Financial markets are full of pricing anomalies, and their existence is often explained by human behavior. Behavioral finance postulates that cognitive irrationality is manifested in biases like the disposition effect (the tendency of people to sell assets that have increased in value, but keeping assets that have dropped in value in portfolio) or overconfidence bias (the tendency of people to be more confident in their own abilities). There are some papers which directly link investment decision making caused by these biases to actual physiology of investors (for example, a known impact of testosterone on investment performance). A new research paper written by Nofsinger, Patterson, and Shank examines not only testosterone but also cortisol levels of testing subjects and then compares their performance in a mock investment contest. Both hormones are strongly related to higher portfolio turnover and inability to accept losses, with cortisol levels even more significant than testosterone.

Author: Nofsinger, Patterson, Shank

Title: On the Physiology of Investment Biases: The Role of Cortisol and Testosterone

In many academic fields like physics, chemistry or natural sciences in general, laws do not change. While economics and theory of investing try to find rules that would be true and always applicable, it is not that simple, there is a “complication“ – human. Psychology of humans is very complex. In the one hand, it creates anomalies in the market, that academics study and practitioners use. On the other hand, after an anomaly is discovered, often, the strategy becomes less profitable.

While for academics, it is just another research question, investors may be worried that the anomaly is arbitraged away, and it will become unprofitable in their portfolios. In this article, we will look deeper on whether the anomaly can be arbitraged away, if the profits are lower for the specific strategy once the strategy becomes well-known, and even if the strategies can be timed. Quantpedia‘s readers are often interested in these common topics, and we will try to shed some light on them.

The U.S. (and the world’s) economy is currently entering a recession. Right now, everybody can see it, the only question is how deep it will be. But is it possible in a real-time predict if the economy will enter a recession? And will that information help us to better set % allocation of equities in our portfolio? Most of the macroeconomic data shows recession in macroeconomic reports with a significant lag. There are multiple different forecasting models which try to predict recession or at least estimate the probability that we are entering into one. We are presenting one interesting research paper written by Jonathan Hartley which shows that prediction markets (betting markets created for the purpose of trading the outcome of events) can be successfully used as a complementary tool in various economic forecasting tools. Prediction markets can be used to measure risk in U.S. equities, credit spreads, the U.S. Treasury yield curve, and U.S. dollar foreign exchange rates.

Author: Hartley

Title: Recession Prediction Markets and Macroeconomic Risk in Asset Prices

Markets are in turmoil, and there exist very few investors who are unscathed by current global events related to coronavirus pandemic. It’s a good time to revisit how are various groups of algorithmic trading strategies navigating current troubled times. The selected sample for this short article consists of 7 well-known equity factor strategies – size, value, momentum, quality, investment, short-term reversal and low volatility factors.

Our analysis shows that we have two groups of factors: strong winners and bad losers. There is no middle ground. A current bear market is ruthless, equity long-short factor strategies either totally nailed it and had a stellar performance or totally disappointed.

Mirror, mirror on the wall, what’s the best factor model of them all? We at Quantpedia are probably not the only one asking this question. A lot of competing factor models are described in the academic literature and used in practice. That’s the reason why we consider a new research paper written by Matthias Hanauer really valuable. He compared several commonly employed factor models across non-U.S. developed and emerging market countries and answered the question from the beginning of this paragraph. Which model seems the winner? The six-factor model proposed in Barillas et al. (2019) that substitutes the classic value factor in the Fama and French (2018) six-factor model for a monthly updated value factor …

Authors: Hanauer

Title: A Comparison of Global Factor Models

Various risk parity methodologies are a popular choice for the construction of better diversified and balanced portfolios. It is notoriously hard to predict the future performance of the majority of asset classes. Risk parity approach overcomes this shortcoming by building portfolios using only assets’ risk characteristics and correlation matrix. A new research paper written by Lohre, Rother and Schafer builds on the foundation of classical risk parity methods and presents hierarchical risk parity technique. Their method uses graph theory and machine learning to build a hierarchical structure of the investment universe. Such structure allows better division of assets into clusters with similar characteristics without relying on classical correlation analysis. These portfolios then offer better tail risk management, especially for skewed assets and style factor strategies.

Authors: Lohre, Rother and Schafer

Title: Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations