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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

Deep learning, predictability, and optimal portfolio returns

Babiak, Mykola; Baruník, Jozef - 2020

Persistent link: https://www.econbiz.de/10012542701

Subsampled factor models for asset pricing : the rise of Vasa

De Nard, Gianluca; Hediger, Simon; Leippold, Markus - In: Journal of forecasting 41 (2022) 6, pp. 1217-1247

Persistent link: https://www.econbiz.de/10013465694

Long-term forecasting in asset pricing : machine learning models' sensitivity to macroeconomic shifts and firm-specific factors

Qian, Yihe; Zhang, Yang - In: The North American journal of economics and finance : a … 78 (2025), pp. 1-20

Persistent link: https://www.econbiz.de/10015434433

Asset allocation with factor-based covariance matrices

Conlon, Thomas; Cotter, John; Kynigakis, Iason - In: European journal of operational research : EJOR 325 (2025) 1, pp. 189-203

Persistent link: https://www.econbiz.de/10015433232

The battle of the factors : macroeconomic variables or investor sentiment?

Mascio, David A.; Molyboga, Marat; Fabozzi, Frank J. - In: Journal of forecasting 42 (2023) 8, pp. 2280-2291

Persistent link: https://www.econbiz.de/10014432891

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

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

Certainty of uncertainty for asset pricing

Jiang, Fuwei; Kang, Jie; Meng, Lingchao - In: Journal of empirical finance 78 (2024), pp. 1-23

Persistent link: https://www.econbiz.de/10015101663

Machine learning risk models

Kakushadze, Zura; Yu, Willie - In: Journal of risk & control 6 (2019) 1, pp. 37-64

Persistent link: https://www.econbiz.de/10012237599