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.
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Iglesias Caride, M., Bariviera, A.F., Lanzarini, L. (2018). Stock Returns Forecast: An Examination By Means of Artificial Neural Networks. In: Berger-Vachon, C., Gil Lafuente, A., Kacprzyk, J., Kondratenko, Y., Merigó, J., Morabito, C. (eds) Complex Systems: Solutions and Challenges in Economics, Management and Engineering. Studies in Systems, Decision and Control, vol 125. Springer, Cham. https://doi.org/10.1007/978-3-319-69989-9_23
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DOI: https://doi.org/10.1007/978-3-319-69989-9_23
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