Does the investment in sophisticated machine learning algorithm research and development pay off? It is an important question, especially in light of the increasing costs related to the R&D of such algorithms and the possibility of decreasing returns for some methods developed in the more distant past. A recent paper by Azevedo, Hoegner, and Velikov (2023) evaluates the expected returns of machine learning-based trading strategies by considering transaction costs, post-publication decay, and the current high liquidity environment. The obstacles are not low, but research suggests that despite high turnover rates, some machine learning strategies continue to yield positive net returns.

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.

Over the last decade, investing responsibly and deploying capital for “ethically” correct and sustainable growth has been quite a theme. We dedicated a few blogs to this theme and have a separate ESG category for trading strategies in our database. It is often easy to commit financial resources to noble ideas during liquidity abundance. However, how do these methodologies fare during crisis times, such as when the GFC (Global Financial Crisis) or COVID-19 hit? That’s the question that a new paper by Henk Berkman and Mihir Tirodkar tries to answer.

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!

More and more, a few mega-cap companies dominate the US stock market performance. Financial journals come up with different names for those stocks every few years. They are now called the “Magnificent Seven”, but we all remember FAANG, right? Naturally, several questions arise – Is the current status quo, when the stock market capitalization is highly concentrated among the few extremely large companies, an exception or rule over history? And what’s the impact of this concentration on the performance of the one particular factor – the Size premium? We present the research paper written by Emery and Koëter that tries to answer those questions. 

The aim of this article is multifold. It aims to answer the research question: does a portfolio consisting of top innovators outperform the S&P 500 index? To address this question, a strategy of investing long in top innovators according to their ranking is developed, and its performance is compared to that of the broad-based index. Based on the common belief that higher innovativeness carries higher risk, it aims to evaluate the volatility associated with innovative stocks. Additionally, it aims to analyze the impact of sector factors on the portfolio’s performance. Finally, it conducts a comparative analysis between the portfolio’s performance and that of the ARK Innovation ETF (ARKK), which specifically focuses on investing in companies relevant to the theme of disruptive innovation.