scholarly journals Deep Learning Methods In Predicting Indonesia Composite Stock Price Index (IHSG)

2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Arief Fadhlurrahman Rasyid ◽  
Dewi Agushinta R. ◽  
Dharma Tintri Ediraras
Keyword(s):  
Deep Learning ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Learning Methods ◽  
Stock Price Index

The stock price changes at any time within seconds. The stock price is a time series data. Thus, it is necessary to have the best analysis model in predicting the stock price to make decisions to avoid losses in investing. In this research, the method used two models Deep Learning namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting Indonesia Composite Stock Price Index (IHSG). The dataset used is historical data from the Jakarta Composite Index (^JKSE) stock price in 2013-2020 obtained through Yahoo Finance. The results suggest that Deep learning methods with LSTM and GRU models can predict Indonesia Composite Stock Price Index (IHSG). Based on the test results obtained RMSE value of 71.28959454502723 with an accuracy rate of 92.39% for LSTM models and obtained RMSE value of 70.61870739073838 with an accuracy rate of 96.77% on GRU models.

scholarly journals ANALISIS FAKTOR PENENTU INDEKS HARGA SAHAM GABUNGAN DI INDONESIA

2019 ◽  
Vol 1 (4) ◽  
pp. 37
Author(s):  
Yulizar Fikri ◽  
Ali Anis
Keyword(s):  
Economic Growth ◽  
Foreign Exchange ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Secondary Data ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Foreign Exchange Reserves ◽  
Stock Price Index

This study aims to determine the analysis of the determinants of the composite stock price index in Indonesia. The independent variables in this study are inflation as X1, foreign exchange reserves as X2, exchange rates as X3, and economic growth as X4, and the dependent variable of the composite stock price index as Y. The data used are secondary data in the formof time series data from 2010Q1 until 2019Q2, with data collection techniques, namely documentation from Bank Indonesia publications, the Central Statistics Agency, investing. comsite and library research. The research methods used are: (1) Multiple Linear Regression, (2) Classical Assumption Test (3) coefficient of determination. The results of this study indicate that:(1) inflation does not significantly influence the composite stock price index. (2) foreign exchange reserves have a significant positive effect on the composite stock price index. (3) the rupiah exchange rate has an influence on the composite stock price index and (4) economic growth hasno significant effect on the composite stock price index.

scholarly journals Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Nahla F. Omran ◽  
Sara F. Abd-el Ghany ◽  
Hager Saleh ◽  
Abdelmgeid A. Ali ◽  
Abdu Gumaei ◽  
...  
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Disease Outbreaks ◽  
Handling Time ◽  
Series Data ◽  
Learning Methods ◽  
Death Cases

The novel coronavirus disease (COVID-19) is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait.

scholarly journals ANALISIS PENGARUH KURS, BI RATE DAN INFLASI TERHADAP IHSG DI BEI PERIODE 2014-2017

KEUNIS ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 64
Author(s):  
Esty Nidianti ◽  
Edi Wijayanto
Keyword(s):  
Exchange Rate ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Series Data ◽  
Simultaneous Test ◽  
Stock Price Index ◽  
The Exchange Rate ◽  
Analyze Data

<p><em>The aim of this study was to determine the effect of macro economic conditions which including the exchange rate, BI rate and inflation of the composite stock price index. The study had used quantitative approach. Determination of the sample was based on time series data periode January 2014 – December 2017 by using saturation sampling method, which resulted 48 as number of samples. This study also had chosen multiple linier regression as attempts to analyze data. The simultaneous test (F test) resulted that the exchange rate, BI rate, and inflation had given significant effect on the stock price index. Meanwhile, the partial test (t test) had indicated that the exchange rate variable and BI rate significantly influenced the stock price index. In contrast, rate of inflation had not showed significant effect on the stock price index. </em><strong><em></em></strong></p>

scholarly journals The Influence of the World Gold Price, the Dow Jones Islamic Market, and the Composite Stock Price Index on the Jakarta Islamic Index for the 2014 - 2018 period

2021 ◽  
Vol 4 (1) ◽  
pp. 13
Author(s):  
Siti Chaerunisa Prastiani
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Price Index ◽  
Oil And Gas ◽  
Time Series Data ◽  
Research Output ◽  
Gold Price ◽  
Series Data ◽  
The World ◽  
Stock Price Index

This study aims to determine how much influence the variables of the World Gold Price and Stock Prices with proxies: Dow Jones Islamic Market (DJIM) stock prices, and the Composite Stock Price Index (IHSG), on the Jakarta Islamic Index (JII). This study uses a quantitative approach, namely data that is measured in a numerical scale, based on the 2014-2018 Time Series data relating to variables sourced from the Central Statistics Agency, the Indonesia Stock Exchange and the Directorate General of Oil and Gas. This research uses one of the SPSS Series. The variables in this study consist of World Gold Price (X1), Dow Jones Islamic Market (DJIM) (X2), Composite Stock Price Index (IHSG) (X3) against the Jakarta Islamic Index (JII) (Y). The purpose of this research is to know each variable partially or simultaneously from the variable World Gold Price, Dow Jones Islamic Market and the Jakarta Islamic Index. Research Output expected by an Accredited journal

scholarly journals PENGARUH INFLASI, BI RATE, DAN NILAI TUKAR RUPIAH TERHADAP PERUBAHAN INDEKS HARGA SAHAM.SEKTOR CONSUMER GOODS

