Stock Price Index Research Articles

Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering

In this paper, we propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose grouping the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents.

Pengaruh Faktor-Faktor Ekonomi Makro terhadap Jakarta Islamic Index (JII) Periode 2010-2019

This study aims to determine and analyze the influence of macroeconomic factors such as Gross Domestic Product (GDP), Inflation Rate, SBI Interest Rate, Rupiah / US Dollar exchange rate on the JII (Jakarta Islamic Index) Stock Price Index on the Indonesia Stock Exchange for the period 2010-2019. This study uses an associative quantitative approach which is intended to examine the relationship and influence between macroeconomic factors variables on the JII stock price index variable. Meanwhile, the analysis method used is multiple linear regression. The results of this study indicate that only the rupiah/US dollar exchange rate has a significant negative effect on the JII stock price index. While macroeconomic factors such as Gross Domestic Product, Inflation Rate, SBI Interest Rate have no significant effect on the JII stock price index.

Study on the Type of the type of wealth and tax

The Japanese economy is facing a new phase from a long-term recession over the past 30 years to an annual economic growth of around 2%. With the recovery of the Japanese economy, not only foreigners but also Japanese capital are returning to Japan. Already, the Japanese Composite Stock Price Index has exceeded 39000 points before the bubble burst. Changes in the yen exchange rate are also inducing the inflow of foreign capital into Japan. In this economic trend, investors are paying attention to the Japanese capital market, and in particular, the wealthy in Japan are focusing on the Japanese domestic market to increase their assets. In particular, the Japanese financial market is attracting attention as a relatively safe asset due to the unstable environment at home and abroad. According to this trend, asset management of the wealthy in Japan is also carried out in various ways. Sometimes there are considerable losses due to asset management failures. That is why it has emerged recently to use various tax savings. Companies and individuals show the same behavior, but on the contrary, the government sometimes strengthens monitoring in various ways to prevent a decrease in tax revenue. This phenomenon has recently become common in Japan. This paper will help the wealthy in Japan understand their asset management and the Japanese government's response by examining the types of asset management and measures to manage assets through tax savings. It is also expected that problems with asset management and implications for government policies will be used as reference for various countries. Therefore, this paper will focus on the asset management and tax savings of the wealthy in Japan.

Determinants JCI of the Indonesian Stock Exchange (Comparison of IHSG Projection of OLS Method and ARIMA and GARCH Methods for the Period 2015-2023)

This study aims to examine the effect of interest rate, exchange rate, inflation, gross domestic product and LQ-45 index variables on the movement of the Composite Stock Price Index using the ordinary least square method. Comparison in predicting the movement of the stock price index through three models, and then compared with the projection results with OLS, ARIMA and GARCH methods. This research was conducted using monthly data on JCI, interest rates, exchange rates, inflation, gross domestic product and the LQ-45 index for the period May 2015 - December 2023. From the results it can be concluded that interest rates have a significant negative effect on JCI, exchange rates have a significant negative effect on JCI, inflation has a significant negative effect on the value of JCI, gross domestic product has a significant negative effect on JCI and the LQ-45 index has a positive effect on JCI. The results of the comparison of JCI projections with 3 models showed that the OLS model offers more accurate results and has the lowest MAPE value in predicting stock price movements.

