Forecast Combination For Asset Classes Etfs: Insights On Market Efficiency and Arbitrage

The Exchange-Traded Funds (ETFs) have transformed asset allocation, allowing investors to gain exposure to diverse asset classes with a single instrument. In turn, forecast combination models have emerged as advantageous methods for improving prediction accuracy. While the Efficient Market Hypothesis (EMH) posits that prices fluctuate randomly, making abnormal returns unattainable, empirical evidence reveals autocorrelation in stock returns, challenging the EMH's strict interpretation. This raises the question of whether new econometric and machine learning methods can predict asset values more effectively. We investigate the effectiveness of forecast combination in predicting financial asset classes. Analyzing ETFs across equity, fixed income, commodities, and cryptocurrency markets, we test the predictive accuracy of ten econometric and machine learning models, and preliminary results suggest that ensemble methods can indeed outperform simple models. Comparisons between forecasts and futures market prices reveal potential inefficiencies, suggesting opportunities for spot-futures index arbitrage. These findings contribute to discussions on market efficiency and highlight the role of forecast combinations in improving asset predictability and portfolio management.