Industry concentration and markups in the US have been rising over the last 3- 4 decades. However, the causes remain largely unknown. This paper uses machine learning on regulatory documents to construct a novel dataset on compliance costs to examine the effect of regulations on market power....

The objective of this paper is to enrich the literature and explain why machine learning took the lead on theoretical and empirical studies. We applied machine learning techniques to professional tennis data while investigating multiple approaches based on our personal tennis understanding. This...

The rapid development of technology requires all professionals, such as the accounting profession, to continue to develop ways of working that are fast and accurate so that they are not outdated and effective in achieving goals. With increasingly sophisticated technology, accountants need to...

This study applies machine learning models to explore the predictive power of ten categories of financial indicators in Chinese stock market. Meanwhile, we assess whether influential financial indicators cluster into specific categories that hold greater importance for stock return prediction....

Stock market index enhancement is a popular strategy among hedge funds. The algorithm tries to adjust the weights of individual stocks of a benchmark index to boost performance of the target portfolio with respect to the original benchmark. Therefore, the key to success of this strategy is the...

Recent innovations in financial technology (Fintech) have changed the landscape of financial services. Fintech is not limited to digital payment systems but also includes blockchain technology and cryptocurrency. Investors are increasingly looking for exposure to financial innovations. However,...

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...

This study utilizes machine learning techniques, notably classification and regression trees (CART) and support vector regression (SVR), to predict corporate credit ratings. While SVR marginally outperforms in accuracy, CART offers interpretability. However, unconstrained models can produce...

We contribute to the literature by analyzing forecast combination methods in the context of machine learning to predict equity returns. Whilst individual models lack robustness, forecast combinations display stability and are able to produce improved results with Sharpe ratios up to 3.16. We use...