Factor allocation

Even though gold is generally a volatile asset, it is often considered a key diversifier, hedging against inflation or protecting during economic uncertainties. According to the authors (Pim van Vliet and Harald Lohre), in times of extreme macroeconomic events, including war, hyperinflation, or major economic recessions, gold investing is widely regarded as a safe haven. However, using gold as a hedge comes at the cost of lower returns. The authors explored the importance of gold in investment portfolios and its ability to reduce the risk of losses combined with bonds and stocks. Compared to many existing studies, they also consider a longer timeframe and the impact of inflation.

Sector/industry picking or country picking can be a profitable trading style but is usually much more challenging than it seems at first sight. Building a good trading model requires a lot of research and dedication. Unfortunately, due to the limited numbers of industries and countries, sorting them on aggregate characteristics can wash out important cross-sectional variations in the characteristics and lead to concentrated portfolios prone to noisier realized returns.

In their fresh Dimensional Fund Advisors research piece, Dong, Huang, and Medhat (2023) touch on the question of whether investors should systematically emphasize certain industries or countries to increase expected returns. Their overhead view provides new insights and sums that investors will likely be better off pursuing premiums in the larger cross-section of individual securities and maintaining broad diversification across the smaller cross-sections of industries and countries.

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.

Science has been in a “replication crisis” for more than a decade. But what does it mean to us, investors and traders? Is there any “edge” in purely academic-developed trading strategies and investment approaches after publishing, or will they perish shortly after becoming public? After some time, we will revisit our older blog on this theme and test the out-of-sample decay of trading strategies. But this time, we have hard data – our regularly updated database of replicated quant strategies.

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.

Today, we will look closer into the hood of life expectancy of investment strategies and try to answer the critical question on which many, in some sense, if not all, trading strategies are built: what happens with anomalies after their discovery? The paper’s authors, with the sweet, simple name Anomaly Discovery and Arbitrage Trading, analyze a stylized model of anomaly discovery, which has implications for both asset prices and arbitrageurs’ trading. Their original research produced an arbitrageur-based asset pricing model that shows that discovering an anomaly reduces the correlation between the returns of its long- and short-leg portfolios: HFs (professional arbitrageurs) use to increase (unwind) such trades when their wealth increases (decreases), further supporting the view that the discovery effects work through arbitrage trading. This effect is more substantial when arbitrageurs’ wealth is more volatile.