Factor allocation

In the ever-evolving world of finance, the quest for stable returns and risk mitigation remains paramount. Traditional asset classes, such as stocks and bonds, have long been the cornerstone of investment portfolios, but their inherent volatilities and susceptibilities to market fluctuations necessitate a more diversified approach. Enter the domain of alternative risk premia (ARP) – strategies designed to capture returns from diverse sources of risk, often orthogonal to traditional market risks. Our exploration in this blog post delves deep into this subject, shedding light on which ARP strategies can truly serve as robust diversifiers in the complex financial tapestry.

You may remember our blog post from the end of March, where we tested the current state-of-the-art LLM chatbot. Time flies fast. More than six months have passed since our last article, and half a year in a fast-developing field like Artificial intelligence feels like ten times more. So, we are here to revisit our article and try some new hacks! Has the OpenAI chatbot made any significant improvement? Can ChatGPT be used as a backtesting engine? We retake our risk parity asset allocation and test the limits of current AI development again!

In a game of poker, it is usually said that when you do not know who the patsy is, you’re the patsy. The world of finance is not different. It is good to know who your counterparties are and which investors/traders drive the return of anomalies you focus on. We discussed that a few months ago in a short blog article called “Which Investors Drive Factor Returns?“. Different sets of investors and their approaches drive different anomalies, and we have one more paper that helps uncover the motivation of investors and traders for trading and their impact on anomaly returns.

The motivation for this study comes from the idea of simplifying the concept of relative valuation among the countries. There exist several ideas for relative value approaches that compare the “visible price” (or market capitalization) of the stock market to some unseen “intrinsic value” of the market. The ideas of what we can use to measure the unseen “intrinsic value” of each individual country/market are numerous – it may be a number derived from GDP (like in a Buffet Indicator), total earnings of listed companies in the selected country (Shiller’s CAPE ratio), or ratios derived from yields, demographic, etc., etc. We asked ourselves – can we create a relative valuation model and use just the price data?

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.