Indonesia Composite Index Research Articles

Merton Model of Default Risk and Stock Return: Evidence from Indonesian Stock Market

The study of the characteristics of risk and return has received great attention. Several studies in finance literature have tested whether default risk influences firms’ stock returns, but the results are often conflicting. Previous research derives varying empirical results because they refer to default risk indicators and samples from different equity markets. The main objective of this study is to evaluate the effect of default risk on stock return using data taken from non-financial companies on the Indonesia Composite Index (IDX Composite) in Indonesia for the 2008-2017 research period. This study uses Merton’s (1974) model as done by Vassalou & Xing (2004) to build a proxy for the risk of default. The advantage of this model is that it considers the volatility of firms’ assets in estimating default risk. Companies can have similar equity and debt levels but possibly have very different default probabilities. The results of the study show that default risk has a positive and significant effect on equity returns.

ANALISIS KINERJA PORTOFOLIO MAGIC FORMULA PADA INDEKS LQ45 DI BURSA EFEK INDONESIA

The rapid growth of active investors in Indonesia stock market raises awareness on the importance of investors’ skill needed in analyzing and choosing profitable stock. One of the method knows to analyze profitable stock in the market is Magic Formula introduce by Joel Grenblatt -part of value investing. Magic Formula is a method on choosing a profitable stock in a simple way based on combination of both High Earnings Yield and High Return on Capital. The period use in this research is from 2016 – 2020. This research use LQ 45 stock listed in Indonesia Stock Exchange. This qualitative research used secondary data which is analyzed using backtesting simulation. The result of this research shows that Magic Formula’s Stock Portfolio build based on data consist of different number of stock: 15, 20, and 25 stock; combination of both high Earnings Yield and High Return on Capital. The Magic Formula’s Portfolio formed based on the LQ 45 stock has higher aritmathic average return compare to LQ 45 return and even the 15-stock-based portfolio beat the Indonesia Composite Index average return. However on the CAGR rate of return, the calculation show that the return of the Magic Formula Portfolio below the return of Indonesia Composite Index

Sentiment Analysis of Indonesian Economics News Summary on The ICI using Long Short Term Memory

Many investors use news summary as a reference while deciding whether to purchase, sell, or hold shares as part of their investment activity. The information provided by the news, on the other hand, frequently fails to meet expectations. Therefore, this study aims to classify economic news summary using sentiment analysis method followed by analyzing the results to determine the correlation between Indonesian economic news summaries and Indonesia Composite Index (ICI) trends. The dataset for this study is a collection of news articles from the Kontan online news site. This study begins with the collection of a dataset that includes 1720 training data and 440 test data that have been automatically summarized using the cosine similarity approach and labeled (positive/negative) based on the ICI trend the next day after the data was crawled. We use Term Frequency – Inverse Document Frequency (TF-IDF) as the word weighting technique and Long Short Term Memory (LSTM) method as the classifier to build the model. From the model training process that has been carried out for eight trials by tuning hyperparameters, the training accuracy is 86% and the F1-Score is 89.3%. While the testing accuracy and F1-score are 68.1% and 75.8% consecutively

Performances of Crypto, Market Indexes, and Gold Before and During the COVID-19 Pandemic: A Comparative Analysis

Research aims: This study aims to investigate empirical evidence of a comparison of investment instruments, including Bitcoin, Indonesia Composite Index (ICI), and gold, before and during the COVID-19 pandemic.Design/Methodology/Approach: Analytical methods employed comparison study using secondary data. The Microsoft Excel program was utilized to calculate formulas for each variable. Then, data were statistically processed using the SPSS application, i.e., independent sample test, testing for differences. The sample used in this study was the closing price of Bitcoin, the price of the ICI, and the price of gold, with monthly data from the beginning of January 2018 to the end of December 2021, to demonstrate a significant difference between the risks of Bitcoin, ICI, and gold before and during the COVID-19 pandemic.Research findings: The hypothesis test results revealed that before the COVID-19 pandemic, the investment risk of ICI and gold was the lowest, with a significance level of 0.000 (0.0000.05) on a different t-test at a 5% significance level. Thus, there was a significant difference in investment risk between ICI and gold. Meanwhile, during the COVID-19 pandemic, the risk of investing between Bitcoin and the ICI was the lowest, with a significance level of risk of was 0.000 (0.000 0.05) on different tests at the significance levels of 5%. In short, there was a big difference in investment risk between Bitcoin and the ICI.Theoretical contribution/Originality: This study provides additional literature on decision-making, especially on risk.

