Stock price forecasting model based on modified convolution neural network and financial time series analysis

Cao Jia-sheng · Jinghan Wang

120 citations0/0 files storedInternational Journal of Communication Systems

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

Summary To forecast the future trend of financial activities through its rules, a convolutional neural network (CNN) is used to forecast stock index. Firstly, a CNN stock index prediction model is constructed, the structural parameter relationship of the CNN model is analyzed, and a CNN model algorithm is implemented. Secondly, the influence of model parameters on prediction results is discussed, and the stock index prediction model based on CNN‐support vector machine (SVM) is established. At last, the empirical analysis is made, and the results show that the two prediction models are feasible and effective. It is concluded that the use of neural networks for financial prediction can deal with the continuous and categorical prediction variables and obtain good prediction results.

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References

2018Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction 2017New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic 2016Forecasting Aggregate Stock Market Volatility Using Financial and Macroeconomic Predictors: Which Models Forecast Best, When and Why?

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