2021 ◽  
Vol 4 (1) ◽  
pp. 51-63
Author(s):  
Diah Budi Pratiwi ◽  
Damayanti Damayanti ◽  
M. Iqbal Iqbal Harori
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Consumer Goods ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Series Data ◽  
A Value ◽  
Stock Price Index ◽  
Positive Effect

This research aims to find out the macroeconomic influence of inflation, bi rate, and rupiah exchange rate on changes in the stock price index of consumer goods sector. The independent variables that used in this research are Inflation (X1), BI Rate (X1), and Rupiah Exchange Rate (X3) and Consumer Goods Sector Stock Price Index as dependent variable. The data in this research is a time series data that includes inflation, BI Rate, and Rupiah exchange rate data for the period 2016-2020. The samples in this research amounted to 60 samples that taken by using census sampling techniques. The data in this research was analyzed by using multiple linear regressions with simultaneous variable results of Inflation, BI rate, and Rupiah Exchange Rate significantly affecting changes in the Consumer Goods Sector Stock Price Index with a value of R Square is 0.382 or 38.2%. While the results partially show that variable inflation has a significant and positive effect, variable rupiah exchange rates has negatively affect on changes in the Stock Price Index of the Consumer Goods Sector. As for the variable BI Rate has no significant effect on changes in the Stock Price Index of the Consumer Goods Sector. ABSTRAK   Penelitian ini bertujuan untuk mengetahui pengaruh ekonomi makro inflasi, bi rate, dan nilai tukar rupiah terhadap perubahan indeks harga saham sektor consumer goods. Variabel bebas yang digunakan pada penelitian ini yaitu Inflasi (X1), BI Rate (X1), dan Nilai Tukar Rupiah (X3) serta Indeks Harga Saham Sektor Consumer Goods sebagai variabel terikat. Data pada penelitian ini merupakan data time series yang meliputi data Inflasi, BI Rate, dan Nilai Tukar Rupiah untuk periode tahun 2016-2020. Sampel pada penelitian ini berjumlah 60 sampel yang diambil dengan menggunakan teknik sampling sensus. Data pada penelitian ini dianalisis dengan menggunakan regresi linier berganda, dengan hasil secara simultan, variabel Inflasi, BI rate, dan Nilai Tukar Rupiah berpengaruh signifikan terhadap perubahan Indeks Harga Saham Sektor Consumer Goods. Secara parsial, variabel inflasi berpengaruh signifikan dan positif, serta variabel nilai tukar rupiah berpengaruh negatif terhadap perubahan Indeks Harga Saham Sektor Consumer Goods. Sedangkan untuk variabel BI Rate tidak berpengaruh secara signifikan terhadap perubahan Indeks Harga Saham Sektor Consumer Goods.

scholarly journals Analysis The Effect of Trading Volume Activity and External Factors to Composite Stock Price Index

2020 ◽  
Vol 4 (3) ◽  
pp. 201-214
Author(s):  
Mega Barokatul Fajri ◽  
Wihandaru Wihandaru ◽  
Adi Lukman Hakim
Keyword(s):  
Stock Price ◽  
Trading Volume ◽  
Price Index ◽  
Time Series Data ◽  
Stock Exchange ◽  
External Factors ◽  
Series Data ◽  
Volume Activity ◽  
Negative Effect ◽  
Stock Price Index

This research as a purpose to analyze the effect of trading volume activity and external factors such as exchange rates, BI Rate to composite stock price index listed on the Indonesian Stock Exchange. The object of this research is on the Indonesia Stock Exchange and Bank Indonesia. In this study, the data used were time-series data and the sampling method used was purposive sampling. The method of analysis used in this study is multiple regression models. Based on the analysis that has been done, it is known that the trading volume activity and BI Rate has no effect on the composite stock price index, while the exchange rate has a negative effect on the composite stock price index.

scholarly journals PENGARUH JUMLAH UANG BEREDAR, KURS US DOLLAR DAN INDEKS HARGA KONSUMEN TERHADAP INDEKS HARGA SAHAM SEKTOR PERDAGANGAN DI BURSA EFEK INDONESIA

Media Ekonomi ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 93
Author(s):  
Nurlia Rahmatika
Keyword(s):  
Exchange Rate ◽  
Money Supply ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Sampling Technique ◽  
Series Data ◽  
Independent Variable ◽  
Stock Price Index

