• We use the same data set as Moskowitz, Ooi & Pedersen (MOP), looking at 12-month TSMOM
• Only 8 out of 55 individual assets have a t-stat > 1.65 (p < 0.1)
  • me: Seems like the wrong way of looking at it. That's like seeing that the majority of individual stocks have low t-stats and concluding that the equity risk premium doesn't exist
• On a pooled regression, we find t = 4.34
• But we believe the pooled regression overstates significance by not controlling for varying asset means
• We use two bootstrap methods, (1) a "wild" bootstrap using fitted pooled regression residuals, and (2) a "paired" bootstrap that resamples the predictor and dependent variable simultaneously
  • Our bootstraps produce 5% critical values of 12.53 and 4.83 respectively, larger than the 4.34 t-stat
  • me: idk what any of that means, hopefully it will make sense later
• We propose a Time Series History (TSH) strategy that goes long/short assets with positive/negative historical mean returns. TSH performs virtually the same as TSMOM
  • Both TSH and TSMOM get returns mainly from long sides. Short sides have insignificant returns and Sharpes
  • The same is true under various weighting schemes (vol-weighted, equal-weighted, etc.)

• Equity indexes, government bonds, currencies, and commodities from 1985 to 2015
• Returns always refer to excess returns unless otherwise stated

• MOP assumes the 12-month vol-adjusted return predicts next month's vol-adjusted return using this equation: \(r / \sigma = \alpha + \beta r_{12-1} / \sigma + \mathcal{E}\)
  • me: the actual equation is more complicated but that's the basic idea

• In their pooled regression, MOP imposed a common intercept on all asset returns, which assumes all assets have the same mean return, but they don't
  • me: That is, your TSMOM strategy will go long the assets with positive mean return and it looks like trend is working but really the positive return from TSMOM is explained by the positive mean return of the assets
• Could change the equation to account for "fixed effects" (i.e. different means for different asset classes): \(r / \sigma = \alpha_2 + \beta_2 r_{12-1} / \sigma + \mu_i / \sigma_i + \mathcal{E}\) where \(\mu_i\) is the mean for that particular asset class
• If all assets have the same Sharpe ratio then \(\beta = \beta_2\). But $β > \(\beta_2\) if the Sharpes are not equal
• It's non-trivial to control for fixed effects because using the full-period \(\mu_i\) introduces look-ahead bias
• Volatility scaling without controlling for fixed effects further exacerbates the upward bias. Even when all assets have equal mean, vol scaling generates positive fixed effects
  • me: I think this is because vol scaling (assuming equal means) causes you to put more money in the assets with higher Sharpe ratios, which produces a higher overall Sharpe ratio even if TSMOM doesn't "work"
• me: I do think it's possible to control for all these biases: construct the time series history (TSH) portfolio as described below, and regress TSMOM on top of it. But the authors didn't do that

• Wild bootstrap:
  • Take the empirically-estimated \(\alpha, \beta\) parameters of MOP equation above, and all the \(\mathcal{E}\) residuals
  • Generate T new observations (where T is the size of the original sample) as \(r / \sigma = \alpha + \beta r_{12-1} / \sigma + \mathcal{E} v\) where \(v\) is a random variable chosen to be either 1 or -1
  • So basically take the same data series but randomly flip half the residuals
  • Then run a pooled regression on the random data to re-estimate the parameters
  • Repeat 1000 times to see how often we get a \(\beta\) at least as large as the original \(\beta\) from MOP
  • Define the bootstrap t-stat as the t-stat of the 975th-largest \(\beta\)
    • me: I think the idea is, find the t-stat that you would need to get p < 0.05, without assuming the data follows a t-distribution…I don't get how that's the right way to do it though, wouldn't you need to set \(\beta = 0\) when generating new observations?
• Pairs bootstrap:
  • Sample T pairs of (\(r / \sigma\), \(r_{12-1} / \sigma\)) (with replacement) from the real data and run pooled regressions on each sub-sample
• 12-month TSMOM had wild bootstrap t-stat 12.53, pairs bootstrap t-stat 4.83 (original data had t-stat 4.34)
• me: Maybe I'm misunderstanding but I don't get how this is supposed to demonstrate anything? Like, if you take the \(\beta\) from your original sample and then you generate more samples and take the 97.5%ile, obviously it will produce a larger \(\beta\) than your original sample? Wouldn't it make more sense to take the 97.5%ile of samples generated with \(\beta = 0\)? And if that number is larger than your actual data, then it means your data isn't as significant as the t-stat suggests

• We control for fixed effects by subtracting the asset's average return from each month's return
• Controlling for fixed effects produces a t-stat of 3.37 (Table 8)
  • me: as the authors mentioned above, this has look-ahead bias, so it underestimates the t-stat, although I don't think it matters much
• Bootstrapping after controlling for fixed effects produces a t-stat of 10.13 (wild) or 3.93 (pairs)
  • me: I think these are also underestimated due to look-ahead bias, but not sure

• TSH strategy goes long assets with positive historical (excess) returns and short assets with negative historical (excess) returns

• Fama-French 4-factor regression from Table 10: (t-stats in parentheses, p-values in brackets. * = 0.05, ** = 0.01)

  	Return	Alpha	Beta	UMD
  equal-wt TSMOM	0.39**	0.15 (1.9)	0.02 (0.6)	0.60 (10**)
  equal-wt TSH	0.25**	0.05 (0.8)	0.25 (8**)	0.13 (2.8*)
  vol-wt TSMOM	1.16**	0.64 (3.7**)	0.05 (0.8)	1.19 (10**)
  vol-wt TSH	0.74**	0.32 (2.18*)	0.46 (10**)	0.33 (3.8**)
  equal-wt TSMOM - TSH	0.14 [0.19]	0.10 [0.26]
  vol-wt TSMOM - TSH	0.42 [0.07]	0.32 [0.08]

  (SMB and HML loadings are not as important but TSMOM had significant negative SMB loading, and TSH had significant HML loading)

• me: TSH was more driven by beta, whereas TSMOM was more driven by UMD and had ~1 SD more alpha. They don't look that related (would have been nice to see their correlation)
• Table 10 also includes the AQR three-factor model (value + momentum everywhere). On that model, TSMOM has less alpha and more loading on momentum, and TSH and TSMOM have the ~same alpha
  • me: I don't know exactly how AQR factors are constructed but like, it makes sense that TSMOM is largely explained by the AQR momentum factor
• Performance of long legs was much stronger than short legs; short legs had close to zero return (for both TSMOM and TSH)
  • me: I wonder how TSMOM long vs. short legs would look if you broke down by asset class. I know equity shorts have ~zero return, I bet bonds have weak-to-zero return, but I would bet commodity and currency shorts do better
• The performance of TSMOM seems to mainly stem from the difference in mean returns, not from the times series predictability of the past 12-month return
  • me: I don't think this conclusion is supported by the data. See My thoughts

• The fixed-effects criticism is fair but I don't think the "Trading strategy" section supports the authors' conclusion. Before reading it, I was skeptical that their approach would be able to explain the fact that TSMOM has zero correlation to equities/bonds, and indeed they failed to explain it. TSMOM had better performance than TSH, more alpha, much less beta, and much more UMD
• Controlling for fixed effects reduced the t-stat from 4.34 to 3.37. 3.37 is still pretty high and it suggests that fixed effects only account for some of TSMOM's performance
  • Intuitively, the fact that TSMOM has zero correlation to equities/bonds means it should not be at all explained by fixed effects—unless you think you can pick winning assets within asset classes
• IMO the authors' claim that TSMOM and TSH performed "virtually the same" is not justified. You could rightly say the difference was non-significant but you can't say there's no difference
• TSMOM's relatively weak alpha was largely because it was heavily explained by UMD. That does not weaken the case for TSMOM as an investing strategy, it only weakens TSMOM as an addition to a portfolio that already trades momentum
• TSH's performance largely comes from beta, which means it isn't that appealing as an addition to an equity portfolio. In contrast, TSMOM has minimal loading on beta. If the two strategies had identical performance otherwise, that alone would make TSMOM much more compelling than TSH
• Much of the appeal of TSMOM comes from its right skew / shallow drawdowns. A linear regression misses this
  • especially considering UMD has a big left skew and TSMOM has a big right skew, which means TSMOM alpha might not be that big, but it must have a mega right skew. would arguably be worth it to invest in TSMOM even with negative alpha (by analogy, if put options had zero alpha, they would be a no-brainer investment)
• But I do think you could fairly argue that TSMOM's alpha on top of TSH isn't statistically strong
  • I would like to see an extension of the data back to the early 1900s like [2018-10-04] AQR: A Century of Evidence on Trend-Following Investing (2014). My money would be on TSMOM getting a statistically significant improvement over TSH in that case
• As for the bootstrapping thing, it doesn't seem like a valid method to me, but the authors are better at statistics than I am. I suspect I'm misunderstanding. Let's say they're right and a t-stat of 4.34 actually corresponds to a p-value > 0.05. A Century of Evidence on Trend-Following Investing found a Sharpe ratio of 0.77—similar to MOP, but going back to 1880. That paper didn't report a t-stat but if my math is right, the t-stat is simply the Sharpe ratio divided by the square root of the number of months, so TSMOM!1880–2013 has a t-stat of 30. OP paper says the t-stat of 4.34 (or 3.37 controlling for fixed effects) is weaker than it sounds. Okay, how about a t-stat of 30?

• We extend Brooks (2017) and focus solely on economic trend
• Economic trend and price trend both stem from the premise that asset prices tend to under-react to new information
• Using prices as a signal of news is convenient, but it's only a proxy for fundamentals
• Economic trend requires identifying fundamental variables in real time
• Our strategy uses similar signals as Brooks (2017) but extends to commodities and includes 2017–2022
  • Brooks (2017) excluded commodities because it was unclear how to define cross-sectional economic momentum, but we're doing trend only
• Price trend tends to perform well in persistent drawdowns; economic trend tends to perform well in drawdowns that have economic catalysts

• Pre-1987, "risk aversion" is defined as 12-month equity momentum. Post-1987, It's defined as an equal combo of 12-month equity momentum and 12-month changes in credit spreads
• We form price trend using a combination of 1-month, 3-month, and 12-month returns

• Positive Sharpe ratio in every asset except one which was a tiny bit negative
• Economic trend had positive return during each of the 5 worst US equity drawdowns and the 5 worst US 10Y drawdowns

• Correlation = 0.4
• Sharpe ratios:
  • economic trend: 1.1
  • price trend: 1.4
  • 50/50 blend: 1.5

• This paper addresses some of my concerns with [2025-03-19 Wed] AQR: A Half Century of Macro Momentum (2017) by giving performance for individual signals, as well as out-of-sample performance on commodities and in the 5 years since the last paper was published
• Would still like to see a replication by an independent team. There are live funds by non-AQR firms that trade on economic trend (for example Arrow Macro Fund) but AFAIK they haven't published any research

• Five signals based on one-year trends:
  1. change in GDP growth forecast
  2. change in inflation forecast
  3. FX depreciation (vs. export-weighted average)
  4. 2Y bond yield
  5. equity market return
• For each of equity indexes / currencies / govt bonds (10Y) / interest rates (3mo), go either long or short based on what the five signals indicate
  • me: aren't "currencies" usually defined as local-currency 3-month rates?
• "long-short" portfolios are market-neutral and compare macro signals to the cross-sectional country average; "directional" portfolios use absolute macro signals. basically cross-sectional momentum vs. trend
• Macro momentum strategy uses half long-short, half directional

• Asset class positioning based on 1-year change in momentum signals:

  	GDP growth	inflation	FX depreciation	2Y yield	equity price
  equity indexes	+	-	+	-	+
  currencies	+	+	-	+	+
  govt bonds	-	-	-	-	-
  interest rates	-	-	-	-	-

• Many of the data series don't start until ~1990. Performance before and after 1990 look qualitatively similar
• 0.5–0.8 Sharpe for individual asset classes; Sharpe 0.5–0.9 for individual signals

• Macro momentum vs. US equities shows a "smile" much like trendfollowing
• Macro momentum performs well in drawdowns insofar as drawdowns are preceded by deteriorating fundamentals
  • me: I thought equity drawdowns were a leading indicator? Could it be a leading indicator but also under-react?
• During the 10 largest increases in real yield, macro momentum returned an average of 12% annualized, compared to –14% for US 10Y Treasuries and 11% for US equities
  • macro momentum returned 20% average during the top 5 increases, but had a couple negative returns during the next 5