PREDICTION INDONESIA COMPOSITE INDEX USING INTEGRATION DECOMPOSITION- NEURAL NETWORK ENSEMBLE DURING VUCA ERA

The Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) era causes turmoil in the capital markets, stocks, commodities, etc. The impact is a decline in the Composite Stock Price Index (IHSG) in 2020. Therefore, future data is needed to inform investors and business people when making portfolio decisions. This paper develops a decomposition and Neural Network (NN) integration model to predict ICI during the VUCA era. The results are presented empirically to show the model's effectiveness in reducing prediction errors. First, the actual data is converted into three components; second, with the Neural Network Ensemble (NNE) approach where the initial step of decomposition results is trained using artificial NN with architecture, training data, and topology to produce individual networks; The output is selected using Principal Component Analysis (PCA) and becomes input to the ensemble model, then combined using a simple average and weighted average. The empirical results from ICI predictions illustrate: (1) decomposition has the potential to overcome data that is characterized by high volatility; (2) NNE is able to reduce errors (MSE≤0.100e-4, MAE≤0.01) compared to individual networks (MSE=0.0024 MAE=0.0376); (3) ensemble combinations using weighted averages (MSE≤3.00e-5,MAE≤0.002) are superior to simple averages (MSE≤5.00e-5,MAE≤0.01); (4) the integration carried out shows effectiveness in predicting ICI and provides better prediction results.

PM10 AIR QUALITY INDEX MODELING USING ARFIMA-GARCH METHOD: BUNDARAN HI AREA OF DKI JAKARTA PROVINCE

Air quality is an essential factor in urban life, and its’ assessment often relies on the concentration of measurable air pollution parameters. One critical parameter is Particulate Matter (PM), particularly PM10, which comprises solid or liquid particles dispersed in the air from various sources. One of the methods employed for predicting stock index prices is ARFIMA. ARFIMA is used to model long memory data characterized by a slowly decreasing Autocorrelation Function (ACF) plot (hyperbolic) or a difference value in the fractional from. This method is widely used due to its ability to handle nonstationarity issues in time series. However, the time series data often contain heteroskedasticity problems. Data with heteroscedasticity are then further addressed using the GARCH model, because it can model volatility changes occurring over longer periods and capture the persistence of volatility. The ARFIMA-GARCH model can explain long-memory patterns in time series data and address heteroscedasticity issues. The data are sourced from the Jakarta open data web, which is integrated with DLH DKI Jakarta Province. The aim of this research was to forecast the PM10 air quality index at the Bundaran HI Area in the Province of DKI Jakarta for the next 14 days, from January 1st to January 14th, 2021, using an ARFIMA model enhanced with GARCH. The analysis reveals that the best model is ARFIMA ([17], d, [1])-GARCH (1,1). Forecasting using this model resulted in a MAPE of 3.47%, indicating that the model is highly capable of forecasting several periods.

Comparative Analysis Of Stock Prices, Stock Returns, And Stock Liquidity Before And After The Stock Split In Banking Companies Listed On The Idx In 2021-2023

This research seeks to compare stock prices, abnormal returns (as a proxy for stock returns), and trading volume activity (TVA) (as a proxy for liquidity levels) before and after a stock split. Utilizing an event study approach, observations were conducted on the average stock prices, abnormal returns, and TVA for five days before and five days after the stock split. Secondary data were employed with purposive sampling techniques. The data utilized in this study comprise the stock split announcement dates, which are treated as the event date (t+0), the daily closing stock prices of companies undergoing stock splits within the observation period, the daily traded share volume, the total shares outstanding, and the daily Composite Stock Price Index (IHSG). The sample consisted of three banking companies listed on the IDX that carried out stock splits between 2021 and 2023. The results reveal no significant differences in stock prices, abnormal returns, and TVA before and after the stock split.

COMPARISON OF VECTOR AUTOREGRESSIVE (VAR) AND VECTOR ERROR CORRECTION MODELS (VECM) FOR THE COMPOSITE STOCK PRICE INDEX (JCI) IN INDONESIA

The capital market has a major role for economic development which is able to encourage capital formation and sustain economic growth. Financial liberalisation between countries in the world will make capital markets in each country interconnected. If there is a large movement in the global stock index, it will also have an impact on the JCI in Indonesia. In addition to global indices, macroeconomics is also seen as capable of influencing capital markets. Inflation and interest rates are macroeconomic components that can be used. The purpose of this research is to be able to see the relationship between one variable and another, namely the Composite Stock Price Index (JCI), Dow Jones Industrial Average (DJIA), inflation rate, interest rate. The VAR/VECM analysis approach was used in this study. Data was taken from 2017 to 2023. The data used for modelling is 84 observations. Based on the results, Inflation and DJIA have a long-term influence on the increase of JCI, while interest rates have no long term influence on JCI in Indonesia. With the model projection for the next 10 years. The projection results state that the variability of JCI in Indonesia is most dominantly explained by the JCI variable itself with a proportion of 95.21619%, while inflation contributes 0.193202%, interest rates 0.249170%, and DJIA index 4.341438%. The results of this analysis can be used as a consideration of strategies in increasing the JCI in Indonesia to maintain the health and stability of the economy.

