NDLI: Beating the S&P 500 index — A successful neural network approach

Content Provider	IEEE Xplore Digital Library
Author	Sethi, M. Treleaven, P. Del Bano Rollin, S.
Copyright Year	2014
Description	Author affiliation: Centre for Financial Comput., Univ. Coll. London, London, UK (Sethi, M.; Treleaven, P.; Del Bano Rollin, S.)
Abstract	The systematic trading of equities forms the basis of the asset management industry. Analysts are trying to outperform a passive investment in an index such as the S&P 500 Index. However, statistics have shown that most analysts fail to consistently beat the index. A number of Neural Network based methods for detecting trading opportunities on Futures contracts on the S&P 500 Index have been published in the literature. However, such methods have generally been unable to demonstrate sustained performance over a significant period of time. The authors of this paper show, through the application of over ten years of experience in quantitative modelling and trading, a different type of Neural Network approach to beating the S&P 500 Index. Rather than trading Futures contracts, it is shown that by using Neural Networks to intelligently select just a handful of stocks a performance significantly in excess of a buy and hold position on the S&P 500 Index could have been achieved over a seven year period. The effect of transaction costs is also considered.
Starting Page	3074
Ending Page	3077
File Size	913691
Page Count	4
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889625
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Indexes Training Market research Portfolios Economics Educational institutions Applications of Neural Networks for Financial Modelling and Forecasting Advanced Computational Intelligence for Algorithmic Trading
Content Type	Text
Resource Type	Article

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