Compare Artificial Neural Networks Model with ARIMA model for Stock Price Prediction – Take Chinese A-shares Stocks as ExampleTools Peng, JIAYI (2020) Compare Artificial Neural Networks Model with ARIMA model for Stock Price Prediction – Take Chinese A-shares Stocks as Example. [Dissertation (University of Nottingham only)]
AbstractStock price prediction will continue to be an attractive topic because stock is an investment tool with high returns along with high risk. Many researches have already applied different forecasting models on predict the developed market stock price, and conclude that the neural networks have a superior performance over the statistical models. As the largest emerging market in the world, the Chinese stock market has experienced a rapidly expansion since its establishment. As the market is fluctuant with less efficiency when compared with other mature markets, the stock price prediction in the Chinese stock market may be more challenging and significant. However, there are not many works focus on the comparison of two types of prediction models when forecasting the Chinese stock market. This paper will compare the performance of two popular prediction models, auto-regressive integrated moving average model (ARIMA) and artificial neural networks (ANNs), on forecasting the Chinese A-shares stocks price. The experiment will forecast the price of seven representative stocks from December 2, 2019 to December 27, 2019, and then use some accuracy measures to estimate the prediction efficiency. The test results indicate that both prediction models have a good performance on forecasting the sample stock prices, but the results cannot prove that one forecasting model is superior than another.
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