The best course of action for every quant researcher is to try to fundamentally understand anomalies and explore their functioning besides the original scope of the academic research papers. The goal of this article was to look for inspiration and further explore the Skewness affect – the tendency of assets with the lowest skewness to outperform assets with the highest skewness. It seems that this anomaly is present not only in commodities but also in currencies, fixed income and equities. Trading strategy that exploits the effect of skewness in the multi-asset setting would earn an annual return of 7.67% when leveraged to the 15% volatility.

Some companies have relatively more of their value in near-term cash flow (for ex. General Motors Corporation). Others (for ex. Tesla), are growth stocks, with a greater proportion of their market value based on long-term expected future cash flow. It seems that coronavirus pandemic has hit mainly the first group, the “low equity duration” companies. A new academic research paper written by Dechow, Erhard, Sloan, and Soliman explains how the equity duration factor can be used to assess how are companies exposed to short-term unexpected macroeconomic events (like COVID-19 pandemic), and how equity duration sensitivity can also explain relative underperformance of value vs growth stocks during the last bear market.

Authors: Dechow, Patricia and Erhard, Ryan and Sloan, Richard G. and Soliman, Mark T.

Title: Implied Equity Duration: A Measure of Pandemic Shutdown Risk

Recent corona-crisis turbulence brought us many unexpected things, and one observation is connected with the fixed-income market. The conventional wisdom says that there is a flight to liquidity during troubled times and crises. Traditionally, liquid assets are US Treasuries or high-quality corporate bonds. Therefore, in theory, the pandemic should have been connected with buying pressure of high-quality liquid assets. However, as shown by a novel, insightful research from Ha, Xiao and Zeng, the exact opposite held. There was a very unusual sellout of liquid assets such as high quality fixed income as mutual funds tried to meet their redemption requests.

Authors: Yiming Ma, Kairong Xiao and Yao Zeng

Title: Mutual Fund Liquidity Transformation and Reverse Flight to Liquidity

Summer is in full swing; therefore, we have prepared some “summer reading” for you. Ten new Quantpedia Premium strategies have been added into our database, and seven new related research papers have been included in existing Premium strategies during last month.

Additionally, we have produced 15 new backtests written in QuantConnect code. Our database currently contains nearly 330 strategies with out-of-sample backtests/codes.

Also, five new blog posts, that you may find interesting, have been published on our Quantpedia blog:

During past months we made a set of articles analyzing the performance of equity factors and selected systematic strategies during coronavirus crisis. These articles were short-ranged with data only from the start of the year 2020, which is enough for the purpose of the quick blog posts, but very short-sighted to see the nature of these strategies. Therefore, we expanded the time range by 20 years. For a better understanding of hedge possibilities of these strategies, we have added a comparison to essential safe-haven assets, not only to equities.

Whenever traders want to assess the stock market’s mood, there is one really popular and useful index the most of them turn to. Yes, you guessed it right, it’s CBOE’s VIX Index. And which index can we use if we want to determine the mood of the cryptocurrencies? We can turn to a paper written by Fabian Woebbeking, which offers the methodology to compute two cryptocurrency volatility indexes (CVX & CVX76). The CVX and CVX76 Indexes also extract the market’s expectation of future volatility from option prices, but from options on the Bitcoin. The research suggests that the cryptocurrency option market has finally reached a sufficient market size to extract stable cryptocurrency volatility information.

Authors: Fabian Woebbeking

Title: Cryptocurrency Volatility Markets