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...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

We focus on the stock selection step of the index tracking problem in passive investment management and incorporate constant changes in the dynamics of markets into the decision. We propose an approach, using machine learning techniques, which analyzes the performance of the selection methods...

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 articlemodels the dependence risk and resource allocation characteristics of two 20-stock coal–uranium and oil–gas sector portfolios fromthe Australian market in the context of the global financial crisis of 2008–2009. The modeling framework implemented consists of pair vine copulas...

The aim of this paper is to assess the effectiveness and risk in the stock exchange market in Central and Eastern Europe countries (CEE) in view of the largest stock exchanges: NYSE2‑LSE‑HKSE2. The implementation of this objective was based on an analysis of basic stock market indicators and...

Value-at-risk (VaR) is a useful risk measure broadly used by financial institutions all over the world. VaR has been extensively used to measure systematic risk exposure in developed markets like of the US, Europe and Asia. This paper analyzes the accuracy of VaR measure for Pakistan's emerging...

The experimental approach is applied to explore the value of unidentified historical information in stock-return prediction. Return sequences were randomly drawn cross section and time from historical S&P500 data. Subjects were requested to predict returns or select stocks from 12 preceding...