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A bibliometric analysis of machine learning econometrics in asset pricing
Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17
Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...
Persistent link: https://www.econbiz.de/10013475217
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Persistence in factor-based supervised learning models
Coqueret, Guillaume - In: The Journal of finance and data science : JFDS 8 (2022), pp. 12-34
In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they are trained on long samples, with long-term returns as dependent variables. In addition, we...
Persistent link: https://www.econbiz.de/10014433680
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A Machine Learning Approach to the Fama-French Three- and Five-Factor Models
Diallo, Boubacar - 2019
This research proposes new estimations of the Fama-French three- and five-factor models via a machine learning approach. Speci fically, it uses a Bayesian optimization-support vector regression (BSVR) approach to obtain predictions of portfolio returns. On data from fi ve industries' portfolio...
Persistent link: https://www.econbiz.de/10012864313
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Sentiment-Driven Speculation in Financial Markets with Heterogeneous Beliefs : A Machine Learning Approach
Di Francesco, Tommaso; Hommes, Cars H. - 2023
This paper proposes an heterogenous asset pricing model in which different classes of investors coexist and evolve, switching among strategies over time according to a fitness measure. In the presence of boundedly rational agents, with biased forecasts and trend following rules, rational or...
Persistent link: https://www.econbiz.de/10014350871
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Factor Models, Machine Learning, and Asset Pricing
Giglio, Stefano; Kelly, Bryan T.; Xiu, Dacheng - 2022
We survey recent methodological contributions in asset pricing using factor models and machine learning. We organize these results based on their primary objectives: estimating expected returns, factors, risk exposures, risk premia, and the stochastic discount factor, as well as model comparison...
Persistent link: https://www.econbiz.de/10013322001
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High-Frequency Expectations from Asset Prices : A Machine Learning Approach
Chaudhry, Aditya - 2020
We propose a novel reinforcement learning approach to extract high-frequency aggregate growth expectations from asset prices. While much expectations-based research in macroeconomics and finance relies on low-frequency surveys, the multitude of events that pass between survey dates renders...
Persistent link: https://www.econbiz.de/10012823023
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Asset Pricing and Machine Learning : A Critical Review of Empirical Findings
Bagnara, Matteo - 2021
The latest development in empirical Asset Pricing is the employment of Machine Learning methods to address the problem of the factor zoo. These techniques offer great flexibility and prediction accuracy but require special care as they strongly depart from traditional Econometrics. I review and...
Persistent link: https://www.econbiz.de/10013321948
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Diverging roads: theory-based vs. machine learning-implied stock risk premia
Grammig, Joachim; Hanenberg, Constantin; Schlag, Christian - 2020
We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the … preferable to rely on a theory-based approach instead of engaging in the computerintensive hyper-parameter tuning of statistical … models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine …
Persistent link: https://www.econbiz.de/10012163064
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Extracting statistical factors when betas are time-varying
Gagliardini, Patrick; Ma, Hao - 2019 - This version: July 2019
This paper deals with identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is nonparametric regarding the way the loadings vary in time as functions of common shocks and individual...
Persistent link: https://www.econbiz.de/10012176811
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Macroeconomic Content of Characteristics-Based Asset Pricing Models : A Machine Learning Analysis
Rytchkov, Oleg - 2020
conclusion extends to several theory-motivated macroeconomic factors. Thus, our results suggest that the empirical success of …
Persistent link: https://www.econbiz.de/10012845051