Our new study aims to investigate the lead-lag effect between prominent, widely recognized stocks and smaller, less-known stocks with similar ticker symbols (for example, TSLA / TLSA), a phenomenon that has received limited attention in financial literature. The motivation behind this exploration stems from the hypothesis that investors, especially retail investors, may inadvertently trade on less-known stocks due to ticker symbol confusion, thereby impacting their price movements in a manner that correlates with the leading stocks. By examining this potential misidentification effect, our research seeks to shed some light on this interesting factor.

In one of our older posts titled ‘Case Study: Quantpedia’s Composite Seasonal / Calendar Strategy,’ we offer insights into seasonal trading strategies such as the Turn of the Month, FOMC Meeting Effect, and Option-Expiration Week Effect. These strategies, freely available in our database, are not only examined one by one, but are also combined and explored as a cohesive composite strategy. In partnership with Deltaray, using MesoSim — an options strategy simulator known for its unique flexibility and performance — we decided to explore and quantify how our Seasonal Strategy performs when applied to options trading. Our motivation is to investigate whether this strategy can be improved in terms of risk and return. We aim to systematically harvest the VRP (volatility risk premium) timing the entries using calendar strategy to avoid historically negative trading days.

The replication crisis in social sciences (and, of course, finance) is an often covered topic (see also our articles How do Investment Strategies Perform After Publication and In-Sample vs. Out-of-Sample Analysis of Trading Strategies). In vs. out-of-sample tests are usually performed on equity factors as data are available. However, the Copenhagen Business Schools, in close cooperation with AQR Capital Management, went in a different direction and built a database of realistic corporate bond data and took a closer look at the precision of corporate bonds forecasting methodologies. We applaud them for that, as working with the corporate bond data is challenging, and their work sheds a little light on this important part of the financial markets.

We mentioned multiple times that we at Quantpedia love historical analysis that spans over a long period of time as it offers a unique glimpse into the different macro environments and periods of political and economic instabilities. These long-term studies help a lot in risk management, and they also help investors set the right expectations about the range of outcomes in the future. Historical analysis of equity and fixed-income markets is not rare, but currency markets are less explored. Therefore, we are happy to investigate a recent paper by Joseph Chen that analyzes carry, momentum, and value strategies in the currency markets over the 200-year history.

There was a time in history when researchers believed that we, as a human species, act ultimately reasonably and rationally (for example, when dealing with financial matters). What arrived with the advent of Animal spirits (Keynes) and later Behavioral Finance pioneers such as Kahneman and Tversky was the realization that it is different from that. We often do not do what is in our best interest; quite the contrary. These emotions are hardly reconcilable with normal reasoning but result in market anomalies.

Researchers love to find causes and reasons and link behavioral anomalies to stock market performance. A lot of anomalies are related to various sentiment measures, derived from a alternative data sources and today, we present an interesting new possible relationship – investors’ mood and sentiment proxied by music sentiment!

The degree of stock market efficiency is a fundamental question of finance with considerable implications for the efficiency of capital allocation and, hence, the real economy. Return predictability is a cornerstone that allows investors to estimate their returns with ranging precision. Some anomalies allow one to exploit loopholes in global markets and capture substantial alpha, which violates the Efficient Market Hypothesis (EMH). However, whether this alpha arrives from risk premia or its source is mispricing is still puzzling academics around the globe, and they wrap their head around solving these tricky question.