October’s is coming, and we continue our short series of introductory articles about portfolio clustering methods we will soon use in our new Quantpedia Pro report. In the previous blog, we introduced three clustering methods and discussed the pros and cons of each one. Additionally, we showed a few examples of clustering, and we presented various methods for picking an optimal number of clusters.

This section demonstrates the Partitioning Around Medoids (PAM) – a centroid-based clustering method, Hierarchical Clustering, which uses machine learning and Gaussian Mixture Model based on probability distribution and applies all three methods to an investment portfolio that consists of eight liquid ETFs.

This blog post is the continuation of series about Quantconnect’s Alpha market strategies. Part 1 is here and Part 2 can be found here. This part is related to the factor strategies notoriously known from the majority of asset classes. We continue in the examination of factor strategies built on top of social trading strategies, but in this part, the investment universe is reduced based on the insights of the last part. So, without further ado, we continue where we have left last time.

As mentioned several times, Quantpedia is a big fan of transferring ideas from one asset class to another. This article is another example; we use an idea originally tested on Chinese stocks and apply it to the commodity futures investment universe. The resultant return new asymmetry investment factor in commodities is an interesting trading strategy unrelated to other common factors and has a slightly negative correlation to the equity market and can be therefore used as an excellent diversifier in multi-asset multi-strategy portfolios.

Hello all,

What have we accomplished in the last month?

– A new important Crisis Hedge Quantpedia Pro report
– 10 new Quantpedia Premium strategies have been added to our database
– 10 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 10 new backtests written in QuantConnect code
– And finally, 12 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Numerous new businesses are emerging related to autonomous traffic, clean energy, biotechnology, etc. Without any doubt, these new companies look promising and at least the technology behind them seems to be the future. Moreover, this novel trend is also supported by the most prominent index creators S&P and MSCI. Both providers have created numerous thematic indexes connected to these hot industries. The popularity has caused that ETFs are nowhere behind, and as a result, these thematic indexes could be easily tracked. However, popularity itself does not guarantee the best investment, and we should be interested in these indexes in greater detail. A vital insight provides the novel research paper of Blitz (2021). The findings are interesting – the thematic investors bet against quantitative investors or, more precisely, against the most common factors that are well-known from the asset pricing models.