Predicting Markets Better: A New Way to Learn from Each Other

Making money and staying safe in the financial market depends on good predictions. Markets are connected, so understanding one can help understand others. While current methods work, they often pick up on noise and miss the complex links between markets. We're introducing a new model that learns from the connections between two markets. It separates shared features from specific ones and removes any interference. We used a special kind of neural network to extract features and capture shared and unique details.

Our model also uses a technique called Dynamic Time Warping to balance the similarities and prediction accuracy. We tested it with data from big companies like Apple, Samsung, and cryptocurrencies like Bitcoin. The results showed our model did better than current top methods in predicting errors, assessing risks, tracking changes, and making prediction curves. This helps market players trust their decisions more.

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