Stock Price Index Research Articles

COMPARISON OF APARCH-TYPE MODELS: DOES THE CONTINUOUS AND JUMP COMPONENTS OF REALIZED VOLATILITY IMPROVE THE FITTING?

This study aims to extend an APARCH-X(1,1) model to the APARCH-CJ(1,1) by separating the exogenous variable X into two components: continuous and discontinuous (jump). The study was based on the application of models to 1-min intraday high-frequency data from the Tokyo Stock Price Index from 2004 to 2011, where its dependent variable is daily return and its exogenous variability is Realized Volatility. As a basic framework, the return errors follow a Normal distribution. An Adaptive Random Walk Metropolis (ARWM) method was constructed in the Markov Chain Monte Carlo algorithm to estimate model parameters so that the model fits the observed return time series. By visual inspection, the parameter trace plots showed good convergence of the Markov chains, indicating that the ARWM method is efficient in estimating the studied models. Based on the results of the Akaike Information Criterion for model fitting to data, this study found that APARCH-CJ(1,1) is inferior to APARCH(1,1).

Contagion Effect Pasar Finansial Antar Negara ASEAN

This study aims to determine whether stock price indices influence each other in the ASEAN region and whether there is a contagion effect between financial markets in the ASEAN region. The data used in this research is time series data from January 3, 2017 to December 30, 2021 from the official websitesinvesting.com and wsj.com. This research is a quantitative research using the GARCH analysis model. The results of the study show that stock price indices influence each other in the ASEAN region and there is a contagion effect between financial markets in the ASEAN region

Analysis of Macroeconomi Factor Effect on the Stock Price Index in Jakarta Islamic Index

The country’s economy slowdown causes the affected exchange rate or exchange rate to weaken, and Bank Indonesia’s interest rate policy at the time of the economic downturn decreases, it has an impact on the stock index in the JII. This research aims to determine the influence of macroeconomics on the IHS in JII. The method used is quantitative research, with an analysis model using the VAR technique. The data used is secondary data of a time series. The research results show that the influence of the ISSI and BI rate responded positively, while inflation responded positively. The exchange rate variable responded negatively to JII shocks until the end of the period. The results of the FEVD test show that the largest contribution was JII, inflation, ISSI, and BI rate, and the smallest contribution to JII was the exchange rate. Implication and recommendation this research are Bank Indonesia as the institution responsible for controlling inflation, can maintain inflation stability, lower the exchange rate against the dollar and maintain interest rate stability.

The Influence of Macroeconomics and the EIDO Index on Stock Price Movements

In general, stock price movements in Indonesia have been described with the Composite Stock Price Index (IHSG). This study aims to determine the impact of the variables Exports, Exchange Rates, Money Supply, Interest Rates, and the EIDO Index (MSCI Indonesia) on stock price movements. This data used secondary data with a timeseries data. Observations were made on the closing price of the Composite Stock Price Index each month, and involved macroeconomic factors such as, Export rate, Exchange Rates, Interest Rates, Money Supply, as well as the EIDO Index variable for the period of January 2019 to June 2023. In this study, the data analysis model used is Multiple Linear Regression, and the data is processed using E-views 10. In the results of this study, it was found that the level of exports, money supply, exchange rate, EIDO index respectively have a significant effect on positive direction to stock price movements. Meanwhile, interest rates have no significant effect on stock price movements.

Comparative Analysis of Stock Price Index Before and After The Announcement of Silicon Valley Bank (SVB) Bankruptcy

The recent bankruptcy of Silicon Bank Valley (SVB) has sent shockwaves globally, given SVB’s status as a top-16 bank in the United States specializing in financing startups. The collapse of SVB has ignited concerns about a potential crisis, prompting a crucial research initiative to assess the likelihood of such a scenario. Since SVB’s bankcruptcy announcement, a notable occurence has been the sustain depression of the Composite Stock Price Index (IHSG) for several days. Consequently, this study aims to scrutinize and assess whether there are statistically significant variations in the stock price index before and after SVB’s bankcrupty announcement, not only within Indonesia but also throughout the ASEAN region. Utilizing quantitative approach, this research relies on secondary data in the form of stock price indices from multiple ASEAN stock exchanges. The observation period spans 14 days before and after March 10, 2023 (the announcement date). The analysis reveals that the stock price indices of JKSE, SETI, KLSE, and STI exhibited significant variations before and after the SVB announcement. However, the PSEI stock price index demonstrated no discernible differences. These findings shed light on the potential interconnectedness of SVB’s bankcruptcy with stock market movements in the ASEAN region.

