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 …

Thanks to quantitative easing, we see record-low interest rates. While yields for short to intermediate maturities in the US are lower than the inflation but still positive, other developed markets such as Japan or European countries even have bond yields negative. Still, it does not implicate that investors have withdrawn from the fixed income markets. Both individual and institutional investors still participate in bond trading. However, the critical question is how these conditions influence the investors. Does their behavior change? Do they reach for yield and prefer riskier bonds in the search for (positive) real yields? In this blog post, we present three novel research papers that offer insights into this topic.

Various research papers show that market sentiment, also called investor sentiment, plays a role in market returns. Market sentiment refers to the general mood on the financial markets and investors’ overall tendency to trade. The mood on the market is divided into two main types, bullish and bearish. Naturally, rising prices indicate bullish sentiment. On the other hand, falling prices indicate bearish sentiment. This paper shows various ways to measure market sentiment and its influence on returns.

Additionally, we take a look at an overnight anomaly in combination with three market sentiment indicators. We analyse the Brain Market sentiment indicator in addition to VIX and the short-term trend in SPY ETF. Our aim is not to build a trading system. Instead, it is to analyze financial markets behaviour. Overall the transaction costs of this kind of strategy would be high. However, more appropriate than using this system on its own would be to use it as an overlay when deciding when to make trades.