scholarly journals IMPLEMENTASI PAKET SHINY PADA PEMODELAN MULTISCALE AUTOREGRESSIVE UNTUK DATA HARGA SAHAM BBRI

2021 ◽  
Vol 10 (3) ◽  
pp. 315-324
Author(s):  
Bahtiar Ilham Triyunanto ◽  
Suparti Suparti ◽  
Rukun Santoso
Keyword(s):  
Nonparametric Regression ◽  
Stock Price ◽  
Time Series Data ◽  
Model Performance ◽  
Series Data ◽  
Nonparametric Regression Model ◽  
Decomposition Level ◽  
R Software ◽  
Interactive Display ◽  
Sample Data

Stocks are an investment that attract people because they can earn large profits by having claim rights to the company's income and assets so investors have to observe stock price movements in the future to achieve investment goals. One of the statistical methods for time series data modeling is ARIMA. However, modeling assumptions must be fulfilled to use that method so an alternative model is proposed, namely nonparametric regression model, which has no modeling assumptions requirement. In this study, the nonparametric regression multiscale autoregressive (MAR) with two different filter and decomposition level J are compared to choose the best model and forecast it. The data are closing stock price, high stock price and low stock price of BBRI’s stocks that divided into 2 parts, namely in sample data from March 19, 2020 to February 4, 2021 to form a model and out sample data from February 5, 2021 to March 23, 2021 used for evaluation of model performance based on MAPE values. The chosen best model for each stock price are the MAR model with  wavelet haar filter and decomposition level 5 for the closing stock price which produces a MAPE value of 1.194%, the MAR model with wavelet haar filter and decomposition level 5 for the high stock price which produces a MAPE value of 1.283%, and the MAR model with a wavelet haar filter and decomposition level 5 for the low stock price which produces a MAPE value of 1.141%, indicating that the models have excellent forecasting capability. In this study, Graphical User Interface (GUI) using R software with the help of shiny package is also built, making data analyzing easier and generating more interactive display output.

scholarly journals PEMODELAN HARGA EMAS DUNIA MENGGUNAKAN METODE NONPARAMETRIK POLINOMIAL LOKAL DILENGKAPI GUI R

2022 ◽  
Vol 10 (4) ◽  
pp. 605-616
Author(s):  
Jody Hendrian ◽  
Suparti Suparti ◽  
Alan Prahutama
Keyword(s):  
Time Series Data ◽  
Arima Model ◽  
Model Performance ◽  
Polynomial Model ◽  
Gaussian Kernel ◽  
Series Data ◽  
Nonparametric Model ◽  
Local Polynomial ◽  
Local Point ◽  
Sample Data

Investing in gold is a flexible choice because it can be sold at any time and used as an emergency fund. Investors should have the knowledge to predict data from time to time to achieve investment goals. One of the statistical methods for time series data modeling is ARIMA. The ARIMA model is strict with the assumptions that the data must be stationary, the residuals must be normally distributed, independent, and with constant variance, so an alternative model is proposed, namely nonparametric regression model, which has no modeling assumptions requirement. In this study, the daily world gold price data will be modeled using a local polynomial nonparametric model as an alternative because the assumptions in the ARIMA are not fulfilled. The data is divided into 2 parts, namely in sample data from January 2, 2020 to November 30, 2020 to form a model and out sample data from December 1, 2020 to December 31, 2020 used for evauation of model performance based on MAPE values. The chosen best model is the local polynomial model with Gaussian kernel function of degree 5, bandwidth of 373, and local point of 1744 with an MSE value of 482.6420. The local polynomial model out sample data MAPE value is 0.61%, indicating that the model has excellent forecasting capability. In this study, Graphical User Interface (GUI) using R software with the help of shiny package is also built, making data analyzing easier and generating more interactive display output. 

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

ANALISA PREDIKSI INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN METODE ARIMA

2020 ◽  
Vol 2 (2) ◽  
pp. 454
Author(s):  
Julkifli Purnama ◽  
Ahmad Juliana
Keyword(s):  
Capital Market ◽  
Stock Price ◽  
Time Series Data ◽  
Arima Model ◽  
Secondary Data ◽  
Actual Data ◽  
Series Data ◽  
Analysis Technique ◽  
The Right ◽  
The Capital Market

Investment in the capital market every manager needs to analyze to make decisions so that the right target to produce profits in accordance with what is expected. For that, we need a way to predict the decisions that will be taken in the future. The research objective is to find the best model and forecasting of the composite stock price index (CSPI). Data analysis technique The ARIMA Model time series data from historical data is the basis for forecasting. Secondary data is the closing price of the JCI on July 16 2018 to July 16 2019 to see how accurate the forecasting is done on the actual data at that time. The results of the study that the best Arima model is Arima 2.1.2 with an R-squared value of 0.014500, Schwarz criterion 10.83497 and Akaike info criterion of 10.77973. Results of forecasting actual data are 6394,609, dynamic forecast 6387,551 selisish -7,05799, statistics forecas 6400,653 difference of 6,043909. For investors or the public can use the ARIMA method to be able to predict or predict the capital market that will occur in the next period.

