Stock Price Index Research Articles

Predicting stock price of construction companies using weighted ensemble learning

Modeling the behavior of stock price data has always been one of the challenging applications of Artificial Intelligence (AI) and Machine Learning (ML) due to its high complexity and dependence on various conditions. Recent studies show that this will be difficult to do with just one learning model. The problem can be more complex for companies in the construction sector, due to the dependency of their behavior on more conditions. This study aims to provide a hybrid model for improving the accuracy of prediction for the stock price index of companies in the construction section. The contribution of this paper can be considered as follows: First, a combination of several prediction models is used to predict stock prices so that learning models can cover each other's errors. In this research, an ensemble model based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Classification and Regression Tree (CART) is presented for predicting the stock price index. Second, the optimization technique is used to determine the effect of each learning model on the prediction result. For this purpose, first, all three mentioned algorithms process the data simultaneously and perform the prediction operation. Then, using the Cuckoo Search (CS) algorithm, the output weight of each algorithm is determined as a coefficient. Finally, using the ensemble technique, these results are combined and the final output is generated through weighted averaging on optimal coefficients. The proposed system was implemented, and its efficiency was evaluated by real stock data of construction companies. The results showed that using CS optimization in the proposed ensemble system is highly effective in reducing prediction error. According to the results, the proposed system can predict the price index with an average accuracy of 96.6 %, which shows a reduction of at least 2.4 % in prediction error compared to the previous methods. Comparing the evaluation results of the proposed system with similar algorithms indicates that our model is more accurate and can be useful for predicting the stock price index in real-world scenarios.

Sequential monitoring for conditional quantiles of general conditional heteroscedastic time series models

AbstractIn this study, we introduce an online monitoring procedure designed to sequentially detect change points in the conditional quantiles of location‐scale time series models. This statistical process control issue holds great significance in risk management, particularly in measuring the value‐at‐risk or expected shortfall of financial assets. Our approach employs suitable detectors, including cumulative sum statistics. We then define a stopping rule and determine control limits based on asymptotic theorems to signal an anomaly. To further evaluate the proposed methods, we conduct a comprehensive empirical study analyzing various aspects of our monitoring procedures when applied to location‐scale time series models. Additionally, we perform a real data analysis using the daily returns of the Korea Composite Stock Price Index (KOSPI) and EuroStoxx 50 indices to affirm the adequacy of the proposed monitoring procedures in real‐world applications.

ANALISIS PENGARUH VARIABEL MAKROEKONOMI TERHADAP JAKARTA ISLAMIC INDEX (JII) (PERIODE 2011-2022)

This research aims to analyze the influence of macroeconomic variables on the movement of the Jakarta Islamic Index (JII) during the period 2011-2022. The considered macroeconomic variables encompass economic growth, inflation, interest rates, exchange rates, and the Composite Stock Price Index (IHSG). The analytical method employed is regression analysis, complemented by classical assumption tests. The research findings indicate that inflation, interest rates, and IHSG significantly influence the movement of JII, while economic growth and exchange rates do not exhibit significant effects. The implications of these findings underscore the importance of considering macroeconomic factors in making investment decisions within the Sharia-compliant capital market, particularly concerning JII. However, it is essential to acknowledge certain limitations in this study, such as data constraints and generalization limitations. Therefore, the results should be interpreted cautiously, taking into account other potential factors that may impact the movement of JII.

A Study on the Asset Management and Taxation Types of the Rich in Japan

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. 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.

Pemodelan Regresi Semiparametrik B Spline (Studi Kasus: Pengaruh Harga Emas dan Minyak Mentah Dunia Terhadap Indeks Harga Saham Gabungan)

