In this paper, we approximate the empirical findings of Papadamou and Markopoulos (2012) on the NOK/USD exchange rate under a Machine Learning (ML) framework. By applying Support Vector Regression (SVR) on a general monetary exchange rate model and a Dynamic Evolving Neuro-Fuzzy Inference System...

We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional...

This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won-U.S. dollar (KRW/USD) exchange rate and the U.S. and Korean stock market returns. We construct international asset allocation...

We develop Residual MisPricing (RMP), an index capturing mispricing relative to a linear benchmark asset pricing model, from the structure imposed by no-arbitrage. RMP is fully conditional and depends only on the returns of basic assets. Return data for several economies reveal that RMP is...

This paper documents that the housing cycle, measured by the residential investment share, is a strong in-sample and out-of-sample predictor for the dollar up to twelve quarters. Housing construction is negatively associated with risk premia in equity and bonds, but positively with foreign...

We investigate the use of machine learning techniques into building statistically stable systematic allocation strategies. Traditionally, allocation processes usually rely on variations of Markowitz framework such as Mean Variance allocation, Maximum Diversity, Risk Allocation , Value at Risk,...

Most publications in Financial ML seem concerned with forecasting prices. While these are worthy endeavors, Financial ML can offer so much more. In this presentation, we review a few important applications that go beyond price forecasting:1. Portfolio construction2. Structural breaks3. Bet...

One of the most exciting recent developments in financial research is the availability of new administrative, private sector and micro-level datasets that did not exist a few years ago. The unstructured nature of many of these observations, along with the complexity of the phenomena they...

This article reviews ten notable financial applications where ML has moved beyond hype and proven its usefulness. This success does not mean that the use of ML in finance does not face important challenges. The main conclusion is that there is a strong case for applying ML to current financial...

We demonstrate the value of machine learning in accounting through a detailed examination of litigation risk, an important and frequently used estimate in the literature. We evaluate a comprehensive set of twelve machine learning techniques and benchmark their performance against the logistic...