The cross-sectional stock return predictions via quantum neural network and tensor network

Paper: arXiv 2304.12501

Authors: Nozomu Kobayashi, Yoshiyuki Suimon, Koichi Miyamoto, Kosuke Mitarai

Abstract

In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical models such as linear regression and neural networks. To evaluate their abilities, we construct portfolios based on their predictions and measure investment performances. The empirical study on the Japanese stock market shows the tensor network model achieves superior performance compared to classical benchmark models, including linear and neural network models. Though the quantum neural network model attains a lowered risk-adjusted excess return than the classical neural network models over the whole period, both the quantum neural network and tensor network models have superior performances in the latest market environment, which suggests the capability of the model’s capturing non-linearity between input features.

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10 (capped from 6.5: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces cutting-edge quantum and quantum-inspired machine learning architectures (QNN and Tensor Networks) to cross-sectional stock return prediction. It features high mathematical complexity in quantum circuit representations and validates the models via empirical portfolio backtesting on the Japanese stock market.

Research Flowchart

  flowchart TD
    A[Research Goal: Evaluate QNN & Tensor Networks for Stock Predictions] --> B[Input Data: Japanese Stock Market Equities & Features]
    B --> C[Model Training & Comparison]
    
    C --> D1[Quantum Neural Network - QNN]
    C --> D2[Tensor Network - TN]
    C --> D3[Classical Benchmarks: Linear Regression & NN]
    
    D1 --> E[Portfolio Construction & Performance Evaluation]
    D2 --> E
    D3 --> E
    
    E --> F[Key Outcome: TN achieves superior performance; QNN & TN excel in recent non-linear market environments]