scholarly journals THE APPROACH OF BOX JENKINS TIME SERIES ANALYSIS FOR PREDICTING STOCK PRICE ON LQ45 STOCK INDEX

Profit ◽  
2019 ◽  
Vol 13 (01) ◽  
pp. 18-25
Author(s):  
Isnaini Nuzula Agustin ◽  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stock Price ◽  
Stock Index ◽  
Series Analysis

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.

A NEW APPROACH OF BIVARIATE FUZZY TIME SERIES ANALYSIS TO THE FORECASTING OF A STOCK INDEX

2003 ◽  
Vol 11 (06) ◽  
pp. 671-690 ◽  
Author(s):  
YU-YUN HSU ◽  
SZE-MAN TSE ◽  
BERLIN WU
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Trading Volume ◽  
Arma Model ◽  
Future Trend ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
New Approach ◽  
Series Analysis ◽  
Closing Price

In recent years, the innovation and improvement of forecasting techniques have caught more and more attention. Especially, in the fields of financial economics, management planning and control, forecasting provides indispensable information in decision-making process. If we merely use the time series with the closing price array to build a forecasting model, a question that arises is: Can the model exhibit the real case honestly? Since, the daily closing price of a stock index is uncertain and indistinct. A decision for biased future trend may result in the danger of huge lost. Moreover, there are many factors that influence daily closing price, such as trading volume and exchange rate, and so on. In this research, we propose a new approach for a bivariate fuzzy time series analysis and forecasting through fuzzy relation equations. An empirical study on closing price and trading volume of a bivariate fuzzy time series model for Taiwan Weighted Stock Index is constructed. The performance of linguistic forecasting and the comparison with the bivariate ARMA model are also illustrated.

Time Series Analysis Using ARCH Models: A Case Analysis of Australian Stock Index

2017 ◽  
Vol 3 (1) ◽  
pp. 74-80 ◽  
Author(s):  
Prabhat Mittal
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stochastic Processes ◽  
Stock Price ◽  
Stock Exchange ◽  
Case Analysis ◽  
Stock Index ◽  
Arch Model ◽  
Arch Models ◽  
New Class

Australian All Ordinaries Stock Index has been in the headline since 1997 for its tear jerking effect on the stock exchange. Present work attempts to develop a realistic time-series model to explain the behavior of the stock price data during 2 January 1997 to 29 December 2006 collected from www.yahoofinance.com. To begin with residual analysis reveals that assumption of constant one period ahead forecast variance does not hold true. Accordingly, a new class of stochastic processes, called Autoregressive Conditional Heteroscedastic (ARCH) is studied. To this end, Computer programs on Ms-Excel have been used to fit the ARCH model.

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