• Both likely driven by market under-reaction to news
• They are expected to diverge at market turning points. By construction, trendfollowing will not immediately turn on a price reversal. But if price reversals are preceded by macro reversals then macro momentum may profit on average
  • me: but you still can't trade at the point of a macro reversal if you're using a 1-year lookback, and in fact the 1-year price trend signal might reverse before the 1-year macro signal, because the market could react to the first (say) 3 months of macro news. although if markets under-react, you shouldn't expect a strong price reversal
• Trend-following (constructed as in "A Century of Evidence on Trend-Following Investing") and macro momentum were correlated at 0.4
  • So they are indeed related, but also diversify each other

• Trend-following + macro momentum had a Sharpe of 1.4, compared to 1.1–1.2 for either alone

  	Excess Return	Stdev	Sharpe	Max DD
  Trend	12.1%	11.2%	1.1	–24%
  Macro Momentum	13.0%	10.7%	1.2	–22%
  50/50 Combo	12.6%	9.3%	1.4	–13%

• Trend-following and macro momentum each hedge each other's worst drawdowns
  • me: Why? I would expect the worst times for both to occur when fundamental trends whipsaw, in which cases prices would also whipsaw
  • ex: In Apr 1999–Dec 2000, macro momentum showed bad fundamentals but equities ripped anyway. When the bubble burst in 2001, trend-following suffered a drawdown, but macro momentum was correctly positioned short equities and long bonds
    • me: US equity peak was in March 2000, not 2001. Maybe other asset classes peaked in 2001?
• Trend-following and macro momentum both performed well in the top 5 US equity drawdowns, with macro momentum performing slightly better in each case

• "Style Premia" is a market-neutral portfolio using value + momentum + carry across the same four asset classes

  	Excess Return	Volatility	Sharpe Ratio	Max DD
  Style Premia	10.8%	11.0%	1.0	-21.3%
  Macro Momentum	13.0%	10.7%	1.2	-21.6%
  50/50 Combo	11.9%	8.3%	1.4	-10.8%

• correlation = 0.2

• These results look compelling and make me want to consider putting some money in macro momentum
• I'm not convinced yet, I would like to see:
  • Does macro momentum work for each individual signal? What if you choose other signals?
  • Does it work out of sample?
  • What if you used other time horizons (say, 10 or 11 months instead of 12 months)?
  • Some analysis of trading costs (although I expect it should be similar to price trend)
  • A replication by another team—I generally trust AQR but I like to see independent replications to be safe

• Equity: higher dividend/shareholder yield stocks should have riskier fundamentals
• Bonds: Term spreads compensate for illiquidity risk and inflation risk
• Commodities: Backwardation exists because producers hedge against price drops, and hedging provides capital for operations before the commodity is produced
• Currencies: Higher short-term rates are compensation for inflation or consumption-growth risk

Three ways to turn carry signals into carry strategies

1. Calendar spreads: Compare front-month to back-month contracts, go long/short to harvest roll yield
2. Cross-sectional carry: Buy securities with relatively high yields, short securities with relatively low yields
3. Time series carry: Long positive expected carry, short negative expected carry. Portfolio may be net long or net short at various times
   • For things like bonds with nearly-always positive carry, this produces positive average beta

An investor who goes long futures with high carry and hedges by shorting spot will have zero profit after all costs are considered

me: if that's true, how is carry different from just multi-asset beta?

• Carry relative value is defined as the z-score of a security's carry relative to its historical carry
• We construct portfolios using either cross-sectional or time series carry, and using either raw carry or carry z-score

• Basically, Time-Series Z-Score >= Time-Series Raw > Cross-Sectional Z-Score > Cross-Sectional Raw
• Multi-Strat, Multi-Sector performed ~as well as the best multi-sector strat
  • best performing combo was Multi-Sector Time-Series (combining raw + z-score), but it only did slightly better than the full combo
  • worst (by far) multi-sector strategy was Cross-Sectional Raw Carry—0.29 Sharpe, vs. 0.88 for the full combo
    • and –60% max drawdown vs. –34%, even though both are scaled to 10% vol
• Carry did best in bonds and commodities. Had similar Sharpe in currencies, but with worse drawdowns. Equity carry had notably worse Sharpe

Performance (all targeting 10% vol):

• All variants had correlations of +/- 0.2 to S&P 500 and US 10Y

• I was motivated to read this because I listened to interviews with Corey Hoffstein / Newfound people and they talked about how people have this idea that carry has bad drawdowns / high correlation to equity drawdowns, but it doesn’t have to be that way. But the never explained what they meant
• Having read the white paper, it looks like cross-sectional carry has bad drawdowns, but time series carry does not
• I am not a big fan of traditional (cross-sectional) carry. Time series carry looks potentially more compelling, but I don't want to change my allocation based on a single white paper
• RSSY (managed by Resolve/Newfound) presumably uses a carry ensemble. I don't know of any other carry products that do that (e.g. AFAIK AQR carry is purely cross-sectional, at least its index looks like it's cross-sectional based on the fat drawdowns)
• Resolve's backtest index has a Sharpe 1.05, Ulcer performance index (UPI) 1.90. AQR's carry index over the same period has Sharpe 0.61, UPI 0.45. I trust this less than the whitepaper (it's a short-horizon backtest with no trading costs) but the relatively high UPI does look nice

• We consider the size, value, momentum, and low-vol factors
• For value and momentum we only look at the large-cap half to ensure good liquidity. For size factor we take NYSE 30th to 70th percentiles as small-caps
• Long-only factors e.g. "Value-RF" are the long leg of the factor minus the risk-free rate
• For low-vol factor, equal-weighted (?) long side at 130% and short side at 70% to achieve zero beta

• Long/short has additional risks in the form of leverage, counterparty risk, and lower liquidity

• Based on my own tests using the Ken French data, these results vary based on choice of factors. If you exclude the size factor and add in the quality factor, long/short beats long-only under the paper's assumptions
  • I might write up a full blog post about this
• The paper might be too unfavorable toward long-short. For example perhaps the short side will see less factor decay because it's not as popular as long-only
  • But my guess is the factor decay will be similar on both sides, e.g.[2018-07-28 Sat] Blitz: Are Exchange-Traded Funds Harvesting Factor Premiums? finds that there is as much money in negative-factor-loaded ETFs as in factor-loaded ETFs. On the other hand, the recent widening of the value premium was driven more by the short side than the long side, suggesting a (relatively) bigger short-side premium in the future
• Although I think this paper overstates its results, I still think long-only and long/short are both reasonable choices. I use AAVM rather than (say) AQR's long/short funds, mainly due to greater concentration and better tax-efficiency

https://www.youtube.com/watch?v=4Cb6JK3-co4

pain near elbow bone, on outside when supine

1. Lying down, wrap band around elbow and pull tight. Squeeze fist and release 20x
2. Lacrosse ball massage
3. Pronated wrist curls (very light weight)
4. Straight-elbow reverse crossovers (cable or light band)
   • retract scapula; keep arms parallel with ground
5. Standard grip exercises

https://www.youtube.com/watch?v=9Ajfmx7qXvE

pain on crease side of elbow, toward body when supine

1. Lacrosse ball massage
   • if you find a spot that hurts, hold there and rotate wrist / flex grip
2. Supinated wrist curls (very light weight)
3. Hold band in supinated grip with forearms horizontal; stretch outward and hold 5 sec. do 10 reps
   • should feel in back of shoulder. if feel in front, move elbows forward
4. Same as #3 but with elbows up and forearms vertical

• More reactive (shorter-term) trend signals better capture short-term trends, but are more prone to whipsawing

• We looked at the correlations between moving average crossover signals of different speeds, from 10 over 30 to 40 over 120
• Actual correlations are lower than the correlations you'd see if returns were IID

• Actual correlations:

  	10x30	20x60	30x90	40x120
  40x120	0.49	0.79	0.95	1

• me: the article didn't look at longer-term signals so it's kinda useless

• The study reviewed the 20 most-cited meta-analyses in strength and conditioning. 85% of them contained at least one statistical error
• An analysis that does these can still be useful, but be aware of the limitations

Comprehensive overview of the quality of plant- And animal-sourced proteins based on the digestible indispensable amino acid score, Table 2 (see also full article)

• limited by lysine: wheat, corn, rice, hemp, oat
• limited by methionine + cysteine: fava bean, pea, soy
• limited by leucine: quinoa[1]
• Note: Figure 2 says DIAAS scores for adults are higher across the board than for 6mo–3y (e.g. soy is >100 instead of 91)
• FAO report Table 5 gives recommended mg/g by age group. It recommends much less leucine/lysine/methionine for adults than for 6mo-3y, so the limitations from the above paper systematically underestimate protein quality

[1] Quinoa protein: Composition, structure and functional properties

note: hemp protein tryptophan value is unknown, so I just put it as 100

Take one 500mg lysine pill per 28g wheat protein to push the DIAAS up to 94. (= 0.5 / (48 / 1000 * (0.94 - 0.57)))

• UW study gave each money food at libitum, then in the CR group restricted calories proportionally for each individual based on their intake
• NIA study gave control group a fixed amount of food based on National Research Council guidelines, and gave CR group 30% less food than that
  • calorie intake was determined by weighing uneaten food, as reported in a different paper on the same study (Age-related decline in caloric intake and motivation for food in rhesus monkeys, 2005)
  • the fixed upper intakes likely prevented the worst right-tail outcomes

• UW ate processed foods, NIA ate whole foods. UW ate primarily whey protein, corn, sucrose (45%), corn oil, cellulose (5g). NIA ate soybean and fish meal, wheat, corn, sucrose (7%), cellulose (6-9g)
• NIA split monkeys into juvenile/adolescent (J/A) and old. CR reduced mean/median lifespan 2–3 years in J/A group but had a small and mixed effect in old group (Figure 1 & Table 3)
  • me: possibly b/c CR increases premature deaths and decreases late deaths. should look at causes of death (accidental/infectious vs. chronic disease)
• UW control (ad libitum) had highest bodyweight; UW CR and NIA control + CR had similar bodyweights for female, and for males NIA CR had lower bodyweights than NIA control (Figure 2). Body fat % had similar relationship to bodyweight (Figure 3)
  • even though NIA CR group ate ~25% fewer calories than NIA control group (Figure 4)
  • me: weird that NIA females had only small difference in bodyweight, but NIA males had large difference
  • me: for NIA the number of individuals at each age appears to fluctuate randomly. how is that possible?
• Both UW and NIA showed later onset of all-cause age-related morbidities in the CR group, but with the difference being greater for UW (Figure 6). Cancer and diabetes followed the same pattern, but for NIA, CR had more heart disease than control
  • both NIA groups had less heart disease than either UW group. UW and NIA CR groups had equal cancer rates
• me: Monkeys eat less as they age. Therefore, the caloric upper bound in the NIA control group should be less restrictive for monkeys whose bound is set when they're young, and therefore CR should show a more prominent effect in NIA older monkeys. That's kind of the opposite of what we see: in J/A cohort, CR is harmful, but in old cohort, there's no consistent effect
• me: some concerns about extrapolating these results
  1. lab conditions are different from real world in many ways
  2. the monkeys were sedentary. they were individually housed in small cages (NIA 2005). it seems likely that physical activity largely negates the benefits of calorie restriction (e.g. hunter-gatherers who eat a lot of sugar but are physically active have very low rates of diabetes: Hunter-gatherers as models in public health)
     • physical activity was tested in a different paper, but it was tested inside a metabolic chamber that was about the same sizes as the cages, i.e., too small for meaningful exercise (Long-term calorie restriction decreases metabolic cost of movement and prevents decrease of physical activity during aging in rhesus monkeys, 2013)

Impact of caloric restriction on health and survival in rhesus monkeys: the NIA study

• In the old-onset cohort, CR group did not have better survival, but did have lower triglyceride, cholesterol, and glucose levels
• In the young-onset (J/A) cohort, CR group did not have better survival or better biomarkers, but did (maybe) have less age-related disease (p=0.06) (age-related disease was not reported for old-onset cohort, for some reason)

