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Contribution Details
Type | Master's Thesis |
Scope | Discipline-based scholarship |
Title | Stressing Predicted Stock Prices - The Effect of Macroeconomic Shock Scenarios on Future Equity Prices |
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Institution | University of Zurich |
Faculty | Faculty of Business, Economics and Informatics |
Date | 2022 |
Abstract Text | Recent research has placed enormous interest in the accurate prediction of stock prices. By extending conventional regression techniques, this thesis implements the concept of stress testing on an equity level using several machine learning methods, including trees, gradient boosting and neural networks (long short-term memory). The models are adapted using a novel time-series approach intended to preserve stationarity and extend the neural network using a bidirectional layer and weight penalization. Stock price changes are being forecasted and subject to severe scenarios, as commonly used on banking levels. Trees and gradient-boosted models have been identified to yield superior results in terms of common loss metrics. The implementation of macroeconomic shock scenarios reveals that predicted stock price changes are less dependent on severe scenarios using trees and gradient-boosted models; however, they are affected by material changes using neural networks. It is further investigated which macroeconomic parameters are of significant fraction in terms of forecasting stock price changes, whereas inflation, treasury and unemployment rates are among the most robust features. |
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