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How to Build a Systematic Innovation Factor in Stocks

February 2, 2024

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.

Join the Race: Quantpedia Awards 2024 Await You

January 26, 2024

Two weeks ago, we promised you a surprise, and now it’s finally time to unveil what we have prepared for you :).

Our Quantpedia Awards 2024 aims to be the premier competition for all quantitative trading researchers. If you have an idea in your head about systematic/quantitative trading or investment strategy, and you would like to gain visibility on the professional scene, then submit your research paper, and you can compete for an attractive list of prizes. All info about the prizes, submission process, expert committee, and our partners are described in detail on our dedicated subpage: Quantpedia Awards 2024. But we will also give you a quick overview in this blog post.

Machine Learning Execution Time in Asset Pricing

January 16, 2024

Machine Learning will quite certainly continue to be a hot topic in 2024, and we are committed to bringing you new developments and keeping you in the loop. Today, we will review original research from Demirbaga and Xu (2023) that highlights the critical role of machine learning model execution time (combination of time for ML training and prediction) in empirical asset pricing. The temporal efficiency of machine learning algorithms becomes more pivotal, given the necessity for swift investment decision-making based on the predictions generated from a lot of real-time data. Their study comprehensively evaluates execution time across various models and introduces two time-saving strategies: feature reduction and a reduction in time observations. Notably, XGBoost emerges as a top-performing model, combining high accuracy with relatively low execution time compared to other nonlinear models.

Cyber Risk and the Cross-Section of Stock Returns

December 12, 2023

In today’s fast world, where information flows freely and transactions happen at the speed of light, the significance of cybersecurity cannot be overstated. But it’s no longer just a concern for IT professionals or tech enthusiasts. The specter of well-documented hacks and phishing incidents casts a long shadow over investors, acting as powerful illustrations of how security breaches, vulnerabilities, and cyber threats can reverberate through financial markets. In this blog post, we’ll delve into the intricate relationship between cybersecurity risk and stock performance, uncovering how these digital hazards can influence financial markets.

What Can We Extract From the Financial Influencers’ Advice?

December 1, 2023

Social media are often the main and primary choice of information in almost every area of our lives, and they also influence the financial decisions of retail traders and investors. A lot of people give opinions anywhere on the Internet; some are respected, others are disrespected, some are more well-known, and others obscure. But the power of those people, financial influencers, as a group, is substantial as they create the market sentiment. But what’s the real value of their advice? Can we extract useful information from their opinions?

Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions

November 13, 2023

Machine learning models have been successfully employed to cross-sectionally predict stock returns using lagged stock characteristics as inputs. The analyzed paper challenges the conventional wisdom that more training data leads to superior machine learning models for stock return predictions. Instead, the research demonstrates that training market capitalization group-specific machine learning models can yield superior results for stock-level return predictions and long-short portfolios. The paper showcases the impact of model regularization and highlights the importance of careful model design choices.

Decreasing Returns of Machine Learning Strategies

November 10, 2023

Traditional asset pricing literature has yielded numerous anomaly variables for predicting stock returns, but real-world outcomes often disappoint. Many of these predictors work best in small-cap stocks, and their profitability tends to decline over time, particularly in the United States. As market efficiency improves, exploiting these anomalies becomes harder. The fusion of machine learning with finance research offers promise. Machine learning can handle extensive data, identify reliable predictors, and model complex relationships. The question is whether these promises can deliver more accurate stock return predictions…

Is It Good to Be Bad? – The Quest for Understanding Sin vs. ESG Investing

November 2, 2023

What are our expectations from the ESG theme on the portfolio management level? The question is whether ESG investing also offers some kind of “alternative alpha”, or outperformance against the traditional benchmarks. There are managers and academics who are enthusiastic and hope for the outperformance of the good ESG stocks. However, the academic research community is really split. Some academic papers show positive alpha for “Saints” (good ESG stocks); others show significantly positive alpha for “Sinners” (bad ESG stocks). So, how it’s in reality? Is it “Good to be Bad”? Or the other way around?

What’s the Key Factor Behind the Variation in Anomaly Returns?

October 13, 2023

In a game of poker, it is usually said that when you do not know who the patsy is, you’re the patsy. The world of finance is not different. It is good to know who your counterparties are and which investors/traders drive the return of anomalies you focus on. We discussed that a few months ago in a short blog article called “Which Investors Drive Factor Returns?“. Different sets of investors and their approaches drive different anomalies, and we have one more paper that helps uncover the motivation of investors and traders for trading and their impact on anomaly returns.

Performance of Factor Strategies in India

August 31, 2023

India is a big emerging market, actually the second biggest after China. We primarily look at developed markets, mostly the U.S. and Europe, and from Emerging Markets, China at most, and we are aware that we neglect this prospective country. We would like to correct this notion and give attention to a country that is (along with China) being cited as a new potential rising superpower and already looking to take the lead of Emerging Markets (EM) countries. Today, we would like to review the paper that analyzes the performance of main equity factors (with an emphasis on the Quality factor) and is a good starting point to understand the specifics of factor investing strategies in India.