See the relevant section of Outlive: A Critical Review

• Standard argument against splitting: for small donors, marginal cost-effectiveness is unlikely to change between first and last dollar. This seems right to me

• I believe you should donate to charities in proportion such that, if all like-minded EAs donated in that proportion, the outcome would be best
• If everyone lump-donates based on the funding situation as of several months ago, then the top charity receives all the money. If everyone proportionally donates, then every charity receives the correct amount of money

• I like this position. Some conceivable objections:
  1. EAs' conclusions about where to donate might not be that correlated
  2. Are we setting a norm for all EA, or just for the individual who asked my opinion? I want to avoid unilaterally doing something that only pays off when everyone does it, unless everyone is going to do it

• I agree that those are cruxes

• wrt correlation, what type of correlation are we interested in? All EAs? Donors to a particular cause? etc.
• Could ask GiveWell, GWWC, etc. about the correlation among donations they have visibility into

• The relevant question is, is the correlation strong enough to collectively hit diminishing returns?
• Correlation within a cause area is a bigger deal

• Agree that within-cause correlation is the natural unit of analysis. Cross-cause I don't think donation splitting makes sense

• Could get correlations from the EA Forum donation election
• This conversation has moved me from correlation being a medium-large problem to a small-medium problem. Still confused about how to think about correlation

• In terms of decision theory, I lean toward lump sum because humans tend to reason in various messy ways, so your own reasoning isn't much evidence about other people's reasoning

• Insofar as we're making a public recommendation, we should tell people to coordinate on some sort of mild donation splitting strategy

• UMD controlling for SUE has worse return than UMD (but still positive). SUE controlling for UMD has better return than SUE

• Most studies found that combined works better than separate
• AA found that separate works better than combined for their specific implementation style. Reason for the difference is unclear. Some possibilities: equal weighing; long only; different metric (EBIT/EV instead of B/M)
• Difference is small either way. That plus the inconsistent results suggests it doesn't really matter

• Value premium is insignificant in the 40% largest stocks

https://www.thelancet.com/journals/landia/article/PIIS2213-8587(18)30288-2/fulltext

• me: Authors are aware of the confounding issues with illnesses causing weight loss so I assume they accounted for it but I didn't read in detail
• For most specific causes, BMI has a U-shaped mortality, with the right side being worse than the left side
  • Causes where the left side is worse: lung cancer, prostate cancer, neurological (dementia etc.), falls, suicide, interpersonal violence
  • Causes with a symmetric U shape: musculoskeletal, accidents (aggregate, including falls), respiratory
  • Pretty much everything else is higher on the right: all cancers, blood/endocrine, cardiovascular
• Confirms that the healthiest BMI range is 18–25
• All-cause mortality is minimized at 23 kg/m² under age 70, and BMI 25 over 70
  • me: From graphs, looks like ideal BMI slowly increases with age from ~22 at <50 to to ~26 at >80; and is slightly lower for men than for women, and women can tolerate BMIs up to ~28 without much impact on mortality

https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)30175-1/fulltext Supplementary Appendix

• Healthiest BMI is 20 to 25. Mortality significantly increases just below or above this range
• Table 3 finds mortality is minimized at 22.5–25. 20–22.5 is nearly identical, and mortality considerably increases <20 and >25
  • me: look at the hazard ratios, not the deaths per participant, because deaths per participant needs to be adjusted in various ways

• Hazard ratios for cause-specific mortality (appendix eTable 15)

  	18.5–20	20–22.5	22.5–25	25–27.5
  cardiovascular disease	1.14	0.98	1.00	1.11
  coronary heart disease	0.95	0.89	1.00	1.18
  stroke	1.15	1.01	1.00	1.05
  respiratory disease	1.73	1.22	1.00	1.00
  cancer	1.01	0.96	1.00	1.05

  • me: Notice the heterogeneity: 20–22.5 is better for heart disease and cancer, but much worse for respiratory disease

• Ideal BMI by age (appendix eFigure 2)

  Age	BMI
  35–49	20–22.5
  50–69	22.5–25
  70–89	25–27.5

• Ideal BMI by sex (appendix eFigure 3) is the same for both sexes, but females have lower hazard ratios across the board (i.e. lower penalty for being under- or over-weight)
• Across 6 steps of successively stricter inclusion criteria, low-BMI hazard ratios consistently go down and high-BMI hazard ratios consistently go up. 20–22.5 starts out with HR ~1.2 and descends to 1.01. 18.5–20 starts at HR ~1.4 and descends to ~1.2
  • me: Presumably we still aren't perfectly controlling for everything. Extrapolating the trend suggests that 18.5–22.5 is healthier than the data suggest

https://www.bmj.com/content/353/bmj.i2156

• Ideal BMI was 23–24 in never-smokers, 22–23 in healthy never-smokers, and 20–22 in never-smokers in studies with >20 years of follow-up
  • me: I believe the argument is that low BMI could be caused by undiagnosed illnesses that don't show up in shorter-term studies. Sounds plausible but I don't fully understand why using longer studies fixes this problem. Perhaps the idea is you filter out anyone who dies within the first 5 years, which eliminates most people who had some undiagnosed disease

• In our replication of the original size effect paper Bantz (1981) that looked from 1936 to 1975, regressing size against CAPM, we got an SMB t-stat of only 1.21 and negative alpha; and a top minus bottom decile t-stat of 1.78 and 2.5% alpha (t=0.66)
• Much of small-cap outperformance is just beta (or all of it, if you use SMB construction)
• Why is our result weaker than that of Bantz (1981)?
  • Older databases had more errors such as delisting bias
• 1926–2017 SMB looks about the same but alpha t-stat is still non-significant
  • Other major factors (HML, MOM, RMW, BAB) all have good alpha t-stats

• me: their data actually shows that it slightly strengthened but it still sucks

• Measuring size by book equity, book assets, sales, PPE, or number of employees resulted in no reliable size premium
  • me: most of these are gonna have negative loading on the value factor

• If you ignore January, SMB has 0.0% return 1927–2017

• SMB is negative in Europe, global, and North America; marginally positive in Pacific; and somewhat but non-significantly positive in global ex-US
  • me: I don't get how it's negative in global but positive in global ex US
• SMB is negative in 13/23 individual countries
• SMB is larger than in US in emerging markets (data starts in 1994) (alpha t-stat 1.27)

• Country equity index SMB (long small countries, short big countries) has a modest size premium (alpha t-stat 1.68)
• Corporate bond SMB has negative alpha
• Currency SMB (using GDP to measure size) has a weak premium in developed markets (alpha t-stat 0.41) and a weak negative premium in emerging markets (alpha t-stat –0.95)

• Removing stocks with size < $5M eliminates the small-cap premium
• Decile 1–10 has 4.3% alpha; but if you remove the 30% smallest stocks from the small decile, alpha drops to –2.3%

• Our estimate suggests a 63 bps market impact for small decile vs 24 bps for median and 10 bps for large decile

• SMB has negative alpha when regressed on the liquidity factor

• One story says small firms have more exposure to earnings growth, which is nondiversifiable risk. But SMB has ~zero alpha on CAPM so SMB can't be getting returns from earnings growth
  • me: I don't get this. If empirical evidence of alpha=0 screens off theory, why are you even considering theories?
• Some say small stocks are mispriced because they're hard to trade. But for them to earn a premium, they must be systematically underpriced, not randomly mispriced. And small stocks are hard to short so if anything they should be overpriced

• Various factors are stronger in small caps: low vol, value, momentum, profitability. But that implies a larger spread among small caps, not that small caps should outperform large caps
• The value premium is bigger in small caps, but small value stocks are the same size as small growth stocks

• Overweighting small caps is good as long as you tilt toward value/momentum/etc. and be mindful of trading costs

• Size effect disappears when controlling for CAPM
• Adding CAPM+HML+UMD doesn't much change SMB's alpha, but SMB does have positive and significant alpha when regressed on CAPM+HML+UMD+QMJ or Fama-French 5-factor (excluding SMB)
  • This works with market cap and with other measures of size
  • This mainly happens because small-caps tend to be junky, so controlling for profitability/quality makes small-caps look better
    • Low-quality companies have trouble growing so they end up with lower market caps
• Controlling for quality, the size effect continued to work past the 1980s; it exists outside of January while diminishing January returns; and it works internationally
  • me: This raises a bunch of unanswered questions about why controlling for quality produces such a strong size effect

• Size and value have 2x as many papers as the next most popular (momentum)

• Some papers suggest factor premiums are larger when there are limits to arbitrage, so you might expect factor premiums to be stronger on the short side
• Momentum crashes are driven by the short side
• One study found abnormal returns are stronger on the short side for 11 different factors
• Borrowing fees are 3x higher than normal for the short leg of value, momentum, low-vol, and profitability portfolios
• Can isolate long and short legs by going long factor/short market or long market/short factor
• We look at HML, WML (momentum), RMW (profitability), CMA (investment), and VOL (low-vol)
• Sharpe ratio ordering for an equal-weighted combination of factors is long leg > long/short > short leg for both US large-caps and small-caps
• Short legs have zero to negative alpha on top of long legs; long legs have alpha on top of short legs
• Novy-Marx found that the low-vol factor was subsumed by the (Novy-Marx) profitability factor, and Fama & French found that HML was subsumed by profitability and investment. We find that these conclusions are entirely driven by the short side
• me: I would've liked to have seen some discussion of why this is true. Wouldn't you expect the short leg to be stronger than the long leg due to constraints?
  • They partially explained it due to factors being more correlated on the short leg, but that doesn't explain why the long legs for individual factors outperform the short legs

• Sharpe ratios:

  	Long Leg	Short Leg	Long/Short
  Value	0.40	0.37	0.40
  Momentum	0.61	0.46	0.55
  Profitability	0.31	0.43	0.41
  Investment	0.49	0.40	0.49
  Low-Vol	0.53	0.54	0.58
  All	1.10	0.69	0.86

• Long legs have similar Sharpes to long/shorts. When combined, long leg has better Sharpe
• Returns are comparable for long and short legs, but short legs have higher vol
• Correlations are lower on the long leg than the short leg. Average pairwise correlation is –0.04 for long legs, 0.31 for short legs, and 0.19 for long/short
• Each factor's short leg has negative alpha when regressed on long legs, with the sole exception of low-vol which has small positive alpha (but long leg over short legs has more alpha). Combined-factors short leg has negative alpha
• The Sharpe-maximizing portfolio allocates 2.6% to short low-vol and 0% to any other short legs. When including market beta, the Sharpe-maximizing portfolio allocates 0% to any short leg

• Sharpe ordering is Long > L/S > Short for both large caps and small caps
• Combined-factor short leg has –1.95% alpha in large caps and –0.04% alpha in small caps

• Combined-factor Long > L/S > Short Sharpe ordering holds in every decade; short leg has negative alpha in every decade
• Long > L/S > Short for equal-weighted portfolios
• Originally we hedged with 50% large-cap equities + 50% small-cap equities. If instead we define the hedge as the "neutral" portfolio (the middle 40% by e.g. B/M), results look similar
  • Similar result when using fully market-cap weighted portfolios instead of the Fama-French style 50% large/50% small
• Results hold when we use 5x5 portfolios instead of 2x3 portfolios (size x factor)
  • MVO does allocate more to short legs in smaller-size portfolios, from 2% in mega-caps up to 46% in micro-caps
    • me: this makes sense if smaller stocks are harder to short
• Short leg has had worse drawdowns than long leg especially in late 90s and 2008-9; shorts have more negative skewness and higher kurtosis
• In Europe/Japan/Asia Pacific, long leg and long/short perform comparably both outperform short leg; short leg has positive alpha over long leg, but less alpha than long leg has. Globally, Long > L/S and short leg has negative alpha

• Profitability + investment subsumes value on the short side and on long/short, but not on the long side (–0.13% alpha on long/short, 1.98% alpha on the long side)

