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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

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

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

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

Deep learning, predictability, and optimal portfolio returns

Babiak, Mykola; Baruník, Jozef - 2020

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

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

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

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

Robust test of long run risk and valuation risk model

Gopalakrishna, Goutham - 2017

This paper tests the long run risk and valuation risk model using a robust estimation procedure. The persistent long run component of consumption growth process is proxied by a news based index that is created using a random forest algorithm. This news index is shown to predict aggregate long...

Persistent link: https://www.econbiz.de/10011819242

Mispricing and uncertainty in international markets

Sandulescu, Mirela; Schneider, Paul - 2021

. Return data for several economies reveal that RMP is countercyclical and related to financial uncertainty. RMP further shows …

Persistent link: https://www.econbiz.de/10012487677