Enhancing Multistep Prediction of Multivariate Market Indices Using Weighted Optical Reservoir Computing
This work addresses the problem of accurate multistep prediction for multivariate market indices, which is crucial for financial analysts and traders, though it appears incremental as it builds on existing reservoir computing techniques.
The paper tackled stock index prediction by proposing a weighted optical reservoir computing system that integrates market, macroeconomic, and technical data, achieving significantly higher performance than state-of-the-art methods like linear regression, decision trees, and LSTM neural networks.
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.