| 000 | 01578naa a2200241 a 4500 | ||
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| 003 | AR-LpUFIB | ||
| 005 | 20250311170453.0 | ||
| 008 | 230201s2018 xx o 000 0 eng d | ||
| 024 | 8 |
_aDIF-M7780 _b7997 _zDIF007109 |
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| 100 | 1 | _aIglesias Caride, Martín | |
| 245 | 1 | 0 |
_aStock returns forecast : _ban examination by means of artificial neural networks |
| 300 | _a1 archivo (401,0 kB) | ||
| 500 | _aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) | ||
| 520 | _aThe 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. | ||
| 534 | _aComplex Systems: Solutions and Challenges in Economics, Management and Engineering, 125, pp. 399-410. | ||
| 650 | 4 | _aREDES NEURONALES | |
| 653 | _aíndice Bovespa | ||
| 700 | 1 | _aBariviera, Aurelio F. | |
| 700 | 1 | _aLanzarini, Laura Cristina | |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-69989-9_23 |
| 942 | _cCP | ||
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