Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for September 2023 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for September 2023 (Final)
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Investing Expertise

Can analysts, experts and gurus really give you an investing/trading edge? Should you track the advice of as many as possible? Are there ways to tell good ones from bad ones? Recent research indicates that the average “expert” has little to offer individual investors/traders. Finding exceptional advisers is no easier than identifying outperforming stocks. Indiscriminately seeking the output of as many experts as possible is a waste of time. Learning what makes a good expert accurate is worthwhile.

10 Great Stock Picks for 2022?

In late 2021, Forbes “queried Morningstar to identify some of the top-performing fund managers, all of whom consistently beat their benchmarks on a longer-term basis over either a three-year, five-year or ten-year period. Forbes spoke with five top portfolio managers overseeing nearly $25 billion in assets. Here are their best stock ideas for the coming year,” as published in December 2021 as “10 Great Stock Picks for 2022 from Top-Performing Fund Managers”:

ViacomCBS (VIAC)/Paramount (PARA)
Madison Square Garden Entertainment (MSGE)
Signature Bank (SBNY)
SiteOne Landscape Supply (SITE)
Snap (SNAP)
Affirm (AFRM)
Silvergate Capital (SI)
Snowflake (SNOW)
Paramount Resources (PRMRF)
Mirion Technologies (MIR)

How did these picks perform? To check, we collect end-of-2021 and end-of-2022 dividend-adjusted prices for the 10 picks and calculate the annual total return for each. We then compare the average of these returns to the annual total return for SPDR S&P 500 ETF Trust (SPY). Using the specified annual data for 2021 and 2022, we find that:

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Benefit of Complexity in Machine Learning Models

Is model complexity (large number of parameters) more an analytical benefit in predicting asset returns, or more an avenue to discover in-sample luck? In their March 2023 paper entitled “Complexity in Factor Pricing Models”, Antoine Didisheim, Shikun Ke, Bryan Kelly and Semyon Malamud examine the theoretical relationship between input complexity and output accuracy for machine learning asset pricing models. They focus on a complexity wedge, the combination of overfitting (data snooping) and limits to learning that causes in-sample performance of a trained model to exceed out-of-sample performance. They apply ridge shrinkage (controlled by a regularization parameter that sets the strength of an overfitting penalty) to suppress data snooping bias and improve the limits to learning. They assess model performance by out-of-sample Sharpe ratio and out-of-sample pricing errors of optimal portfolios. They test theoretical conclusions on a broad sample of publicly traded U.S. stocks and a set of 110 monthly stock return factors, the latter augmented by a random feature generator that expands the 110 raw factors to any desired number of derivative factors. Using monthly data for the 110 stock return predictors and monthly U.S. stock returns during February 1963 through December 2019, they find that: Keep Reading

Hedge Fund Arbitrage of New Anomalies

Do hedge funds rapidly move to exploit, and thereby weaken/extinguish, newly discovered stock return anomalies? In the December 2022 version of their paper entitled “Anomaly Discovery and Arbitrage Trading”, Xi Dong, Qi Liu, Lei Lu, Bo Sun and Hongjun Yan measure the post-publication role of hedge funds on 99 published stock return anomalies (or latest working paper dates if unpublished). For each anomaly, they:

  1. Calculate a five-year rolling correlation of monthly returns between the extreme tenths (deciles 1 and 10) of anomaly stock sorts, minus the correlation between deciles 5 and 6 to control for unrelated trends.
  2. Analyze via quarterly SEC Form 13F holdings aggregate U.S. hedge fund differential trading of extreme decile stocks.

Using monthly returns for the 99 anomalies as available starting in 1926 and hedge fund SEC Form 13F filings as available starting 1981, both through 2020, they find that: Keep Reading

Suppressing Long-side Factor Premium Frictions

Are their practical ways to suppress the sometimes large reduction in academic (gross) equity factor premiums due to trading frictions and other implementation obstacles? In their March 2023 paper entitled “Smart Rebalancing”, Robert Arnott, Feifei Li and Juhani Linnainmaa first examine the performance and related turnover of seven long-only factor premiums: annually reformed (end of June) value, profitability, investment, and a composite of the three; and, monthly reformed value and momentum, and a composite of the two. Their long-only factor portfolios hold market-weighted stocks in the top fourth of factor signals. They reinvest any dividends in all stocks in the portfolios, such that dividends do not affect portfolio weights. They test three ways to suppress periodic turnover via a turnover limit:

  1. Proportional Rebalancing – trade all stocks proportionally to meet the turnover limit.
  2. Priority Best – buy stocks with the strongest factor signals and sell stocks with the weakest, until reaching the turnover limit.
  3. Priority Worst – buy stocks that only marginally qualify for the factor portfolio and sell those that just barely fall out (with the strongest buy and sell signals last), until reaching the turnover limit.

They also apply these three turnover suppression tactics to non-calendar reformation, triggered when the difference between the current and target portfolios exceeds a specified threshold. They ignore the 100% initial formation turnover common to all portfolios. Using  accounting data and common stock returns for all U.S. publicly listed firms during July 1963 through December 2020, with portfolio tests commencing July 1964, they find that: Keep Reading

Machine Learning Applied to U.S. Sector Rotation

Can machine learning perfect equity sector rotation? In the January 2023 version of their paper entitled “Deep Sector Rotation Swing Trading”, flagged by a subscriber, Joel Bock and Akhilesh Maewal present a sector rotation strategy guided by multiple-input, multiple output deep learning model. The strategy chooses weekly from among 11 U.S. sectors using exchange-traded fund (ETF) proxies. Specifically, each week during each year, they:

  • Train the machine learning model on the last two years of weekly (Friday close) historical sector ETF prices and volumes and sometimes auxiliary economic data (10-year U.S. Treasury yield, USD currency index, crude oil proxy and stock market volatility) to predict next-week opening and closing prices for each ETF.
  • Compare the predicted return estimate for each ETF to a dynamically updated threshold return to screen for potential buys.
  • Apply additional filters to screen out potential buys with unusual past losses to accommodate investor loss aversion.
  • At the next-week open, allocate available capital to surviving sector ETFs based on respective past win rate (profitable trade) and respective past sector trade momentum.
  • Liquidate all positions just prior to the next-week close.