• Studies have found short-borrowing costs between 0.25% and 1% per year for large caps
• But stocks that factors want to short tend to be more expensive, enough to destroy the premium according to some studies; another found that it destroys 40% of the premium
• Factor shorts often have high short demand and no shares are available to borrow
• Long-leg approach requires ~2:1 leverage to get the same return

https://alphaarchitect.com/2019/11/do-long-only-factor-portfolios-deliver/

• This study contradicts risk-based explanations of factors. Shouldn't the long legs be riskier?
• But behavioral finance would predict the short legs to have higher alpha because they're harder to arbitrage
• me: Just speculating here, perhaps people's aversion to seemingly "bad" stocks is stronger than their attraction to "good" stocks, which makes the long side more mispriced than the short side
  • This is plausible for value: people really dislike value stocks. On the other hand, they love investing in the "next big thing"
  • For momentum, it seems intuitive that people particularly dislike investing at 52-week highs, but they're not particularly enthusiastic about investing at 52-week lows

https://www.redpenreviews.org/reviews/eat-drink-and-be-healthy/

• It got the highest rating in Red Pen Reviews by a huge margin (97% vs. 86% for #2)
  • Red Pen Reviews is run by Stephan Guyenet, who Scott says "provide[s] the best introduction to nutrition I’ve ever seen" (in The Hungry Brain) and "represents the 'scientific consensus' side" (in debates with Gary Taubes), and Mike Israetel says is one of the top nutrition researchers
• The authors appear to have good epistemology; they write about how to draw causality from observational studies and properly address objections that I might have raised (e.g. before reading this, I thought a lot of food <> health correlations might be explainable by income/class/conscientiousness but observational studies contradict that: Greeks are healthier but poorer than Americans, and people who conscientiously followed the old USDA guidelines were less healthy than people who didn't)
• The authors have done a lot of nutrition research themselves
  • This is a plus for obvious reasons, but the authors also cite their own work a lot which is a minus because obviously they think their own work is good

• Real = return after inflation
• Excess = return minus RF, which is the nominal return any investor will get if they currency hedge

• We added economist surveys to our estimate of cash returns. They have not been very accurate historically, but we believe adding them makes our estimate more robust given that the yield curve currently predicts large changes in interest rates
  • Our formula for cash real return: (3-month yield + 10-year yield + economist forecast) / 3 - 10-year inflation forecast

• CMAs primarily depend on two questions:
  1. Are risk premia constant or time-varying? If constant, use historical average. If time-varying, use current yield
  2. Do valuations mean revert?

• The risk-free asset for an investor with an N-year horizon is an N-year Treasury bond
  • me: If you have a 5-year horizon, there's no reason (given EMH) to buy 1-month T-bills instead of 5-year bonds
  • There is not one but there are many risk-free assets, depending on the investor

• CAPM predicts the excess return of stock \(i\): \(R_i - R_{rf} = \beta_i (R_{mkt} - R_{rf}) + \mathcal{E}\)
• Mkt and RF are abstract, but we can replace them with equities and T-bills: \(R_i - R_{bills} = \beta_i (R_{equity} - R_{bills}) + \mathcal{E}\)
• Now suppose the risk-free asset is mis-specified, and it's actually N-year bonds. Then we can write \(R_i - R_{bills} = \beta_i (R_{equity} - R_{bills}) + (1 - \beta_i) (R_{bonds} - R_{bills}) + \mathcal{E}\)
• If you look at \(R_i - R_{bills}\), you should see a negative correlation between a stock's equity beta and its bond beta because the two numbers add up to its "true" beta
• Previous literature by De Franco, Monnier & Rulik (2017) and Coqueret, Martellini & Milhau (2017) found a negative correlation between equity beta and bond beta
• me: Note that this has nothing to do with the equity-bond correlation

• We look at a bunch of portfolio sorts from the Ken French library (10 portfolios formed on industry|size|BM|profitability|investment|etc.) 1963 to 2018, plus bonds from 2- to 30-year maturities. We subtract the T-bill return from all portfolios

• Equity beta vs. N-year bond beta shows a strong negative correlation for all values of N
  • Equity beta vs. 5-year bond beta:
  • Equity vs. bond beta regression lines:

• Table of statistics for equity beta vs. N-year bond beta (intercepts are all ~0)

  	2-year	5-year	7-year	10-year	30-year
  slope	-1.54	-0.92	-0.81	-0.66	-0.40
  t-stat	-20	-21	-21	-21	-18
  r2	0.77	0.80	0.79	0.79	0.73

• 5-year bonds have the highest r², which suggests they're the market-preferred risk-free asset
• 10-year bonds have a slope of -0.66, which implies the market risk-free asset is a 66% position in 10-year bonds, which has very roughly the same duration as a 92% position in a 5-year bond
  • and similarly, -1.54 slope for 2-year bonds implies the risk-free asset is a 154% position

• Result holds in all three sub-periods 1963–1981 (characterized by rising rates), 1982–2000, and 2001-2018
  • In sub-periods, the best-fit duration varied from 2 to 7 years
• Result holds in the UK and Japan (1986–2018) and the Eurozone (1999–2018)
  • Best-fit maturities were 7, 5, and 10 years, respectively

• By Ibbotson data (1926–1994), stocks returned 10.3%, intermediate-term Treasuries returned 5.1%, and inflation averaged 3%
  • A 60/40 portfolio returned 5.1% real
• This seemingly suggests you could withdraw 5% in the first year and increase your withdrawals by 3% per year to match inflation
• Note: An n% withdrawal rate means you withdraw n% the first year and withdraw the same amount after adjusting for inflation in subsequent years. (it does not mean you always withdraw n% of your portfolio)

• I quantify portfolios in terms of portfolio longevity: how long the portfolio will last before withdrawals fully exhaust it
  • This is intuitive for my clients, whose primary goal is making it through retirement, and whose secondary goals is accumulating wealth for their heirs
• I calculated the portfolio longevity for a 50% stocks/50% bonds portfolio for every starting year, up to a maximum of 50 years
• 3% withdrawal rate lasted >50 years for every start year
• 4% withdrawal rate usually lasted >50 years. In the worst case, it lasted ~30 years
• 5% withdrawal rate lasted >50 years less than half the time, and frequently only lasted 20–30 years

• Train at higher frequency (hit muscle groups 2-3x/week)
• Don't specialize. Train all muscles evenly so you don't lose them
• Prioritize high stimulus to fatigue ratio (eg fewer 0 RIR sets, more isolation, do rows or RDL instead of DL)
• Err toward more reps
  • Muscle fibers convert from fast twitch to slow twitch and you gain endurance
• Start at 3 RIR

• To minimize muscle loss, only cut for 8-12 weeks, then eat at maintenance for 5-9 weeks, then cut again
• Eat 4x/day+
• Cut out fats more than carbs. You need carbs for energy
• Take a deload week if fatigue is high. Eat at maintenance during deload b/c the point is to recover
  • If you cut while not heavily stimulating your muscles, your body will eat the muscle
• Don't get greedy, stay around 500 cal deficit. >1k deficit will lose muscle

• Cardio is good. Eating too little triggers muscle loss; cardio lets you eat more while still losing fat
• Non-Exercise Activity Thermogenesis is king
  • All the ways you burn calories without exercising, eg going to the store, doing laundry, pacing while on the phone
• Do cardio with high stimulus:fatigue—burn the most calories with least fatigue
  • Stimulus and fatigue are mostly linear but there are outliers. walking > uphill walking > swimming/elliptical > cycling/HIIT > jogging
  • Jogging burns calories fast but it's fatiguing
• Keep cardio sessions shorter (<1 hour)
• Do cardio far after the last meal and soon before the next one
  • eat right after b/c cardio puts your body into catabolism
  • best time is before breakfast

• On a cut, you need more volume to maintain muscle, but can't recover from as much volume. That means you need frequent deloads. On a bulk, you need less volume to maintain muscle, and can recover from more, so you can start at low volume and increase volume for many weeks before needing to deload
• Eat at maintenance during a deload week

• Our analysis technique has two parts
  1. Index construction: build stock indexes for factors
  2. Return decomposition: parse out the sources of indexes' returns over time to figure out how the factors work
• When we decompose a factor, we need to account for the distorting effects that turnover can have

• Geometric mean return (CAGR)
• Real = return after inflation
• Excess = return minus RF, which is the nominal return any investor will get if they currency hedge

• For methodology, see 2022 Updated estimates
  • The 2023 text is basically identical to the 2022 text

• Forward-looking expected return for emerging – US is >3%, its highest in decades
• Emerging markets have a bigger value spread than developed markets (97th percentile vs. 92nd percentile)

• Performance of institutional managers according to eVestment data (gross of fees) 2012 to 2022:

  	systematic	discretionary
  US large cap	0.06	0.03
  emerging large cap	0.45	0.19

• Emerging markets are exposed to greater currency risk + a carry premium

• Bond yields went up, but equities did not comparably reprice, so bonds look relatively better than last year
• Negative yield curve implies a negative excess return for long-term Treasuries

• The SG Trend Index, established in 2000, beat 60/40 both absolutely and risk-adjusted from 2000–2020, but underperformed 2010–2019
  • SG Trend Index maintained a low/negative correlation over the full period
• In 2010s, markets displayed unusually small moves, which coincided with a stable global economy and few (if any) major crises

• Backdrop:
  1. benign macroeconomic shocks
  2. soft growth and low inflation
  3. central bank policy constrained by low long-duration bond yields
• Central banks suppressed market volatility, preventing price trends
• Trendfollowing strategies capture the tendency of markets to gradually incorporate new information
• 2010s had unusually low GDP growth volatility and few large market moves
• Central banks could suppress economic volatility for two reasons:
  1. They could add monetary stimulus without risking high inflation
  2. Bond yields <3% would normally be highly stimulative, but they weren't in the 2010s because "r-star"—the real policy rate consistent with a neutral policy stance—was very low

• Trend-following has performed well in the more volatile macro environment of 2022
• Several key challenges make it improbable that economic conditions will return to 2010s levels within the next couple years
  1. Inflation would have to reduce. Historically, high inflation takes years to subside
  2. Recent monetary tightening (raising rates) will likely decrease growth in 2023, with many forecasters projecting recession
  3. Central banks appear to face difficult tradeoffs between growth/employment and inflation, so they are less able to suppress market moves

• Some are worried that they're late to the party. We believe there is a reasonable chance that strong returns will continue
• Historically, after 12 months of strong performance trend-following performs about average over the following 12 months (there is no 12-month mean reversion)

• If PE has greater exposure to certain risk factors, then it should earn a compensatory higher return
• Based on economic intuition and empirical evidence, we expect PE to have higher exposure to financial leverage (equity risk), illiquidity premium, size, and value
• PE firms have 2–4x the debt/equity ratios as public companies (avg 100–200% vs. 50%)
• Private Equity's Diversification Illusion (Welch 2017) showed how traditional methods of valuation understate PE's risk. They report equity beta < 1, but a beta of 1.2–1.5 is more realistic
• In principle, a 5–10 year lockup justifies a 4–6% illiquidity premium. But data suggests that the illiquidity premium is largely offset by investor willingness to pay for return-smoothing
• Investors might overpay for PE if they attribute its higher returns to alpha instead of equity risk premium
  • Evidence on private real estate supports this: private real estate has underperformed REITs (Ilmanen, Chandra, and McQuinn (2019) and Ang, Nabar, and Wald (2013)
• PE historically had small cap and value tilts. Value tilt has disappeared more recently

• PE beat large-caps by 2.3% and 1.2x-leveraged small-caps by 0.7%, and lost to small-cap value by 1.6%
  • Corroborated by Stafford (2017), "Replicating Private Equity with Value Investing, Homemade Leverage, and Hold-to-Maturity Accounting"
• IRRs are too gameable. Public market equivalent (PME) is a better metric of relative performance
  • PME compares PE fund to a comparable public market fund where the same amounts of money were invested at the same times
• PE/PME had large EBITDA/EV valuation gaps 1998–2006, but basically no valuation gap 2006–2018. PE outperformance has shrunk, but still positive
  • Some studies have found continued PE outperformance after 2006; others found no outperformance

• Almost always do main lifts at 70% to 90% of max intensity
• Train low volume on main lifts to avoid overuse injuries. Stay 1–2 reps shy of failure
• Do high volume accessory work: 4–6 sets of 6–15 reps
• Train each muscle group 2–3 times per week
• Bulk/cut phases build muscle faster than maintaining constant body fat
  • Move half a pound per week from 10–15% up to 20%
    • me: That's basically 12 weeks of pushing PRs, then deload and 12 weeks of lighter work while cutting
    • me: Half a pound probably isn't enough to make me hungry. I lost half a pound per week by accident during the sandwich era
• Bodybuilding-style training has much lower injury rate than powerlifting-style

