Research

We examine whether the US Securities Exchange Act of 1934 significantly stabilized the market by introducing mandatory disclosure of information. We argue that mandatory information disclosure can curb stock manipulation by enhancing transparency, thereby reducing excess stock volatility. After a comprehensive assessment of the voluntary disclosure practices of companies listed on the New York Stock Exchange before 1934, we find that those with poor disclosure practices experienced a significantly greater reduction in volatility after the implementation of the act compared with those with good disclosure practices. Further analysis reveals that the liquidity of these companies with poor disclosure practices also improved significantly more than that of companies with better disclosure, and the improvement in liquidity was linked to the decrease in their volatility. Given that one key purpose of the act’s legislators was to reduce excess market volatility, our findings provide empirical support for considering this legislative aim successful.

We propose a simple, linear-regression-based method for prediction of the time series of stock returns. The method achieves out-of-sample performances comparable to machine learning methods while having ignorable computational costs. The key component of the method is to integrate a straightforward cross-market factor screening into the iterated combination method proposed by Lin et al., (2018). Our empirical results on the U.S. stock market show that the method outperforms many state-of-the-art machine learning methods in certain periods. The method also exhibits greater utility gain and investment profits in most periods after considering transaction costs.

We investigate the performances of two methods of complete subset averaging—complete subset linear averaging (CSLA) and complete subset quantile averaging (CSQA)—on the problem of corporate bond return prediction. We find that the two methods are overwhelmingly better than univariate linear regression and simple forecast combination. Meanwhile, CSQA is better than CSLA in most cases. For practical implementation, we also provide discussions on the selection of the hyperparameter k when applying these complete subset averaging methods.

We employ an ensemble learning approach, “stacking”, to refine and combine a variety of linear and nonlinear individual stock return prediction models. In an application of forecasting U.S. market excess return, stacking with a simple structure can outperform the traditional historical mean benchmark, Mallows model averaging, simple combination forecast, complete subset regression, combination elastic net forecast, and several other models in terms of both in- and out-of-sample performance measures on a consistent basis. More importantly, we find that the out-of-sample gains of stacking are especially evident during extreme downside market movements. Overall, stacking can generate substantive improvements in market excess return predictability.

This paper investigates how the COVID-19 pandemic affects the connectedness network of stock market volatility in 19 economies around the world. Our method builds on the Diebold-Yilmaz volatility network model to construct the volatility spillover index, and uses lag sparse group LASSO to accommodate the high-dimensional system. We find that the outbreak of the COVID-19 pandemic strengthens the overall volatility connectedness, and the global connectedness level remains high throughout 2020. In particular, connections across different continents have become stronger during this period. However, China is shown to be disconnected from the global volatility connectedness network until late November 2020. We find evidence that China is not the main source of volatility spillover during the COVID-19 pandemic.

近期，经济学研究的方法论问题重新在中文经济学界引起关注。陆铭教授最近 把经济学研究中的一种现象概括为“过度模型化”：模型构建未能很好纳入现实情 境，出现“为了让模型成立而忽略现实”；过度追求方法复杂性和精确性，出现“为 了让方法成立而牺牲问题”(陆铭, 2026)。本文赞同对这一现象的观察，但认为其所 指向的并非中国经济学近期才有的特殊现象，而是经济学长期面对的基本问题。本 文认为，这一问题的关键不在于“模型化”，而在于如何判断“过度”。为此，本文 提出行为公设、制度框架与经验校准构成的三元框架：经济解释需要说明行动者如 何形成目标、信念和选择方式，行动者处于何种规则和收益后果之下，以及相关机 制在经验上具有多大量级和适用边界。所谓“过度”，可以理解为三者之间的失衡 或断裂。本文以李嘉图式抽象及熊彼特对其“李嘉图恶习”的批评、门格尔与历史 学派之争、米塞斯与弗里德曼围绕先验演绎和经验检验的分歧为线索，说明关于模 型、制度语境和经验证据之间关系的争论贯穿经济学方法史。随后，本文讨论现代 因果识别进入传统历史研究时引发的争议，说明经验工具进入既有命题时仍需面对 机制解释、制度语境和知识增量问题。最后，本文以拍卖理论和期权定价为例说明， 模型复杂本身并不必然导致“过度”。

We propose a new method for forecasting stock market returns in a data-rich environment: the factor-augmented sum-of-the-parts (FA-SOP) approach. Rather than predicting returns directly, FA-SOP decomposes them into three components—dividend-price ratio, earnings growth, and price-earnings ratio growth (gm)—and models each separately. We emphasize that gm is a more promising target for forecasting due to its stronger connection with macroeconomic conditions and greater variability over time. FA-SOP forecasts gm using latent macroeconomic factors extracted from high-dimensional data, capturing the underlying state of the economy while avoiding overfitting. Applied to S&P 500 returns from 1960 to 2022, FA-SOP outperforms predictive regressions, factor-augmented regressions, and traditional decomposition approaches, yielding robust out-ofsample gains in both statistical and economic terms. Simulations based on a present-value model further show that FA-SOP’s advantage stems from its ability to track the true data-generating process more closely. Our results highlight the value of decomposing returns and focusing on components that are more predictably linked to economic fundamentals.

This study employs an ensemble machine learning method, known as “Stacking," to forecast corporate bond returns. We find that the Stacking method introduces new features into combination forecasts, increasing the predictive model’s power and generating higher statistical and economic gains across bond ratings and maturities. Moreover, the method is efficient for tackling high dimensionality and achieves the best result when using predictors from corporate bond, Treasury, and stock markets jointly. While the overall performance of different Stacking models is satisfactory, simpler Stacking models appear to outperform others and generate optimal forecasts.

We contrast continuous magnitude estimation with discrete directional classification in equity premium prediction. Through a symmetric, single-pass out-of-sample evaluation of traditional econometric and machine learning algorithms, we document a distinct divergence in predictive performance. While continuous models struggle with low signal-to-noise ratios and generally fail to outperform the historical mean, discrete classifiers consistently reveal statistically significant predictability and generate substantial economic utility. We demonstrate that this divergence stems from the inherent sensitivity of continuous models to magnitude noise. Under continuous estimation, algorithms face a structural dilemma: they either overreact to unforecastable extreme shocks or resort to excessive shrinkage, both of which severely limit their market-timing ability during downturns. Conversely, discarding magnitude estimation acts as a form of structural regularization. This transformation frees nonlinear algorithms from extrapolating non-stationary macroeconomic trends, allowing them to utilize persistent high-frequency risk signals to execute timely market exits. Ultimately, our findings suggest that the fundamental choice of the predictive paradigm exerts a first-order impact that outweighs specific algorithmic sophistication.