PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average)

2014 ◽  
Vol 14 (2) ◽  
pp. 60
Author(s):  
Greis S Lilipaly ◽  
Djoni Hatidja ◽  
John S Kekenusa
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Secondary Data ◽  
Research Use ◽  
Autoregressive Integrated Moving Average ◽  
Minimum Data ◽  
Research Show

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average) Greis S. Lilipaly1) , Djoni Hatidja1) , John S. Kekenusa1) ABSTRAK Metode ARIMA adalah salah satu metode yang dapat digunakan dalam memprediksi perubahan harga saham. Tujuan dari penelitian ini adalah untuk membuat model ARIMA dan memprediksi harga saham PT. BRI, Tbk. bulan November 2014. Penelitian menggunakan data harga saham  harian  maksimum dan minimum PT. BRI, Tbk. Data yang digunakan yaitu data sekunder yang diambil dari website perusahaan PT. BRI, Tbk. sejak 3 Januari 2011 sampai 20 Oktober 2014 untuk memprediksi harga saham bulan November 2014. Dari hasil penelitian menunjukkan bahwa data tahun 2011 sampai Oktober 2014 bisa digunakan untuk memprediksi harga saham bulan November 2014. Hasilnya model ARIMA untuk harga saham maksimum adalah ARIMA (2,1,3) dan harga saham minimum adalah model (2,1,3) yang dapat digunakan untuk memprediksi data bulan November 2014 dengan validasi prediksi yang diambil pada bulan Oktober 2014 untuk selanjutnya dilakukan prediksi bulan November 2014. Kata Kunci: Metode ARIMA, PT. BRI, Tbk., Saham THE PREDICTION STOCK PRICE OF PT. BRI, Tbk. USE ARIMA METHOD (Autoregressive Integrated Moving Average) ABSTRACT ARIMA method is one of the method that used to prediction the change of stock price. The purpose of this research is to make model of ARIMA and predict stock price of PT. BRI, Tbk. in November 2014. The research use maximum and minimum data of stock price daily of PT. BRI, Tbk. Data are used is secondary data that taking from website of PT. BRI, Tbk. since January 3rd 2011 until October 20th 2014 to predict stock price in November 2014. From this research show that data from 2011 until October 2014 can be used to predict the stock price in November 2014. The result of ARIMA’s model for the maximum stock price is ARIMA (2,1,3) and the minimum stock price is (2,1,3) can use to predict the data on November 2014 with predict validation that take on October 2014 and with that predict November 2014. Keywords: ARIMA method, PT. BRI, Tbk., Stock

scholarly journals Rainfall Forecast of Merauke Using Autoregressive Integrated Moving Average Model

2018 ◽  
Vol 73 ◽  
pp. 12010 ◽  
Author(s):  
Yenni P. Pasaribu ◽  
Hariani Fitrianti ◽  
Dessy Rizki Suryani
Keyword(s):  
Global Climate ◽  
Human Life ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
Crop Failure ◽  
Maximum Rainfall ◽  
Autoregressive Integrated Moving Average ◽  
Agriculture Sector ◽  
Rainfall Forecast

Climate is an important element for human life, one of them is to agriculture sector. Global climate change leads to increased frequency and extreme climatic intensity such as storms, floods, and droughts. Rainfall is climate factor that causes the failure of harvest in Merauke. Therefore, rainfall forecast information is very useful in anticipating the occurrence of extreme events that can lead to crop failure. The purpose of this research is to model rainfall using autoregressive integrated moving average (ARIMA) model. The ARIMA model can be used to predict future events using a set of past data, including predicting rainfall. This research was conducted by collecting secondary data from Agency of Meteorology, Climatology, and Geophysics (BMKG) from 2005 until 2017, then the data was analyzed using R.3.4.2. software. The analysis result showed that ARIMA model (2.0,2) as the right model to predict rainfall in Merauke. The result of forecasting based on ARIMA model (2.0,2) for one period ahead is 179 mm of average rainfall, 46 mm of minimum rainfall, and 295 mm of maximum rainfall. Thus it can be concluded that the intensity of rainfall in Merauke has decreased and there was a seasonal shift from the previous period.

scholarly journals FORECASTING ARRIVAL OF FOREIGN TOURISTS USING SEASONAL ARIMA BOX-JENKINS

2021 ◽  
Vol 15 (2) ◽  
pp. 223-230
Author(s):  
Nur Ilmayasinta
Keyword(s):  
Moving Average ◽  
Seasonal Pattern ◽  
Secondary Data ◽  
Autoregressive Integrated Moving Average ◽  
Tourism Sector ◽  
Existing Data