• My current program is pretty good, except that I should do higher reps on accessory work (chins, dips, DB bench, face pulls), although I kinda already knew that and I just don't like doing high reps
  • For chins and dips, switch from 5 reps to 8 reps

• Ecological fallacy: at the population level, being a night owl is correlated with creativity, but the very best writers tend to be larks
  • Top early-morning writers don't report major struggles with focusing in the morning, which suggests that they're larks, and night owls might not perform well if they force themselves to write in the morning
• People are bad at scheduling, and if they intend to write later in the day, they won't get around to it
• The day uses up some sort of 'willpower' or 'creativity'
• Sleep appears involved in unconscious processes of creativity. You wake up primed to work, and by instead puttering around making breakfast, you dissipate the potential
  • Or perhaps there is something special about the liminal half-asleep state

• We look at value, momentum, carry, and defensive
• Current valuations aren't useful because holdings can change rapidly
• me: They don't really say anything helpful

• Geometric mean return (CAGR)
• Real = return after inflation
• Excess = return minus RF, which is the nominal return any investor will get if they currency hedge

• 50% chance that 10-year realized equity returns will be off by >3% per year

• Executives want to know what to do. Give them the answer first so you don't waste their time. You can explain the reasoning afterward

• Ideas should form a pyramid: key point > supporting arguments > sub-supporting arguments
• Aim for 3 points at each level of the pyramid

• A few ways of ordering ideas:
  1. Time order: sequence of events with cause-effect relationship
  2. Structural order: thought + supporting ideas
  3. Importance order: most to least important

• Successful people look more places, they work harder, they think longer than less successful people
• The highest quality work comes from people who are emotionally committed to their work
  • Perhaps the best mathematician I know produced good work, but not top quality, because he didn't care deeply about his work
• Courage—willingness to attack difficult problems
• Ability to tolerate ambiguity
  • It is a fine balance between believing what you learn and at the same time doubting things
• You need to think about things from many angles, put "hooks" in them. Connect them in many ways to what you already know so you can later retrieve them in unusual situations
• Most outsiders are kooks, but the best insights often come from them. I have no simple answer except do not dismiss outsiders too abruptly

• You need a vision of who you are and where your field is going
• Some of the greatest work was done under unfavorable conditions
  • What most people believe is the best working conditions for them is seldom, if ever, true
  • The supposedly ideal working conditions of the Institute for Advanced Study has ruined more good people than it has helped

• You must present your results so that others will listen to you
• Lots of good work has been lost because of poor presentation, only to be rediscovered later by others

• Those who have done great things generally report, privately, that it is better than wine, the opposite sex, and song put together
• It is in the struggle and not the success that the real gains appear

• QVAL includes mostly Value, but also Profitability, Profit Growth, Quality, Low Risk, and Accruals
• QMOM includes mostly Momentum, but also Low Risk (albeit a different interpretation) and Seasonality
• They sorta capture Size by equal-weighting
• They don't capture Debt Issuance, Investment, or Skewness
  • but Investment is highly correlated with Value

• Prior research shows that even before fees, individual investors tend to buy stocks that underperform the market
• Prior research finds variables that reliably predict performance: IQ, cognitive abilities, geography, portfolio concentration, age, past performance. But even skilled stock pickers in these studies are unable to beat transaction costs
• Most prior studies examine all individual investors, most of whom trade infrequently, and use data from only a single broker
• We look at all transaction data for the Taiwan stock market 1992–2006
  • Day traders account for 17% of volume
    • This percentage is remarkably stable over time
• Day trading: The purchase and sale of a stock in the same day
• 20% of day traders earned positive abnormal net returns, and about 1% were able to predictably profit
• Profitable day traders earn more in small-cap and high-vol stocks, and in periods around earnings announcements
• Profitable day traders do not appear to make money by providing liquidity
• Aside from past performance, the best predictor of future performance is concentrating in a small number of stocks
  • This suggests that successful day traders gain an information advantage by carefully studying a few stocks

• We face near risks (nuclear war, asteroids), risks of as yet undeveloped technology (nanotech, AI), and long-term risks (sun expansion)
• Homo sapiens has existed 200K years; homo erectus 2M years; median species lasts 2M years
• Anthropic doomsday argument suggests humanity won't last much longer

• Several people have estimated the probability of extinction at 25-50% this century

• NASA spends $4M/year on monitoring asteroids and comets; $1.7B is spent on climate change
• Bioterrorism might be the most severe risk; US spends $5B/year on biodefense
• Unclear risks from high-energy physics experiments; Posner (2004) recommended withdrawing federal support for these experiments
• We have bunkers for government leaders and seed storage facilities, but no way to shelter large numbers of people during a potential extinction event
  • As few as 100 people could repopulate earth. The Americas and Polynesia were originally populated by fewer than 100 founders

• Discounting has been justified based on time preference, consumption growth, uncertainty about future existence, and opportunity costs. None of these justifications applies to the benefits of delaying human extinction
• Justification for time preference is typically descriptive. But observed time preference might only apply to instrumental goods, like money, whose value decays over time
  • It would be difficult to design an experiment in which time preference for an intrinsic good, like happiness, can be separated from forms of non-pure discounting
• We should reject time discounting just as we reject spatial discounting
• It is better to invest in stocks rather than x-risk if: ROC is exogenous to the rate of social savings; ROC is higher than the rate of technological change in extinction countermeasures; marginal cost-effectiveness of countermeasures does not decrease faster than ROC
  • Exogeneity is false
  • Spending on countermeasures tends to accelerate learning
  • If x-risk decreases after space colonization, then we might be living in a particularly influential time

• Suppose, absent extinction risks, humanity survives as long as homo erectus did, at a population of 10 billion. That gives us 1.6 million years remaining, and 1.6e16 life-years
• 1 in a million chance of extinction-level asteroid impact this century
• A system to detect all near-earth asteroids would cost $300M to $2B, and a system to deflect asteroids would cost $1B to $20B to develop
• Assume $20B to run an asteroid deflection program, and a 50% probability of success
• Expected cost is $2.50 per life-year. This is far more cost-effective than many programs we currently fund

• Although the usual justifications for discounting do not apply to extinction, we might conclude that working on human extinction is cost-effective even if we accept discounting
• Using Weitzman (2001)'s gamma discounting, asteroid deflection cost $140 per life-year. At a 1% constant discount, it cost $40,000

• We define fundamental value as the residual income valuation: \(F_t = B_t + \displaystyle\frac{E[X_{t+1} - r B_t]}{1 + r} + \frac{E[X_{t+2} - r B_{t+1}]}{(1 + r)^2}\)
  • where B is book value, X is forecasted earnings, r is expected return (a combination of risk-free rate, assumed 3% equity premium, and beta of the stock in question)
  • That is, fundamental value is current book value plus the next two years' expected earnings in excess of the return on equity
  • If we use infinity years instead of two, this is equivalent to the discounted dividend model

• Value investing works either by prices reverting to fundamentals, or by buying cash flow for cheap. But the latter is not typical
• If fundamentals converge to price, or if the value spread widens, then value will underperform
• One way to assess whether fundamentals help value investors is to cheat and use future earnings expectations: in year N, take analyst forecasts of earnings made in year N+1
  • This strategy performed very well in general, but performed poorly in the dot-com era and for the past few years
  • me: "poorly" means Sharpe ~2, vs. average Sharpe ~6
• We decompose returns into (1) dividend + (2) multiple expansion + (3) change in fundamentals

• Two widely held conclusions in the literature:
  1. Market discount rates are not the right indicator for altruistic projects
  2. Market discount rates are much higher than society's true discount rate
• This implies that altruistic projects should weight the future more highly
• Our goal is not to determine the social discount rate, but to identify a range of considerations on how to allocate resources over the long run
• Section II: Results from behavioral psychology generally suggest giving more weight to the future than a standard discounting approach would
• Section III: The pure uncertainty case for giving greater weight to the future (e.g., Weitzman) is less compelling than many have suggested
• Section IV: Any choice we make could change the choices of other actors and future generations

• Fundamental discounting equation: \(\rho = \delta + \eta g\)
• There are several issues that do not fit into this standard approach

• Weitzman has argued that uncertainty strengthens the argument for prioritizing the future
• Here we suggest that the effects of uncertainty are less clear cut, due to two issues:
  1. Proper modeling of low-probability catastrophic events
  2. Learning on the timing of mitigation policies

• People whom the policymaker does not control are likely to adjust their behavior in response to changes in policy

• We identified four Ds that must be considered
• Our principal conclusion is that analyses that focus on merely one or two of the four Ds will provide an incomplete picture

• Recent drawdown for trendfollowing is commensurate with previous large drawdowns
• Trendfollowing delivered positive returns when global equities declined in late 2015

• Assets invested in trendfollowing peaked in mid-2008 at $210B, and have declined to $120B
• All systematic hedge fund strategies have $500B AUM, or 17% of all hedge fund assets
• Futures market has grown since 2008, so trendfollowing as a % of futures markets has decreased by more than half

• Macro environment has produced few meaningful trends since mid-2016
• Can measure opportunity set for trendfollowers by examining the number and magnitude of trends
  • Recent years have seen few such opportunities
  • People have a perception of strong trends due to S&P performance [remember, this was written in 2017], but S&P is the exception right now

• The strategy is behaving as expected
• We continue to anchor our expectations for trendfollowing to the long-term evidence

• Return premiums come more from interest rate-adjusted carry than from excess spot returns
• Returns are stronger during backwardation, inflation, and economic expansion
• We find positive returns even in contango when inflation is up or the economy is expanding

• "Interest rate-adjusted carry" equals the carry PLUS the risk-free rate, equals the convenience yield
• Can decompose excess return as spot return + carry, or excess of cash spot return + interest rate-adjusted carry. We prefer the latter because both components are economically meaningful
• Paper focuses on excess return over the risk-free rate, not on total return

• We constructed two portfolios:
  1. equal-weighted
  2. long top 1/3, short bottom 1/3 on backwardation

• Size, value, momentum, low vol premiums
  • This is not an exhaustive list, but we use these because data is readily available
• Historical (long-only, large cap) premium sizes in the US from 1963 to 2009, using Ken French data
  • For all but size, factor is taken as the top 30%, equal-weighted. Size factor treats 30th to 70th percentiles as small cap
    • me: the paper also reports value-weighted, but I'm just writing down the equal-weighted results
• Raw long-only portfolios have high correlation to each other because they're all exposed to market beta. But correlations between alphas are lower and sometimes negative

• Size, value, and low-vol have low turnover, but momentum has higher transaction costs
• Will historical premium magnitudes continue in the future? Unclear

• It's better to make a strategic choice to add factor exposure, rather than trying to get factor exposure accidentally by hiring fund managers with good track records
• Active management does not add value, it's better to get passive factor exposure
• Even if you want active management, it's better to choose in advance what allocation to give to each factor
  • This allows you to control overall portfolio risk

• Two approaches: passively managed index funds or actively managed quant funds
  • Ignoring actively managed non-quant funds that accidentally have a factor loading, because it's probably a bad idea to invest in those
• There aren't really any passive momentum funds
  • me: this was true at time of publication, but isn't true anymore
• Passive funds might not be constructed in the most efficient way
• It's more efficient to exploit premiums simultaneously instead of separately, by avoiding contradictory positions e.g. between value and momentum
  • me: this seems only relevant for long/short, but paper is about long-only
• Downside of active quant funds is they're more expensive

• Two prior studies found that Senate and House members beat the market
• Our own deeper analysis finds no evidence that Congress members beat the market
• On close inspection, the previously published findings do not actually show that Congress beats the market
• While isolated members of Congress may trade based on insider info, there is no evidence of widespread insider trading
• Previous studies looked at synthetic portfolios built from particular trades. When we attempt to reconstruct Congress members' investment portfolios, we find that they underperformed the market by 2-3 percentage points before expenses
• We believe Congress members have opportunities to insider trade, but usually choose not to because of the potential costs (criminal prosecution or reputational harm)

• A marginal unit of work has bigger EV when the solution is needed earlier. So we should prioritize urgent scenarios more than would be justified by looking purely at their impact and likelihood

• Assume AI will either come soon (20 years) or later (50 years)

