Stock returns forecast: an examination by means of Artificial Neural Networks

Martin Iglesias Caride, Aurelio Fernandez Bariviera and Laura Lanzarini

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Abstract: The validity of the Efficient Market Hypothesis has been under severe scrutiny since several decades. However, the evidence against it is not conclusive. Artificial Neural Networks provide a model-free means to analize the prediction power of past returns on current returns. This chapter analizes the predictability in the intraday Brazilian stock market using a backpropagation Artificial Neural Network. We selected 20 stocks from Bovespa index, according to different market capitalization, as a proxy for stock size. We find that predictability is related to capitalization. In particular, larger stocks are less predictable than smaller ones.

Date: 2018-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-for
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Citations: View citations in EconPapers (3)

Published in Studies in Systems, Decision and Control, vol 125. Springer, Cham

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1801.07960

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