scholarly journals Big Data Business Actual Analysis: Stock Price Prediction Based on Time Series Model

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Aiwen Rui
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Minimum Principle ◽  
Composite Index ◽  
Characteristic Root ◽  
Return Rate ◽  
Stock Price Prediction ◽  
Sparse Coefficient ◽  
Model Expression ◽  
Return Data

This paper selects the daily closing price data of the Shanghai Composite Index from January 1, 2016 to December 31, 2017, excluding holidays, and preprocesses the data. After taking the logarithm and converting it into the rate of return data, the first-order difference is performed to make it into a stable time series, and then the ARMA(p,q) model is constructed. Through parameter significance test, residual test and characteristic root test, according to the minimum principle of AIC, the optimal model is finally determined to be ARMA(2,5) of sparse coefficient, and the expression of the model is obtained. The GARCH(1,1) model is established for the residual of ARMA(2,5), and the model expression is obtained. In order to directly predict the return rate of the Shanghai Composite Index, the ARIMA(2,1,5) model of the sparse coefficient is constructed for the return rate of the Shanghai Composite Index, and the model expression is obtained. By predicting the Shanghai Composite Index return data on January 2, 2018, it is found that the prediction error of the model is small, and it can be used for subsequent predictions.

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

2020 ◽  
Vol 17 (4) ◽  
pp. 215-227
Author(s):  
Julia Babirath ◽  
Karel Malec ◽  
Rainer Schmitl ◽  
Kamil Maitah ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Risk Management ◽  
Time Series Analysis ◽  
Wiener Process ◽  
Stock Price ◽  
Percentage Error ◽  
Portfolio Performance ◽  
Stock Price Prediction ◽  
Series Analysis ◽  
Augmented Dickey Fuller

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

scholarly journals Failure in Stock Price Prediction: A Comparison bettwen the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes of Existing Machine Learning Algorithms

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Ping Zhang ◽  
Jia-Yao Yang ◽  
Hao Zhu ◽  
Yue-Jie Hou ◽  
Yi Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stock Price ◽  
Financial Time Series ◽  
Learning Algorithms ◽  
Shape Feature ◽  
Curve Shape ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial Time

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

PENGARUH VOLATILITAS NILAI TUKAR, IDR-USD TERHADAP RETURN SAHAM DI BURSA EFEK INDONESIA: PENERAPAN MODEL GARCH

2019 ◽  
Vol 14 (2) ◽  
pp. 95
Author(s):  
Rahmadiva Dianitha Danial ◽  
Brady Rikumahu
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Granger Causality ◽  
Stock Price ◽  
Foreign Exchange Market ◽  
Stock Exchange ◽  
Composite Index ◽  
Granger Causality Test ◽  
Causality Test ◽  
Stock Market Returns

Penelitian ini bertujuan untuk menguji pengaruh  volatilitas return nilai Kurs IDR-USD terhadap volatilitas return pasar saham di Bursa Efek Indonesia. Dari pengambilan data sekunder dari 3 Januari 2012 hingga 29 September 2017 diperoleh data time series sebanyak 1404 hari. Data  dianalisis dengan model  GARCH dan Uji Granger Causality. Berdasarkan hasil permodelan GARCH(1,1), volatilitas kurs mempengaruhi volatilitas IHSG. Uji Granger Causality menunjukkan bahwa volatilitas kurs  dan IHSG memiliki hubungan yang kausal dua arah. Penelitian ini menunjukkan bahwa informasi kurs dapat memprediksikan kondisi harga indeks saham di pasar modal di periode hari berikutnya, begitupun sebaliknya. Prediksi tepat yang dilakukan oleh investor akan mengurangi risiko dan meningkatkan imbal hasil dalam berinvestasi jika pasar uang maupun pasar modal yang sedang bergejolak.  Kata Kunci: GARCH, Volatilitas, IHSG, Nilai Tukar ABSTRACT This study aims to examine the effect of the volatility of the return on the IDR-USD exchange rate toward  the volatility of stock market returns in the Indonesia Stock Exchange. From the data collection from 3 January 2012 until 29 September 2017 we obtained 1404 time series. Analyzing data, this study used  GARCH modeling and Granger Causality Test. The selected GARCH (1,1) modeling result shows that the volatility of exchange rate influences the volatility of Indonesian Composite Index.  Granger Causality test shows that the volatility of exchange rate and volatility of Indonesian Composite Index have two-way granger cause. This study indicates that exchange rate information can predict the condition of stock price index in capital market and movement of Indonesian Composite Index (ICI) can predict exchange rate movement in foreign exchange market. Appropriate predictions by investors will reduce the risk and increase the yield in investing if the money market and capital markets are fluctuating high. Keywords: GARCH, Volatility, ICI, Exchange Rate

scholarly journals A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yang Yujun ◽  
Yang Yimei ◽  
Xiao Jianhua
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Method ◽  
Reference Value ◽  
Prediction Methods ◽  
Hybrid Prediction ◽  
Original Stock ◽  
Stock Price Prediction ◽  
Comparison Results

The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

scholarly journals Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

2019 ◽  
Vol 61 ◽  
pp. 01006 ◽  
Author(s):  
Jakub Horák ◽  
Tomáš Krulický
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Stock Prices ◽  
Stock Price ◽  
Exponential Time ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Artificial Neural ◽  
Time Series Alignment

Accurate stock price prediction is very difficult in today's economy. Accurate prediction plays an important role in helping investors improve return on equity. As a result, a number of new approaches and technologies have logically evolved in recent years to predict stock prices. One is also the method of artificial neural networks, which have many advantages over conventional methods. The aim of this paper is to compare a method of exponential time series alignment and time series alignment using artificial neural networks as tools for predicting future stock price developments on the example of the company Unipetrol. Time series alignment is performed using artificial neural networks, exponential alignment of time series, and then a comparison of time series of predictions of future stock price trends predicted using the most successful neural network and price prediction calculated by exponential time series alignment is performed. Predictions for 62 business days were obtained. The realistic picture of further possible development is surprisingly given based on the exponential alignment of time series.

scholarly journals Research on Volatility of Return of Chinese Stock-Market Based on Generalized Hyperbolic Distribution Family

2021 ◽  
Vol 9 ◽  
pp. 1-7
Author(s):  
Wu Libin ◽  
Liu Shengyu ◽  
Gao Jun
Keyword(s):  
Time Series ◽  
Composite Index ◽  
Parametric Modeling ◽  
Return Rate ◽  
Statistical Characteristics ◽  
Chinese Stock Market ◽  
Generalized Hyperbolic Distribution ◽  
Full Account ◽  
Volatility Model ◽  
Distribution Family

Financial time series often present a nonlinear characteristics, and the distribution of financial data often show fat tail and asymmetry, but this don’t match with the standpoint that time series obey normal distribution of return on assets, etc, which is considered by linear parametric modeling in the traditional linear framework. This paper has a systematic introduction of the definitions of GH distribution family and related statistical characteristics, which is based on reviewing the basic properties of the ARCH/GARCH model family and a common distribution of its disturbance. And select the Shanghai Composite Index and the Shanghai and Shenzhen (CSI) 300 index daily return rate index to estimate volatility model. GH distribution is used for further fitting to disturbance. This is done after take full account of the effective extraction of the model for the disturbance distribution information. The results show that the GH distribution can effectively fitting residuals distribution of the volatility models about series on return rate.

Sign in / Sign up

Export Citation Format

Share Document