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The Expected Returns of Machine-Learning Strategies

July 29, 2024

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.

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Combining Discretionary and Algorithmic Trading

July 25, 2024

The area we want to explore today is an interesting intersection between quantitative and more technical approaches to trading that employ intuition and experience in strictly data-driven decision-making (completely omitting any fundamental analysis!). Can just years of screen time and trading experience improve the metrics and profitability of trading systems through discretionary trading actions and decisions?

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Oh My! I Bought A Wrong Stock! – Investigation of Lead-Lag Effect in Easily-Mistyped Tickers

June 20, 2024

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.

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ESG Investing during Calm and Crisis Periods

April 26, 2024

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.

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Impact of Business Cycles on Machine Learning Predictions

April 15, 2024

As an old investing adage goes, “Everybody’s a genius in a bull market.” It is easy to fall victim to the Dunning-Kruger effect, where attribution bias makes us mistake our luck for abilities. When the business cycles change, there are great problems with precise stock price predictability. And this is not the only problem for humans, who are baffled by many mental heuristics. Machine learning algorithms experience similar problems, too. What is happening, and why is it so? A new paper by Wang, Fu, and Fan gives an explanation and proposes some remedies …

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Music Sentiment and Stock Returns around the World

April 2, 2024

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!

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