Hello and Happy New Year 2025 to all!

Let’s quickly review December’s updates:

– An enhancement of the Saving Plan Analysis report
– A new Youtube video series – QuantBeats
– 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 7 new backtests written in QuantConnect code
– 4 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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

The year 2024 is nearly behind us, so it’s an excellent time for a short recapitulation. In the previous 12 months, we have been busy again (as usual) and have published over 70 short analyses of academic papers and our own research articles. The end of the year is a good opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics ranking). The top 10 is diverse, as usual; once again, we hope that you may find something you have not read yet …

Three new strategies have been added. Three 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, three trading strategies have been backtested in QuantConnect in the previous two weeks.

Seasonality plays a significant role in financial markets and has become an essential concept for both practitioners and researchers. This phenomenon is particularly prominent in commodities, where natural cycles like weather or harvest periods directly affect supply and demand, leading to predictable price movements. However, seasonality also plays a role in equity markets, influencing stock prices based on recurring calendar patterns, such as month-end effects or holiday periods. Recognizing these patterns can provide investors with an edge by identifying windows of opportunity or risk in their investment strategies.

For those who have not yet had the chance to read it, we recommend the latest empirical study by Minghui Chen, Matthias X. Hanauer, and Tobias Kalsbach, which shows that design choices in machine learning models, such as feature selection and hyperparameter tuning, are crucial to improving portfolio performance. Non-standard errors in machine learning predictions can lead to substantial portfolio return variations, and authors are highlighting the importance of robust model evaluation techniques.