S&P500 Forecasting and Trading using Convolution Analysis of Major Asset Classes

TL;DR

This paper presents a convolution-based method to forecast the S&P 500 index by analyzing the time evolution of major liquid futures across four asset classes, outperforming traditional buy-and-hold strategies.

Contribution

It introduces a novel convolution analysis approach using multiple asset classes to improve S&P 500 forecasting accuracy and trading performance.

Findings

Forecasts beat the Buy and Hold strategy.

Uses 42 futures contracts across four asset classes.

Based on smooth shifts and slow dynamics in asset class relationships.

Abstract

By monitoring the time evolution of the most liquid Futures contracts traded globally as acquired using the Bloomberg API from 03 January 2000 until 15 December 2014 we were able to forecast the S&P 500 index beating the Buy and Hold trading strategy. Our approach is based on convolution computations of 42 of the most liquid Futures contracts of four basic financial asset classes, namely, equities, bonds, commodities and foreign exchange. These key assets were selected on the basis of the global GDP ranking across countries worldwide according to the lists published by the International Monetary Fund (IMF, Report for Selected Country Groups and Subjects, 2015). The main hypothesis is that the shifts between the asset classes are smooth and are shaped by slow dynamics as trading decisions are shaped by several constraints associated with the portfolios allocation, as well as rules restrictions imposed by state financial authorities. This hypothesis is grounded on recent research based on the added value generated by diversification targets of market participants specialized on active asset management, who try to efficiently and smoothly navigate the market's volatility.