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.

At the beginning of October, we plan to introduce for our Quantpedia Pro clients a new Quantpedia Pro report dedicated to clustering methods in portfolio management. The theory behind this report is more extensive; therefore, we have decided to split the introduction into our methodology into three parts. We will publish them in the next few weeks before we officially unveil our reporting tool. This first short blog post introduces three clustering methods as well as three methods that select the optimal number of clusters. The second blog will apply all three methods to model ETF portfolios, and the final blog will show how to use portfolio clustering to build multi-asset trading strategies.

Is there any association between the country’s stock market and its gambling policy? Surprisingly, yes, and there’s more to it than one would think. In a new research paper, Kumar, Nguyen and Putnins offer a complex study of gambling activities in 38 countries worldwide to estimate the impact on their financial markets.

The research’s dataset follows that around 86% of the estimated total global gaming revenue comprises traditional gambling forms – casinos, lotteries, sports betting, and many others. Moving to the financial markets, the authors introduce a split of stocks into lottery-like and non-lottery stocks to estimate the amount of gambling in stock markets. Lottery-like stocks are expected to be traded much more often than other stocks. It turns out that 14% of developed markets, 18% of emerging ones and 33% of retail-dominated Asian markets (China, Thailand) is being gambled. Generally, there is 3.5 times more capital gambled in the stock market around the world compared to the traditional ways combined together.

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.