Articulo
Stock Returns Forecast : An Examination By Means of Artificial Neural Networks
Registro en:
issn:2198-4182
issn:2198-4190
Autor
Caride, Martín Iglesias
Bariviera, Aurelio F.
Lanzarini, Laura Cristina
Institución
Resumen
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. Parte de Berger-Vachon, C.; Gil Lafuente, A. M.; Kacprzyk, J.; Kondratenko, Y.; Merigó, J. M.; Morabito, F. C. (eds.) (2018). <i>Complex Systems: Solutions and Challenges in Economics, Management and Engineering</i>. Cham: Springer. Instituto de Investigación en Informática