Revisiting the Effect of Dividend Policy on Firm Performance and Value: Empirical Evidence from the Korean Market

This study investigates the relationship between dividend policy, firm performance, and value within the Korean market, taking into account the unique context of Chaebol ownership structures. Utilizing a robust dataset of 5478 observations from the Korean Composite Stock Price Index, our empirical analysis employs advanced regression models, revealing distinctive effects of various dividend policy measures through the lenses of interest alignment and managerial entrenchment hypotheses. Surprisingly, while cash dividend payments exhibit a robust positive impact on Tobin’s Q and market-to-book ratios, suggesting an overall positive link with market valuations, a closer inspection reveals divergent impacts for Chaebol and non-Chaebol firms. In Chaebol entities, dividend policy proxies consistently demonstrate positive effects on performance metrics, aligning with the interest alignment hypothesis and highlighting strategic signaling efforts. Conversely, non-Chaebol firms exhibit intriguingly negative impacts, supporting the managerial entrenchment hypothesis and implying potential challenges to market value. Firms should prioritize transparent communication on dividend policies for improved investor decision making and enhanced corporate governance in the dynamic Korean market.

PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN MODEL INTERVENSI FUNGSI PULSE

The intervention model is one model that is frequently used to explain how interventions from both internal and external sources can lead to dramatic fluctuations in a time series of data. The Composite Stock Price Index, known as the IDX Composite, is an index that tracks all stock price performance. For the Composite Stock Price Index from 2 October 2020 to 6 June 2022, daily close price data are used in this study. The data showed a sharp reduction starting on 9 May 2020 (T=386) and lasting for the following 4 days, which made the pulse function the likely intervention model. Rising interest rates and high inflation figures from the United States are to blame for the drop in IDX Composite close price. In addition, a lot of profit-taking was done because of the Eid holidays and the expectation of a substantial increase in COVID-19. The best intervention model created is ARIMA ([3],1,0) with an intervention order of b=0, r=0, and s=11, which can then be used to forecast Composite Stock Price Index for the following period. This is based on the outcomes and analyses. The sMAPE value in the research utilizing this model was 0.98%, suggesting very strong forecasting capabilities.

Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression.

This study proposes a control chart that monitors conditionally heteroscedastic time series by integrating the Huber support vector regression (HSVR) and the one-class classification (OCC) method. For this task, we consider the model that incorporates nonlinearity to the generalized autoregressive conditionally heteroscedastic (GARCH) time series, named HSVR-GARCH, to robustly estimate the conditional volatility when the structure of time series is not specified with parameters. Using the squared residuals, we construct the OCC-based control chart that does not require any posterior modifications of residuals unlike previous studies. Monte Carlo simulations reveal that deploying squared residuals from the HSVR-GARCH model to control charts can be immensely beneficial when the underlying model becomes more complicated and contaminated with noises. Moreover, a real data analysis with the Nasdaq composite index and Korea Composite Stock Price Index (KOSPI) datasets further disclose the validity of using the bootstrap method in constructing control charts.

The volatility mechanism and intelligent fusion forecast of new energy stock prices

AbstractThe new energy industry is strongly supported by the state, and accurate forecasting of stock price can lead to better understanding of its development. However, factors such as cost and ease of use of new energy, as well as economic situation and policy environment, have led to continuous changes in its stock price and increased stock price volatility. By calculating the Lyapunov index and observing the Poincaré surface of the section, we find that the sample of the China Securities Index Green Power 50 Index has chaotic characteristics, and the data indicate strong volatility and uncertainty. This study proposes a new method of stock price index prediction, namely, EWT-S-ALOSVR. Empirical wavelet decomposition extracts features from multiple factors affecting stock prices to form multiple sub-columns with features, significantly reducing the complexity of the stock price series. Support vector regression is well suited for dealing with nonlinear stock price series, and the support vector machine model parameters are selected using random wandering and picking elites via Ant Lion Optimization, making stock price prediction more accurate.

The effect of macro variables on Indonesian stock exchanges in pandemic COVID-19

During the COVID-19 pandemic, it is crucial for stock traders inside a specific nation to conscientiously examine and assess the current monetary conditions and macroeconomic data. This study examined the influence of macroeconomic factors, namely unemployment, inflation, interest rates, and exchange rates, on the Indonesian stock market index within the framework of the COVID-19 pandemic. This research employs quantitative approaches, notably autoregressive distributed lag (ARDL) analysis, to evaluate the dynamics of time series data. The current analysis shows that a variety of factors, such as inflation, interest rates, and exchange rates, have an impact on the stock price index. Furthermore, the results of this study indicate that the deviation from the long-term equilibrium in the short term is rectified at a monthly rate of 33.47%. As a result of completing an exhaustive analysis of this study, it is of the utmost importance for the Indonesian government to concentrate its attention on the issues of unemployment, inflation, interest rates, and currency rates during the COVID-19 epidemic. This is done with the intention of achieving stability on the stock exchange. In addition, it is strongly suggested that investors do a comprehensive analysis of the volatility of stock prices by incorporating a variety of macroeconomic elements.