Indonesia's economy is influenced by many factors, including the tourism sector. Through this tourism sector, it is possible for many foreign tourists to visit Indonesia. There are so many foreign tourists who come to Indonesia, forecasting is needed to find out the estimates of foreign tourists in the following months based on existing data. The method that used is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The foreign tourist’s coming to Indonesia through Soekarno Hatta Airport were taken from the center agency on statistics (BPS) Indonesia. Data on the number of foreign tourists who come to Indonesia through Soekarno Hatta Airport is data with a seasonal pattern. The data used is secondary data obtained from Soekarno Hatta Airport for the period January 2010 to June 2015. In this case it is used to predict the value of the data for the next 6 months using the best model is the . Forecasting results show the number of each month increases from the previous year. In July it showed the highest yield of 342536, which was 297878 in the previous year. Forecasting results show the number of each month increases from the previous year. In July, the highest yield was 342536, which was 297878 in the previous year.

scholarly journals Comparison of Financial Models for Stock Price Prediction

2020 ◽  
Vol 13 (8) ◽  
pp. 181
Author(s):  
Mohammad Rafiqul Islam ◽  
Nguyet Nguyen
Keyword(s):  
Neural Network ◽  
Stochastic Process ◽  
Predictive Models ◽  
Stock Price ◽  
Financial Models ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Neural Network

Time series analysis of daily stock data and building predictive models are complicated. This paper presents a comparative study for stock price prediction using three different methods, namely autoregressive integrated moving average, artificial neural network, and stochastic process-geometric Brownian motion. Each of the methods is used to build predictive models using historical stock data collected from Yahoo Finance. Finally, output from each of the models is compared to the actual stock price. Empirical results show that the conventional statistical model and the stochastic model provide better approximation for next-day stock price prediction compared to the neural network model.

scholarly journals Prediksi Harga Tutup Saham PT. Garuda Indonesia,Tbk Menggunakan Metode ARIMA

Jurnal MIPA ◽  
2017 ◽  
Vol 6 (1) ◽  
pp. 74
Author(s):  
Johanes Lusikooy ◽  
Nelson Nainggolan ◽  
Julia Titaley
Keyword(s):  
Secondary Data ◽  
Research Use ◽  
Closing Price ◽  
Research Show

Tujuan dari penelitian ini adalah untuk menerapkan model ARIMA dalam memprediksi harga tutup saham PT. Garuda Indonesia,Tbk. 25 Oktober 2016 sampai 7 November 2016. Penelitian ini menggunakan data harga tutup saham PT. Garuda Indonesia,Tbk. Data yang digunakan yaitu data sekunder yang diambil dari website perusahaan PT. Garuda Indonesia,Tbk. sejak 1 Januari 2013 sampai 24 Oktober 2016.Dari hasil penelitian menunjukkan bahwa data 1 Januari 2013 sampai 24 oktober 2016 dapat digunakan untuk memprediksi harga tutup saham 25 oktober 2016 sampai dengan 7 November 2016. Hasilnya model ARIMA untuk harga tutup saham adalah ARIMA (3,1,3) yang dapat digunakan untuk memprediksi data 25 oktober 2016 sampai dengan 7 November 2016muaThe purpose of this research is to applying model of ARIMA to predict stock closing price of PT.Garuda Indonesia,Tbk. in 25 October 2016 until 7 November 2016. The research use data of stock closing price daily of PT. Garuda Indonesia,Tbk. Data are used  is secondary data that taking from website of PT. Garuda Indonesia,Tbk. since 1 Januari 2013 until 24 October 2016. In this research show that data from 1 January 2013 until 24  October 2016 can be used to predict the stock closing price in 25 October 2016 to 7 November 2016. The result of ARIMA’s model for stock closing price is ARIMA (3,1,3) can use to predict the data on 25 October 2016 to 7 November 2016

scholarly journals Stock Price Movement through Technical Analysis: Empirical Evidence from the Information Technology (IT) Sector

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Shalini Singh ◽  
Anindita Chakraborty
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Secondary Data ◽  
Technical Analysis ◽  
Future Price ◽  
Analysis Tool ◽  
Price Movement ◽  
Technical Indicators ◽  
Share Prices ◽  
The Future

