## Ximei Liu* , Zahid Latif** , Daoqi Xiong*** , Sehrish Khan Saddozai** and Kaif Ul Wara****## |

Shanghai stock market | Shenzhen stock market | |
---|---|---|

S1 | 0.980609 | 0.970912 |

S2 | 1.001231 | 1.000403 |

S3 | 0.990148 | 0.984291 |

S4 | 1.017246 | 1.004301 |

S5 | 1.013732 | 1.007665 |

S6 | 0.973261 | 0.965074 |

S7 | 1.002248 | 1.016953 |

S8 | 0.986677 | 0.992662 |

S9 | 0.997649 | 1.001223 |

S10 | 1.006260 | 1.016948 |

S11 | 0.991607 | 0.995377 |

S12 | 1.039331 | 1.044192 |

Fig. 2 shows the monthly data of Shanghai composite index and Shenzhen component index from January 2001 to December 2014 and these indices are represented by {SH} and {SZ}, respectively.

Using the structure method of the seasonal index, the seasonal indices of the two stock markets are shown in Table 1.

Table 1 above show that there is no seasonal effect on Shanghai and Shenzhen stock markets, respectively. Therefore, there is no need to eliminate seasonal fluctuations while using the ARIMA model.

As can be seen from Fig. 2, {SH} and {SZ} are stationary series. In addition, the ARIMA model sequence is smooth so we needed a differential stationary series. Fig. 3 represents the second order differential sequence diagram of the {SH} and {SZ}.

We can comprehend from Fig. 3 that the ADF value of the second order differential sequences of Shanghai and Shenzhen stock markets is greater than the absolute value of the critical value at 1%, 5%, and 10% significant levels. Consequently, the null hypothesis of the unit root is excluded, so sequences are smooth and d = 2.

We usually use the sample autocorrelation and partial autocorrelation analysis for model identification and order determination. However, this judgment is very subjective, hence, we use EVIEWS software to establish multiple models as well as the BIC criteria to compare the models. The results are shown in Table 2.

According to the minimum BIC criterion, we finally choose ARIMA (4, 2, 6) to forecast the Shanghai and Shenzhen stock markets.

An integrated neural network is mainly composed of the input layer, hidden layer, input delay layer, and output layer. We should therefore set the number of delay layers between the input and output layer, and the hidden layer of the study neural network before applying the actual model.

Firstly, the algorithm involves importing of the historical data, setting up of the training set, validation set, test set, and the number of delay layers and the hidden layers. Secondly, the algorithm should train the network. Thirdly, the algorithm should decide which network to choose according to the error autocorrelation curve. Finally, the algorithm should give the predictive output and detect the neural network model.

Data can be divided into the following three categories: the training set that is used to train the data; the validation set that is used to validate the network model, that is, whether it is feasible or not; the test set is used to assess the prediction ability of the network model. In this article, the data set parameters include: training set, 70%; validation set, 15%; and the test set, 15% [13-15].

In this study, we used MATLAB tools to construct the network model which is combined with the dynamic neural network GUI toolkit to build a sequence prediction model. The effect of the neural network prediction model is shown mainly by visual analysis through error and error autocorrelation figures.

We can see from Figs. 4 and 5 that the coarser line in the vertical direction is the difference between the target value and the forecast value. It is important to observe that the thinner the line, the better would be the forecast. We can see from Figs. 6 and 7 that when lag = 0, the error of the lag has the largest value; the other cases with values that are less than the confidence interval are preferred. These figures show that the model error within the confidence interval and the effect of the neural network model is good.

In this section, we used the neural network model to predict the Shanghai composite index and the Shenzhen component index. This was followed by the analysis of the training and test values. These results show that the test values and the actual values are close to each other, and the precision is very high; hence, it is proved that the neural network model is effective. It provides great reference value for investors. The forecast for Shanghai and Shenzhen are shown in Figs. 8 and 9, respectively.

In order to analyze and compare the ARIMA and neural network models (Table 3), we chose the average absolute error as the evaluation index. The average absolute error values of the ARIMA and the neural network models for Shanghai and Shenzhen stock markets are shown in Table 4.

Table 4 shows that the prediction accuracy of the neural network model is higher than the prediction accuracy of the ARIMA model. The neural network model depicts the changing pattern of the stock price in a comprehensive way; therefore, the neural network is a more effective forecasting method than the ARIMA model.

The stock market is facing a rapidly changing external environment that increases the uncertainty of the prediction factors. In order to describe the changing pattern of the stock price more accurately and comprehensively, we used a hybrid ARIMA and neural network model to forecast the Shanghai and Shenzhen stock markets.

Firstly, we used the monthly closing prices of Shanghai composite index and Shenzhen component index from January 2001 to December 2014. This is because these data are the latest and most representative.

Secondly, we selected the optimal ARIMA model to forecast Shanghai and Shenzhen stock markets by using the BIC criteria, EVIEWS and the SPSS software.

Thirdly, we used the neural network model to forecast the Shanghai and Shenzhen stock markets using the MATLAB software.

Finally, we compared the optimal ARIMA model with the neural network model. The calculated results show that the neural network model improved the predictive ability of the Shanghai and Shenzhen stock markets. This model is also able to reflect the development trends of stock markets and can provide investors with more constructive investment advice.

She is a teacher at Zhejiang Sci-Tech University, China. She graduated from Beijing University of Posts and Telecommunications. She has been engaged in the telecommunications, financial forecasting and other engineering sectors since 2011. She holds postgraduate qualifications in business administration as well as in different fields of telecom engineering.

He earned his Ph.D. degree from Beijing University of Posts and Telecommunications, China. He has been working in the telecommunication sector of Pakistan, since 2003. He is the author of several publications in the field of management. His research interests are in the fields of information and communication technology (ICT), role of ICT in the development of rural fields, one belt, one road, laying of optical fibre along with China-Pakistan Eonomic Corridor and ICT diffusion.

She earned her Ph.D degree from the School of Economics and Management at Beijing University of Posts and Telecommunications, China. She received her postgraduate degree in Sociology from Bahauddin Zakariya University Multan, Pakistan. Previously, she worked as a lecturer at Bahaudin Zakariya University Multan Pakistan. Her research interests are in HRM, talent management and employee psychology.

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