Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that...

Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle credit risk problems. However, the...

We introduce a robust, flexible and easy-to-implement method for estimating the yield curve from Treasury securities. This method is non-parametric and optimally learns basis functions in reproducing Hilbert spaces with an economically motivated smoothness reward. We provide a closed-form...

We design a novel empirical framework to examine market efficiency through out-of-sample(OOS) predictability. We frame the classic empirical asset pricing problem as a machine learningclassification problem. We construct classification models to predict return states. The prediction- based...

We show that machine learning methods, in particular extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained...

Out-of-sample prediction of profitability is a critical step in fundamental analysis and yet even sophisticated regression models do not generate predictions that significantly outperform random walk predictions. We employ random forests with classification trees, a method from machine learning,...

Financial strength ratings (FSRs) have become more significant particularly since the recent financial crisis of 2007-09 where rating agencies failed to forecast defaults and the downgrade of some banks. The aim of this paper is to predict Capital Intelligence banks' financial strength ratings...

In this paper we propose the use of machine learning methods to estimate inequality of opportunity. We illustrate how our proposed methods - conditional inference regression trees and forests - represent a substantial improvement over existing estimation approaches. First, they reduce the risk...

Small businesses that have demonstrated high levels of pre-disaster local involvement are more likely to take an active role in community resilience during a disaster, regardless of their own financial security. Our investigation of small business survey responses about COVID-19 impacts finds...

We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to...