Quantpedia in November 2023

Hello all,

Being a trader or portfolio manager is a stressful job. Sometimes, you outperform your peers, and other times you underperform. What’s important is to understand the drivers behind those swings in the performance.

We at Quantpedia want to help with that; therefore, we have prepared a new Alpha Analysis report for our Quantpedia Pro clients. You can compare the performance of your model portfolio (built from any combination of our strategies, ETFs, or your uploaded equity curves) to your desired benchmark and investigate the differences. The new functionality provides comprehensive factor analysis, builds “synthetic alpha” that’s explainable by systematic factors, and enables users to identify the primary drivers of underperformance or outperformance of your model portfolio or trading strategy.

You can dig deeper into the intricacies of your alpha, figure out what was (or wasn’t) working and analyze the contribution of individual factors to your total out-performance. We believe that this new functionality will help you to make more informed decisions, improve your investment strategies, and ultimately achieve better results.

Let’s also quickly recapitulate Quantpedia Premium development:

13 new Quantpedia Premium strategies have been added to our database
11 new related research papers have been included in existing Premium strategies during the last month
8 new backtests were written in QuantConnect code. Our database currently now contains nearly 730 strategies with out-of-sample backtests/codes.

Additionally, 4 new articles were published on the Quantpedia blog in the previous month:

What Can We Extract From the Financial Influencers’ Advice?
Authors: Ali Kakhbod, Seyed Mohammad Kazempour, Dmitry Livdan, and Norman Schuerhoff
Title: Finfluencers

Military Expenditures and Performance of the Stock Markets
Authors: Cyril Dujava, Radovan Vojtko
Title: Military Expenditures and Performance of the Stock Markets

Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions
Authors: Clint Howard
Title: Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions

Decreasing Returns of Machine Learning Strategies
Authors: Nusret Cakici and Christian Fieberg and Daniel Metko and Adam Zaremba
Title: Predicting Returns with Machine Learning Across Horizons, Firms Size, and Time

Yours …

Radovan Vojtko
CEO & Head of Research

Are you looking for more strategies to read about? Visit our Blog or Screener.

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We’ve already analysed tens of thousands of financial research papers and identified more than 700 attractive trading systems together with hundreds of related academic papers.

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