Application of neural networks to evaluate the stock index on the basis of stock performance and interest rate levels

original titel: Anwendung von Neuronalen Netzen zur Evaluierung des Aktienindex auf Basis von Aktienverläufen und Zinsniveau

Abstract

This study explores the use of neural networks to evaluate stock indices based on stock price movements and interest rate levels. It aims to understand the relationship between these factors and infer the implications for stock performance. This research is motivated by the prevailing assumption of a link between interest rates, inflation and their impact on stock prices and indices. The use of artificial intelligence (AI) in financial markets is becoming increasingly common for risk mitigation and early risk detection. However, this particular investigation using AI techniques is novel. The relationship between stock prices, index values, and interest rates is non-linear, necessitating the use of neural networks (NNs) for analysis. The study uses different NN models, mainly feedforward and recurrent NNs, to validate the predictive ability of the data. The research provides a comprehensive motivation for the problem statement in the context of financial markets and the application of AI methods in evaluating the relationship between interest rates and stocks and indices. The basic concepts of NNs are well summarized, despite extensive existing research. The study also includes an economic perspective on AI. Financial markets, with a focus on stocks, indices, and interest rates, are thoroughly examined. A special chapter deals with the datasets used for NN training, discussing their plausibility and preparation for applicability. The implemented NN models are presented along with identified limitations and discussions. These limitations are mitigated using various AI techniques, such as hyperparameter optimization. The study of the relationship between interest rates and stock or index values using NNs reveals ambiguous interpretations for different labeling approaches, although they can inform investment decisions. The study bridges two different disciplines: financial markets and artificial intelligence. It connects these fields through the hypothesis investigated. The critical and reflective discussions of the applied methods and results show an intensive discourse on the topic.

Type
Publication
MA Akademie Verlags und Druck-Gesellschaft mbH
Alexander Maximilian Röser
Alexander Maximilian Röser
Senior Consultant @WEPEX

My research interests include Artificial Intelligence, Data Science and Digital Transformation .