Index Prediction

“It Looks All the Same to Me”: Cross-index Training for Long-term Financial Series Prediction ArXiv ID: 2511.08658 “View on arXiv” Authors: Stanislav Selitskiy Abstract We investigate a number of Artificial Neural Network architectures (well-known and more ``exotic’’) in application to the long-term financial time-series forecasts of indexes on different global markets. The particular area of interest of this research is to examine the correlation of these indexes’ behaviour in terms of Machine Learning algorithms cross-training. Would training an algorithm on an index from one global market produce similar or even better accuracy when such a model is applied for predicting another index from a different market? The demonstrated predominately positive answer to this question is another argument in favour of the long-debated Efficient Market Hypothesis of Eugene Fama. ...

Hunting Tomorrow’s Leaders: Using Machine Learning to Forecast S&P 500 Additions & Removal ArXiv ID: 2412.12539 “View on arXiv” Authors: Unknown Abstract This study applies machine learning to predict S&P 500 membership changes: key events that profoundly impact investor behavior and market dynamics. Quarterly data from WRDS datasets (2013 onwards) was used, incorporating features such as industry classification, financial data, market data, and corporate governance indicators. Using a Random Forest model, we achieved a test F1 score of 0.85, outperforming logistic regression and SVC models. This research not only showcases the power of machine learning for financial forecasting but also emphasizes model transparency through SHAP analysis and feature engineering. The model’s real world applicability is demonstrated with predicted changes for Q3 2023, such as the addition of Uber (UBER) and the removal of SolarEdge Technologies (SEDG). By incorporating these predictions into a trading strategy i.e. buying stocks announced for addition and shorting those marked for removal, we anticipate capturing alpha and enhancing investment decision making, offering valuable insights into index dynamics ...