• Most EV comes from scenarios where (post-)humanity colonizes space
• Extrapolating from current welfare of humans + farmed animals + wild animals, unclear whether we should support spreading sentient beings to other planets
• Future agents will likely care morally about the same thing we value, or about things we are neutral towards. Unlikely they will hold the reverse of our values. This suggests positive EV of the future
• Other intelligent life in the universe might colonies space, which might be worse in expectation than if humans do it
• If the universe is already filled with disvalue that future agents could alleviate, this gives further reason to reduce x-risk
• X-risk reduction might also prevent global catastrophes that could set social progress on a worse course
• Efforts to reduce x-risk seem positive in expectation from most consequentialist views

• The long-term future is a primary moral concern
• We should aim to satisfy our reflected moral preferences
• Consequentialism
• Linear aggregation

• Altruism likely evolved as a shortcut solution to coordination problems
• Christiano:
  • As agents become better at long-term planning, altruistic emotions become less adaptive
  • Long-term planning will reduce the selection pressure on selfishness
  • Agents who care about the long-term future will be favored by natural selection
• Shulman: Agents who want to create value would only be slightly disadvantaged in direct competition with agents who only care about expanding
• Tomasik: Humane values only have a mild degree of control over the present. So it would be surprising if this changed significantly
• me: I feel like none of this really supports the headline. Long-term planning isn't the same as altruism

• Part 1.2 claims that current people have other-regarding preferences, and future people probably will, as well. While it's clearly true that people have other-regarding preferences, they don't seem to have the necessary kinds of preferences
  • eg people don't generally have preferences about (individual) wild animals or non-kin future people. People kind of care about the global poor but not enough to do anything to help them. People only reliably care about those with whom they can form cooperative relationships
• Much as a paperclip maximizer knows that you don't want to turn the universe into paperclips but doesn't care, humans know that (eg) animals don't like being eaten but they don't care. This seems true based on my many interactions with fellow humans
• Maybe could argue that people have extremely weak "purely altruistic" preferences, which is sufficient to make the far future net positive in expectation
  • I think most of the upside EV of the future comes from situations where people end up forming stronger "purely altruistic" preferences, because of CEV or greater reflection or something. So if the far future is net positive in expectation, that's the main reason
• On Beckstead's argument in Part 3.1: I am not convinced that we are in fact on a trend of social progress, rather than mere economic progress. Additionally, our current trajectory might be idiosyncratically bad; maybe in most possible worlds, we would have already banned factory farming by now (although this seems less likely)
  • Current values do look better than most historical values, so this gives some evidence that the current trajectory is better than average
  • The other argument in part 3.1 only works if you presuppose that extinction is bad, so it's circular reasoning
• In the conclusion, I think by "robustly positive", they mean "neutral or positive in expectation across many value systems"

• Political institutions only care about short time horizons
• Countries' laws and policies substantially affect people's moral norms

• Three major categories
  1. Epistemic determinants: actors' states of knowledge
  2. Motivational determinants: actors' goals and motivations
  3. Institutional determinants: features of institutional context that prevent even well-informed, motivated actors from adopting longtermist policy
• People rationally discount future impacts due to uncertainty
• People irrationally discount future impacts due to attentional biases

• First three are moderate, soft-power reforms that could be implemented immediately

• If capital owners spend 3% per year, their wealth accumulates at rate r - 3, not r
• If the number of descendants doubles every 35 years, wealth accumulates at r - 5
• Factoring in taxation, wealth grows at r - 7

• Even if capital accumulates at r, I am skeptical that a wealth tax is a good idea
• Suppose the economy is composed of workers and capitalists. Workers immediately consume earnings, while capitalists set their consumption according to the Ramsey model
• The government imposes a tax on capital of T per period, which is transferred to workers
• Steady-state return on capital is \(r = g/\eta + \delta + T\)
• In this system, the level of inequality is stable
• Consumption of workers is maximized at T = 0 because taxation reduces capital reinvestment and therefore economic growth
• A government controlled by capitalists that only cares about maximizing capitalist consumption would make T as negative as possible
• Wealth inequality is minimized by making T as large as possible
• A consumption tax would equalize standards of living without discouraging capital accumulation

• While past returns do not predict future returns, past volatility does largely predict future volatility
• True in stocks, bonds, currencies, commodities
• We can volatility-target our portfolio by adjusting leverage based on current vol

• Examined the impact of vol targeting on 60 assets
• Vol scaling reduces portfolio volatility and kurtosis, and increases Sharpe
• High vol is associated with low return, so vol-targeting behaves similarly to trendfollowing

• Examined US stocks and VIX 1990-2018
• Higher current vol predicts lower future returns
• Strategies that time vol have generated alpha
• Insurance against future volatility appears "too cheap" right after volatility rises
  • But the r² is only 0.008
• Investors initially under-react to volatility news, followed by a delayed overreaction

• Suppose we have a wide range of problems, such as in a field of research
• Law of logarithmic returns: In areas of endeavor with many disparate problems, the returns in that area will tend to vary logarithmically with resources invested

• We have an area containing several problems, of varying benefit and difficulty, and that difficulties are distributed smoothly across several orders of magnitude

• Optimal problem selection: Choose problems in order of benefit-cost ratio
• Difficulty-based selection: Easiest problems first
• Naive problem selection: Work on all problems equally in parallel
• If you knew everything about benefits and difficulties, you'd do optimal problem selection. If you knew nothing, you'd do naive problem selection

• Since (by assumption) there is no significant correlation between difficulty and benefit, the benefit from solving all problems at a particular difficulty is constant across difficulty levels
• Difficulty is evenly distributed on a log scale, therefore research returns grow logarithmically

• Benefit/cost ratio will be roughly evenly distributed across orders of magnitude, giving logarithmic returns for a central range of projects
  • me: marginal returns decrease more quickly for early and late projects

• Problems will get solved in difficulty order

• Both optimal and naive look roughly logarithmic, so anything in between should look roughly logarithmic as well

• Logarithmic returns to research do follow from this model, but the conclusion is kind of embedded into the model with the assumption that benefits and difficulties are distributed logarithmically

• The benefits of reducing x-risk may overwhelm those of safeguarding future consumption
  • me: not sure what "safeguarding future consumption" means
• Reducing x-risk directly increases expected utility, and future utilities are only subject to discount by the uncertainty of their realization (0.01% or less); while increasing future consumption is subject to an additional discount on future economic values (me: I think this means the discount due to consumption growth)
• Thus, strong action on climate change mitigation maybe justified even if the economic discount rate is high
• An increase in extinction probability may either increase or decrease our willingness to invest to reduce these probabilities

• Discount by \(r = \delta + \eta g\)
• We should use a zero rate of pure time preference, but may use positive \(\delta\) due to uncertainty about whether future utility will be realized
• r is the discount rate on future consumption, not on utility. Economics agree r should roughly equal the market real risk-free rate
  • [ ] What if the real risk-free rate is negative, as it is currently?
• Stern used a 0.1% probability of extinction, but this is too high. It should be more like 0.01%, or even much less than that
  • 0.1% implies only 37% chance of surviving 1000 years, and 0.004% chance of surviving 10,000 years
    • me: I find 37% for 1000 years pretty plausible. I agree we should use a lower rate for >1000 years, but that doesn't seem as relevant for climate change
• Most empirical estimates find \(\eta < 2\) (e.g., 1.6 for Evans (2004))
• It doesn't change much to truncate our consideration to only a few hundred years, because the discounted utility of everything after that is small
• For extinction, we care more about \(\delta\) than r, so we put much more weight on extinction even if it happens more than a few hundred years from now

• If we are confined only to consumption choices, then higher P(extinction) makes climate change appear less serious because we discount the future more heavily
• If we can also reduce P(extinction), then higher \(\delta\) might mean we care more about x-risk

• Extinction due to climate change seems possible via three conditions: imperfect governance, positive feedbacks, and a tipping point
• Extinction may be unlikely, but the main point is that we cannot rule it out

• Consider an effort that will reduce P(extinction) \(\delta\) by proportion \(\alpha\), to \((1 - \alpha) \delta\)
• Assume no changes in consumption from consumption growth, population growth, etc.
• Present value of utility for year t is \(U \cdot (1 - \delta)^t\)
  • The full utility stream from year t to infinity has present value \(U \displaystyle\frac{(1 - \delta)^t}{\delta}\)
• Let \(B_\infty\) be the benefit of reducing \(\delta\) by proportion \(\alpha\). Then \(\displaystyle\frac{\partial B_\infty}{\partial \delta} = \frac{-\alpha}{1 - \alpha}(1 - \delta)^{t1} \frac{1 + t_1 \delta / (1 - \delta)}{\delta^2}\)
• \(\frac{\partial B_\infty}{\partial \delta} < 0\), which means an increase in \(\delta\) decreases the benefit from lowering it by proportion \(\alpha\). This is true even when \(t_1 = 0\) (i.e., the lowering takes effect right away)
  • For the special case of \(t_1 = 0\), \(\displaystyle\frac{\partial B_\infty}{\partial \delta} = \frac{-\alpha}{1 - \alpha} \delta^2\)
• Why does larger \(\delta\) see a smaller benefit?
  1. First effect: The same proportionate decrease (\(\alpha\)) in \(\delta\) results in a larger absolute decrease if \(\delta\) is larger
  2. Second effect: Larger \(\delta\) means future utility matters less. This effect dominates
• Intuition for the opposite: Suppose your house is threatened by bushfire. The higher the probability of destruction, the more willing you are to invest in things like paving a fire clearance
  • Explanation for this intuition is that time horizon is finite
• When end time T is finite, sign of \(\displaystyle\frac{\partial B_T}{\partial \delta}\) can be positive or negative. It can increase as T increases, but eventually must start decreasing for sufficiently large T

• In 1950's, Heinz von Foerster found that population growth follows hyperbolic growth, culminating at infinity in 2026
• According to von Foerster, technological growth is a function of population, because each person has some probability of developing a technological advancement; and technology enables larger population
  • This allows the population growth rate to accelerate
• This matches reality, with population doubling times getting increasingly shorter
• Then, contrary to history, people started having fewer kids than their resources could support (the demographic transition)

• World GDP doubling time had been exponentially decreasing (suggesting hyperbolic growth) up until about 1960. Now we just get a flat 2% GDP growth
• Using von Foerster's model, this is because population has stopped growing hyperbolically

• Industrial Revolution does not show up on a world GDP graph
• Until about 1500, increases in productivity only occurred due to population. GDP per capita was about the same everywhere

• The singularity got cancelled because we no longer have a surefire way to convert money into researchers
  • The old way was money -> food -> population -> researchers
• AI might enable money -> more AIs -> more research, which could restart hyperbolic growth
• Presumably you would eventually hit some other bottleneck, but things could get very strange before that happens

• Unlike viruses, humans can improve fitness within (rather than across) generations via memetics
• Some inventions, such as the printing press, accelerate the rate of intellectual evolution
• Von Foerster et al. (1960) observed growing population growth, fitting an infinite population by 2026
  • Population growth peaked in 1968 and has fallen since then
• Gross world product (GWP) back 10,000 years better fits a power law than an exponential curve
• Fit has GWP = infinity by 2047
• me: Based on this data, it looks like hyperbolic growth stopped around 1980 (Scott says 1960), and it became exponential. We only get GWP = infinity at 2047 if we insist on fitting to a power law
  • me: Perhaps this implies altruists should be trying to get people to have more kids?

