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. Additionally, more professional investors usually follow popular assets, and this market segment is probably significantly more efficient.

So, we went in this direction. We took a well-known commodity momentum factor strategy and investigated its performance among commodity futures that were part of the S&P GSCI respectively BCOM commodity indexes and then compared the strategy’s performance with a variant that traded only non-indexed commodity futures. As we had expected, the trading strategy using exotic assets performed significantly better.

We started to systematically search for systematic cryptocurrency trading strategies in academic research approximately two years ago. This article is a short analysis of the performance of systematic crypto strategies in 2021. We conclude that non-price predictors offered advantages over price-based only trading strategies in the previous year-to-date and 12-months periods.

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.

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.

After a month, we are back with a year-to-date performance analysis of a few selected trading strategies. In the previous article, we were writing about the performance of equity factors during the coronavirus crisis. Several readers asked us to take a look also on different types of trading strategies, so we are now expanding to other asset classes. We picked a subset of strategies that can be used as a hedge at the times of market stress (at least, that’s what the source academic research papers indicate) and checked how they fared.