The Influence of World Oil Prices and Economic Growth on the Composite Stock Price Index through Foreign Exchange Rates on the Indonesia Stock Exchange

The Influence of World Oil Prices and Economic Growth on the Composite Stock Price Index through Foreign Exchange Rates on the Indonesia Stock Exchange

Volatility Index, Exchange Rate, Economic Growth On Stock Indexes

This study aims to analyse the effect of economic growth on the stock price index in Indonesia and Malaysia during the COVID-19 pandemic. Using regression analysis, this study measures the relationship between economic growth and the stock price index, focusing on both countries' fiscal and monetary policies. The results show that economic growth has a positive and significant effect on the stock price index in Indonesia and Malaysia, reflecting investors' optimism towards the economic outlook. Economic stimulus policies and monetary measures the government and central bank take are essential in supporting economic recovery and stock market stability. This research provides important insights for investors in making more informed investment decisions based on economic growth indicators. This study also emphasises the importance of responsive and coordinated economic policies to support economic growth and stock market stability in times of crisis.

Analisis Pengaruh Transmisi Kebijakan Moneter Melalui Jalur Suku Bunga Dan Harga Aset Terhadap Inflasi Di Indonesia Tahun 2017-2023

This study aims to analyze the effect of monetary policy transmission through the interest rate channel using the Bank Indonesia Certificate Interest Rate (RSBI) and the Interbank Money Market Interest Rate (RPUAB) indicators and through the asset price channel using the Composite Stock Price Index (CSPI) and Real Investment (IR) indicators. The data used is secondary data obtained from the official website of Bank Indonesia (BI) and the Central Bureau of Statistics (BPS). The data analysis used is the Vector Error Correction Model (VECM) with the Eviews 10 application. The results of this study indicate that in the long run the Bank Indonesia Certificate Interest Rate (RSBI) has a significant negative effect on inflation, the Interbank Money Market Interest Rate (RPUAB) has a significant positive effect on inflation, the Composite Stock Price Index (CSPI) has a significant negative effect on inflation, and Real Investment (IR) has a positive and significant effect on inflation. While in the short term, the Bank Indonesia Certificate Interest Rate (RSBI) has a negative and insignificant effect on inflation, the Interbank Money Market Interest Rate (RPUAB) has a negative and insignificant effect on inflation, the Composite Stock Price Index (CSPI) has a negative and insignificant effect on inflation, and Real Investment (IR) has a positive and insignificant effect on inflation.

Performance evaluation of housing price index prediction model using DNN and SVR

This study used the base interest rate, mortgage interest rate, deposit interest rate, consumer price index, exchange rate, and composite stock price index as variables to predict the housing price index. The variables were normalized using the min-max normalization method. Machine learning methods, Deep Neural Networks (DNN) and Support Vector Regression (SVR), were employed to construct a prediction model, and the fit of the model was evaluated using the Root Mean Square Error (RMSE). The analysis results of the DNN model indicated that when applying the softplus activation function, the RMSE was 0.1298, resulting in the smallest prediction error, and the correlation coefficient between the predicted and actual values was highest at 0.9342. The analysis results of the SVR model using the Gaussian RBF kernel showed that the RMSE was smallest at 0.2781 when the parameters were set to C=100 and Gamma=0.1. Therefore, the DNN model was found to be more fit for predicting the housing price index. Additionally, it was confirmed that the consumer price index had a non-linear effect with a positive relationship, and the base interest rate had a linear effect with a negative relationship.

