Fast sharpness-aware training for periodic time series classification and forecasting
Various deep learning architectures have been developed to capture long-term
dependencies in time series data, but challenges such as overfitting and computational time …
dependencies in time series data, but challenges such as overfitting and computational time …
Portfolio Management Transformed: An Enhanced Black–Litterman Approach Integrating Asset Pricing Theory and Machine Learning
H Ko, J Lee - Computational Economics, 2025 - Springer
This study proposes a novel Black–Litterman portfolio model that leverages machine
learning predictions based on size, book-to-market, momentum, and volatility. Our model …
learning predictions based on size, book-to-market, momentum, and volatility. Our model …
A self-attention based cross-sectional return forecasting model with evidence from the Chinese market
X Xiao, X Hua, K Qin - Finance Research Letters, 2024 - Elsevier
This study introduces a novel model based on self-attention mechanisms to generate out-of-
sample forecasts of cross-sectional returns. This model is designed to capture the non …
sample forecasts of cross-sectional returns. This model is designed to capture the non …
Equity trading
C Fohlin - Research Handbook of Financial Markets, 2023 - elgaronline.com
Equity trading 359 key provisions unify the definition of equity as I use the term in this
chapter, which focuses on ownership stakes in publicly traded corporations and surveys a …
chapter, which focuses on ownership stakes in publicly traded corporations and surveys a …
Multi-Factor Model with Time-Varying Volatility: A Multi-Task Learning Approach
B Son, H Kim, J Lee - Available at SSRN 4840857 - papers.ssrn.com
This paper introduces a novel neural network-based multi-factor asset pricing model
incorporating time-varying volatility dynamics. Utilizing a multi-task learning framework, it …
incorporating time-varying volatility dynamics. Utilizing a multi-task learning framework, it …