Cointegration Analysis of Macro Economic Factors, Index FTSE, on the Indonesian Stock Exchange Period 2017-2022

This research was explain about analyzing and knowing the development of the global exchange stock price FTSE index by analyzing to the Indonesia’s Stock Exchange (BEI) and to determine cointegration between FTSE 100, data Inflation, USD/IDR exchange Rates, and Interest Rate. Combined to IHSG. Data collection method was data from 5 years starting from 2017 to 2022 on a monthly basis. The sampling method is nonprobability sampling with a sampling technique, namely purposive sampling. The analysis was carried out using the Johansen Cointegration Test and VECM test which processed by Eviews 10 software. Results showed no significancy short-term relationship between FTSE 100, Inflation, USD/IDR Exchange Rates, Interest Rate and Composite Stock Price Index (IHSG ). But, on the other side have a significant long-term relationship between them.

Multi-Layer Perceptron-Based Classification with Application to Outlier Detection in Saudi Arabia Stock Returns

We aim to detect outliers in the daily stock price indices from the Saudi Arabia stock exchange (Tadawul) with 2026 observations from October 2011 to December 2019 provided by the Saudi Authority for Statistics and the Saudi Central Bank. We apply the Multi-Layer Perceptron (MLP) algorithm for detecting outliers in stock returns. We select the inflation rate (Inflation), oil price (Loil), and repo rate (Repo) as input variables to the MLP architecture. The performance of the MLP is evaluated using standard metrics for binary classification, namely the false positive rate (FP rate), false negative rate (FN rate), F-measure, Matthews correlation coefficient (MCC), accuracy (ACC), and area under the ROC curve (AUC). The results demonstrate the efficiency and good performance of the MLP algorithm based on different criteria tests.

Robust monitoring conditional volatility change for time series based on support vector regression

This study considers a robust monitoring procedure aimed at detecting an anomaly of conditional volatility from sequentially observed time series following a (nonlinear) generalized autoregressive conditional heteroscedastic (GARCH) model. We employ a specifically designed cumulative sum (CUSUM) method hybridized with the asymmetric Huber support vector regression, named AHSVR. AHSVR-GARCH model provides an effective way to model nonlinear GARCH time series while significantly augmenting the performance of the proposed monitoring process in terms of stability and detection ability. Monte Carlo simulations illustrate the functionality of AHSVR-GARCH monitoring scheme, demonstrating AHSVR-GARCH model’s superiority over the standard SVR-GARCH model when monitoring GARCH-type time series. Data analysis using the S&P 500, NASDAQ index, and Korea Composite Stock Price Index (KOSPI) also affirms our method’s efficacy in real-world applications.

Determinants Indonesia Composite Index on the Indonesia Stock Exchange

This study aimed to determine and analyze the movement of the composite stock price index by observing the variables of inflation, exchange rates, interest rates, and the amount of money circulating in companies listed on the Indonesia Stock Exchange. The population in this study is all monthly time series data, including the Composite Stock Price Index (CSPI) for the period January 2019 to December 2020, with as many as 24 months of observation for each variable. The sample selection method uses a saturated sample because the Composite Stock Price Index includes the average stock price of all companies listed on the Indonesia Stock Exchange. The data were processed by quantitative descriptive techniques using multiple linear regression statistical tests. The results of this study indicate that inflation and interest rates positively affect the Composite Stock Price Index. Meanwhile, the Exchange Rate and the Money Supply have no effect on the Composite Stock Price Index on the Indonesia Stock Exchange. In the Covid-19 pandemic, economic growth slowed, causing the Indonesian economy to decline.

THE IMPACT OF THE COVID-19 PANDEMIC ON THE STOCK PRICES OF MANUFACTURING COMPANIES ON THE INDONESIA STOCK EXCHANGE

The coronavirus, or COVID-19, is caused by the SARS-CoV-2 virus, first discovered in China in late 2019 and as of June 2021. Since then, the virus has spread worldwide. Indonesia has reported more than 31,000 cases of COVID-19 (Gugus Tugas Percepatan Penanganan COVID-19, 2020), making it the second-highest country in ASEAN, according to CNN Indonesia (2022). The global tally has surpassed 6.7 million cases, with over 395,000 fatalities. This pandemic has not only affected the health sector but has also impacted various other economic sectors, including the stock market. Developing countries like Indonesia have felt the repercussions, evident in the movements of the Composite Stock Price Index (IHSG). Studying the IHSG's trends before and during the COVID-19 pandemic is crucial as a guide for governments and companies in managing foreign fund flows, maintaining index stability, and preserving economic value (Nurmasari, 2020). In this context, the Indonesia Stock Exchange (PT. Bursa Efek Indonesia) and the Financial Services Authority (Otoritas Jasa Keuangan or OJK) have introduced new policies to prevent a decline in the capital market caused by Covid-19. The decision by the Indonesia Stock Exchange states that these measures are taken to reduce the spread of the virus.