INFERENCE IN MILDLY EXPLOSIVE AUTOREGRESSIONS UNDER UNCONDITIONAL HETEROSKEDASTICITY

Mildly explosive autoregressions have been extensively employed in recent theoretical and applied econometric work to model the phenomenon of asset market bubbles. An important issue in this context concerns the construction of confidence intervals for the autoregressive parameter that represents the degree of explosiveness. Existing studies rely on intervals that are justified only under conditional homoskedasticity/heteroskedasticity. This paper studies the problem of constructing asymptotically valid confidence intervals in a mildly explosive autoregression where the innovations are allowed to be unconditionally heteroskedastic. The assumed variance process is general and can accommodate both deterministic and stochastic volatility specifications commonly adopted in the literature. Within this framework, we show that the standard heteroskedasticity- and autocorrelation-consistent estimate of the long-run variance converges in distribution to a nonstandard random variable that depends on nuisance parameters. Notwithstanding this result, the corresponding t-statistic is shown to still possess a standard normal limit distribution. To improve the quality of inference in small samples, we propose a dependent wild bootstrap-t procedure and establish its asymptotic validity under relatively weak conditions. Monte Carlo simulations demonstrate that our recommended approach performs favorably in finite samples relative to existing methods across a wide range of volatility specifications. Applications to international stock price indices and U.S. house prices illustrate the relevance of the advocated method in practice.

Pengaruh Earning Per Share, Return on Equity dan Price to Book Value Terhadap Harga Saham Periode 2019-2021 (Studi Kasus pada Perusahaan Sub Sektor Otomotif dan Komponenya di BEI)

The phenomenon that occurred during the Covid-19 pandemic that spread in Indonesia in early 2020 has an impact on all aspects of life, including the capital market. The spread of Covid-19 in Indonesia has caused fear for investors so that many of them choose to leave risky assets. This affects the movement of the Composite Stock Price Index (IHSG). The problem in this study aims to determine the effect of EPS (Earning Per Share), ROE (Return on Equity) and PBV (Price to Book Value) on stock prices in automotive companies listed on the IDX for the period 2019 - 2021. The type of research used is quantitative research with secondary data sources. The population in this study is automotive companies listed on the Indonesia Stock Exchange for the period 2019 - 2021 as many as 15 companies. Sampling using purposive sampling technique as many as 10 companies that meet the criteria. Data analysis techniques using multiple linear regression analysis techniques. The results of the study indicate that EPS can influence stock prices, and ROE also influences stock prices, and PBV influences company value. Simultaneously, the variables EPS (Earning Per Share), ROE (Return on Equity) and PBV (Price to Book Value) influence stock prices.

Analyzing the Selective Stock Price Index Using Fractionally Integrated and Heteroskedastic Models

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile’s stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China’s economic slowdown in 2012 and the subprime crisis in 2008.

Impact of Mergers & Acquisitions Announcements on Stock Prices

This study examines the impact of M&A announcements on stock prices in India, by utilizing event study methodology and a dataset of 15 announcements. It identifies dynamic trends in stock performance before and after theannouncements. The Efficient Market Hypothesis (EMH), which posits that the market incorporates all relevant information, is evident in stock price volatility. The post-COVID-19 market environment has heightened investor caution due to substantial losses. The research, covering M&A announcements from 2014 to 2023, uniquely contributes by analysing pre- and post-COVID investor sentiments. Although substantial results were produced, the analysis of Abnormal Returns (AR) and Cumulative Average Abnormal Returns (CAAR) indicates that M&A announcements did not significantly impact overall stock performance, thereby supporting the EMH. These findings provide valuable insights into market responses to M&A in India, benefiting investors, business strategists, policymakers, and other stakeholders.

