Blog - QuantPedia

Using Inflation Data for Systematic Gold and Treasury Investment Strategies

February 21, 2025

Inflation significantly impacts the prices of gold and treasury bonds through various mechanisms. Gold is often viewed as a hedge against inflation, while treasury bonds exhibit a more complex relationship influenced by interest rates and investor behavior. This relationship between inflation, gold, and treasuries is well understood, but the real question is whether we can systematically capitalize on it. In this article, we explore how inflation data can be used to build trading strategies—and as our findings suggest, the answer is a definite yes.

Does the Image-Based Industry Classification Outperform?

February 18, 2025

For decades, investors and analysts have relied on traditional industry classifications like GICS, NAICS, or SIC to group companies into sectors and peer groups. However, these rigid categorizations often fail to capture the evolving nature of businesses, especially in an era of technological convergence and rapid industry shifts. Machine learning (ML) offers a more dynamic and data-driven alternative by analyzing company visuals—such as logos, product images, and branding elements—to identify similarities that go beyond predefined classifications. A recent study applies this approach to construct new industry groupings and tests them in industry momentum and reversal. The results show that ML-generated groups lead to superior performance, once again highlighting the potential of image-based classification in financial analysis.

Dangers of Relying on OHLC Prices – the Case of Overnight Drift in GDX ETF

February 14, 2025

Can we truly rely on the opening price in OHLC data for backtesting? While the overnight drift effect is well-documented in equities, we investigated its presence in gold using the GLD ETF and then extended our analysis to the GDX – Gold Miners ETF, where we observed an unusually strong overnight return exceeding 30% annualized. However, when we tested execution at 9:31 AM using 1-minute data, the anomaly diminished significantly, suggesting that the extreme return was partially a data artifact. This finding highlights the risks of blindly trusting OHLC open prices and underscores the need for higher-frequency data to validate execution assumptions.

Quantpedia in January 2025

February 10, 2025

Hello all,

What have we accomplished in the last month?

– An updated Seasonality Analysis report
– 10% discount code for those who help us and fill out our survey
– an invitation to Quantpedia Awards 2025 competition with a $25.000 prize pool
– 9 new Quantpedia Premium strategies have been added to our database
– 8 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 8 new backtests written in QuantConnect code
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Quantpedia Premium Update – February 8th

February 10, 2025

Five new strategies have been added. Four new related research papers have been included into existing strategy reviews and two short free blog posts have been published during last few weeks. Plus, four trading strategies have been backtested in QuantConnect in the previous two weeks.

Join the Race Once Again: Quantpedia Awards Competition Is Back!

February 4, 2025

Last year, we promised our readers that the Quantpedia Awards would be back! And now it’s again time to unveil what we have prepared for you.

For a quick recapitulation (for those who were not around in 2024, when we started this activity for the first time), our Quantpedia Awards 2025 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 2025. However, we will also give you a quick overview in this blog post.

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.

Quantpedia Premium Update – January 23rd

January 23, 2025

Four new strategies have been added. Four new related research papers have been included into existing strategy reviews and three short free blog posts have been published during last few weeks. Plus, four trading strategies have been backtested in QuantConnect in the previous two weeks.

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…