  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.
