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Estimating the volatility of asset pricing factors

Becker, Janis; Leschinski, Christian - 2018 - This version: May 20, 2018

Models based on factors such as size, value, or momentum are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid individual assets, this measure is...

Persistent link: https://www.econbiz.de/10011860248

Financial Machine Learning

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

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

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

Asset Pricing : Time-Series Predictability

Rapach, David; Zhou, Guofu - 2022

Asset returns change with fundamentals and other factors, such as technical information and sentiment over time. In modeling time-varying expected returns, this article focuses on the out-of-sample predictability of the aggregate stock market return via extensions of the conventional predictive...

Persistent link: https://www.econbiz.de/10013322523

Sparse Multivariate Modeling for Stock Returns Predictability

Bernardi, Mauro; Bianchi, Daniele; Bianco, Nicolas - 2021

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

Persistent link: https://www.econbiz.de/10013239660

Forecasting Stock Returns

Rapach, David; Zhou, Guofu - 2023

We survey the literature on stock return forecasting, highlighting the challenges faced by forecasters as well as strategies for improving return forecasts. We focus on U.S. equity premium forecastability and illustrate key issues via an empirical application based on updated data. Some studies...

Persistent link: https://www.econbiz.de/10014351279

Expected Returns and the Predictability of Equity Lending Fees

Andrews, Spencer - 2022

Using a novel equity lending dataset, this paper is the first to show that expected returns strongly and negatively predict future equity lending fees. In comparing two expected return measures, I find that a rational expected return has stronger predictive power of future short selling activity...

Persistent link: https://www.econbiz.de/10013491786

Can Deep Neural Networks Outperform Fama-MacBeth Regression and Other Supervised Learning Approaches in Stock Returns Prediction With Asset-Pricing Factors?

Teng, Huei-Wen; Li, Yu-Hsien - 2022

In asset pricing, most studies focus on finding new factors such as macroeconomic factors or firm characteristics to explain risk premium. Investigating whether these factors are useful in forecasting stock returns remains active research in the field of finance and computer science. This paper...

Persistent link: https://www.econbiz.de/10014235825

An artificial stock market : asset pricing and endogenous expectations using neural nets

Sommer, Jörg; Hanusch, Horst - 1998

In this paper the relatively new technique of neural nets is integrated in a traditional model of portfolio choice. On the basis of Arrow’s State Preference Model the investment decision depends on the expectation building process which consists of two components. The individual information...

Persistent link: https://www.econbiz.de/10009781736

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