<em>Technical analysis forecasts the future asset prices with the use of their historical prices, trading volumes, market action and primarily through the uses of charts that predicts the future price trends. Technical analysis guides the investor to track the market with different indicators which is convenient for their study. Technical indicators aids to analyse the short-term price movement of the shares, most importantly it indicates the turning point and helps in projecting the price movement. This paper is prepared to employ the technical analysis tool to IT index companies. Indicators have been analysed using share prices of companies for 1 years, i.e., from January 2015- December 2015. Study is performed using secondary data, which has been collected from NSE website. The Technical Indicators used for the study are Bollinger Bands and MACD (Moving Average Convergence and Divergence). The purpose of the study is to find the best technical indicator to analyse the share prices.</em>

scholarly journals Penerapan Model ARIMA-ARCH untuk Meramalkan Harga Saham PT. Indofood Sukses Makmur Tbk

2021 ◽  
Vol 3 (3) ◽  
pp. 171-177
Author(s):  
Yulvia Fitri Rahmawati ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroscedasticity

Abstract– The stock price is the value of the stock in the market that fluctuates from time to time. Time series data in the financial sector generally have quite high volatility which can cause heteroscedasticity problems. This study aims to model and to predict the stock price of PT Indofood Sukses Makmur Tbk using the ARIMA-ARCH model. The data used is daily stock prices from 2nd June 2020 to 15th February 2021 as training data, while from 16th February 2021 to 1st March 2021 as testing data. ARIMA-ARCH model is a model that combines Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH), which can be used to overcome the residues of the ARIMA model which are indicated to have heteroscedasticity problems. The result showed that the model that could be used was ARIMA(1,1,2)-ARCH(1). This model can provide good forecasting result with a relatively small MAPE value of 0.515785%. Abstrak– Harga saham adalah nilai saham di pasar yang berfluktuasi dari waktu ke waktu. Data runtun waktu di sektor keuangan umumnya memiliki volatilitas cukup tinggi yang dapat menyebabkan masalah heteroskedastisitas. Penelitian ini bertujuan untuk memodelkan dan meramalkan harga saham PT Indofood Sukses Makmur Tbk menggunakan model ARIMA-ARCH. Data yang digunakan adalah harga saham harian dari 2 Juni 2020 hingga 15 Februari 2021 sebagai data training, sedangkan dari 16 Februari 2021 hingga 1 Maret 2021 sebagai data testing. Model ARIMA-ARCH merupakan suatu model yang menggabungkan Autoregressive Integrated Moving Average (ARIMA) dan Autoregressive Conditional Heteroscedasticity (ARCH), yang dapat digunakan untuk mengatasi residu dari model ARIMA yang terindikasi memiliki masalah heteroskedastisitas. Hasil penelitian menunjukkan bahwa model yang dapat digunakan adalah ARIMA(1,1,2)-ARCH(1). Model tersebut mampu memberikan hasil peramalan yang baik dengan perolehan nilai MAPE yang relatif kecil yaitu 0,515785%.

scholarly journals Model ARIMA-GARCH Pada Peramalan Harga Saham PT. Jasa Marga (Persero)

2021 ◽  
Vol 3 (3) ◽  
pp. 164-170
Author(s):  
Fransisca Trisnani Ardikha Putri ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Moving Average ◽  
Garch Model ◽  
Conditional Heteroskedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Conditional Heteroskedasticity

Abstract– PT Jasa Marga is a great reputation company, the leader in comparable businesses, has a steady income, and paying dividends consistently. This paper aims to find the best model to forecast stock price of PT Jasa Marga using ARIMA-GARCH. The data used is daily stock price of PT Jasa Marga from March 2020 to March 2021. Autoregressive Integrated Moving Average (ARIMA) is a method that can be used to forecast stock prices. However, an economical data tend to have heteroscedasticity problems, one of the methods used to overcome them is Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Future stock price of PT Jasa Marga is forecasted with ARIMA-GARCH model.  The data is modeled with ARIMA first, if there is heteroscedasticity, combine the model with GARCH model. The result of this study indicated that ARIMA (1, 1, 1) – GARCH (2, 2) is the best model, with MAPE 1,5647 Abstrak– PT Jasa Marga adalah perusahaan yang reputasinya baik, terdepan di perusahaan-perusahaan sejenis, stabil pendapatannya, dan pembayaran devidennya konsisten. Paper ini bertujuan untuk mencari model terbaik dalam meramalkan harga saham PT Jasa Marga menggunakan ARIMA-GARCH. Data harga saham yang diolah yaitu data sekunder dari PT Jasa Marga pada Maret 2020 hingga Maret 2021. Autoregressive Integrated Moving Average (ARIMA) sebagai metode yang dapat dimanfaatkan guna meramalkan harga saham. Akan tetapi, data tentang ekonomi cenderung memiliki masalah heteroskedastisitas, metode yang umum dipakai untuk mengatasinya adalah Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Harga saham PT Jasa Marga diramalkan dengan model ARIMA-GARCH.  Data terlebih dahulu dimodelkan dengan ARIMA, jika didapati adanya heteroskedastisitas, maka model tersebut dikombinasikan dengan GARCH. Penelitian ini menghasilkan ARIMA (1,1,1)-GARCH(2,2) sebagai model terbaik dengan MAPE 1,5647.

