In this paper, the use of the machine learning algorithm is examined in derivation of the determinants of price movements of stock indices. The Random Forest algorithm was selected as an ideal representative of the nonlinear algorithms based on decision trees. Various brokering and investment...

This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

This paper formulates and solves an economic decision problem of the acquisition of costly information in data-driven decision making. The paper assumes an agent predicting a random variable utilizing several costly explanatory variables. Prior to the decision making, the agent learns about the...

This paper focuses on the spatial variation in the uptake of social security benefits following a large and detrimental exogenous shock. Specifically, we focus on the Global Financial Crisis (GFC) and the onset of the COVID-19 pandemic. We construct a two-period panel of 66 Territorial...

We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

The adoption of wind energy has grown significantly in recent years. New, cost-effective technologies have been developed, led by customer awareness of green technologies and a legal framework proposed at the European Union level. The stochastic nature of wind speed is transferred to wind...

Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as...

We investigate various statistical methods for forecasting risky choices and identify important decision predictors. Subjects (n=44) are presented a series of 50/50 gambles that each involves a potential gain and a potential loss, and subjects can choose to either accept or reject a displayed...

Where should better learning technology (such as machine learning or AI) improve decisions? I develop a model of decision-making in which better learning technology is complementary with experimentation. Noisy, inconsistent decision-making introduces quasi-experimental variation into training...