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Kelly, Bryan T.; Xiu, Dacheng - National Bureau of Economic Research - 2023

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...

Persistent link: https://www.econbiz.de/10014322889

Financial Machine Learning

Kelly, Bryan T.; Xiu, Dacheng - 2023

Persistent link: https://www.econbiz.de/10014349505

Behavioral Portfolio Management with Layered ESG Goals and Ai Estimation of Asset Returns

Dash, Gordon H.; Kajiji, Nina - 2021

research on portfolio sustainability, network theory, and the interconnectedness of financial returns. Additionally, we provide …

Persistent link: https://www.econbiz.de/10013322710

Investors' behavior in alternative asset classes

Papavasiliou, Ellie; Topaloglou, Nikolas; Tsomidis, Georgios - In: Spoudai : journal of economics and business 72 (2022) 3/4, pp. 3-55

investor personae in the Behavioral Finance literature, namely, the Cumulative Prospect Theory, the Markowitz and the Loss …

Persistent link: https://www.econbiz.de/10014246136

Dynamic Portfolio Selection and Asset Pricing under Neo-Additive Probability Weighting

He, Xue Dong; Sun, Yu - 2023

We study a dynamic portfolio selection problem in which an agent trades a stock and a risk-free asset with the objective of maximizing the rank-dependent utility of her wealth at the terminal time of the investment horizon. Due to time inconsistency, we consider three types of agents,...

Persistent link: https://www.econbiz.de/10014350423

Automated machine learning and asset pricing

Healy, Jerome V.; Gregoriou, Andros; Hudson, Robert - In: Risks : open access journal 12 (2024) 9, pp. 1-12

specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can …

Persistent link: https://www.econbiz.de/10015066381

Firm Characteristics and Expected Stock Returns

Han, Yufeng - 2020

We analyze the joint out-of-sample predictive ability of a comprehensive set of 299 firm characteristics for cross-sectional stock returns. We develop a cross-sectional out-of-sample R2 statistic that provides an informative measure of the accuracy of cross-sectional return forecasts in terms of...

Persistent link: https://www.econbiz.de/10012852228

Structural Deep Learning in Conditional Asset Pricing

Fan, Jianqing; Ke, Zheng Tracy; Liao, Yuan; Neuhierl, … - 2022

networks. Our method is guided by economic theory and employs time-varying conditional information on alphas and betas carried …” of machine learning predictions by incorporating finance theory into the learning, and provide an economic interpretation … to capture time-varying features such as latent factors of the model. We formally establish the asymptotic theory of the …

Persistent link: https://www.econbiz.de/10013406180

Machine Learning and the Implementable Efficient Frontier

Jensen, Theis Ingerslev; Kelly, Bryan T.; Malamud, Semyon; … - 2022

We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading...

Persistent link: https://www.econbiz.de/10013492674

Asset Pricing Under Keeping Up with the Joneses and Heterogeneous Beliefs

He, Xue-zhong - 2013

When agents agree to disagree about the expected growth rate of the aggregate endowment process, we study the asset price dynamics under “Keeping up with the Joneses” (KUJ) meaning that each agent maximizes the expected life-time CRRA utility of his relative consumption to the other agent in...

Persistent link: https://www.econbiz.de/10013091259