<p dir="ltr"><span>The increases of the world gold and crude oil prices have a big role as a main factor that effect composite stock price index, the effect can make investors to buy stock from Bursa Efek Indonesia. Regression semiparametric used in this research for a purpose to get combined parametric and nonparametric with B Spline approach. B Spline is a development of spline to overcome weaknesses in making singular matrix at a high order spline with many knot points and close together. Variable parametric component is composite stock price index with crude oil price, and variable nonparametric component is composite stock price index with gold price that got obtained from January 2015 until December 2022. The result from this research is best regression semiparametric B-Spline modelling can be obtained using some combination of order and knot points. The optimal point is obtained on 2nd order using 4 knot point (1.135;1.319,15;1.320,75;1.323,25) with a minimum GCV value is 100.227,8. The best measure of goodness with a coefficient of determination value (R-Square) obtained a value 78,8%, because the value is more than 67% make it as a strong model. MAPE value is 3,37% that has a value less than 10 %, make this model have a perfect forecasting ability.</span></p><p><span id="docs-internal-guid-09fba3ae-7fff-a25a-8197-7dd90ea8b1e1"><span>Keywords: </span><span>Gold; Crude Oil; Composite Stock Price Index; Semiparametric B Spline; GCV</span></span></p>

Dampak Nilai Fundamental Terhadap Harga Saham Pada Sektor Perbankan Tahun 2020 - 2022

This research examines the impact of fundamental values on share prices in Indonesian banking companies from 2020 to 2022. It investigates the influence of earnings per share, book value per share, and the composite stock price index on share prices, with the debt-to-equity ratio as a control variable. Using purpose sampling, 43 companies were observed. The findings suggest that earnings per share, book value per share, and the composite stock price index influence share prices, while the debt-to-equity ratio does not. It advises banks to focus on increasing net profits by optimizing products and services and managing expenses and non-performing loans. Additionally, banks should monitor asset growth, which positively correlates with share price increases.

Analisis Kausalitas Nilai Tukar Rupiah/USD, Dow Jones Industrial Average (DJIA), Nikkei225, Shanghai Index Composite (SSEC) Terhadap Indeks Harga Saham Gabungan (IHSG)

The COVID-19 pandemic has had a negative effect on global stock markets. In addition, the increase in the number of confirmed cases of COVID-19 further increased the volatility of the exchange rate. This study aims to determine whether there is a causal relationship between the Exchange Rate, the Dow Jones Industrial Average (DJIA), the Nikkei225 Index, and the Shanghai Stock Exchange Composite Index (SSEC) and the Composite Stock Price Index (CSPI). The research method used is the Augmented Dickey Fuller Unit Root Test, and Granger Causality. The results of the study using Granger Causality indicate that the Exchange Rate, DJIA Index, and Nikkei225 Index have a causal relationship to the Composite Stock Price Index. The SSEC index does not have a causal relationship with the Composite Stock Price Index. This means that the JCI movement has the influence of the Exchange Rate, the DJIA Index, and the Nikkei225 Index.

THE INFLUENCE OF PROFITABILITY RATIOS, DER, PBV, AND COVID19 ON STOCK RETURNS

Investment is one of the activities that influences the growth of the Indonesian economy. Investment not only helps in economic development in Indonesia but also helps companies optimize their growth and provide a good response to investors. Investors have the main goal, namely achieving high returns or stock returns. However, high returns can also carry high risks, because shares have no certainty that they will rise or fall quickly and suddenly. Sudden changes in stock prices play an important role in determining stock prices and returns which can rise or fall very sharply so that they can create uncertainty for investors in making decisions to invest in a stock. The aim of this research is to determine the effect of PBV, ROE, NPM, EPS, DER, COVID on stock returns. To find out what factors cause what, this research uses a comparative causal research strategy. Companies in the Financial sector (consisting of 106 shares) which are included in the Composite Stock Price Index (also called IHSG) are the focus of this research. Data is taken from the application (Stockbit), in obtaining financial data. that researchers will use (idx.co.id). PBV has a positive effect on stock returns. Meanwhile, ROE, NPM, EPS, DER, COVID have no effect on stock returns

Pengaruh Inflasi, Nilai Tukar, dan Produk Domestik Bruto terhadap Indeks Harga Saham di Jakarta Islamic Index

The purpose of this research is to find out whether inflation, exchange rates, and PDB affect the JII stock price index. This research method is quantitative. Data obtained from secondary data in the form of a time series of 32 samples. This study uses Eviews version 13 in data processing. The results of this study on the t test stated that inflation did not significantly affect the JII stock price index based on t count < t table (1.497416 <2.048407) and the number of probs. (0.1463 > 0.05). The exchange rate has no significant relationship to the JII index as shown by t count < t table (-5.675294 < 2.048407 and total prob. (0.000 < 0.05). Meanwhile, PDB has a significant effect on the JII stock price index from the t count > t table (5.628365 > 2.048407) and the number of probs (0.000 <0.05). Whereas in the simultaneous test the variables inflation, exchange rate, and PDB affect the JII stock price index as evidenced by f count > f table (12.511175 > 2.946685) and a significance level (0.000 <0.05).

