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 due, 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…
The application of alternative data is currently a strong trend in the investment industry. We, too, analyzed few datasets in the past, be it ESG data, sentiment, or company fillings. This article continues the exploration of the alt-data space. This time, we use the research paper by Joenväärä et al., which shows that lexically diverse hedge funds outperform lexically homogeneous as an inspiration for us to analyze various lexical metrics in 10-K & 10-Q reports. Once again, we show that it makes sense to transmit ideas from one research paper to completely different asset class.
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.
- a new Crisis Hedge Quantpedia Pro report — currently, the stocks indexes around the world are close to all-time highs, and an optimistic mood is in the air. Corona crisis is nearly forgotten (at least financial markets act as if nothing had happened), but there will be a crisis at some time in the future. And it’s better to prepare for the worst during calm periods than to look for quick help under stress in volatile times. …
The improvement in collective human knowledge and technological progress brings many changes in our society. Numerous new businesses are emerging related to autonomous traffic, clean energy, biotechnology, etc. Without any doubt, these new companies are promising and at least the technology behind them seems to be the future. Perhaps the futuristic vision makes these companies so popular among investors.
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…
In part 1 of our article, we analyzed tendencies and trends among the Top 10 quantitative strategies of 2021. Thanks to Quantpedia Pro’s screener, we published several interesting insights about them.
In part 2 of our article, we got deeper into the first five specific strategies, which are significantly outperforming the rest in 2021.
Today, without any further thoughts, let’s proceed to the five single best performing strategies of 2021 as of August 2021.
As we have mentioned several times, the best course of action for a quant analyst who wants to develop a new trading strategy is to understand a well-known investment anomaly/factor fundamentally and then improve it. Quantpedia is a big fan of transferring ideas derived from academic research from one asset class to another. But that’s not the only possibility of improvement — we can try to embrace Roger Ibbotson’s theory of popularity, which states that popular assets/securities are usually overpriced compared to less-known (exotic) assets/securities. …