Equal-weighted benchmark portfolios are sometimes overshadowed by the more popular market capitalization benchmarks but are still popular and often used in practice. One of the advantages of equal-weighted portfolios is that academic research shows that in the long term, they tend to outperform their market-cap-weighted peers, mainly due to positive loadings on well-known factor premiums like size and value. So, if equal weighting outperforms market-cap weighting (in the long term), what options do we have if we want to outperform equal weighting? A recent paper by Cirulli and Walker comes to our aid with an interesting proposal …

Does the investment in sophisticated machine learning algorithm research and development pay off? It is an important question, especially in light of the increasing costs related to the R&D of such algorithms and the possibility of decreasing returns for some methods developed in the more distant past. A recent paper by Azevedo, Hoegner, and Velikov (2023) evaluates the expected returns of machine learning-based trading strategies by considering transaction costs, post-publication decay, and the current high liquidity environment. The obstacles are not low, but research suggests that despite high turnover rates, some machine learning strategies continue to yield positive net returns.

The area we want to explore today is an interesting intersection between quantitative and more technical approaches to trading that employ intuition and experience in strictly data-driven decision-making (completely omitting any fundamental analysis!). Can just years of screen time and trading experience improve the metrics and profitability of trading systems through discretionary trading actions and decisions?

Five 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.

It is not easy to build a robust trend-following strategy that will withstand different difficult market conditions and bring consistent results. The author of today’s work was not frightened by this task and delivered a full framework on how to design a robust trend-following strategy step by step.

Hello all,

What have we accomplished in the last month?

– A new Attribution Analysis report for Quantpedia Pro clients
– An exclusive promo from QuantInsti
– 11 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
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month