Their benchmark is buying and holding the S&P 500 Index with reinvested dividends. Using weekly inputs as described during January 2012 through December 2022, they find that:

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Performance of Barron’s Annual Top 10 Stocks

Each year in December, Barron’s publishes its list of the best 10 stocks for the next year. Do these picks on average beat the market? To investigate, we scrape the web to find these lists for years 2011 through 2022, calculate the associated calendar year total return for each stock and calculate the average return for the 10 stocks for each year. We use SPDR S&P 500 ETF Trust (SPY) as a benchmark for these averages. We source most stock prices from Yahoo!Finance, but also use Historical Stock Price.com for a few stocks no longer tracked by Yahoo!Finance. Using year-end dividend-adjusted stock prices for the specified stocks-years from the end of 2010 through the end of 2022, we find that: Keep Reading

Aggregate Net Insider Trading and Future Stock Market Returns

Does aggregate insider stock buying and selling offer clues about future stock market returns? In their January 2023 paper entitled “Aggregate Insider Trading in the S&P 500 and the Predictability of International Equity Premia”, Andre Guettler, Patrick Hable, Patrick Launhardt and Felix Miebs investigate relationships between net aggregate insider trading and future stock market excess returns at horizons from one month to one year. They define net aggregate insider trading as unscheduled open market insider purchases minus sales, divided by purchases plus sales. They focus on S&P 500 firm insider trading and S&P 500 Index excess returns (relative to the U.S. Treasury bill yield). They also consider U.S. non-S&P 500 insider trading. They further look at insider trading and stock market excess returns within Canada, France, Germany, Great Britain and Italy. Using monthly aggregations of the specified insider trading data from 2iQ and monthly stock market index returns during January 2004 through December 2018, they find that:

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GMO Forecast Accuracy Test

A subscriber suggested an update of “GMO’s Stunningly Accurate Forecast?” with out-of-sample testing of GMO forecasts. To investigate, we test GMO’s 7-Year asset class real return forecasts of December 31, 2010, July 31, 2013, June 30, 2014 and November 2015. We first match the 11 GMO asset classes covered in these forecasts to exchange-traded funds (ETF), as follows:

  1. U.S. equities (large cap) – SPDR S&P 500 ETF Trust (SPY).
  2. U.S. equities (small cap) – iShares Russell 2000 ETF (IWM).
  3. U.S. high quality – Invesco S&P 500 Quality ETF (SPHQ).
  4. International equities (large cap) – iShares MSCI EAFE ETF (EFA).
  5. International equities (small cap) – iShares MSCI EAFE Small-Cap ETF (SCZ).
  6. Emerging equities – iShares MSCI Emerging Markets ETF (EEM).
  7. U.S. bonds (government) – iShares 20+ Year Treasury Bond ETF (TLT).
  8. International bonds (government) – SPDR Bloomberg Barclays International Treasury Bond ETF (BWX).
  9. Emerging bonds – iShares J.P. Morgan USD Emerging Markets Bond ETF (EMB).
  10. Inflation-indexed bonds – iShares TIPS Bond ETF (TIP).
  11. Short-term U.S. Treasuries (30 days to 2 years) – iShares 1-3 Year Treasury Bond ETF (SHY).

We adjust monthly ETF returns for inflation using monthly changes in U.S. Consumer Price Index (CPI). We then calculate the real compound annual growth rate (CAGR) for each over specified forecast horizons. Using GMO forecasts, dividend-adjusted ETF prices and CPI data during December 2010 through November 2022, we find that: Keep Reading

Can Investing Research Be Made Scientific?

Should investors presume that, in the absence of falsifiable theories, the body of factor investing research is largely spurious? In the January 2023 version of his paper entitled “Causal Factor Investing: Can Factor Investing Become Scientific?”, Marcos Lopez de Prado reviews the current state of confusion about causality in factor investing research and discusses ways to resolve that confusion. Specifically, he addresses:

  • Differences between association and causation.
  • Why the study of association alone does not create scientific knowledge.
  • How observational studies, natural experiments and simulated interventions support investigation of causality.
  • The current state of causal confusion in econometrics and factor investing studies.
  • How to transform factor investing into a truly scientific discipline.

Based on many references and the logic of the scientific method, he concludes that:

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Will Machines Revolutionize Investing?

Given that finance is ultimately tied to human emotions, does the body of research support belief that abilities of machine learning to handle large amounts of data, non-linearities and variable interactions will revolutionize investing? In their January 2023 paper entitled “How Can Machine Learning Advance Quantitative Asset Management?”, David Blitz, Tobias Hoogteijling, Harald Lohre and Philip Messow study application of machine learning to investing from the perspective of a prudent practitioner. They define machine learning models as initially guided software programs that subsequently learn by themselves and then make predictions about unseen (out-of-sample) data. They describe benefits and pitfalls of machine learning versus classical (linear regression) econometrics. They discuss critical design choices for applying machine learning models to asset management. They focus on the ability of machine learning to beat the stock market, but also discuss asset risk forecasting, optimal portfolio construction and trading optimization. Based on the body of research and their collective experience in asset management, they conclude that: Keep Reading

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