Using Quantum Machine Learning to Predict Asset Prices in Financial Markets

Using Quantum Machine Learning to Predict Asset Prices in Financial Markets

Abstract:

In the financial world, a lot of effort is spent on predicting future asset prices. Gaining even a modest increase in forecasting capability can generate enormous profits. Some statistical models identify patterns, trends, and correlations in past prices, and apply those patterns to forecast future values. A more novel approach is the use of artificial intelligence to learn underlying trends in the data and predict future prices. As quantum computing matures, its potential applications in this task have also become increasingly more interesting. In this thesis, several different models of these various types are implemented: ARIMA, RBM, LSTM, and QDBM (Quantum Deep Boltzmann Machine). These models are trained on historical asset prices and used to predict future asset prices. The model predictions are then also used as the input for a simulated trading algorithm, which investigates the effectiveness of these predictions in the active trading of assets. The predictions are performed for ten different assets listed on the NYSE, NASDAQ, and XETRA, for the five-year period from 2018 to 2022. The assets were chosen from varying industrial sectors and with diverse price histories. Trading based on the model predictions was able to either match or outperform the classic buy-and-hold approach in nine out of the ten assets tested.

Author:

Maximilian Adler

Advisors:

Claudia Linnhoff-Popien, Jonas Stein, Jonas Nüßlein, Nico Kraus (Aqarios GmbH)

Student Thesis | Published November 2023 | Copyright © QAR-Lab
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