New strategy:
#425 – Impact of Overnight Returns and Daytime Reversals to Future Stock Returns
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Bactest period: 1993 – 2017
Indicative performance: 5.28%
Estimated volatility: 6.43%
Source paper:
Akbas, Ferhat and Boehmer, Ekkehart and Jiang, Chao and Koch, Paul D.: Overnight Returns, Daytime Reversals, and Future Stock Returns: The Risk of Investing in a Tug of War With Noise Traders
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3324880
Abstract:
A high frequency of positive overnight returns followed by negative trading day reversals during a month suggests a persistent daily tug of war between opposing investor clienteles, who are likely composed of noise traders overnight and other investors during the day. We show that a more persistent tug of war predicts higher future returns, both for individual stocks and the overall market. Our results are stronger in situations where it is riskier to trade against noise traders. Additional tests further support the conclusion that investors demand a premium for trading stocks that are more prone to this “noise trader risk.”
New research papers related to existing strategies:
#44 – Paired Switching
Clare, Seaton, Smith, Thomas: Absolute Momentum, Sustainable Withdrawal Rates and Glidepath Investing in US Retirement Portfolios from 1925
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3347183
Abstract:
A significant part of the development in pension provision in many countries is the emergence of ‘Target Date Funds’ or TDFs. In this paper we examine the proposition of de-risking through life and the guidance offered by TDFs in the decumulation phase following retirement. We investigate the withdrawal experience associated with Glidepath Investing in the US since 1925 for conventional bond-equity portfolios. We find one very powerful conclusion: that smoothing the returns on individual assets by simple absolute momentum or trend following techniques is a potent tool to enhance withdrawal rates, often by as much as 50% per annum! And, perhaps of even greater social relevance is that it removes the ‘left-tail’ of unfortunate withdrawal rate experiences, i.e. the bad luck of a poor sequence of returns early in decumulation. We show that diversifying assets over time by switching between an asset and cash in a systematic way is potentially more important for the retirement income experience than diversifying one’s portfolio across asset classes. We also show that Glidepath investing is only sensible within a few years of the target date. This finding provides succour to enthusiasts for target date investing in the face of the growing hostility in the literature.
#62 – Shorting Overvalued Stocks
Khan, Peddireddy, Rajgopal: Earnings Quality on the Street
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3347217
Abstract:
To understand how practitioners operationalize evaluations of earnings quality, we obtain a proprietary dataset of 1,029 reports on aggressive reporting practices over 2003-2015 for 348 unique firms published by a research firm (RF) that sells such data to institutional clients. From these reports, we identify 121 measures of poor earnings quality under four major categories: (i) sales quality; (ii) margin quality; (iii) cash flow quality; and (iv) others. As a first-cut to short-list stocks for detailed fundamental analysis, the RF appears to screen for larger, growing firms with lower barriers to arbitrage. The firms flagged by the RF also have higher M-score, F-score, and total and abnormal accruals. The average two (251) days abnormal return after the stock is first flagged by the RF is -1.30 (-18.5) percent, and such return is incremental to the return attributable to mispricing of accruals. Modified Jones and Dechow-Dichev models of abnormal accruals do not appear to capture RF identified signals well suggesting that such models are too coarse to pick up nuanced fundamental analysis conducted by the RF. In out of sample analyses, we find that the RF signals are associated with future restatements, AAERs, and GAAP-related lawsuits after controlling for other earnings quality indicators. We develop an improved earnings quality indicator (RFSCORE) for firms in the retail, durable manufacturing, and business services sectors using the RF’s signals which are based on granular, context- and industry-specific fundamental analysis. To the Street, our paper suggests that fundamental analysis, beyond just the magnitude of accruals, can predict future stock returns. To academics, our research demonstrates that granular, context-specific analysis of public data can supplement and improve the workhorse models used to identify poor earnings quality.
#21 – Momentum Effect in Commodities
#322 – Rank Effect for Commodities
#424 – Long-Run Reversal in Commodity Returns
Geczy, Samonov: Two Centuries of Commodity Futures Premia: Momentum, Value and Basis
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3336406
Abstract:
Using hand-collected data of commodity futures contracts going back to 1877, we replicate in the pre-sample history the well-documented cross-sectional commodity factor premia of momentum, value and basis. All three premia remain significantly positive in the additional 80-plus years of pre-sample data. Compared to a long-only passive basket of commodity futures, a long-only premia portfolio more than doubles its Sharpe in both the early and recent samples, suggesting a more optimal way to obtain portfolio’s commodity exposure while maintaining its beneficial inflation hedging property.
And four additional related research papers have been included into existing free strategy reviews during last 2 weeks:
Can we explain abudance of equity factors just by data mining?
Chen: The Limits of P-Hacking: A Thought Experiment
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3358905
Abstract:
Suppose that asset pricing factors are just p-hacked noise. How much p-hacking is required to produce the 300 factors documented by academics? I show that, if 10,000 academics generate 1 factor every minute, it takes 15 million years of p-hacking. This absurd conclusion comes from applying the p-hacking theory to published data. To fit the fat right tail of published t-stats, the p-hacking theory requires that the probability of publishing t-stats < 6.0 is infinitesimal. Thus it takes a ridiculous amount of p-hacking to publish a single t-stat. These results show that p-hacking alone cannot explain the factor zoo.
And three insights from academic research related to #14 – Momentum Factor Effect in Stocks:
Souza: A Critique of Momentum Anomalies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3341275
Abstract:
This paper offers theoretical, empirical, and simulated evidence that momentum regularities in asset prices are not anomalies. Within a general, frictionless, rational expectations, risk-based asset pricing framework, riskier assets tend to be in the loser portfolios after (large) increases in the price of risk. Hence, the risk of momentum portfolios usually decreases with the prevailing price of risk, and their risk premiums are approximately negative quadratic functions of the price of risk (and the market premium) theoretically truncated at zero. The best linear (CAPM) function describing this relation unconditionally has exactly the negative slope and positive intercept documented empirically.
Butt, Virk: Momentum Crashes and Variations to Market Liquidity
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3314095
Abstract:
We document that the variation in market liquidity is an important determinant of momentum crashes that is independent of other known explanations surfaced on this topic. This relationship is driven by the asymmetric large return sensitivity of short-leg of momentum portfolio to changes in market liquidity that flares the tail risk of momentum strategy in panic states. This identification explains the forecasting ability of known predictors of tail risk of momentum strategy such that the contemporaneous increase in market liquidity predominantly sums up the trademark negative relationship between predictors and future momentum returns. Our results are robust using a different momentum portfolio and alternative measures of market liquidity that make a substantial part of the common source of variation in aggregate liquidity.
Kolari, Liu: Market Risk and the Momentum Mystery
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3280559
Abstract:
This paper employs the ZCAPM asset pricing model of Liu, Kolari, and Huang (2018) to show that momentum returns are highly related to market risk arising from return dispersion (RD). Cross-sectional tests show that momentum risk loadings and RD risk loadings are similarly priced in momentum portfolios. Comparative analyses find that zero-investment momentum portfolios and zero-investment return dispersion portfolios earn high returns relative to other risk factors. Further regression tests indicate that zero-investment momentum returns are very significantly related to zero-investment return dispersion returns. We conclude that the momentum mystery is explained by market risk associated with return dispersion for the most part.
