Comparative Empirical Study on CAPM and Stock Price Prediction by BP Neural Network in Shanghai’s A-share
- 10.2991/assehr.k.200401.049How to use a DOI?
- CAPM, stock price prediction, BP neural network
Capital Asset Pricing Model (CAPM) is aimed to quantify the relationship between risk and expected rate of return. It has been tested and improved constantly by a number of researchers both abroad and in China. This study achieves a comparative empirical test of CAPM on E-V model, E-S model and GLS model in Shanghai A-share market. However, all of three model cannot adapt to Shanghai A-share market wonderfully. Now, computer science is a new instrument that can be applied to describe the capital market. Therefore, as a comparison of results, we select a single stock and use the simplest neural network, BP neural network with algorithms to optimize the weights and thresholds between the network layers for prediction. The results show that machine learning is a good method for stock prediction compared with only considering a single factor, which should be further explored in our future learning.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Man Fang AU - Xuefei Wang AU - Mengting Zhang AU - Yue Li AU - Jingyi Zhang PY - 2020 DA - 2020/04/06 TI - Comparative Empirical Study on CAPM and Stock Price Prediction by BP Neural Network in Shanghai’s A-share BT - Proceedings of the International Conference on Education, Economics and Information Management (ICEEIM 2019) PB - Atlantis Press SP - 225 EP - 232 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200401.049 DO - 10.2991/assehr.k.200401.049 ID - Fang2020 ER -