Technical analysis testing in forecasting Socially Responsible Investment Index in Indonesia Stock Exchange

Abstract

This study aims to examine whether the Autoregressive Integrated Moving Average (ARIMA) model is appropriate to be applied in the Indonesia Stock Exchange, especially for the socially resposible investment stocks. For the ARIMA model combines the autoregressive and moving average method, so it is viewed as a useful tool to predict the stock prices. Those methods are frequently used methods to forecast the stock prices. The data used in this study were daily SRI-KEHATI Index during the period of June 8, 2009 to July 17, 2017. The results showed that the daily SRI-KEHATI Index data were not stationary data, thus this data needed to be transformed. The transformation was done by using the first seasonal differencing transformation process. After being transformed, those data became stationary. Furthermore, this study found that ARIMA (3,1,1) was a model, which might be appropriate and fit with the data condition. This method was also relevant to be applied in the Indonesia Stock Exchange in order to forecast the stock prices.

Mapping the research on sustainable stock indices: a bibliometric review from 2001 to 2022

“…Thus, the increased interest from global investors boosted the research based on SSI. The academicians focussed more on analysing performance and forecasting of SSI, connectedness and volatility spillover among SSI and other traditional investment options to impart significant diversification benefits of investing in SSI (Arias Fogliano de Souza Cunha and Samanez, 2013; Irfan et al , 2021; Pulungan et al , 2018; Statman, 2006). It is relevant to observe that the publication sample for review consists of only documents published until March 2022.…”

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

“…The results indicate that the ARIMA model can compete well with existing techniques for stock price prediction, especially in the short term. Pulungan et al 24 applied a combination of autoregressive (AR) and moving average (MA) methods. The data needs to be stationary for ARIMA to be applied efficiently.…”

“…The well-known traditional statistics time series forecasting methods, such as ARIMA and its variants 17,[21][22][23][24][25][26][27][28][29] are still used a lot because of their efficiency level. Table 1 presents a summary of ARIMA-based approaches for SPF.…”