No doubt, the financialization in commodities was a significant breakpoint in markets and research as well. Many commodity strategies in Quantpedia’s screener are linked to financialization. It would be naive to think that the speculation in the commodity futures which has emerged did not influence the dynamics of the market. With the increased speculative trading, the Commodity Futures Trading Commission started collecting the net positions, but this dataset did not include all the data and often was connected with misreporting (and is not published anymore). The novel research paper of Adams, Collot and Rossi (2021) offers a different insight on this topic. It shows how to measure the influence using the term structure of commodity prices, focusing on crude oil. The authors suggest that during normal times, the term structure of crude oil futures should be smooth. They consider the term structure that starts with spot price and includes futures with one to twelve-month maturities, but they omit the one and two-month futures (since those are mostly used for speculation). The key finding is that when they estimate the missing futures based on the other prices using the smooth spline interpolation, this estimated term structure curve can be compared to the realized one. The deviation from the predicted (estimated) curve can be interpreted as the degree of speculation in commodity futures markets. As a result, with the mathematical modelling, the authors offer an interesting insight into speculation without any external datasets.

From the perspective of an indifferent observer, it seems that the world is accelerating from decade to decade. The average speed of information processing increases, and we consume more and more knowledge in a shorter and shorter amount of time. So, we have decided to start a new information stream for a people who have adjusted to this tempo.

It is striking how cryptocurrencies are both similar and dissimilar to the more established asset classes at the same time. On the one hand, many findings from traditional asset classes also apply to this novel class. On the other hand, this “new” world with its own characteristics brings many novel “problems” that attract researchers. This week’s blog presents several research papers connected to the pump and dump schemes in cryptos. These pumps and dumps are nothing new, and we already know them from the stock market. However, there are some notable differences…

ESG scores are the modern trend in the financial markets, and while this sustainable investing has its critics, it seems to become a regular part of the markets. Frequently, and probably rightfully, ESG is criticized for the lack of commonality across various “scorers”, and as a result, there might be a large dispersion among the score of one firm. The reason is that the score usually consists of different metrics and aggregation methodology. Apart from this “long-term” score, investors can easily recognize the “short-term” score, which can be proxied by negative incidents such as pollution, poor social aspects, social or governance scandals and so on. Moreover, these incidents could be more informative about (un)sustainable practice compared to ESG scores. These ESG incidents are studied by the novel research of Simon Glossner (2021). Using incidents news, the author provides interesting results that mainly support proponents of sustainable investing. Poor ESG performance proxied by incidents predicts more incidents in the future, lower profitability which should subsequently spill to negative performance in future. For example, portfolios consisting of negative incidents stocks significantly underperform the market for both US and European stocks. Therefore, this research paper is a compelling addition to the literature that, apart from social aspects, connects ESG also with performance.

This article is a primer into the methodology we use for the Portfolio Risk Parity report, which is a part of our Quantpedia Pro offering. We explain three risk parity methodologies – Naive Risk Parity (inverse volatility weighted), Equal Risk Contribution and Maximum Diversification. Quantpedia Pro allows the design of model risk parity portfolios built not just from the passive market factors (commodities, equities, fixed income, etc.) but also from systematic trading strategies and uploaded user’s equity curves.

The time flies very fast, and like every month, I have again a bunch of interesting improvements I would like to present to all of you. We again have two new reports for Quantpedia Pro (the Market Phase Analysis and the Portfolio Risk Parity reports) that I will describe soon.

But first, let’s recapitulate Quantpedia Premium development. Ten new Quantpedia Premium strategies have been added to our database, and twelve new related research papers have been included in existing Premium strategies during the last month. Additionally, we have produced 11 new backtests written in QuantConnect code. Our database currently contains over 430 strategies with out-of-sample backtests/codes.

Additionally, four new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month …