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Predicting Prices of Tech Stocks using the Transformer

Zitouni, Théodore LU and Grull, Sebastian LU (2024) NEKH02 20241
Department of Economics
Abstract
Accurately predicting future stock prices is a dream for many investors. The inherent uncertainty and complexity of financial markets makes price prediction a difficult task. However, the recent advancements of neural networks in the field of machine learning have introduced new possibilities for accurately predicting future prices. We explore the application of the Transformer, a recent model which has proven to have powerful performance on a number of tasks. By leveraging the Transformer, we seek to challenge the efficient market hypothesis. The hypothesis states that asset prices fully reflect all available information, making it impossible to consistently outperform the market by accurately predicting future prices. We demonstrate the... (More)
Accurately predicting future stock prices is a dream for many investors. The inherent uncertainty and complexity of financial markets makes price prediction a difficult task. However, the recent advancements of neural networks in the field of machine learning have introduced new possibilities for accurately predicting future prices. We explore the application of the Transformer, a recent model which has proven to have powerful performance on a number of tasks. By leveraging the Transformer, we seek to challenge the efficient market hypothesis. The hypothesis states that asset prices fully reflect all available information, making it impossible to consistently outperform the market by accurately predicting future prices. We demonstrate the Transformers ability to relatively accurately predict future prices for five stocks, with a mean absolute percentage error of 3.18% on average. We contribute to an ongoing discussion on the validity of the efficient market hypothesis and how the Transformer can be leveraged for this task. (Less)
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author
Zitouni, Théodore LU and Grull, Sebastian LU
supervisor
organization
course
NEKH02 20241
year
type
M2 - Bachelor Degree
subject
keywords
Price Prediction, Neural Networks, Efficient Market Hypothesis, Machine Learning, Transformer
language
English
id
9158003
date added to LUP
2024-09-24 09:00:38
date last changed
2024-09-24 09:00:38
@misc{9158003,
  abstract     = {{Accurately predicting future stock prices is a dream for many investors. The inherent uncertainty and complexity of financial markets makes price prediction a difficult task. However, the recent advancements of neural networks in the field of machine learning have introduced new possibilities for accurately predicting future prices. We explore the application of the Transformer, a recent model which has proven to have powerful performance on a number of tasks. By leveraging the Transformer, we seek to challenge the efficient market hypothesis. The hypothesis states that asset prices fully reflect all available information, making it impossible to consistently outperform the market by accurately predicting future prices. We demonstrate the Transformers ability to relatively accurately predict future prices for five stocks, with a mean absolute percentage error of 3.18% on average. We contribute to an ongoing discussion on the validity of the efficient market hypothesis and how the Transformer can be leveraged for this task.}},
  author       = {{Zitouni, Théodore and Grull, Sebastian}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Predicting Prices of Tech Stocks using the Transformer}},
  year         = {{2024}},
}