Autoencoder asset pricing models

S Gu, B Kelly, D Xiu - Journal of Econometrics, 2021 - Elsevier
We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su
(KPS, 2019), our model allows for latent factors and factor exposures that depend on …

[PDF][PDF] Implementing the Capital Asset Pricing Model in Forecasting Stock Returns: A Literature Review

J Mandala, JP Soehaditama… - Indonesian …, 2023 - journal.formosapublisher.org
Harry Markowitz developed the portfolio theory model in 1952. His theory is how risk-averse
investors create optimal portfolios that maximize expected returns for a given level of risk …

Asset pricing with fading memory

S Nagel, Z Xu - The Review of Financial Studies, 2022 - academic.oup.com
Building on evidence that lifetime experiences shape individuals' macroeconomic
expectations, we study asset prices in an economy in which a representative agent learns …

Identifying long‐run risks: A Bayesian mixed‐frequency approach

F Schorfheide, D Song, A Yaron - Econometrica, 2018 - Wiley Online Library
We document that consumption growth rates are far from iid and have a highly persistent
component. First, we estimate univariate and multivariate models of cash‐flow …

Ambiguity and the historical equity premium

F Collard, S Mukerji, K Sheppard… - Quantitative …, 2018 - Wiley Online Library
This paper assesses the quantitative impact of ambiguity on historically observed financial
asset returns and growth rates. The single agent, in a dynamic exchange economy, treats …

[图书][B] Economic dynamics: theory and computation

J Stachurski - 2009 - books.google.com
A rigorous and example-driven introduction to topics in economic dynamics, with an
emphasis on mathematical and computational techniques for modeling dynamic systems …

Autoencoder asset pricing models

S Gu, BT Kelly, D Xiu - 2019 - papers.ssrn.com
We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su
(KPS, 2019), our model allows for latent factors and factor exposures that depend on …

Forecasting value-at-risk using deep neural network quantile regression

I Chronopoulos, A Raftapostolos… - Journal of Financial …, 2024 - academic.oup.com
In this article, we use a deep quantile estimator, based on neural networks and their
universal approximation property to examine a non-linear association between the …

Volatility, valuation ratios, and bubbles: An empirical measure of market sentiment

C Gao, IWR Martin - The Journal of Finance, 2021 - Wiley Online Library
We define a sentiment indicator based on option prices, valuation ratios, and interest rates.
The indicator can be interpreted as a lower bound on the expected growth in fundamentals …

Machine learning for continuous-time finance

V Duarte, D Duarte, DH Silva - The Review of Financial Studies, 2024 - academic.oup.com
We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-
time models in finance. We approximate value and policy functions using deep learning and …