The Effect of Corporate Governance on the Degree of Agency Cost in the Korean Market

This study examines the relationship between corporate governance (CG) and agency costs using Korean market data, particularly for chaebol firms. The final sample includes 660 firm-year observations between 2016 and 2020 for Korean non-financial firms listed on the Korean Composite Stock Price Index (KOSPI). This study employs an ordinary least-squares panel data regression model using two proxies for agency costs, namely, asset utilization ratio and operating expense ratio, and six CG individual metrics as independent variables (CG score, protection of shareholder rights, board structure, disclosure, audit organization, and managerial discretion and error management). We find that firms with high CG experience lower agency costs than those with low CG. Moreover, our evidence suggests that firms can decrease agency costs by improving the quality of CG. The results of our regression model also support the idea that CG is effective in reducing agency costs for chaebol firms but not for non-chaebol firms. Finally, our findings suggest that the implementation of effective CG mechanisms in firms might improve managerial behavior through better decision-making to maximize the value of firms.

Effect of Inflation, Rupiah Exchange, Dow Jones Index, Nasdaq Index, and S & P500 Index Against Combined Stock Price Index

This research aims to determine empirical evidence of the influence of inflation, Rupiah exchange rate, Dow Jones index, Nasdaq index, A & P500 index on the Composite Stock Price Index. The research uses quantitative methods and uses classical assumption tests, multiple regression analysis, and hypothesis testing. The data used is secondary data during the period January 2019 – November 2023, resulting in a total of 59 samples from each variable using the purposive sampling method. The research results show that inflation has an effect on the Composite Stock Price Index, the Rupiah exchange rate has no effect on the Composite Stock Price Index, the Dow Jones Index has an effect on the Stock Price Index Combined, the Nasdaq Index influences the Composite Stock Price Index, the S & P500 Index influences the Composite Stock Price Index, and Inflation, the Rupiah Exchange Rate, the Dow Jones Index, the Nasdaq Index, and the S & P500 Index simultaneously influence the Composite Stock Price Index.

Forming an Optimal Portfolio with a Single Index Model: Empirical study on banking stocks in the LQ45 Index

The aim of forming an optimal portfolio in this research is to determine the composition of the shares that form the portfolio. Apart from that, this research also aims to determine the expected return and risk of the portfolio being formed. The research sample is 5 banks included in the LQ45 Index. The data used is monthly stock prices. Data analysis uses a single index model, where the reference index is the Composite Stock Price Index on the Indonesian Stock Exchange. The results of the analysis found that there were shares of 4 banks that were included in the optimal portfolio. The proportion of each stock in the portfolio is as follows: Bank Mandiri 60.13%, Bank BRI 26.71%, Bank BCA 8.91%, and Bank BNI 4.25%. The portfolio formed is able to minimize risk compared to the shares that form the portfolio.

Capital Market Efficiency and Economic Growth: Evidence from Bangladesh

This study explores the influence of capital market efficiency on Bangladesh’s economic growth, using time series data covering market capitalization, total market turnover, and stock price indices from 2011 to 2022. The application of a multiple regression model reveals that the Bangladesh capital market has the potential to contribute significantly to the country’s economic growth. This potential is attributed to the notable characteristics of high market capitalization and relatively robust market liquidity. Consequently, it is recommended that market organizations and regulations be structured to attract a substantial number of both domestic and foreign investors, encouraging substantial listings, investments, and trading. Such measures are crucial to achieving the central objective of optimally allocating economic resources for the sustained and resilient growth of the nation.

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.