Comparison of Stock Price Prediction Models using Pre-trained Neural Networks

C. Anand
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引用次数: 36

Abstract

Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures inclusive of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used in this paper for stock price prediction of an organization by using the previously available stock prices. The National Stock Exchange (NSE) of India dataset is used for training the model with day-wise closing price. Data prediction is performed for a few sample companies selected on a random basis. Based on the comparison results, it is evident that the existing models are outperformed by CNN. The network can also perform stock predictions for other stock markets despite being trained with single market data as a common inner dynamics that has been shared between certain stock markets. When compared to the existing linear models, the neural network model outperforms them in a significant manner, which can be observed from the comparison results.
基于预训练神经网络的股价预测模型比较
包括神经网络在内的几种智能数据挖掘方法在过去十年中被学术界广泛采用。在经济快速发展的今天,股票市场数据预测和分析发挥着重要作用。一些非线性模型如神经网络、广义自回归条件异方差(GARCH)和自回归条件异方差(ARCH)以及线性模型如自回归综合移动平均(ARIMA)、移动平均(MA)和自回归(AR)可用于股票预测。本文使用卷积神经网络(CNN)、长短期记忆(LSTM)、循环神经网络(RNN)、多层感知器(MLP)和支持向量机(SVM)等深度学习架构,通过使用先前可用的股票价格对组织进行股票价格预测。使用印度国家证券交易所(NSE)数据集训练具有日收盘价的模型。对随机选择的几个样本公司进行数据预测。从对比结果可以看出,现有模型的性能明显优于CNN。该网络还可以对其他股票市场进行股票预测,尽管它是用单一市场数据作为某些股票市场之间共享的共同内部动态进行训练的。与现有的线性模型相比,神经网络模型的表现明显优于线性模型,这从对比结果中可以看出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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