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. The proposed approach is a trend following trading strategy 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. Simulations provide empirical evidence of directional predictability of the S&P500 Index, thus enhancing the financial assets' forecasting toolkit of quantitative trading academics and professionals.

S&P500 Forecasting and trading using convolution analysis of major asset classes / Papaioannou, Panagiotis; Dionysopoulos, Thomas; Russo, Lucia; Giannino, Francesco; Janetzko, Dietmar; Siettos, Constantinos. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - 113:(2017), pp. 484-489. [10.1016/j.procs.2017.08.307]

S&P500 Forecasting and trading using convolution analysis of major asset classes

Giannino, Francesco;Siettos, Constantinos
2017

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. The proposed approach is a trend following trading strategy 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. Simulations provide empirical evidence of directional predictability of the S&P500 Index, thus enhancing the financial assets' forecasting toolkit of quantitative trading academics and professionals.
2017
S&P500 Forecasting and trading using convolution analysis of major asset classes / Papaioannou, Panagiotis; Dionysopoulos, Thomas; Russo, Lucia; Giannino, Francesco; Janetzko, Dietmar; Siettos, Constantinos. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - 113:(2017), pp. 484-489. [10.1016/j.procs.2017.08.307]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/696194
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