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Essays on computational portfolio management and asset pricing / Sebastian Konstantin Schäfer. Wuppertal, September 30, 2023
Content
Introduction
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning
Introduction
Background and related literature
Reinforcement Learning
Related work
Deep Q-learning for Portfolio Management
Experiments
Methodology
Hyperparameters
Results
Conclusion
A Deep Learning Stack for Asset Pricing and Portfolio Management
Introduction
Neural networks
Data and network designs
Results
Long portfolios
Long-short portfolios
Performance sources
Cost Sensitivity
Conclusion
Anxiety in Returns
Introduction
Risk aversion, anxiety and investment behavior
Empirical analysis
Data
Empirical results for the anxiety anomaly
Time persistence
In-sample predictive power
Investor's convex reaction to fear
Alternative risk aversion measurement
Out-of-sample predictability
Potential drivers of the anxiety anomaly
Anxiety anomaly and neural networks
Conclusion
Monday Afternoon Reversal
Introduction
Monday afternoon reversal
Data
The Monday afternoon reversal
Predictive regression analysis
Out-of-sample analysis
Overnight versus intraday components
Relationship to systematic risk and firm news
U.S. stock market indices and international ETFs
Investor disagreement and informed trading
Conclusion
Summary and conclusions