The short-term reversal effect is the same as the small market capitalization factor. It has steadily contributed significant excess returns over a long period of time in the past. A single factor alone can achieve an annualized rate of return of 40%+. Unfortunately, the good times did not last long. Since 2019, the performance of the reversal factor has been extremely unstable, with large retracements, poor long-term monotonicity, and even momentum effects in some time periods and in the index domain. So has the short-term reversal effect degenerated into a style factor? How to improve the reversal factor, and in factor investment practice, can momentum and reversal be treated uniformly under the same framework? This article introduces the optimal construction of inversion factors and summarizes two unified frameworks.

There are already a vast number of technical indicators derived from price sequences, and the complexity of comprehensively considering the relationship between volume and price has increased by more than one order of magnitude. From word-of-mouth mantras about the relationship between volume and price, to high-dimensional deep time-series neural networks, from monthly long-term volume and price trends, to instantaneous impacts on the order book, people are endlessly exploring the relationship between volume and price. This article systematically analyzes some representative volume-price relationship indicators from the perspective of factor investment, hoping to provide reference and inspiration for mining volume-price factors in this area.

In the long run, the expected return of low-volatility stocks is higher than that of high-volatility stocks. This is the low-volatility (low-risk) anomaly that has existed in global markets for a long time. This article introduces the main nature and causes of low-risk anomalies, and lists common factor construction methods, hoping to provide an outline and guidance for factor investment in the volatility category among volume and price factors.

Sonnet of Intraday Stock Volatility: Metrics and Decomposition.

This article discusses several measures of high-frequency stock volatility within a day, and summarizes its various decomposition methods from a unified perspective, providing a reference for volatility-related investment practices.

Ke Yun has special flowers in recent years, often growing into new branches.

The anomaly of low volatility (low risk) is a well-known anomaly prevalent in the global secondary market, and the idiosyncratic risk (idiosyncratic volatility) factor calculated based on idiosyncratic rate of return is an outstanding representative among them. After excluding the impact of systemic risk factors, the residual return rate of individual stocks is considered to better represent risk, and the factors constructed from this also have better performance. Under the implicit factor framework, this article introduces the theoretical basis of idiosyncratic volatility factors, empirical evidence in the A-share market, and subsequent improvement ideas.

In the summer of 2023, I started my full-time quantitative trading career.

In quantitative trading, few problems are more frustrating than a strategy going live and then performing far worse than expected. The goal of this article is to give readers a first-pass understanding of what “backtest overfitting” means, why it happens, and what can be done about it.