<em>This study aims to determine the analysis of the influence of the Money Supply (M2), the USD Exchange Rate and the Consumer Price Index.</em> <em>The research methodology used is a quantitative method with time series data and data sources derived from secondary data obtained from the Indonesia Stock Exchange. The sampling technique uses purposive sampling method with monthly data and research period from January 2009 to December 2016. The data analysis technique used is multiple linear regressions.</em> <em>The results of this study indicate that partially the independent variable Amount of Money has a positive and significant effect on the Trading Sector Stock Price Index. While the independent variable USD Exchange Rate and Consumer Price Index has a negative and significant effect on the Trading Sector Stock Price Index. Meanwhile, simultaneously the independent variable consisting of Money Supply, the USD Exchange Rate and the Consumer Price Index together have a significant relationship to the dependent variable, namely the Trade Sector Stock Price Index. </em>

scholarly journals Pergerakan Harga Saham Syariah Pada Saham Bank Syariah Di lihat Dari Faktor Makroekonomi

2018 ◽  
Vol 5 (1) ◽  
pp. 175
Author(s):  
Rais Sani Muharrami ◽  
Shufiatul Zahidah ◽  
Ika Yoga
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Macroeconomic Indicators ◽  
Bank Stock ◽  
Stock Price Index

This study aims to determine the macroeconomic indicators that affect sharia banking stock price index period 2014-2016. Four variables consist of inflation, BI interest rate, rupiah exchange rate and SBIS are considered to have an effect on the syariah bank stock price index. This research uses quantitative method. This study uses monthly time series data which is analyzed by multiple linear regression. The data used are secondary data with 36 data from January 2014-December 2016. Data collection is taken with documentation techniques sourced from the official website of Bank Indonesia and yahoofinance.com. The results showed that inflation did not significantly influence the sharia bank stock price index. While the BI interest rate, the exchange rate of rupiah and SBIS have a significant influence on PT Bank Panin Dubai Syariah Tbk stock price index. From the results of this study, it can be concluded that the indicators considered in the PT Bank Panin Dubai Syariah Tbk stock price index are BI rate, rupiah exchange rate and SBIS.

scholarly journals Analisis Intervesi Fungsi Step Efek Program Tol-Laut Terhadap Pergerakan Harga Saham TMAS.JK

2019 ◽  
Vol 5 (01) ◽  
pp. 47-54
Author(s):  
Wigid Hariadi
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Vice President ◽  
Step Function ◽  
Series Data ◽  
Intervention Analysis ◽  
Analysis Model ◽  
External Events ◽  
Port Construction

Abstract. Intervention analysis is used to evaluate the effect of external events on a time series data. Sea-highway program is one of the leading programs Joko Widodo-Jusuf Kalla in presidential election 2014. So the author want to modeling the effect from Sea-highway programs on stock price movement in the shipping sector, TMAS.JK (Pelayaran Tempuran Emas tbk). After analyzing, proven that it has happened intervention on movement of daily stock price TMAS.JK caused by Sea-highway programs. Intervention I, on 11 August 2014, which was efect as a result of the election of the Joko Widodo-Jusuf kalla pair as President and vice President Republic of Indonesia on 22 july 2014. Intervention II, on 10 november 2014, president Joko Widodo speech in APEC about Sea-highway Program, and offering investment in port construction to foreign country. So that the model of time series analysis that right is intervention analysis model multi input step function, where the model is ARIMA (2,1,0), StepI (b=0, s=2, r=1), StepII (b=3, s=0, r=1).  Keywords: Intervention Analysis, Multi Input, Step Function, Sea-highway.    Abstrak. Analisis intervensi digunakan untuk mengevaluasi efek dari peristiwa eksternal pada suatu data time series. Program Tol-Laut merupakan salah satu program unggulan pasangan Joko Widodo-Jusuf Kalla dalam pemilu 2014. sehingga, penulis ingin memodelkan efek dari Program Tol-Laut terhadap pergerakan harga saham dibidang pelayaran, TMAS.JK (Pelayaran Tempuran Emas tbk). Setelah dilakukan analisis data, terbukti bahwa terjadi intervensi pada pergerakan harga saham harian TMAS.JK yang disebabkan oleh efek dari program Tol-Laut. Dimana intervensi I, pada tanggal 11 Agustus 2014, yang diduga sebagai dampak dari terpilihnya pasangan Joko widodo-Jusuf Kalla sebagai presiden dan wakil presiden Republik Indonesia pada tanggal 22 Juli 2014. Intervensi II, pada tanggal 10 November 2014, pidato Presiden Joko Widodo di forum APEC mengenai program  tol  laut, dan  menawarkan investasi dibidang pembangunan pelabuhan  kepada bangsa asing. Sehingga model analisis time series yang tepat adalah model analisis intervensi multi input fungsi step, dimana modelnya adalah ARIMA (2,1,0), StepI (b=0, s=2, r=1), StepII (b=3, s=0, r=1). Kata kunci: Analisis intervensi, Multi Input, fungsi step, Tol-Laut.

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