factor investing - QuantPedia

Are Sector-Specific Machine Learning Models Better Than Generalists?

May 14, 2025

Can machine learning models better predict stock returns if they are tailored to specific industries, or is a one-size-fits-all (generalist) approach sufficient? This question lies at the heart of a recent research paper by Matthias Hanauer, Amar Soebhag, Marc Stam, and Tobias Hoogteijling. Their findings suggest that the optimal solution lies somewhere in between: a “Hybrid” machine learning model that is aware of industry structures but still trained on the full cross-section of stocks offers the best performance.

Fear, Not Risk, Explains Asset Pricing

April 17, 2025

With financial markets increasingly whipsawed by geopolitical tensions and unpredictable policy shifts from the Trump administration—investors are once again questioning how to understand risk, fear, and the true drivers of returns. A recent and compelling paper dives into this debate with a provocative thesis: in “Fear, Not Risk, Explains Asset Pricing,” authors Rob Arnott and Edward McQuarrie argue that traditional models built on quantifiable risk have failed to explain real-world returns, and that fear—messy, emotional, and deeply human—is the missing piece.

Navigating Market Turmoil with Quantpedia Tools: A Rational Guide for Portfolio Management

April 7, 2025

The recent imposition of sweeping global tariffs by President Donald Trump has triggered a sharp and sudden selloff across global equity markets. In times like these, it’s natural for panic to set in. However, as quantitative investors, our strength lies in data-driven decision-making, risk management, and maintaining discipline when others lose theirs.

Rather than reacting emotionally, the prudent course of action is to reassess the robustness of our portfolios. Are we diversified across uncorrelated strategies? Do we have components in place that act as hedges during market crises? Fortunately, the tools provided by Quantpedia can help investors, traders, and portfolio managers identify, test, and deploy crisis-resilient strategies in a structured and evidence-based manner.

Front Running in Country ETFs, or How to Spot and Leverage Seasonality

April 1, 2025

Understanding seasonality in financial markets requires recognizing how predictable return patterns can be influenced by investor behavior. One underexplored aspect of this is the impact of front-running—where traders anticipate seasonal trends and act early, shifting returns forward in time. We have already explored seasonality front-running in commodities, stock sectors, and crisis hedge portfolios. Our new research examines whether this phenomenon extends to country ETFs, an asset class where seasonality has been less studied. By applying a front-running strategy to a dataset of country ETFs, we identify opportunities to capitalize on seasonal effects before they fully materialize. Our findings indicate that pre-seasonality drift is strongest in commodities but remains present in country ETFs, offering a potential edge in portfolio construction. Ultimately, our study highlights how front-running seasonality can enhance ETF investing, providing an additional layer of market timing beyond traditional trend-following approaches.

How Mega Tech Stocks Impact Factor Strategies

March 26, 2025

The dominance of mega-tech stocks, particularly the “Magnificent 7,” in both U.S. and global equity indexes has a profound impact on factor portfolios. When constructing value-weighted smart beta strategies, these portfolios often end up heavily concentrated in a few individual stocks. This concentration introduces idiosyncratic risk, skewing the risk profiles of factor strategies. While no active strategy can entirely avoid the influence of these high-market-cap stocks, it is critical to limit their exposure to reduce idiosyncratic risk and improve the stability of factor-based approaches.

Seasonality Patterns in the Crisis Hedge Portfolios

January 30, 2025

Building upon the established research on market seasonality and the potential for front-running to boost associated profits, this article investigates the application of seasonal strategies within the context of crisis hedge portfolios. Unlike traditional asset allocation strategies that may falter during market stress, crisis hedge portfolios are designed to provide downside protection. We examine whether incorporating seasonal timing into these portfolios can enhance their performance and return-to-risk ratios, potentially offering superior risk-adjusted returns compared to static or non-seasonal approaches.

It’s About the Price of Oil, Not ESG

January 23, 2025

The growing urgency of climate change has increased scrutiny of companies’ ESG (Environmental, Social, and Governance) practices. Investors are now more inclined to support firms that demonstrate strong ESG commitments, often willing to pay a green premium for sustainable investments. However, is the spread in performance between the ‘Sin’ and ‘Saint’ stocks driven by the ESG factor or some other omitted variable? The recent study by Zhan Shi and Shaojun Zhang suggests that the hidden force that may be in play is the price of the oil.

Out-of-Sample Test of Formula Investing Strategies

January 16, 2025

Can we simplify the complexities of the stock market and distill them into a simple set of quantifiable metrics? A lot of academic papers suggest this, and they offer formulas that should make the life of a stock picker easier. Some of the most compelling methodologies within this realm are the F-Score, Magic Formula, Acquirer’s Multiple, and the Conservative Formula. These quantitative strategies are designed to identify undervalued stocks with robust fundamentals and potential for high returns. But do they really work out-of-sample? A new paper by Marcel Schwartz and Matthias X. Hanauer tries to answer this interesting question…

Refining ETF Asset Momentum Strategy

January 10, 2025

Today’s research introduces a refined ETF asset momentum strategy by combining a correlation filter with selective shorting. While traditional long-short momentum strategies usually yield suboptimal results, the long leg proves effective on its own, and the correlation filter demonstrates significant value for improving the timing and performance of the short leg. We propose a final strategy of going long on 4 top-performing ETFs while selectively shorting 1 ETF with a 30% weight. Our findings demonstrate that this combined long-short selective hedge strategy significantly outperforms standalone momentum strategies and the benchmark, delivering superior risk-adjusted returns and effective hedging during unfavorable market conditions.