Impact of Commodity Price and Exchange Rate on IHSG with Geopolitics as Moderation

This research aims to determine the influence of world gold prices, world oil prices, world coal prices, and exchange rates on the Indonesian composite stock index. The tests of the ponder are Indonesia's world oil prices, world gold prices, world coal prices, trade rate, geopolitical chance record, and composite stock prices for Indonesia, data from www.investing.com and www.policyuncertainty.com January 2020 to December 2023. The method used to analyze the data in this research is multiple regression analysis and residual tests for moderator variables. Through F test analysis it is known that simultaneously all variables X have a significant influence on the Indonesian Composite Stock Price Index at the 5% level. Based on these results, the research hypothesis is accepted. Furthermore, through t-test analysis (subtest), the variables of world gold prices, world oil prices, world coal prices, and exchange rates have a partially significant influence on the Indonesian Composite Stock Price Index at a confidence level of 100. It turns out to be 95% (a = 5%). It can be concluded that changes in these variables influence changes in the Indonesian composite stock index. In addition, geopolitical variables cannot moderate the relationship between all variables X and Y. This shows that geopolitical factors do not play a significant role in this context. The variation in the Indonesian Composite Stock Price Index of 88.0 can be explained by combining all variables X. The remaining 12.0% can be explained by other factors not included in the estimation model.

PENGARUH KEBIJAKAN UTANG, LIKUIDITAS DAN PROFITABILITAS TERHADAP NILAI PERUSAHAAN DENGAN HARGA SAHAM SEBAGAI VARIABEL INTERVENING PADA PERUSAHAAN YANG TERDAFTAR DALAM INDEKS LQ45 DI BURSA EFEK INDONESIA TAHUN 2020-2022

The LQ45 Index is one of the stock price indices. Selecting stock values ​​with a high level of liquidity can be the right choice for investors to reduce the risk of future losses. The shares of companies listed in the LQ45 Index are a collection of 45 companies with liquidity selected with various selection criteria. The level of liquidity in the LQ45 Index has high stocks and a large market capitalization. This study aims to determine the effect of Debt Policy, Liquidity, and Profitability on Company Value with Stock Price in LQ45 companies as an intervening variable. This study uses a quantitative research method. The population in this study consisted of companies listed in the LQ45 Index, with a sample of 12 companies. The sampling technique used in this study is purposive sampling. This study's data analysis and hypothesis testing used the Partial Least Squares-Structural Equation Modeling (PLS-SEM) method. The results of hypothesis testing regarding the direct impact using Smart PLS 3.0 show that debt policy has a negative but insignificant effect on stock prices, liquidity has a negative but insignificant effect on stock prices, profitability has a significant negative effect on stock prices, debt policy has a significant negative effect on company value, liquidity has a significant negative effect on company value, profitability has a negative but insignificant effect on company value, stock prices have a negative but insignificant effect on company value. The indirect effect hypothesis test results show that the debt policy variable has a positive but insignificant effect on company value through stock prices, liquidity has a positive but insignificant effect on company value through stock prices, and profitability has a positive but insignificant effect on company value through stock prices.

Forecasting international tourist arrivals in South Korea: a deep learning approach

Purpose This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism. Design/methodology/approach This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism. Findings This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management. Originality/value This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

Reaksi Pasar Modal terhadap Faktor Ekonomi dan Faktor Non Ekonomi di Indonesia

The purpose of this study is (1) to examine how much influence economic and non-economic factors have on the movement of the Jakarta Composite Index (CSPI), (2) to examine the most dominant factor in the movement of the Composite Stock Price Index (CSPI). The method in this study uses monthly data and is processed using the Vector Auto Regression (VAR) analysis technique. the results of the variance decomposition test before social / political turmoil showed that the most dominant contribution was recorded at the CSPI itself 61 percent, with economic factors 21 percent and non-economic factors 18 percent. However, the results of the variance decomposition test after social / political turmoil showed that the most dominant contribution was recorded in non-economic factors, 52 percent, with economic factors 36 percent, and CSPI itself at 18 percent. Thus, there are differences before and after social / political turmoil.