Pengaruh Harga Minyak Dunia Dan Makroekonomi Terhadap Pergerakan Indeks Harga Saham Gabungan (IHSG) Di Bursa Efek Indonesia (BEI) Periode 2018-2022

Abstract
 This study aims to determine the effect of world oil prices and macroeconomics on the movement of the Jakarta Composite Index (IHSG) on the Indonesian Stock Exchange (IDX) for the 2018-2022 PERIOD. By using the multiple linear regression analysis method, it can be seen how the relationship between world oil and macroeconomic variables as independent variables with the composite stock price index variable as the dependent variable. The data used in this study is monthly data for all variables in the 2018-2022 period. Then from the research results it was found that the world oil price variable and the inflation variable had a positive and significant effect on the JCI. The exchange rate variable has a negative and significant effect on the JCI. While the interest rate variable has no effect on the JCI variable partially..

A Hybrid Model for Forecasting Realized Volatility Based on Heterogeneous Autoregressive Model and Support Vector Regression

In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the HAR model, and the residuals are used to train the SVR model. The residual component is then predicted using the SVR model, and the results from both the HAR and SVR models are combined to obtain the final prediction. The second model is a weight-based model, which is a combination of the HAR and SVR models and uses the same independent variables and dependent variables as the HAR model; we adjust the contribution of the two models to the predicted values by giving different weights to each model. In particular, four volatility models are used in RV forecasting as basic models. For empirical analysis, the RV of returns of the Tokyo stock price index and five individual stocks of TOPIX 30 is used as the dataset. The empirical results reveal that according to the model confidence set test, the weight-type model outperforms the HAR model and the residual-type HAR–SVR model.

RESEARCH ON EMPIRICAL DISTRIBUTIONS OF INDEX RETURNS: EVIDENCE FROM INTERNATIONAL MARKETS

The paper reviews the currently held and quite persistent assumptions about the statistical distributions behind financial market returns used to make inferences for the purposes of risk management. We use data from 30 years of daily observations from the 15 largest and oldest stock price indices around the world and test the return data against the most popularly assumed distributions, breaking the data arrays into 100, 250, 750, and 250-day estimation windows at 0.95 and 0.99 confidence levels. We find returns in different estimation windows to be inconsistent with single distributions. When returns in short windows fit Gaussian distributions better, returns in longer estimation windows do not fit any suggested distributions at all. The normal distribution seems to be a better choice for 100-day windows, with longer window rejection rates for all suggested distributions being too high for reliable inference.

Complex network analysis of global stock market co-movement during the COVID-19 pandemic based on intraday open-high-low-close data

This study uses complex network analysis to investigate global stock market co-movement during the black swan event of the Coronavirus Disease 2019 (COVID-19) pandemic. We propose a novel method for calculating stock price index correlations based on open-high-low-close (OHLC) data. More intraday information can be utilized compared with the widely used return-based method. Hypothesis testing was used to select the edges incorporated in the network to avoid a rigid setting of the artificial threshold. The topologies of the global stock market complex network constructed using 70 important global stock price indices before (2017–2019) and after (2020–2022) the COVID-19 outbreak were examined. The evidence shows that the degree centrality of the OHLC data-based global stock price index complex network has better power-law distribution characteristics than a return-based network. The global stock market co-movement characteristics are revealed, and the financial centers of the developed, emerging, and frontier markets are identified. Using centrality indicators, we also illustrate changes in the importance of individual stock price indices during the COVID-19 pandemic. Based on these findings, we provide suggestions for investors and policy regulators to improve their international portfolios and strengthen their national financial risk preparedness.

Assessing the Influence of ESG Factors on Stock Prices: An Analysis of Nifty 50 Companies

This study examines the relationship between sustainability reporting, stock performance, and ‘Environmental’, ‘Social’, and ‘Governance’ (ESG) characteristics of Indian companies. The goal is to clarify the influence of environmentally conscious activities on enterprises. This research addresses the lack of knowledge regarding the impact of ESG ratings on stock performance in the Indian market, which has been overlooked in previous studies that mostly focused on the US and European markets. The study employs a quantitative methodology to gather stock prices and ESG ratings for companies in the Nifty 50 index. It then constructs portfolios that reflect the companies with the greatest and lowest ESG scores. The data is examined by OLS regression and Pearson correlation tests, indicating that ESG has a detrimental effect on stock prices in India. This suggests that enhancing ESG scores may not be advantageous for firms. Nevertheless, the study reveals disparities between companies with high and low ESG scores in relation to Sustainable Development Goals (SDGs), regulatory frameworks, anti-corruption initiatives, and ESG methodology. This presents opportunities for Indian businesses to improve their sustainable practices and generate value for stakeholders.