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 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.

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

A Sectoral Analysis of the Role of Stock Market Development on Economic Growth: Empirical Evidence from Indian Economy

2017 ◽  
Vol 18 (4) ◽  
pp. 911-923 ◽  
Author(s):  
Madhu Sehrawat ◽  
A.K. Giri
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Trade Openness ◽  
Series Data ◽  
Causality Test ◽  
Long Run ◽  
Short Run

The present study examines the relationship between Indian stock market and economic growth from a sectoral perspective using quarterly time-series data from 2003:Q4 to 2014:Q4. The results of the autoregressive distributed lag (ARDL) approach bounds test confirm the existence of a cointegrating relationship between sector-specific gross domestic product (GDP) and sector-specific stock indices. The empirical results reveal that sector-specific economic growth are significantly influenced by changes in the respective sector-specific stock price indices in the long run as well as in the short run. Apart from that, the control variables, such as trade openness and inflation, act as the instrument variables in explaining the variations in the sector-specific GDP of the economy. The results of Granger causality test demonstrate unidirectional long-run as well as short-run causality running from sector specific stock prices to respective sector GDP. The findings suggest that economic growth of the country is sensitive to respective sub-sector stock market investments. The findings highlight the reasons for cyclical and counter-cyclical business phase for the overall economy.

Terrorism and Stock Market Linkages: An Empirical Study from a Front-line State

2017 ◽  
Vol 18 (2) ◽  
pp. 365-378 ◽  
Author(s):  
Imtiaz Arif ◽  
Tahir Suleman
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Negative Impact ◽  
Stock Exchange ◽  
Series Data ◽  
Front Line ◽  
Long Run ◽  
Factor Index ◽  
The Impact

This article investigates the impact of prolonged terrorist activities on stock prices of different sectors listed in the Karachi Stock Exchange (KSE) by using the newly developed terrorism impact factor index with lingering effect (TIFL) and monthly time series data from 2002 (January) to 2011 (December). Johansen and Juselius (JJ) cointegration revealed a long-run relationship between terrorism and stock price. Normalized cointegration vectors are used to test the effect of terrorism on stock price. Results demonstrate a significantly mixed positive and negative impact of prolonged terrorism on stock prices of different sectors and show that the market has not become insensitive to the prolonged terrorist attacks.

AN INTEGRATED DATA CHARACTERISTIC TESTING SCHEME FOR COMPLEX TIME SERIES DATA EXPLORATION

2013 ◽  
Vol 12 (03) ◽  
pp. 491-521 ◽  
Author(s):  
LING TANG ◽  
LEAN YU ◽  
FANGTAO LIU ◽  
WEIXUAN XU
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Data Exploration ◽  
Series Data ◽  
Economic Time Series ◽  
Noise Pattern ◽  
Complex Time ◽  
Testing Scheme ◽  
Sample Data ◽  
Economic Time

In this paper, an integrated data characteristic testing scheme is proposed for complex time series data exploration so as to select the most appropriate research methodology for complex time series modeling. Based on relationships across different data characteristics, data characteristics of time series data are divided into two main categories: nature characteristics and pattern characteristics in this paper. Accordingly, two relevant tasks, nature determination and pattern measurement, are involved in the proposed testing scheme. In nature determination, dynamics system generating the time series data is analyzed via nonstationarity, nonlinearity and complexity tests. In pattern measurement, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) are measured in terms of pattern importance. For illustration purpose, four main Chinese economic time series data are used as testing targets, and the data characteristics hidden in these time series data are thoroughly explored by using the proposed integrated testing scheme. Empirical results reveal that the natures of all sample data demonstrate complexity in the phase of nature determination, and in the meantime the main pattern of each time series is captured based on the pattern importance, indicating that the proposed scheme can be used as an effective data characteristic testing tool for complex time series data exploration from a comprehensive perspective.

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