• I devised a stochastic model for the evolution of GWP
• A stochastic model starting in 10,000 BC has "singularity time" varying from 4,000 BC to >5,000 AD
• According to this model, based on where we are now, median singularity time in 2047, and singularity is all but inevitable by 2100
• Stochastic model run up through 1700 predicts a lower GWP in the 1800's than we actually saw, suggesting the industrial revolution was indeed an anomaly

• If technological growth and economic growth can accelerate each other, this produces hyperbolic growth
• Solow model: labor, capital, human capital, and technology go into production. Then, some production is consumed and some is fed back into the factors of production
  • Solow treated labor and technology as growing exogenously, not as taking input from production
• Under Solow model, fixed (exponential) technological growth leads to exponential growth of production
• Romer model treated all four factors of production as growing endogenously, and as experiencing depreciation
  • This results in an unstable model—the amount of each factor either goes to infinity in finite time or decays to zero

• One explanation is that growth may stabilize to exponential, so we won't see GWP = infinity
• A hyperbolic model might not be wrong, but it loses accuracy outside a regime
• Economic growth is limited by natural resources (including solar energy)
  • Under an extension of the Romer model, the economy continues growing hyperbolically until all natural resources are used up, at which time it crashes to zero

• Predictions of infinity tell us two key things
  1. If the patterns of history continue, then some sort of economic explosion will take place again, the most plausible channel being AI
  2. The propensity for explosion is a sign of instability in the human trajectory

• We wan to promote good value changes and avoid bad value changes, but distinguishing between them can be difficult
• EAs seem to think value drift will affect EAs less than people in general, and themselves in particular even less. This could be overconfidence bias
• Being connected with the EA community, getting involved in EA causes, being open to new ideas, prioritizing a sustainable lifestyle, and certain personality traits seem associated with less value drift
• The study of EAs' experiences with value drift is neglected, so further research is likely to be highly beneficial

• I interviewed 18 EAs

• Value drift is highly neglected, and any research is potentially highly impactful
• Some ideas about future research:
  • Find literature on value drift in other social movements
  • Replicate this study with improved interview questions and larger/more representative sample
  • Survey people's self-perceptions of likelihood of value drift and compare to actual value drift
  • Interview/survey people who have left EA movement
  • Conduct longitudinal analyses of values
  • Philosophical research on moral issues surrounding value drift

• Strong Longtermism: The primary determinant of the value of our actions is the effect of those actions on the long-run future
  • me: This is distinct from regular longtermism, which merely claims that most of the value of the world exists in the far future, with no claim as to our effect on it. A "Kaufman-esque" longtermist might still endorse global poverty
• Hinge of History Hypothesis (HoH): We are living at the most influential time ever
• In the EA community, those who believe strong longtermism usually also believe HoH, but it is not obvious why these claims should be associated
• This post will introduce a view called outside-view longtermism: we should expect the future to continue the trends of the past, and now is most likely not an exceptional time

• Value Lock-in view: If we develop AGI, that AGI's goals will likely determine the trajectory of the future, so it is overwhelmingly important that we ensure it has good goals
• Time of Perils view: We are living in a period of unusually high extinction risk
  • Outside-view support: Extinction risk has been unusually high since the development of nuclear weapons
  • Inside-view argument: Extinction risk due to forthcoming technologies like synthetic biology
  • We need not only that x-risk is high, but that mitigation is particularly cost-effective
• Outside-view arguments
  • We're early on in human history, and thus have a lot of ability to influence the future
  • We're in a period of unusually high economic and technological growth
  • By virtue of still being on one planet, we're both unusually able to coordinate and unusually likely to go extinct

• If we're in one of the most (but not the most) influential times, that's still vulnerable to the reasoning above
• Yudkowsky's view on AI takeoff entails that we're in the most influential time ever, not merely a particularly influential time
• If now is enormously influential time but some future time matters even more, we should invest for that future time

• Implications are non-obvious
• Most obvious action is invest more
  • This seems like it would have been a good strategy at many times in the past

• A directly self-defeating theory would defeat its own purpose if successfully implemented
• An indirectly self-defeating theory defeats its own purpose if agents try to implement it, because they will fail
• A donor can seemingly always do better by donating later, but then will end up never donating at all, apparently producing a paradox
• We can produce the same paradox without reference to time: Suppose you can name any amount of money, and a third party will donate that much money. No matter what sum you choose, you could have picked something higher
  • Paradox arises because gains are potentially infinite
  • me: You can make an even more similar paradox by saying the third party will donate the reciprocal of the number you name, so you want to name a number arbitrarily close to 0 but you can't say 0
• At some point the universe will almost surely come to an end. With perfect foresight, we could adopt a spending schedule that spends all our resources just as the world ends

• We cannot reliably foresee the end of the world. Even so, zero time preference does not entail PIPS
• If deferring consumption does not maximize utility, then agents should not defer consumption
• UU is not indirectly self-defeating. The real problem is that it gives us only the vaguest guidance about what to do

• Arguably, if we are uncertain about what savings rate maximizes utility, we should choose one that reflects the market interest rate
  • me: sounds like this is saying to set consumption = interest rate, but I'm not sure
• It would not make sense to invest in greenhouse gas mitigation if we could invest the money and then build the walls and pumps and everything we need if sea levels rise
• But the rate of return might fall in the future

• Additional income does not provide much happiness above about $20K per year. It is far more important to minimize the risk of global catastrophe than to increase consumption
• How do we account for x-risk in discounting?
• One approach is to treat extinction as infinitely bad, so no amount of discounting can make it disappear
• Another approach is to carry on discounting as usual, subject to the constraint of avoiding x-risk
  • me: I think the author's claim is that we must ensure extinction has probability 0

Each line gives an estimate from a particular paper

• Community-building was #2 cause priority according to 2019 EA Leaders survey, and institution-building is a form of community-building. How does it compare to other forms?

• Investing in epistemic institutions seems valuable and neglected
• Groups working on community-building and groups working on improving decision-making should join forces more

• All the models I discuss assume the only purpose of accumulating capital is for eventual consumption
• Consumption can take place over an infinite time horizon
• Models assume consumption is fundamentally the same across periods, and the only difference is the amount of capital available

• Future models could be created that might better reflect reality
• The difficulties encountered in these models are instructive about the nature of the problem

• Value investing has underperformed over the last 12 years
• Primary driver was the value-growth spread widening
• Book value may not be the right measure. Including intangibles improves on P/B, particularly post-1990
• With today's value-growth spread at an extreme, the stage is set for potentially historic outperformance

• HML return can be divided into three components
  1. Revaluation: relative valuation between the growth and value baskets
  2. Profitability: growth stocks are more profitable than value stocks
  3. Migration: stock-level mean reversion in valuation multiples (growth stocks become value stocks, and vice versa)
• Structural = profitability + migration
• Historically, 50% of the high valuations of growth stocks are explained by better future profitability
• Historically, 2/3 of variation in HML is explained by revaluation

• Attribution of value factor returns

  	Total	Revaluation	Structural	Profitability	Migration
  1963–2007	6.2	0.2	6.	-13.2	19.2
  2007–2020	-6.1	-7.2	1.1	-15.9	17.0
  Full	3.3	-1.5	4.8	-13.9	18.7

• Structural value performance still beat growth, but by a much smaller margin than historically
• Underperformance of value was mostly explained by revaluation
  • me: This could make sense as a reaction to the structural value premium falling. Or maybe better explained by HML (B/P) becoming a worse value metric due to the rise of intangibles
• From 2007–2020, the value factor experienced a 55% drawdown, but a 64% drop in relative valuation
• me: Reduction in structural return is mostly driven by small-caps. Large-cap HML structural return went from 3.5% 1963–2007 to 2.0% 2007–2020, so not much change

• r(revaluation, profitability) = –0.32; r(revaluation, migration) = –0.43; r(revaluation, migration) = –0.04
  • When HML benefits from a tailwind of upward revaluation, this tends to get offset by lower profitability and migration
• If we account for these correlations, a regression predicts that if the value spread mean-reverted to its historical 50th percentile within one year, HML would experience a 77% return (on a log scale, meaning value more than doubles relative to growth)
  • Even a reversion to the 95th percentile valuation would entail a 37% return
  • This assumes reversion to historical structural return of 4.8%
• Historically, the value spread halfway mean-reverts every 2.2 years

• What expectations do you get if you assume structural return stays near 1%?
• Historically, how has structural return varied? How unusual is 1%?
• What is the structural return using metrics other than B/P?
• 2023-01-30: I'm not sure the concept of structural return is even coherent. I don't know how to think about migration. The AQR paper seems cleaner b/c it directly predicts fundamentals growth as a function of the current price/fundamental ratio
  • Thinking about it, I think RAFI concept is reasonable but I'm not sure. But the migration component feels more like revaluation: when an individual stock experiences multiple contraction, it might switch from value to growth or vice versa. Migration happens when a value stock does better than expected or a growth stock does worse than expected, so it's a component of structural return

• High-yield spread: Yield difference between below-investment-grade bonds and Treasury bonds
• Small-cap value tends to underperform when high-yield spread increases, probably because investors become scared that these companies are more likely to go under or be unable to refinance their debt
• In Q1 2020, the high-yield spread grew from 3.6% to 8.8%, which explains small value's poor performance
• High-yield spreads this high tend to occur about once per decade
• Small value tends to recover quickly after high-yield spread increases, generally beating the market over the subsequent year

• This chart shows one-year forward returns for the smallest and cheapest decile vs. the S&P 500

  HY Spread	Value	S&P 500
  <4%	15%	10%
  4-6%	16%	12%
  >6%	33%	13%

• Even though small value generally performed poorly in the last decade, it outperformed the market in 2011-2013 and in 2016 when the high-yield narrowed

• Historically, value performs better when the growth/value ratio is high, measured here by the free cash flow spread
• Current FCF spread is nearly 6x

• This chart one-year forward returns for the smallest and cheapest decile vs. the S&P 500

  FCF Spread	Value	S&P 500
  <3.5x	22%	14%
  3.5-4.5	16%	13%
  >4.5	29%	7%

https://alphaarchitect.com/2020/04/13/trend-following-reality-you-need-trends-to-trend-follow/

• Key question: Has trendfollowing stopped working in the last decade?
• Trend-following strategies perform well when markets are in chaos, but markets have been stable 2010-2018
• Paper examines 67 markets across equities, bonds, commodities, and currencies back to 1877, looking at 1-, 3-, and 12-month trend-following
• Trend-following performs better when markets have large moves. If you plot the regression of the absolute magnitude of market moves vs. trend-following performance over 2010-2018 versus over the prior sample (1880-2009), the fitted lines look nearly the same
• Poor recent performance is mostly explained by lack of big market moves

[2020-12-04 Fri]

• Sharpe ratio of trendfollowing can be predicted with linear regression \(\alpha + \beta | SR |\)
  • |SR| is the absolute Sharpe ratio of the underlying market (absolute b/c trends can be positive or negative)
  • \(\beta\) is the extent to which market Sharpe ratios predict trendfollowing performance
  • \(\alpha\) is the absolute expected return of trendfollowing
• \(\alpha\) is negative: on markets with zero Sharpe ratio, trendfollowing has negative performance
• |SR| has been weaker 2010-2018, which explains poor trendfollowing performance
• If we plot a regression line using data from 2010-2018, it looks nearly identical to a regression on 1880-2009. If trendfollowing efficacy had degraded, we would expect to see a shift down (lower \(\alpha\)) or shallower slope (lower \(\beta\)), but we don't
• Trendfollowing has not been substantially less diversified 2010-2018, so this does not explain performance. Diversification actually worked slightly in favor of performance
• Among three hypotheses (market moves, trend efficacy, diversification), market moves explain almost 100% of the poor performance 2010–2018. See Exhibit 7
  • Trend efficacy did explain poor performance 1900–1909. 1940–1949 had poor trend efficacy, but this was more than made up for by unusually large market moves
  • 2010–2018 actually has amazingly average trend efficacy. Most decades are higher or lower
  • 2010–2018 is by far the worst decade for market moves
• When large market moves did occur 2010–2018, trendfollowing profitability was consistent with expectations based on long-term evidence
• There is little reason to expect a fundamental shift in markets that would cause permanently smaller market moves
• Looking ahead, this suggests that trend-following strategies should be able to deliver performance more in line with full sample results going forward if the size of market moves reverts to levels more consistent with the long-term historical distribution of returns

• Valuation timing has strong statistical forecasting power, but weak outperformance in a portfolio
• Valuation better predicts 5-10 year returns than over shorter time horizons (e.g., next quarter)

• Higher valuation predicts lower future returns
• 1900-2015, top quintile valuation (CAPE > 22) had 3% 10-year forward return, while bottom quintile (CAPE < 11) had 10%
• Hindsight bias: we know the distribution of valuations over the sample period
  • If we define valuation quintiles using a rolling 60-year window of past data, cheap quintile still outperforms expensive quintile, but by a smaller margin

• T-stats for CAPE as predictor of returns over future time periods

  10yr	1yr	1mo
  3.4	1.9	2.3

• We test a strategy that scales equity investment from 50% to 150% based on valuation. That way it isn't binary and it has the same average % investment as buy-and-hold

