"It Looks All the Same to Me": Cross-index Training for Long-term Financial Series Prediction

The study titled "It Looks All the Same to Me" investigates the potential of cross-index training in financial forecasting using Artificial Neural Networks. By examining various architectures, the researchers sought to determine if training on one global market index could enhance prediction accuracy for another index. The predominantly positive results suggest that such cross-training is effective, reinforcing the Efficient Market Hypothesis of Eugene Fama, which posits that markets are efficient and reflect all available information. This research contributes to the ongoing discourse on financial modeling and could influence future investment strategies, as it opens avenues for leveraging machine learning across diverse market conditions.