scholarly journals PENGARUH FAKTOR FUNDAMENTAL DAN RISIKO SISTEMATIK TERHADAP HARGA SAHAM PERUSAHAAN DI PASAR MODAL SYARIAH (Studi Kasus di Jakarta Islamic Index Periode 2014-2017)

2020 ◽  
Vol 6 (10) ◽  
pp. 1984
Author(s):  
Vega Zahrah Narendra Duhita ◽  
Nisful Laila
Keyword(s):  
Stock Price ◽  
Secondary Data ◽  
Quantitative Approach ◽  
Statistical Tool ◽  
Insignificant Effect ◽  
Regression Test ◽  
Systematic Risks ◽  
Level Of Significance ◽  
Research Period ◽  
Research Show

This research aims to find out and explain the influence of financial fundamental factors (DER, NPM, ROA, TATO, EPS, and PER) and systematic risks (Stock Beta) on stock price of the companies which were registered on Jakarta Islamic Index period 2014-2017. It uses quantitative approach to find out the relation between one variable and another. Data used is a secondary data including the value of every ratio used and closi stock price in ecery company. The object of the research is 16 companies which were registered on Jakarta Islamic Index during the research period. This research focuses on whether the variables used affect companies’ stock price by using multiple linier regression test and statistical tool IBM SPSS 25 and determining the level of significance 5%. The results of the research show that the fundamental factors and systematic risks simultaneously have a positive and significant effect on companies’ stock price on JII during period 2014-2017. Meanwhile, the variables of DER, ROA, TATO, EPS partially have a positive and significant effect, the variables of NPM, PER, and stock beta have insignificant effect on companies’ stock price on JII during period 2014-2017.Keywords: Fundamental Analysis, Stock Price, Systematic Risks, Jakarta Islamic Index

FORECASTING STOCK PRICES ON THE LQ45 INDEX USING THE VARIMAX METHOD

2021 ◽  
Vol 14 (1) ◽  
pp. 98-107
Author(s):  
Dinul Darma Atmaja ◽  
Widowati Widowati ◽  
Budi Warsito
Keyword(s):  
Formation Process ◽  
Stock Prices ◽  
Predictive Value ◽  
Stock Price ◽  
Moving Average ◽  
Exogenous Variable ◽  
Initial Model ◽  
Vector Autoregressive ◽  
Autoregressive Integrated Moving Average ◽  
Model Formation

Forecasting using the Autoregressive Integrated Moving Average (ARIMA) method is not appropriate to predict more than one stock price because this method is only able to model one dependent variable. Therefore, to expect more than one stock prices, the ARIMA method expansion can be used, namely the Vector Autoregressive Integrated Moving Average (VARIMA) method. Furthermore, this research will discuss forecasting stock prices on the LQ45 index using the Vector Autoregressive Integrated Moving Average with Exogenous Variable (VARIMAX) method. Then, after the initial model formation process, the best model is the VARIMAX (0,1,2) model. Finally, the results of this study using the VARIMAX (0,1,2) model obtained the predictive value of the prices and the error values of stocks on the LQ45 index.

Application of Enhanced Hidden Markov Model in Stock Price Prediction

2020 ◽  
Vol 3 (1) ◽  
pp. 70-78
Author(s):  
Donata D. Acula ◽  
Teofilo De Guzman
Keyword(s):  
Neural Networks ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Stock Price ◽  
Hidden Markov ◽  
Moving Average ◽  
High Price ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
Stock Price Prediction

The main focus of this research is the enhancement of the Hidden Markov Model by using some features of Neural Networks and the forecasted values of predictors by Seasonal Autoregressive Integrated Moving Average. The enhanced method was used to predict the close price of stocks whose predictors are open price, high price, low price, and volume of Apple and Nokia data. The performance of the method was measured using the Mean Absolute Percentage Error of the predicted price. The result was compared against the actual close price by using the paired T-test. The testing of the hypothesis showed that the Enhanced Hidden Markov Model obtained more than 94% accuracy rate. It also shows that in Apple data, the predicted close price of the Enhanced Hidden Markov Model is significantly better than the predicted close price of Neural Networks. Using Nokia data, the test claims that there is no difference between the performance of Enhanced Hidden Markov Model and Neural Network in prediction. 

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