Forecast Combination for Asset Classes: Insights on Market Efficiency and Arbitrage

Authors: Rodrigo Baggi Prieto Alvarez, Jorge Miguel Bravo

Abstract:

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 econometric models, machine learning methods and forecast combinations can predict asset prices more effectively. Also, comparing forecasts with futures market prices may reveal potential arbitrage opportunities, offering insights into market inefficiencies. Using ETFs indices from January 1st, 2015, to September 30th, 2024, across equity markets (S&P 500, Russell 2000, MSCI Developed Markets and MSCI Emerging Markets), fixed income (7-10 Year Treasury Bond, Developed Markets Treasury Bond, Emerging Markets Treasury Bond and U.S. Corporate Bonds), commodity (Gold Shares ETF) and crypto (ProShares Bitcoin ETF), this paper tests the predictive accuracy of traditional econometric models (ARIMA, ETS), machine learning (SVM, Random Forest, XGBoost) and forecast combinations (ARIMA-SVR, ARIMA-ANN, Ridge Regression and LASSO). Preliminary results suggest that ensemble methods can indeed outperform simple models, indicating that combinations like the Ridge Regression and LASSO are superior to econometric and machine learning models individually. Also, prediction accuracy is better for fixed income ETFs, aligned with the lower volatility of these assets, while models show higher forecast error for crypto and equity ETFs. Finally, initial comparisons between forecasts and the futures market prices reveal potential inefficiencies, suggesting opportunities for spot-futures index arbitrage. Providing empirical evidence on the application of forecasting models to a significant group of financial assets, these findings contribute to discussions on market efficiency and highlight the role of ensemble methods in improving asset price predictability and portfolio management.

Keywords: ETF, asset prediction, forecast combination, EMH, spot-futures index arbitrage