Indonesia Composite Index and Market Reaction in Indonesia Due to Covid-19 Pandemic

This study aims to examine the impact of the Covid-19 pandemic on the Indonesia Composite Index (ICI) and the reaction of the Indonesian capital market to events related to the Covid-19 pandemic in Indonesia. There were two events observed, the first event was the announcement of the first positive case of Covid-19 in Indonesia, and the second event was the determination of the status of a national disaster by the Indonesian government. Testing of effect of the Covid19 pandemic on stock prices was carried out by using an independent sample t-test against the average ICI before and after the two events. Testing the market reaction using the event study methodology. The test results found that the ICI average decreased significantly after the announcement of the first positive Covid-19 case. The ICI average also decreased significantly after the determination of the national disaster status. The average ICI return did not differ significantly before and after the two events. The market reaction test found no significant abnormal returns around the two events. The Covid-19 pandemic had an impact on the Indonesian capital market, marked by a decline in the ICI, although investors in the capital market did not react excessively, so there was no significant abnormal return.

Hyperparameter Optimization of Long-Short Term Memory using Symbiotic Organism Search for Stock Prediction

Producing the best possible predictive result from long-short term memory (LSTM) requires hyperparameters to be tuned by a data scientist or researcher. A metaheuristic algorithm was used to optimize hyperparameter tuning and reduce the computational complexity to improve the manual process. Symbiotic organism search (SOS), which was introduced in 2014, is an algorithm that simulates the symbiotic interactions that organisms use to survive in an ecosystem. SOS offers an advantage over other metaheuristic algorithms in that it has fewer parameters, allowing it to avoid parameter determination errors and produce suboptimal solutions. SOS was used to optimize hyperparameter tuning in LSTM for stock prediction. The stock prices were time-series data, and LSTM has proven to be a popular method for time-series forecasting. This research employed the Indonesia composite index dataset and assessed it using root mean square error (RMSE) as a key indicator and the fitness function for the metaheuristic approach. Genetic algorithm (GA) and particle swarm optimization (PSO) were used as benchmarking algorithms in this research. The hybrid SOS-LSTM model outperformed GA-LSTM and PSO-LSTM with an RMSE of 78.799, compared to the GA-LSTM model with an RMSE of 142.663 and the PSO-LSTM model with an RMSE of 529.170.

Causality Analysis of the Brent Oil Prices, the Gold Prices and the Exchange Rates (USD/IDR) on Indonesia Composite Index (Empi rical Study January 2016 – April 2021)

Global economic conditions are in turmoil due to the COVID-19 pandemic since 2019. Many policies are implemented to suppress the spread of the virus, which then has an impact on economic activity. It influences investors' attitudes and changes the choices in investing. This study aims to analyze the causal relationship between the Indonesia Composite Index (ICI) and gold prices, oil prices and the exchange rate (USD/IDR). This study used monthly data for the period January 2016 – April 2021 with an analysis of the Vector Error Correction Model (VECM), which is a restricted VAR model with the Eviews 10 tool. The long-term VECM estimation results show that Brent oil prices and gold prices have a negative effect while the exchange rate (USD/USD) IDR) the ICI. The Granger causality test shows that the ICI has a one-way causality relationship with the exchange rate (USD/IDR).

Causality Analysis of the Brent Oil Prices, the Gold Prices and the Exchange Rates (USD/IDR) on Indonesia Composite Index (Empi rical Study January 2016 – April 2021)

Causality Analysis of the Brent Oil Prices, the Gold Prices and the Exchange Rates (USD/IDR) on Indonesia Composite Index (Empi rical Study January 2016 – April 2021)

PASAR MODAL INDONESIA: ANALISIS PERTUMBUHAN SELAMA PANDEMI COVID-19

The purpose of this study is to evaluate the impact of the COVID-19 pandemic on the Indonesian Capital Market. Data analysis uses descriptive statistics by comparing the Indonesia Composite Index (ICI) growth, market capitalization, transaction volume, investors, total securities ownership, and value trading per month during 2020 and comparing them to 2019. The results show that the COVID-19 pandemic affects the growth and development of the Indonesian capital market during 2020 in the January-November period when compared to the previous year in the same period. The analysis results show that the ICI, market capitalization, transaction volume, trade value, and total securities ownership grew negatively during 2020 in the January-November period when compared to the same period in the previous year. However, amidst the negative growth of the Indonesian capital market, there is good news, namely that the number of the individual investors has increased during 2020 in the January-November period.

Do pandemic COVID 19 and business cycle influence the Indonesia composite index?

The impact of the Covid 19 pandemic on the Indonesian economy was worse than the impact of the global financial crisis in 2008. We can see the business cycle from the contraction of economic growth, volatility of the exchange rate, and inflation. The shocks to these business cycles could create systemic risks and influence financial system stability. Financial system stability is more sensitive to shocks from changes in one of the macroprudential indicators, namely the Indonesia Composite Index (ICI). This study will examine the effect of the Covid 19 pandemic and the business cycle on ICI. This research was conducted in Indonesia using quarterly secondary data from 2008-2021. The data was obtained from Bank Indonesia. The model used is the Auto Regressive Distributed Lag (ARDL) model. The purpose of using this model is to see the long-term and short-term relationships between the variables studied. The results show that the Covid 19 pandemic and business cycle variables such as; economic growth, exchange rate, and inflation have a relationship on the ICI both in the short and long term. The decline in economic growth during a crisis is difficult to avoid. However, the government must continue to accelerate the economic growth so it does not decline sharply during the crisis. Monetary authorities must also maintain exchange rate stability and inflation so it does not fluctuate unsteadily.

