What does one of the most popular and well-known metrics, VIX, tell us about future returns? Academic research (Bansal and Stivers, July 2023) shows that a common, intuitive 20/80 thumb rule can be applied as time-variation in the returns earned from equity-market exposure can be explained well by a simple 2-term risk-return specification, which predicts (1) much higher returns 20% of the time following after VIX exceeds a high threshold at around its 80th percentile and (2) lower excess returns following a high market sentiment. They argue that VIX and market sentiment tend to measure complementary aspects of risk: the level of risk (VIX) and the price of risk or risk appetite (sentiment), and that, thus, both terms should be accounted for when evaluating time variation in the equity market’s risk premium.

Following the recent release of the popular large language model ChatGPT, the topic of machine learning and AI seems to have skyrocketed in popularity. The concept of machine learning is, however, a much older one and has been the topic of various research and technology projects over the last decade and even longer. In this article, we would like to discuss what machine learning is, how it can be used in quantitative trading, and how has the popularity of ML strategies increased over the years.

In recent years, the diversification potential of commodities has come under scrutiny. While the majority of studies examining the role of commodities in a portfolio typically focus on U.S. investors or those dealing primarily with U.S. dollar-denominated assets, Dequiedt et al. (2023) offer a unique perspective by considering the viewpoint of domestic investors in a sample of 38 developed and emerging countries. The study explores the relationship between diversification benefits of commodities for local investors and country’s level of commodity risk exposure. Findings reveal that incorporating commodities tends to enhance the Sharpe ratio of the optimal domestic asset portfolios in most countries with low commodity dependence but doesn’t benefit highly commodity-dependent ones.

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?

If there were a Superbowl of Finance for equities, it’d definitely be FOMC (Federal Open Market Committee) meetings. Investors and traders from around the world gather and make their decisions on the brink of releasing a statement and following the press conference. Shah, Paturi, and Chava (May 2023) contribute with a new cleaned, tokenized, and labeled open-source dataset for FOMC text analysis of various data categories (meeting minutes, speeches, and press conferences). They also propose a new sequence classification task to classify sentences into different monetary policy stances (hawkish, dovish, and neutral) and show the application of this task by generating a hawkish-dovish classification measure from the trained model that they later use in an interesting trading strategy.

Seasonality effects, one of the most fascinating phenomena in the world of finance, have captured the attention of investors and researchers worldwide. Since these anomalies are often driven by factors other than general market trends, they usually don’t correlate strongly with market movements, which can help reduce the portfolio’s overall risk. Following the theme of our previous article Are There Seasonal Intraday or Overnight Anomalies in Bitcoin?, we decided to extend the data and conduct a more in-depth analysis of our earlier findings. This article explores potential seasonal patterns related to Bitcoin, focusing on whether these patterns are influenced by factors such as current market trends or the level of volatility in the market.