Analysis and Economic Prediction of Stock Index Based on Index Tracking and ARIMA Model

Zhang, Xiaoduo; Zhai, Rui; Gao, Wen

doi:10.1088/1742-6596/1903/1/012015

Cited by 6 publications

(7 citation statements)

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“…Therefore, it can better describe the future direction of financial markets and predict the prices in the future, so it is widely used to analyze stock prices, exchange rate, and financial derivatives pricing. Zhang et al employed ARIMA model to set up stock portfolio [2]. Ahmar et al compared different variants of ARIMA model and tested prediction ability respectively [3].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Prediction Using ARIMA and LSTM Models: An Application to CSI 300 Closing Prices

Zhang

2024

HSET

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In the development of the real economy, the performance of the stock market can well reflect its development status, so it is of great value to select effective forecasting methods to make a relatively accurate prediction of stock prices. The research and forecast object of this paper is the daily adjusted closing price of CSI 300 index. The sample data is derived from China Securities Data Website and is composed of six characteristic data for 261 days from July 1, 2022 to July 26, 2023. This paper compares the prediction ability of Autoregressive Integrated Moving Average (ARIMA) model and Long Short Term Memory (LSTM) networks. For the deviation function, this paper chooses MAE and RMSE two methods. By comparing the prediction ability of the models, the following conclusions are drawn: The daily closing price of CSI 300 has autocorrelation and long-term memory, which enables the prediction of future price based on historical data. Compared with ARIMA model, LSTM has stronger prediction ability, but the prediction has a lag, which is not conducive to reflecting the drastic trend changes in the short term. More characteristic information needs to be added to characterize to obtain more accurate results.

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Section: Introductionmentioning

confidence: 99%

Stock Price Prediction Using ARIMA and LSTM Models: An Application to CSI 300 Closing Prices

Zhang

2024

HSET

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“…Stock price prediction models can be divided into traditional regression models, traditional machine learning models and deep learning models. Traditional regression models, such as polynomial regression, autoregressive integrated moving average (ARIMA) 1 tend to be less accurate compared to other models. Traditional machine learning models for stock prediction include support vector machine 2 , random forest 3 , K proximity value 4 , etc [20][21][22][23][24][25][26][27] .…”

Section: Introductionmentioning

confidence: 99%

Kweichow moutai stock price forecasting using transformers

Peng,

Wang,

Wang

2023

International Conference on Images, Signals, and Computing (ICISC 2023)

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This paper develops a model for predicting Kweichow Moutai stock prices combining both fundamental analysis (Composite Index, earnings per share, Market capitalisation in circulation) and technical analysis (past stock prices) based on the transformer deep learning method. The approach improves the accuracy of stock price prediction that relies solely on fundamental analysis or technical analysis.

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“…Because the stock market contributes to the stability and development of global finance, more and more researchers are paying attention to stock price prediction [1]. Among the methods available, the ARMA model [2], the ARIMA model [3] and GARCH model are the most widely and frequently used by these researchers. These financial time series models are considered to be important tools for stock market forecasting, which can basically help investors make accurate judgements.…”

Section: Introductionmentioning

confidence: 99%

A Research on Neural Network and An Improved Model For Stock Prediction

Dai¹

2023

HBEM

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In this paper, Back Propagation Neural Network (BPNN) and Firefly Algorithm-Back Propagation Neural Network (FA-BPNN) are studied for the purpose of stock price prediction. The result shows that, compared with BPNN, the average prediction time of FA-BPNN increases by 3.2239s, the average relative prediction accuracy increases by 1.2739 %, and the corresponding variance decreases by 0.0003. It shows that the prediction accuracy obtained using FA-BPNN is higher. This is because the weighted value of FA-BPNN can effectively find the extreme value of the loss function, thus avoiding local optimum. But the prediction time is slightly longer, because the algorithm needs to sacrifice the tracking time in order to improve the prediction accuracy. Meanwhile, the variance corresponding to the relative prediction accuracy of FA-BPNN is the smallest. This demonstrates that the prediction effect of FA-BPNN is superior, and the prediction results are more stable and robust after each training. The above outcomes demonstrate that the FA-BPNN model has excellent performance in short-term stock forecasting. It has some feasibility and can be of use to stock market investors.

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Analysis and Economic Prediction of Stock Index Based on Index Tracking and ARIMA Model

Cited by 6 publications

References 0 publications

Stock Price Prediction Using ARIMA and LSTM Models: An Application to CSI 300 Closing Prices

Stock Price Prediction Using ARIMA and LSTM Models: An Application to CSI 300 Closing Prices

Kweichow moutai stock price forecasting using transformers

A Research on Neural Network and An Improved Model For Stock Prediction

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