Studies using machine learning techniques for return forecasting have shown considerable promise. However, as in empirical asset pricing, researchers face numerous decisions around sampling methods and model estimation. This raises an important question: how do these methodological choices impact the performance of ML-driven trading strategies? Recent research by Vaibhav, Vedprakash, and Varun demonstrates that even small decisions can significantly affect overall performance. It appears that in machine learning, the old adage also holds true: the devil is in the details.

Short sellers often have a bad reputation, seen as market disruptors who profit from declining prices. Yet, they play a crucial role in making markets more efficient by identifying overvalued assets and correcting mispricings. A recent study uncovers another surprising aspect of their behavior: rather than just demanding liquidity, the most informed short sellers actually provide it. Using transaction-level data, the research shows that these traders supply liquidity, especially on news days and when trading on known anomalies, challenging the conventional view of short sellers as merely aggressive market participants.

Today, we will venture a little into the fundamental analysis corner, and we will give you a glimpse of an intriguing paper (Hillenbrand and McCarthy, 2024) that discusses the advantages of using ‘Street’ earnings over traditional GAAP earnings. The paper suggests that ‘Street’ earnings provide better valuation estimates and improved financial analysis. Is this a way how to improve the performance of the struggling equity value factor?

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.