Gold is a hot topic nowadays, but that is not a surprise given the worldwide situation. Gold is by the majority considered as a hedge, safe haven and often recognized for its ability to preserve the value in the long term. However, gold itself is not the only gold-related investable asset. There are numerous gold-related stocks – producers, explorers and developers. Common sense might suggest that the price of such stocks should reflect the gold prices, but the novel research by Baur and Trench (2020) shows that this logic is not always correct. Results suggest that gold equities cannot be considered as safe havens and investors differentiate between producers, explorers and developers during regular times. On the other hand, during the recent (and lasting) stressful COVID period, all types of gold stocks moved similarly to gold.

Authors: Dirk G. Baur and Allan Trench

Title: Not all Gold Shines in Crisis Times – COVID-19 Evidence

There are very few observations of the attributes of financial markets that are considered by most of the investors as nearly permanent facts. One of the most often cited examples is that over the long interval stocks outperform bonds. But is it really such truth? Over how long interval? 10 years, 20 years, 30 years? As the new and better historical data are becoming available for analysis, they show interesting findings. Let’s show one example. There exist one very long interval during which the return of stocks was nearly equal to bonds. What do you think is the length of such an interval in the case of the US? It’s 150 years! Yes, that’s correct, there was a one-and-half-century long period in the history of the United States when the performance of stocks and bonds was nearly identical. We do not imply that it will be the case in the 21st century. But an important research paper written by Edward McQuarrie shows that investors must prepare for even the most unexpected possibilities when they are making their asset allocation decisions.

Author: McQuarrie

Title: The US Bond Market before 1926: Investor Total Return from 1793, Comparing Federal, Municipal and Corporate Bonds Part II: 1857 to 1926

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

The idea of buying an investment asset for a lower price than a fair-value is the cornerstone of value factor strategies. Various value strategies were popularized by famous investor Benjamin Graham (and his successors like Warren Buffett) and were firstly employed in the stock market. This idea of looking for investment opportunities that can be bought cheaply can also be applied in currency markets – Currency Value Factor strategy. There is, however, one catch – an investor must know the fair-value exchange rate for currencies. The most popular equilibrium exchange rate model used for this purpose is based on PPP (purchasing power parity). A new research paper written by Ca’ Zorzi, Cap, Mijakovic, and Rubaszek analyzes two additional models – Behavioral Equilibrium Exchange Rate (BEER) and the Macroeconomic Balance (MB) approach to assess which model has the best forecasting power.

Authors: Ca’ Zorzi, Cap, Mijakovic, Rubaszek

Title: The Predictive Power of Equilibrium Exchange Rate Models

Professor Robert Shiller’s work and his famous CAPE (cyclically-adjusted price-to-earnings) ratio is well known among the investment community. His methodology for assessing a valuation of the U.S. equity market is not the first one but is surely the most cited and the most discussed. There are numerous papers that tweak or adjust Shiller’s methodology to assess better if U.S. equities are under- or over-valued. We recommend the work of Wang, Ahluwalia, Aliaga-Diaz, and Davis (all from The Vanguard Group ) in which they use a combination of machine learning and a regression-based approach to obtain forecasted CAPE ratio, and subsequently, U.S. stock market returns, more accurately.

Authors: Wang, Ahluwalia, Aliaga-Diaz, Davis

Title: The Best of Both Worlds: Forecasting US Equity Market Returns using a Hybrid Machine Learning – Time Series Approach

The end of the year is a good time for a short recapitulation. Apart from other things we do (which we will summarize in our next blog in a few days), we have published around 50 short blog posts / recherches of academic papers on this blog during the last year. We want to use this opportunity to summarize 10 of them, which were the most popular (based on Google Analytics tool). Maybe you will be able to find something you have not read yet …