[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 …
investors create optimal portfolios that maximize expected returns for a given level of risk …
Identifying long‐run risks: A Bayesian mixed‐frequency approach
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 …
component. First, we estimate univariate and multivariate models of cash‐flow …
Ambiguity and the historical equity premium
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 …
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 …
emphasis on mathematical and computational techniques for modeling dynamic systems …
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 …
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 …
The indicator can be interpreted as a lower bound on the expected growth in fundamentals …
Machine learning for continuous-time finance
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 …
time models in finance. We approximate value and policy functions using deep learning and …