Journal ArticleDOI
Machine Learning in Empirical Asset Pricing
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TLDR
Overall, the paper concludes that machine learning can offer benefits for future research, but researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing.Abstract:
The tremendous speedup in computing in recent years, the low data storage costs of today, the availability of “big data” as well as the broad range of free open-source software, have created a renaissance in the application of machine learning techniques in science. However, this new wave of research is not limited to computer science or software engineering anymore. Among others, machine learning tools are now used in financial problem settings as well. Therefore, this paper mentions a specific definition of machine learning in an asset pricing context and elaborates on the usefulness of machine learning in this context. Most importantly, the literature review gives the reader a theoretical overview of the most recent academic studies in empirical asset pricing that employ machine learning techniques. Overall, the paper concludes that machine learning can offer benefits for future research. However, researchers should be critical about these methodologies as machine learning has its pitfalls and is relatively new to asset pricing.read more
Citations
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Journal ArticleDOI
Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
TL;DR: This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.
Journal ArticleDOI
Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models
TL;DR: In this paper, the authors assess the ability of various machine learning models, in order to forecast the credit ratings of eco-friendly firms, and find that classification and regression trees have the most precision for the credit rating predictions.
Journal ArticleDOI
Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models
TL;DR: In this article , the authors assess the ability of various machine learning models, in order to forecast the credit ratings of eco-friendly firms, and find that classification and regression trees have the most precision for the credit rating predictions.
Journal ArticleDOI
Machine learning solutions to challenges in finance: An application to the pricing of financial products
TL;DR: This paper proposes a machine-learning method to price arithmetic and geometric average options accurately and in particular quickly and it is verified by empirical applications as well as numerical experiments.
Journal ArticleDOI
Machine learning and credit ratings prediction in the age of fourth industrial revolution
TL;DR: In this article, the authors employ the machine learning techniques- a subset of artificial intelligence- in order to predict the credit ratings for the banks in GCC, and suggest that arbitrary forests demonstrate the highest precision, based on the F1 score, specificity and the accuracy scores.
References
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Book
Computing Machinery and Intelligence
TL;DR: If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.
Journal ArticleDOI
The Cross-section of Expected Stock Returns
TL;DR: In this paper, the cross-sectional properties of return forecasts derived from Fama-MacBeth regressions were studied, and the authors found that the forecasts vary substantially across stocks and have strong predictive power for actual returns.
Journal ArticleDOI
A Comprehensive Look at the Empirical Performance of Equity Premium Prediction
Ivo Welch,Amit Goyal +1 more
TL;DR: The authors comprehensively reexamine the performance of variables that have been suggested by the academic literature to be good predictors of the equity premium and find that by and large, these models have predicted poorly both in-sample and out-of-sample (OOS) for 30 years now.
Journal ArticleDOI
Some studies in machine learning using the game of checkers
TL;DR: In this article, two machine learning procedures have been investigated in some detail using the game of checkers, and enough work has been done to verify the fact that a computer can be programmed so that it will lear...