Machine Learning Predictions of International Stock Returns
type: CreativeWork
description: This dissertation is broadly describing predictability of returns on individual stocks in international context. The first chapter covers required prerequisites for any study of fundamental anomalies outside the US. The second chapter studies the predictability of stock returns at an annual frequency. The last chapter then looks at possible profitability of the predictability on liquid universe of stocks at monthly frequency. In the first chapter, we study the role of the choice of a fundamental database on the portfolio returns of a set of 74 fundamental anomalies. We benchmark Compustat by comparing it to Datastream in the US and find systematic differences in the raw financial statements across the databases. These differences only have a small effect on the returns of anomalies when they are constructed on stock-months existing in both databases. Different stock coverage across the databases, however, leads to large statistically and economically significant disparities in the returns. Profitability anomalies yield negative returns on the Datastream universe. In the second chapter, we study statistical significance of 93 fundamental anomalies published in academic journals in a multiple hypothesis setting. We generate a universe of 48,387 data-mined fundamental strategies in order to overcome a problem of not being able to observe strategies that were tried but not published. The multiple hypothesis tests reveal that the number of significant anomalies heavily depends on the precise specification of the tests. We show that the adjustment of standard errors on portfolio returns for heteroskedasticity and autocorrelation is of first order importance and t-statistics on the portfolio returns may not have critical values of the normal distribution. In the third chapter, we study out-of-sample returns on 153 anomalies in equities documented in academic literature. We show that machine learning techniques that aggregates all the anomalies into one mispricing signal ar…
abstract: This dissertation is broadly describing predictability of returns on individual stocks in international context. The first chapter covers required prerequisites for any study of fundamental anomalies outside the US. The second chapter studies the predictability of stock returns at an annual frequency. The last chapter then looks at possible profitability of the predictability on liquid universe of stocks at monthly frequency. In the first chapter, we study the role of the choice of a fundamental database on the portfolio returns of a set of 74 fundamental anomalies. We benchmark Compustat by comparing it to Datastream in the US and find systematic differences in the raw financial statements across the databases. These differences only have a small effect on the returns of anomalies when they are constructed on stock-months existing in both databases. Different stock coverage across the databases, however, leads to large statistically and economically significant disparities in the returns. Profitability anomalies yield negative returns on the Datastream universe. In the second chapter, we study statistical significance of 93 fundamental anomalies published in academic journals in a multiple hypothesis setting. We generate a universe of 48,387 data-mined fundamental strategies in order to overcome a problem of not being able to observe strategies that were tried but not published. The multiple hypothesis tests reveal that the number of significant anomalies heavily depends on the precise specification of the tests. We show that the adjustment of standard errors on portfolio returns for heteroskedasticity and autocorrelation is of first order importance and t-statistics on the portfolio returns may not have critical values of the normal distribution. In the third chapter, we study out-of-sample returns on 153 anomalies in equities documented in academic literature. We show that machine learning techniques that aggregates all the anomalies into one mispricing signal ar…
author: Tobek, Ondrej
language: en
keywords: Machine Learning, Empirical Asset Pricing, International Stock Returns, Anomalies, Predictability of Stock Returns
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/2a0cce4d-9d02-449c-9cfc-1b8c70cc659e/download
license: https://creativecommons.org/licenses/by/4.0/
date published: 2019-08-28
date modified: 2026-07-22T22:24:11Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.44015