Traditional finance assumes that market participants are perfectly rational, which is obviously at odds with reality, so it cannot explain the many forms of mispricing that appear in practice. Behavioral finance treats investors as boundedly rational and combines traditional financial theory with psychology to study which irrational behaviors create mispricing and how those biases connect to asset returns. Factor investing treats human behavioral bias as a source of excess return, so it is worth reviewing the main results of behavioral finance as a whole and linking them to the anomalies seen in markets.

The role of a risk model in an alpha strategy is to predict the covariance matrix of expected returns for the underlying assets, so that portfolio variance can be minimized for a given expected return and the strategy’s Sharpe ratio can be improved. A risk model is no less important than a return model, yet compared with return modeling there is surprisingly little discussion of how to build one. One reason is that most investors simply buy commercial solutions such as Barra; another is that risk models must be judged at the portfolio level and cannot be evaluated through a simple benchmark. This article gives a systematic introduction to the definition and implementation of risk models, including shrinkage estimators, expert factor models, and data-driven statistical models.

In this section, we revisit Quant 4.0 from a systems perspective and explore how all of its components can be integrated into a single architecture. The figure below shows a suggested Quant 4.0 system framework, including an offline research system for quantitative research and an online system for live quantitative trading.

The unexamined life is not worth living.

This article first explains why I chose to pursue an alpha strategy, then reviews the first phase of strategy R&D in the second half of 2023, outlines the tasks of phase two, and finally looks ahead to phase three.

Unlike volume factors, which examine aggregate trading volume over a period, the fund-flow factors in this article are computed from tick-by-tick trade data and study microstructure features created by trading itself, such as whether the counterparty is trading in large or small orders and how posted order sizes are distributed. Tick-by-tick trade data is large enough to create serious engineering challenges for factor computation. This article first introduces a single-machine computation framework and then briefly summarizes the existing research results on fund-flow factors.

XAI (Explainable Artificial Intelligence) has been an important research direction for decades and is crucial to the credibility and robustness of artificial intelligence models. In the quantitative field, improving the explainability of AI can make decision-making processes more transparent and easier to analyze, provide researchers and investors with useful insights, and uncover potential risk exposures. In this article, we will discuss how to leverage XAI in Quant 4.0: the first part introduces common XAI techniques, and the second part connects these techniques to practical quantification scenarios. Knowledge-driven artificial intelligence is an important complementary technology to data-driven artificial intelligence, especially in low-frequency investment scenarios such as value investing and global macro investing. At the end of this article, we introduce how to apply knowledge-driven artificial intelligence to quantitative research.

The automated AI in Quantification 4.0 covers the automation of the entire quantitative process. In this section, we first introduce the overall view of the R&D process, and then discuss how to upgrade it to an automated AI version.

In recent years, the quantitative investment industry has been booming in China and has become an unavoidable topic when discussing the secondary market. It is either mythical or criticized by everyone. So, what exactly is quantitative investment (Quantitative Investment)? What quantitative strategies are there? Are quantitative strategies guaranteed to make money? What are the basic principles for building a quantitative strategy? This article introduces the basic concepts of quantitative investment, its past and present, and looks forward to its future development model.

Backtesting is the core link in quantitative strategy research and development, and it is also the key difference between quantitative investment and traditional active investment. Backtesting refers to conducting simulated transactions on an investment strategy that can be accurately characterized in a historical simulation environment, and using the historical performance of the strategy to infer its future performance, thereby making choices among multiple groups of strategies to form the final investment decision. Based on a brief introduction to backtest overfitting, this article further discusses the quantitative evaluation indicators of the historical performance of trading strategies, focusing on the false positives caused by multiple tests during the backtesting process. Quantitative trading uses computer programs to realize automated trading. Its key difference from traditional active investing is its reliance on “backtesting” to verify the effectiveness of the proposed strategy and estimate its expected performance. In the context of the growth of computer computing power and algorithm development, researchers have implemented an increase in the number of backtests. Multiple tests have led to frequent false positives. The seemingly best strategies during the backtest period lack the ability to generalize to out-of-sample data. This phenomenon is called backtest overfitting. How to estimate the probability of backtest overfitting and adjust expected performance indicators such as the Sharpe ratio to correctly reflect the true performance of the strategy under the influence of multiple tests has become an emerging research direction.

Trading volume is an indispensable part of the volume price factor, but in most cases it appears as a supporting role in the coordination of volume and price, as discussed in Volume Price Relationship Factor and Momentum Reversal Factor. This article focuses on the trading volume itself and discusses two important stock selection factors: turnover factor and illiquidity premium. The trading volume factor has significant predictive effect on stock cross-sectional returns in global markets, especially in immature markets such as A-shares.