Enhancing Multistep Prediction of Multivariate Market Indices Using Weighted Optical Reservoir Computing

TL;DR

This paper introduces a novel weighted optical reservoir computing approach for multistep prediction of multivariate market indices, outperforming traditional methods in capturing market volatility and nonlinear behaviors.

Contribution

The paper presents an innovative optical reservoir computing system that effectively integrates macroeconomic and technical data for improved stock index prediction.

Findings

Outperforms linear regression, decision trees, and LSTM in prediction accuracy.

Effectively captures market volatility and nonlinear behaviors.

Demonstrates potential for real-time, parallel, multi-dimensional data processing.

Abstract

We propose and experimentally demonstrate an innovative stock index prediction method using a weighted optical reservoir computing system. We construct fundamental market data combined with macroeconomic data and technical indicators to capture the broader behavior of the stock market. Our approach shows significant higher performance than state-of-the-art methods such as linear regression, decision trees, and neural network architectures including long short-term memory. It captures well the market's high volatility and nonlinear behaviors despite limited data, demonstrating great potential for real-time, parallel, multi-dimensional data processing and predictions.