title: Three essays on asset pricing: from traditional models to neural networks
Creators: name: Wei, Peng
Publisher: name: The University of Edinburgh
Year: 2025
Subjects: subject: Asset pricing
subject: Deep learning
subject: Neural networks

description: In the ever-evolving landscape of financial markets, understanding and predicting asset prices remain fundamental challenges in financial economics. This thesis advances the field of asset pricing by addressing two critical gaps. First, it investigates the momentum phenomenon in the options market, shedding light on the under-researched area and providing new empirical insights. Second, it bridges traditional asset pricing frameworks with advanced neural network models, exploring both cross-sectional and time-series contexts to capture complex, nonlinear, and time-varying relationships often overlooked by conventional methods. The thesis comprises three distinct yet interconnected studies, each focusing on a specific aspect of asset pricing. These essays are unified by a shared progression, starting with traditional factor models and evolving toward the integration of conventional asset pricing frameworks with neural networks. Chapter 2 examines the momentum phenomenon in the options market, focusing on three popular delta-neutral trading strategies: straddles, strangles, and butterfly spreads, using S&amp;P 100 options data from 2000 to 2019. The study evaluates the robustness of momentum returns by accounting for traditional options risk factors, including the volatility spread, idiosyncratic volatility, market capitalization, the term spread of implied volatility, and the smirk slope of implied volatility. The findings reveal significant momentum returns in both straddle and strangle strategies, which persist after controlling for these traditional risk factors and are unexplained by existing factor models. In contrast, no substantial momentum is observed for butterfly spreads, highlighting the strategy-specific nature of momentum effects in the options market. Chapter 3 addresses the time series predictability of foreign exchange (FX) returns using neural networks. A Deep Prediction Model (DPRED) is introduced, incorporating cross-country interactions and long-memory dependencies. Empirical results indicate the presence of significant cross-country and long-memory effects, with the DPRED model providing improved forecasting accuracy. The findings also suggest that simpler neural network architectures often yield better performance. Chapter 4 examines neural network-based factor models for asset pricing in equity and foreign exchange (FX) markets, and develops a deep latent factor model that incorporates dynamic factor portfolios, extended historical asset dynamics, and cross-asset interactions. A series of progressively simplified deep learning models are designed to investigate the role of these components. The empirical study finds that FX models benefit from historical dynamics, while equity models perform better with dynamic factor weights. Furthermore, cross-asset interactions are significant in FX but minimal in equities. These findings highlight the critical role of tailored neural architectures in optimizing asset pricing models across different financial markets. By integrating traditional asset pricing theories with advanced neural network methodologies, this thesis forms a cohesive narrative and contributes to both the theoretical and empirical asset pricing literature. It enhances our understanding of asset pricing dynamics across diverse financial markets, demonstrating the value of combining theoretical insights with machine learning techniques.