Determinants of The Indonesia Composite Index Movement in The Covid-19 Pandemic Era

The Indonesia Composite Index, or the composite stock price index (IDX Composite), experienced a sharp decline at the beginning of the Covid-19 pandemic, from 6,323 in January to 3,937 in March 2020. If IDX composite continues to plummet in the long term, the worst impact will hit the performance of issuers or public companies. This study aims to analyze the factors that influence the IDX Composite in the era of the Covid-19 pandemic. This research is a quantitative study using ARCH-GARCH analysis techniques and data testing using the EViews program. The population used in this study was IDX Composite, and the sample was IDX Composite from January to December 2020. The results showed that the rupiah exchange rate partially had a significant negative effect on the IDX Composite. Meanwhile, Bank Indonesia's interest rate has a significant and positive effect on the IDX Composite in the era of the Covid-19 pandemic. Meanwhile, Inflation did not significantly affect the IDX Composite in the era of the Covid-19 pandemic.

A New Approach for Robust Mean-Variance Portfolio Selection Using Trimmed k-Means Clustering

In this study, we consider the data preprocessing using trimmed k-means clustering for robust mean-variance portfolio selection. The proposed method trims the outliers in the data preprocessing stage. The optimum portfolio is formed by selecting the stock representation for each cluster using the Sharpe ratio. The optimum portfolio formation is accomplished by robust fast minimum covariance determinant (FMCD) and robust S mean-variance (MV) portfolio model. In the empirical experiment, we use fundamental trading data for the year 2017 (to form the clusters) and daily closing price data of LQ45 index stocks from August 2017 to July 2018 taken from the Indonesian Stock Exchange to form the optimum portfolio. As benchmark for portfolio performance formed in this study, we use the performance of the Indonesia Composite Index (ICI). The results reveal that the proposed method can reliably obtain the optimum portfolio and solve the outliers problem. Moreover, the comparison stage shows that the combination of trimmed k-means clustering and robust portfolio model is better in forming the optimum portfolio than the combination of k-means clustering and robust MV portfolio model. Finally, we also find that the combination of trimmed k-means clustering (with α=10%) and robust FMCD MV portfolio model outperforms portfolios produced by other methods.

Determinant of Indonesia Composite Index During Covid-19 Pandemic

This study aims to determine and analyze the influence in the long run and short run interest rates, IDR exchange rates, inflation and oil prices on ICI on the Indonesia Stock Exchange during the Covid-19 pandemic. The analysis method used is with Error Correction Models (ECM) analysis. The data used in the study is time series data that is compiled periodically on a weekly basis from January 2019 to April 2021. The results showed that interest rates have a significant effect in the long run and have no significant effect in the short run, the IDR exchange rate and world oil prices have a significant effect in the long and short run, while inflation has no significant effect in the long run and short run on ICI.
 
 Penelitian ini bertujuan untuk mengetahui dan menganalisis pengaruh dalam jangka panjang dan jangka pendek suku bunga, kurs IDR, inflasi dan harga minyak duni terhadap IHSG di Bursa Efek Indonesia pada saat pandemi Covid-19. Metode analisis yang digunakan yaitu dengan analisis Error Correction Models (ECM). Data yang dipakai dalam penelitian adalah data time series yaitu data yang disusun berkala secara mingguan dari Januari 2019 sampai dengan April 2021 secara runtun waktu. Hasil penelitian menunjukkan, suku bunga berpengaruh signifikan dalam jangka panjang dan tidak berpengaruh signifkan dalam jangka pendek, kurs IDR dan harga minyak dunia berpengaruh signifikan dalam jangka panjang dan jangka pendek, sedangkan inflasi tidak berpengaruh signifikan dalam jangka panjang dan jangka pendek terhadap IHSG.

The Role of E-money on Business Growth in Indonesia

The objective of this research are to analyze the differences in business growth in Indonesia before and after the e-money, era and to analyze the the role of e-money in business growth in Indonesia.The type of this research is quantitative with comparative and explanatory method. The data used in this research is e-money and Indonesia Composite Index (also known as IHSG). Data analysis technique used in this research are paired t-test and regression. The result shows that there is significant difference of business growth in Indonesia between before and after e-money era. Furthermore, based on regression analysis shows that e-money has significant effect on business growth in Indonesia.

Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Autoregressive Integrated Moving Average (ARIMA)

There are various types of investment in Indonesia, one of which is the Indeks Harga Saham Gabungan (IHSG) or in English it is called the Indonesia Composite Index, ICI, or IDX Composite. IHSG is an important parameter to consider when making an investment considering that IHSG is a joint stock. This study aims to predict the price of the IHSG with data mining techniques using an algorithm that can be used as a reference for investors when making an investment. ARIMA is a model for generating estimates from historical data. Data in this study were collected from the monthly IHSG from January 4, 2010 - November 26, 2019. Based on the correlation plot, two autocorrelations (lag 1, lag 32) were found to be significant. This model can predict with an average percentage error of 0.004 so that this prediction is considered good enough to predict the stock price of the IHSG.

Anlysis Of Factors Affecting Price Movements Of The Joint Stock Index On Indonesia Stock Exchange In 2015-2019

The study aims in determining and analyzing the effect ofInflation Rate, Exchange rate, Interest rate and Oil Prices on Indonesia Composite Index simultaneously and partially on the Indonesian Stock Exchange in 2015-2019. The population that is used in this study is Indonesia Composite Index on the Indonesian Stock Exchange (IDX) period in 2015 to 2019 which totaling 60 months. The samplex selection uses a saturated sampling technique so all polulation are used as samples. This research used quantitative descriptive by testing classic assumption tests and multiple linear regression analysis using IBM SPSS Statistics 21 aplications.Simultaneously Inflation Rate, Exchange rate, Interest rate and oil price have an effect on Indonesia Composite Index. Partially, Infaltion Rate and Oil Price have a significant effect on Indonesia Composite Index while Exchange Rate, Interest Rate have no effect on Indonesia Composite Index in Indonesia Stock Exchange Periode 2015-2019.

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

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.

DETERMINANT YIELD GOVERNMENT BOND TRADED FROM 2016 TO 2021

A Government Bond is a significant financial instrument for the state budget of Indonesia Government. It has become important and quite attractive among other financial instruments. Using BI 7-Day Repo Rate, Inflation, Money Supply, Indonesia Composite Index, and USD/IDR Exchange Rate as independent variables, this research has been done to determine Government Bond Yield. The research population is bond issued by Indonesia Government. The sampling is 10-years traded Government Bond fixed rate serries during 2016-2021 with 300 data collected which since 2020 Indonesia has been facing the Covid-19 pandemic that affected the global economy and financial markets. This research finds that Government Bond Yield positively affected by BI 7-Day Repo Rate, Inflation Rate, and USD/IDR Exchange Rate, while negatively affected by Money Supply, yet Indonesia Composite Index and Covid-19 pandemic has no effect on the Government Bond Yield.

DO FOREIGN FUNDS AFFECT INDONESIAN CAPITAL MARKET LIQUIDITY?

Trading Volume (Liquidity) is the perfect complement to returns. Therefore, research regarding volume becomes an important thing in the capital market. Various factors that influence the Liquidity (TV) are bid-ask spread; interest rates, exchange rates, and foreign funds. The study uses Amihud Illiquidity Ratio (AIR) as a proxy for liquidity. The data is the Indonesian Composite Index for liquidity, and all variables, daily basis; 2016-2019 period, divided into odd and even semester, with the linear regression model. The results showed that the bid-ask spread decreases trading volume, exchange rate and interest rates had a positive effect on AIR, where the greater the depreciation and the higher the interest rates, the lower the liquidity. For 2019, the foreign fund has a negative coefficient, which shows foreign funds increase capital market liquidity. In 2019, there will be a strengthening effect between foreign funds and the exchange rate
 Keyword: liquidity; trading volume; Amihud Illiquidity Ratio; bid-ask spread; exchange rate.

Does The Efficient Market Theory In The Weak Form Exist? Evidence From Indonesia

In the weak-form market efficiency theory, investors cannot predict the movement of all prices because of randomness. This circumstance happens because of a quick market reaction to new information. Conversely, suppose the market is not efficient in this shape; in that case, the investors can obtain an abnormal return. One of the reasons is the thin market, where many inactive stocks to be traded are available. Based on these issues, this research intends to examine this theory by employing runs testing on the daily returns of the Indonesia Composite Index (ICI) between January 2014 and December 2018 for each year and a whole. Once performing this test, this research demonstrates that the daily returns of the ICI are random for both situations. By denoting these facts, this research concludes that the capital market in Indonesia is efficient in a weak form and experiences a decrease in the thin level, reflected by the escalation in trading frequency, volume, and value, as well as the number of dynamic shares transacted. This research suggests that investors without sufficient information should utilize the service of the securities analysts to select the stocks they buy and sell to get the capital gain. 
 
 Keywords – an efficient market in the weak form; market index return; runs test; thin market