• Using rolling 60-year window to determine "normal" valuations. 1958-2015

  	CAGR-RF	stdev	Sharpe
  B&H	5.5	14.9	0.37
  timing	5.4	14.6	0.37

• Results may have been disappointing because CAPE generally drifted upward over this period
• Only applying timing at extreme valuations does not help

• If we adjust the results ex post (a.k.a., cheat) to show no valuation change over the period, value timing now outperforms buy-and-hold
  • These adjustments are not possible without perfect foresight

• Regression analysis shows valuation timing has negative factor loading on 12-month time-series momentum

• Valuation timing after controlling for TSMOM has positive but not statistically significant excess return

  	CAGR-RF	stdev	Sharpe	MaxRelDD
  B&H	5.5	14.9	0.37
  value	5.4	14.6	0.37	-32%
  MOM	5.9	13.8	0.43	-31%
  value+MOM	5.7	13.8	0.41	-17%

  • MaxRelDD is max drawdown relative to B&H
• Value and momentum signals have correlation -0.2

• If pure momentum outperforms value+mom, why not pure mom?
• Recent decades have been particularly bad for value-based timing, which is unlikely to repeat
• Momentum has had periods of sharp underperformance; value+momentum has had smaller drawdowns relative to pure momentum
• Value and momentum are negatively correlated
• Momentum has higher turnover and transaction costs, and the above numbers are gross
• If we construct long/short portfolios using time-series value and momentum, both had positive Sharpe (0.15 and 0.24, respectively, for 1900-2015)
  • Combining these long/short portfolios maximizes Sharpe at around 50% allocation to each (due to negative correlation)
    • Pure momentum has higher Sharpe than pure value, so if correlation were zero, optimal weighting would lean toward momentum

• Can reduce negative exposure to momentum by simply rebalancing less frequently
• Slowing down the value signal holding period from 1mo to 1yr improves Sharpe substantially, and slowing from 1yr to 2yrs improves Sharpe a little
• These implicit methods are probably not as good as explicitly accounting for momentum

• Mathematical genius is not about proving difficult theorems, but about discovering theorems
• On a scale of natural mathematical ability, Hardy rated himself a 25, Hilbert an 80, and Ramanujan and Gauss 100
• Genius requires giftedness (essentially general intelligence), but it also requires productivity and creativity

• Creativity is the bringing into being of something that has not previously existed
• One prominent theory of creativity that seems unpromising and probably wrong: the chance configuration theory, which states that creativity is what happens when you try things at random and they happen to work by sheer chance

• Giftedness may even be orthogonal to genius
  • Wagner was not a prodigy and did not even seem particularly talented (he wasn't good at playing any instruments and he only started seriously focusing on music later in life)
• Giftedness is necessary for genius, but genius also requires productivity and creativity
• Individual differences in countable units of achievement follow a J-curve (log-normal distribution), which suggests a multiplicative model

• NYT calculator is my favorite rent vs. buy calculator because it accounts for opportunity costs
• By default, the calculator makes some questionable assumptions
  • It assumes housing prices will rise faster than rent, but in the long run they must rise at the same rate
  • It assumes housing prices increase 1% faster than inflation, but it looks more likely that prices will simply match inflation
  • It assumes 4% investment return, but a global stock portfolio can be expected to earn closer to 6% (nominal). Adding in "contrarian investing" strategies can get up to maybe 8%

• An individual house is risker than the stock markets
  • :me: if you lever up the global market portfolio to match the risk level of a house, you will get a much higher expected return
• For young people (under ~40), buying a house adds correlation risk: in a local downturn, junior employees face the most job risk, and the best place to get a job might be a different city. Owning a house makes it harder to move
• Retired people face the opposite risk: you don't care about the job market, but you do care about rents rising
• It generally doesn't make sense to time entering the stock market because of the opportunity costs of holding cash. But it does make sense to time the housing market, so it's better to buy a house during a recession
  • :me: I'm not sure that's true because buying during a recession requires liquidating a bigger % of your investments. Although with a mortgage, this doesn't matter much

1. Get everything in writing
2. Always keep the door open
3. Information is power
4. Always be positive
5. Don't be the decision maker
6. Have alternatives
7. Proclaim reasons for everything
8. Be motivated by more than just money
9. Understand what they value
10. Be winnable

• Write down everything they tell you verbally

• Your leverage comes from the fact that they don't know what you're thinking or what you'd be satisfied with
  • Corollary: Don't tell them how much you currently make
• "Yeah, [COMPANY_NAME] sounds great! I really thought this was a good fit, and I'm glad that you guys agree. Right now I'm talking with a few other companies so I can't speak to the specific details of the offer until I'm done with the process and get closer to making a decision. But I'm sure we'll be able to find a package that we're both happy with, because I really would love to be a part of the team."

• Your excitement is one of your most valuable assets in a negotiation. If you're excited, companies will expect you'll work hard and stay for a long time

• "I'll look over some of these details and discuss it with my [FAMILY/CLOSE_FRIENDS/SIGNIFICANT_OTHER]."

• Once you have an offer, reach out to other companies you're interviewing with and let them know you've received an offer
• Mention the name of the company if it's prestigious
• Also send this to companies you applied to but haven't gotten a response

• Generally, you should start interviewing at larger companies earlier—their processes are slower and their offer windows are wider

• Treat an exploding offer as a non-offer
• Convey to them that if the offer is exploding, it's useless to you
• Every time I got an exploding offer and told them I couldn't make a decision in time, the offer immediately extended

• Companies value different skills, and some companies will consider you more valuable than others. Chances are this is where you'll be able to negotiate your strongest offer

• Easier to develop a rapport over phone which can help, but using email makes it easier to say uncomfortable things without being pressured by a recruiter
• Recruiters will always prefer to talk on the phone, but if you want to use email, you can push back on this

• Focus on making nonzero-sum improvements by negotiating along multiple dimensions (signing bonus, stock, commuter benefits, vacation time, etc.)
• Sharing a cake seems like it's zero-sum, but maybe one person prefers frosting and the other wants extra cherries

• Even if you don't have other offers, you still have a BATNA: interview at more companies, go to grad school, go on sabbatical in Morocco, etc.
• Talk about your BATNA, but be sure to re-emphasize that you'd like to reach an agreement

• The company has spent about $25,000 getting to this point, and will have to start over if you turn them down

• Tell them what you like about the offer. Be unfailingly polite
• Asking for more money feels greedy, but not if you need the money. Have a reason for everything.
• I told employers that I was earning-to-give, so since I was donating 33% of my income to charity, I had to negotiate aggressively to leave myself enough to live off
• It can help to give a specific financial goal, e.g., you want to buy a house

• After asking for something, emphasize the unique value you bring

• Be motivated by more than just money
• Salary is the hardest thing for companies to give, so if you want more money, you should structure it outside of salary. A signing bonus is easier because it only gets paid once and it doesn't create the same social unrest as if you got paid a higher salary than your coworkers
• The easiest thing for a company to give is stock

• Companies want to know that you won't keep dragging them along
• Once you reach the intermediate stages, set a deadline, even if it's arbitrary
• I use "a weekend with the family" to make the final decision because it has the added benefit of bringing in other decision-makers
• Always wait until your deadline day to sign an offer, even if you only have one. I've seen offers spontaneously improve as deadlines approached
• Your trump card: "If you can do X, I will sign." Save this for the very end

• Criticisms don't apply as much if you do rolling exposure like we do
  • We buy 3 month puts every month and sell the previous month's puts
• The size of our put holding depends on market valuation. When valuations are expensive, puts look more valuable

• "Naked share" test: Is the title compelling enough that people would share the title text even without the article?

• You might not know as much as experts, but experts usually aren't good at writing, and those who are are usually too busy

• I self-host instead of writing on Medium or Quora so I can control the platform forever

• Schedule writing sessions
• Don't worry about growing an audience yet

• If I hear someone give a provocative opinion, I write it down and maybe turn it into an essay

• Think of yourself as a journalist

• May 6 had been unusually turbulent prior to flash crash
  • By 2:30pm, VIX was up 22% from open
• Buy-side liquidity on E-Mini S&P 500 futures contracts had fallen from $6 billion to $2.6 billion (where liquidity is defined as the value of resting orders); SPY buy-side liquidity fell from $275 million to $220 million
• At 2:32, a large mutual fund complex attempted to sell 75,000 E-Mini contracts (worth $4.1B) as a hedge to an existing equity position
  • Executed via an algorithm ("Sell Algorithm") that attempted to sell an amount equal to 9% of the trading volume over the previous minute, until the whole amount was sold, without regard to price
  • The Sell Algorithm sold the full amount in 20 minutes, which is unusually fast for such a large position (normal would be ~5 hours)
• Cross-market arbitrageurs transferred this sell pressure to other related products (e.g., SPY, or individual equities in the S&P 500)
• HFTs accumulated a net long position of 3,300 E-Mini contracts. Between 2:41pm and 2:44pm, they sold 2,000 contracts, while trading 140,000 contracts
• Due to increased volume from HFT selling, the Sell Algorithm increased its rate of selling

• Sell pressure drove the E-Mini price down 3% in the four-minute period [2:41, 2:44]
  • Cross-market arbitrageurs drove down SPY by the same amount
• HFTs then began to quickly buy and resell to each other
  • Between 2:45:13 and 2:45:27, HFTs traded 27,000 E-Mini contracts (49% of total trading volume), while only net buying 200
• Buy-side market depth fell to $58 million, less than 1% of its depth from earlier that day
• E-Mini fell another 1.7% in those 15 seconds, to reach its intraday low
• At 2:45:28, E-Mini trading was automatically paused for five seconds by the Chicago Mercantile Exchange Stop Logic Functionality
• When trading resumed at 2:45:33, prices began to recover

• The rapid price move triggered pauses in many individual market participants' automated trading systems
• Many liquidity providers stopped trading or widened their spreads
• Due to liquidity providers canceling their orders, liquidity completely evaporated on some securities and ETFs, and some trades executed at $0.01 or $100,000
  • Market makers sometimes put in bids at $0.01 or asks at $100,000 when they're obligated to provide liquidity on a security, but don't want to actively trade it (called "stub quotes")
  • :me: this is why you should always put in limit orders, even when the spread is 1 cent. although to be fair, this only would have happened on securities that were relatively thinly traded to begin with
• The exchanges and FINRA agreed to cancel ("break") all trades that executed at >60% away from the 2:40pm price

• The automated execution of a large sell order can trigger extreme price movements
• The interaction with algorithmic traders can result in widespread loss of liquidity
• Existing market-wide circuit breakers failed to trigger, and may need to be recalibrated
• Many market participants implement their own trading pauses. If many trigger at once, that can cause a liquidity crisis
• Pausing trading is an effective way to re-establish an orderly market
• New circuit breaker pilot program: pause trading across all markets in a security for 5 minutes if that security has experienced a 10% price change in the preceding 5 minutes
• SEC and FINRA have developed more objective standards for when to break trades
  • Pilot program: For stocks priced <$25/$25-$50/>$50, trades will be broken if they are at least 10%/5%/3% away from the circuit breaker trigger price

• This book is about strategies for producing retirement income

• Scenario matrix: Matrix where columns represent results we care about and rows represent possible scenarios
• This book focuses on discrete rather than continuous models to avoid having to deal with analytic functions
• This book uses MATLAB

• Two key questions:
  1. How much money do you have to provide future income?
  2. How long might you need such income?

• Momentum works for equities in many countries, bonds, currencies, commodities, and others
• We will present evidence based on Ken French's publicly available data

• Factor returns over the whole period and out of sample (for US stocks):

  	RMRF	SMB	HML	UMD
  1927-2013	7.7%	2.9%	4.7%	8.3%
  1991-2013	8.2%	3.3%	3.3%	3.6%

  • note that the first papers on momentum were Jegadeesh and Titman (1993) and Asness (1994)

• Sharpe ratios:

  	RMRF	SMB	HML	UMD
  1927-2013	0.41	0.26	0.39	0.50
  1991-2013	0.54	0.29	0.32	0.36

• % positive N-year rolling returns, 1927-2013

  	RMRF	SMB	HML	UMD	HML+UMD
  Sharpe	0.41	0.26	0.39	0.50	0.80
  1-year	71%	58%	63%	81%	81%
  5-year	82%	65%	89%	88%	90%

  • HML+UMD uses 60% HML, 40% UMD
  • UMD is more consistent than RMRF