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

Panagiotis Papaioannou; Thomas Dionysopoulos; Dietmar Janetzko; Constantinos Siettos

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arXiv · 1612.04370

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

Panagiotis Papaioannou·Thomas Dionysopoulos·Dietmar Janetzko·Constantinos Siettos

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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.

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Panagiotis Papaioannou, Thomas Dionysopoulos, Dietmar Janetzko, Constantinos Siettos. 2016-12-13. S&P500 Forecasting and Trading using Convolution Analysis of Major Asset Classes. https://arxiv.org/abs/1612.04370

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