The Term Structure of Machine Learning Alpha.
Abstract Machine learning (ML) models for predicting stock returns are typically trained on
one-month forward returns. Although these models show impressive full-sample gross …
one-month forward returns. Although these models show impressive full-sample gross …
Leveraging corporate governance characteristics for stock crash risk assessment
Causing considerable losses for individual investors, stock crashes are also potential
triggers for wider financial crises. In contrast to existing research that primarily focuses on …
triggers for wider financial crises. In contrast to existing research that primarily focuses on …
Dynamic Asset Allocation Using Machine Learning: Seeing the Forest for the Trees.
C Mueller-Glissmann, A Ferrario - Journal of Portfolio …, 2024 - search.ebscohost.com
High inflation and aggressive monetary policy tightening in 2022 triggered one of the largest
return drawdowns for a US 60/40 portfolio in the last 100 years. In this article, the authors …
return drawdowns for a US 60/40 portfolio in the last 100 years. In this article, the authors …
An Evaluation Framework for Machine Learning and Data Science (ML/DS) Based Financial Strategies: A Case Study Driven Decision Model
Big data and computational technologies are increasingly important worldwide in asset and
investment management. Many investment management firms are adopting these data …
investment management. Many investment management firms are adopting these data …
Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions
C Howard - Reducing Biases and Overfitting in Machine Learning …, 2023 - papers.ssrn.com
Abstract Machine learning has become increasingly popular in asset pricing research.
However, common modeling choices can lead to biases and overfitting. I show that group …
However, common modeling choices can lead to biases and overfitting. I show that group …
Predicting corporate bond illiquidity via machine learning
This paper tests the predictive performance of machine learning methods in estimating the
illiquidity of US corporate bonds. Machine learning techniques outperform the historical …
illiquidity of US corporate bonds. Machine learning techniques outperform the historical …
Optimal order routing with reinforcement learning
L ter Braak, M van der Schans - Available at SSRN 4611420, 2023 - papers.ssrn.com
Many asset managers use sell-side brokers to buy and sell shares of common stock.
Typically, orders are distributed between several brokers using so-called algo wheels. This …
Typically, orders are distributed between several brokers using so-called algo wheels. This …
True Value Investing in Credits through Machine Learning
Value investing in the credit market aims to identify mispricings by determining whether a
bond's credit spread offers a sufficient compensation for its risk. To assess how successful …
bond's credit spread offers a sufficient compensation for its risk. To assess how successful …
[LIVRE][B] The Battle of the Models: Modern Takes on Traditional and Machine Learning Techniques in Empirical Finance
C Howard - 2023 - search.proquest.com
Consensus views in finance must be continuously challenged and re-evaluated. This thesis
uses new techniques and modern perspectives to challenge commonly held beliefs, both …
uses new techniques and modern perspectives to challenge commonly held beliefs, both …
Financial reporting in the era of AI: the response of companies in the Netherlands to the challenges posed by machine readership
A Pocriciuc - 2024 - essay.utwente.nl
This thesis investigates the impact of Artificial Intelligence (AI) on financial reporting
practices in medium to large, listed companies in the Netherlands. By surveying financial …
practices in medium to large, listed companies in the Netherlands. By surveying financial …