Solar energy is a promising renewable energy with the potential to the sustainable development of the world. Efficient photo-thermal conversion is essential for harvesting and conversion of solar energy, leading to the exploration and design of efficient photothermal materials. Herein, we...

With the miniaturization and integration of electronic devices, the thermal management has become an unavoidable problem. However, when the heat sink is closely attached to the electronic device, the use of a metal heat sink will cause the fatigue failure of the electronic device due to the...

We examine the relation between firms’ environmental, social, and governance (ESG) performance and aggregate stock market returns. Based on 38 ESG scores, we construct a market-level ESG index and find its strong positive predictive power on the market returns both in- and out-of-sample, and...

We document empirically that firm fundamentals have explanatory power on the shape of the option implied volatility (IV) curve that is both economically and statistically significant. We find further that, after accounting for fundamentals, the associated IV process can generate overreaction in...

In this paper, we explore what factors drive expected corporate bond returns all over the world. With a novel dataset, and utilizing machine learning models, we find there is strong predictability of corporate bond returns in international markets. However, the documented factors that drive...

Using a novel data set based on individual resumes of public firm employees, we propose a monthly index of aggregate labor flow that measures the dynamics of firm employment. We find that the index can predict the economic outputs significantly: an increase in the index leads to greater...

In this paper, we study portfolio choice problem under estimation risk and show why the 1/N rule is very difficult to beat in applications and studies. First, as long as the dimensionality is high relative to sample size, we show that the usual estimated investment strategies are biased even...

In this paper, we conduct a simulation analysis of the Fama and MacBeth (1973) two-pass procedure, as well as maximum likelihood (ML) and generalized method of moments estimators of cross-sectional expected return models. We also provide some new analytical results on computational issues, the...

We study whether the similarity of firm disclosures on the Compensation Discussion and Analysis (CD&A) has predictability for future stock returns. We find that changes to the language and construction of the CD&As predict firms' future stock returns. A portfolio that longs the CD&A...

Automated machine learning extends the search space to include hyperparameters and algorithm selection. We apply automated machine learning (AutoML) to cross sectional stock return prediction with factors. We formulate factor dimension reduction and hyperparameter tuning in conventional ML...