Low volatility investing is an appealing approach to compound wealth in the stock market for the long term. This particular factor investing style exploits the popular naive notion that lower (higher) risk must always equal lower (higher) overall returns. But in fact, this naive assumption is not true, as low-volatility investments often yield more than their high-volatility counterparts. While low-volatility investing has many advantages, it also results in some disadvantages. How to overcome them? Bernhard Breloer, Martin Kolrep, Thorsten Paarmann, and Viorel Roscovan, in their study Dissecting the Performance of Low Volatility Investing, propose a solution.

Today’s article focuses on investigating long-short anomaly portfolio return predictability in international stock markets, which often undergo mispricing due to investors’ sentiment. A paper by Jiang, Fuwei et al. (Apr 2023), suggests using the AAIG (Global Anomaly Index), and it examines the ability of the aggregate anomaly index to predict future returns in 33 stock markets. While previous research finds that a high aggregate anomaly measure predicts a low return in the U.S. market, this study further demonstrates that the global component of AAI (aggregate anomaly indices) is the key that drives international return predictability and reveals that the global anomaly index is a strong and robust predictor of equity risk premiums not just in the U.S. market but also in international markets, both in- and out-of-sample, consistently delivering significant economic values.

In March, we posted a series of three articles where our goal was to construct a market timing strategy that would reliably sidestep the equity market during bear markets. In this article, we revisit our research to address the forward-looking bias in our final market timing strategy. Upon careful examination, we identified a bias in our macroeconomic trading signal based on the U.S. S&P Composite dividends. To eliminate the issue, we have replaced the signal from U.S. S&P Composite dividends with Housing Starts Growth sourced from FRED, ensuring the strategy is no longer biased.

The unbiased version of our TrendYCMacro strategy, which uses the HOUSE signal, yields an annual excess return of 6.59%, slightly below the 7.10% of the biased version with the DIVIDEND signal. Interestingly, the unbiased version experiences slightly lower annualized volatility at 11.87% compared to the 11.89% of the biased version. Both versions have suffered the same maximal drawdown of -25.13% and exhibit comparable risk-adjusted returns, with the unbiased version having a Sharpe ratio of 0.56 and the biased version having a Sharpe ratio of 0.60.

If different investors share a common goal, why are there differences in strategy choices and portfolio characteristics across investor classes? Elsaify (2022) attempts to provide an answer. In his study, he documents heterogeneity in investors’ processing abilities, which is the key factor influencing investor’s strategy choice and finds that such heterogeneity stems from factor timing ability.

According to the results, hedge funds seem to have the highest attention capacity, the most precise information and excel at factor timing. On the other hand, long-term investors (insurance companies and pension funds), brokers, and short-sellers exhibit low attention capacity because of their timing inability. They spend relatively more attention on the fundamental, their portfolios have the least dispersion and variance and their impact on factor returns is limited.

Bearish trends or deep corrections in international equity markets starting in 2022 and rising interest rates worldwide brought investors’ attention back to not only once-proclaimed dead factor investing. From long-run and short run, during different market cycles, different factors behave differently. What’s fortunate is that it is pretty predictable to some extent. Andrew Ang, Head of Factor Investing Strategies at BlackRock, in his Trends and Cycles of Style Factors in the 20th and 21st Centuries (2022), used Hodrick-Prescott (HP) filter and spectral analysis to investigate different models to draw some general conclusions on most-widely used factors. We will take a look at a few of quite the most interesting ones of them.

Two leading political parties, the Democrats and Republicans, have dominated the United States politic for decades. As a consequence, the significant differences in views on major issues of partisans from different parties may influence their economic expectations. Recent studies found that partisan politics significantly impacts household beliefs and economic decision-making. But do political beliefs matter to institutional investors?