The Potential of Quantum Techniques for Stock Price Prediction

ArXiv ID: 2308.13642 “View on arXiv”

Authors: Unknown

Abstract

We explored the potential applications of various Quantum Algorithms for stock price prediction by conducting a series of experimental simulations using both Classical as well as Quantum Hardware. Firstly, we extracted various stock price indicators, such as Moving Averages (MA), Average True Range (ATR), and Aroon, to gain insights into market trends and stock price movements. Next, we employed Quantum Annealing (QA) for feature selection and Principal Component Analysis (PCA) for dimensionality reduction. Further, we transformed the stock price prediction task essentially into a classification problem. We trained the Quantum Support Vector Machine (QSVM) to predict price movements (whether up or down) contrasted their performance with classical models and analyzed their accuracy on a dataset formulated using Quantum Annealing and PCA individually. We focused on the stock price prediction and binary classification of stock prices for four different companies, namely Apple, Visa, Johnson and Jonson, and Honeywell. We primarily used the real-time stock data of the raw stock prices of these companies. We compared various Quantum Computing techniques with their classical counterparts in terms of accuracy and F-score of the prediction model. Through these experimental simulations, we shed light on the potential advantages and limitations of Quantum Algorithms in stock price prediction and contribute to the growing body of knowledge at the intersection of Quantum Computing and Finance.

Keywords: Quantum Algorithms, Quantum Support Vector Machine (QSVM), Quantum Annealing, Stock Price Prediction, Feature Selection

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper applies advanced quantum algorithms (QA, QSVM, QLSTM) and discusses high-dimensional feature spaces, indicating moderate-to-high math complexity. It uses real-time stock data from four companies, compares quantum vs. classical models with accuracy/F-score metrics, and details a data pipeline, showing solid empirical rigor.

  flowchart TD
    Start["Research Goal: Evaluate Quantum Algorithms for Stock Price Prediction"] --> Data["Data: Raw Stock Prices Apple, Visa, J&J, Honeywell"]

    Data --> Indicators["Feature Extraction: MA, ATR, Aroon Indicators"]
    Indicators --> Selection["Feature Selection: Quantum Annealing & Dimensionality Reduction: PCA"]

    Selection --> ModelQ["Quantum Process: Train QSVM for Binary Classification"]
    Selection --> ModelC["Classical Process: Train Classical SVM for Binary Classification"]

    ModelQ --> ResultsQ["Outcomes: QSVM Accuracy & F-score"]
    ModelC --> ResultsC["Outcomes: Classical Accuracy & F-score"]

    ResultsQ --> Analysis["Key Findings: Comparison of Quantum vs. Classical Performance"]
    ResultsC --> Analysis