Short-term mean reversion trading on equity indexes is a popular trading style. Often, price-based technical indicators like RSI, CCI are used to assess if the stock market is in overbought or oversold conditions. A new research article written by Chainika Thakar and Rekhit Pachanekar explores a different indicator – TRIN, which compares the number of advancing and declining stocks to the advancing and declining volume. TRIN’s advantage is that it’s cross-sectionally based and its calculation uses not only price but also volume information. Thakar& Pachanekar’s research paper is useful for fans of indicator’s based trading strategies and offers a short introduction to TRIN’s calculation together with an example of mean-reversion market timing strategy written in a python code.

Authors: Chainika Thakar, Rekhit Pachanekar

Title: Trading Index (TRIN) – Formula, Calculation & Strategy in Python

While there may be debates about passive and active investing, and even blogs about the numbers of active funds that were outperformed by the market, the history taught us that the outperformance of active or passive investing is cyclical. As a proxy for the active investing, the new Quantpedia’s research paper examines factor strategies and their smart allocation using fast or slow time-series momentum signals, the relative weights based on the strength of the signals and even blending the signals. While the performance can be significantly improved, using those smart approaches, the factors still got beaten by the market in both US and EAFE sample. However, the passive approach did not show to be superior. The factor strategies and market are significantly negatively correlated and impressively complement each other. The combined Smart Factors and market portfolio vastly outperforms both factors and market throughout the sample in both markets. With the combined approach, the ever-present market falls can be at least mitigated or profitable thanks to the factors.

Often, this blog provides novel research that may not include the straightforward trading strategy, yet it is an interesting insight for portfolio managers, risk managers, investors or traders. Novel research of Brogaard et al. (2020) examines the crucial role of market makers during extreme price movements. According to the authors and the past literature, there are two competing theories of how the extreme price movements end, and both are related to the market makers. It is the constrained liquidity provision theory and the strategic liquidity provision. This research tests and explains these competing theories, with findings that are in line with the strategic liquidity provision. The results can be found particularly interesting during extreme price movements because the paper has shown that firstly, liquidity providers scale back and only interfere later. Market makers utilize price pressures in stressful times in a profitable way, since they profit from subsequent reversals.

Authors: Jonathan Brogaard, Konstantin Sokolov and Jiang Zhang

Title: How do Extreme Price Movements End?

Thirteen new Quantpedia Premium strategies have been added into our database, and eleven new related research papers have been included in existing Premium strategies during last month.

Additionally, we have produced 11 new backtests written in QuantConnect code. Our database currently contains over 370 strategies with out-of-sample backtests/codes.

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

Plus we continue to re-run some of our codes on a monthly basis systematically, over 160 codes are at the moment part of this activity.

Nobody can doubt that in the recent period, ESG investing has significantly grown and is a staple part of the financial markets. The academic literature has also grown with the popularity of ESG investing. The negative, mixed and positive results for ESG scores in portfolios have evolved, and generally, there is a consent that ESG scoring can be a vital part of the portfolio management process. It can be observed that in the past, the ESG scores were not priced in the equity market and still, the ESG is not priced in the corporate bond market (apart from Europe). Nowadays, the investors react to the ESG scores, but the research paper of Cui and Docherty (2020) has novel insights that investors may react too much to the ESG. Their research shows that investors overreact to the negative ESG events and stocks connected with negative ESG events sharply fall, but the prices have mean-reverting properties. As a result, there is a reversal after bad ESG events. Stocks firstly sharply fall, but then their prices are reverted to the previous values. Therefore, this paper is interesting from the market pricing or efficiency point, but it also can be utilized by a reversal investor.

In recent years, financial markets have experienced a boom in passive and index-based strategies, which could have caused a change in the trading volume, volatility, beta or correlations. The reason is straightforward: the index investing causes a lot of stocks to move in the same direction. A novel research Shen and Shi (2020), using high-frequency data, suggests that over the last two decades, the patterns mentioned above have changed and the index investing is the cause. Both the trading volume and stock correlations are increased at the end of trading sessions. Betas are firstly dispersed, but in general, converge to one during the rest of the day. Trading volume has high dispersion at the market open, but low dispersion at the market close. Overall, the paper has many important implications for portfolio managers, risk managers and traders as well since it is closely related to the transaction costs, intraday price fluctuations, correlations or liquidity. Moreover, it is full of exciting charts that are worth seeing.