Stock Price Index Research Articles

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.

The Impact of Inflation, Interest Rates, and Exchange Rates on the Jakarta Islamic Index (JII) Stock Price Index on the Indonesia Stock Exchange

The Impact of Inflation, Interest Rates, and Exchange Rates on the Jakarta Islamic Index (JII) Stock Price Index on the Indonesia Stock Exchange

On the Predictability of the DJIA and S&P500 Indexes

We obtained from Standard and Poor�s Corporation the complete 126-year history of the Dow Jones Industrial Average (DJIA) daily closing prices. We are applying rolling window averaging and adaptive learning methodologies, coupled with robust estimation methods, to examine which are the best forecasting models over a broad range of economic and financial conditions during the life of the index, based on daily and monthly stock index prices, and daily, monthly, and semi-annual stock returns. Why is an AR(1) model a reasonable benchmark of stock prices? Why do we have it? What should be our forecasting benchmarks? Do we find forecasting improvements from the Hendry-Castle-Doornik-Clements approach using robust forecasting methodologies and saturation variables in the prices of the index? Given that the DJIA fell over 15% during the first half of 2022, is this one of the worst six-month periods ever? What has happened to the Dow, historically, during such periods in the past with regard to six-month, one-year, and three-year-ahead stock returns? Is capitalism dead or doomed? We report statistically significant forecasting improvement from saturation and robust forecasting techniques during the 1896�June 2022 period. We report forecast stock returns for the next six months and three years that are bullish. In the King�s English, June 30, 2022, was another excellent common stock buying opportunity and Capitalism is not dead.

Predictive Data Analysis: Leveraging RNN and LSTM Techniques for Time Series Dataset

Recurrent neural networks (RNNs) and long short-term memory (LSTM) models have demonstrated tremendous effectiveness in modeling time series data due to the ability they possess to capture temporal dependencies. Stock market data is an example of time series data. Stock price data is very volatile and dynamic therefore one of the most turbulent areas to invest in might be the stock market. The choice to buy or sell stocks is heavily influenced by external circumstances and statistical analysis of previous stock performance. Stock price index forecasting has been a major area of research for many years and many machine learning and deep learning methods have been proposed to simplify this hard process, but little success has been discovered so far.In this paper, the application of RNNs and LSTMs on a stock price dataset to predict future values is explored. We start with Recurrent neural networks (RNNs) to predict the values but one of the major challenges with it is the "vanishing gradient" problem, which makes it difficult for the network to learn long-term dependencies in the data. To overcome this long short-term memory (LSTM) was used which eliminates the vanishing gradient problem. Data were normalized and divided into time steps to determine the relationship between past values and future values to make accurate predictions. The outcomes show that the MLS LSTM method significantly outperforms existing algorithms, achieving a prediction accuracy of 98.1% on the training data set and a 91.97% accuracy on the testing data set.

The Dissemination of Politically-Motivated False Information and Its Influence on the Stock Market

ABSTRACT The rapid growth of online media and communities has increased and accelerated the spread of unverified and false information (“fake news”), with significant political, economic, and social impacts, leading the European Commission to promulgate a “Code of Practice on Disinformation.” Identifying and countering such false information is time- and labor-intensive, and could benefit from the development of tools that automatically identify and flag such information. This study explores the use of deep learning techniques to detect fake news, using decreases in the incidence of emotional vocabulary and subjectivity to enhance detection accuracy, and examines potential correlations between the emotional sentiment of news content and the movement of stock price indexes. Empirical results show that deep learning techniques can be used to effectively detect fake news, with multiple trainings effectively improving detection accuracy and reducing the loss rate. In addition, increased objectivity and the use of fewer words with high emotional sentiment increases news credibility. Finally, news sentiment was found to be correlated with the movement of three of five stock indexes examined.

A Novel Proposal for Improving Economic Decision-Making Through Stock Price Index Forecasting

A Novel Proposal for Improving Economic Decision-Making Through Stock Price Index Forecasting

The Role of Monetary Policy to Stock Market in Indonesia: The Case of the Mining Sector

This study aims to analyze the long-term and short-term effects, causality, and effectiveness of monetary policy on mining subsector stocks in Indonesia. This study uses the Vector Error Correction Model (VECM) method and uses secondary data with an analysis period of 2000 to 2020 with monthly data frequency. The total sample used was 252 months. The results of this study show that in the short term, economic growth and exchange rates hurt the mining subsector stock price index. While the world crude oil price has a positive and significant effect on the mining sub-sector stock price index. In contrast to interest rates that do not affect the mining sub-sector stock price index. Different analyses result in the long term, namely only interest rates and economic growth that affect the mining sub-sector stock price index. Interest rates in the long term have a positive and significant effect, while economic growth has a negative and significant effect on the mining sub-sector stock price index. This study's results also show a one-way causality or reciprocal relationship between the exchange rate and the mining sub-sector stock price index, the mining sub-sector stock price index and oil prices, and interest rates on the mining sub-sector stock price index. At the same time, economic growth has no causality relationship to the mining subsector stock price index. It is expected that business actors will be able to adopt environmentally friendly monetary policies as stipulated by the central bank in Bank Indonesia Regulation Number 24/5/PBI 2022 for the sake of economic sustainability in Indonesia.

PERAMALAN HARGA SAHAM NVIDIA DENGAN METODE DOUBLE MOVING AVERAGE

The movement of NVIDIA's stock price greatly affects investment decisions, so accurate forecasting is very important to influence investment decisions. This research will apply the data mining process for forecasting NVIDIA stock prices using the Double Moving Average method with the application of order 2 and order 3 models. The purpose of this study is to determine the forecasting of the NVIDIA stock price index based on historical data. The results show that stock price forecasting using the Double Moving Average method order 2 model is more accurate and in accordance with actual or actual results. On the other hand, forecasting the Double Moving Average method with the order 3 model produces unsatisfactory forecasts that can be used, making it less suitable for dynamic markets. Therefore, the results of forecasting NVIDIA stock prices using the Double Moving Average method order 2 obtained an accuracy of 98% compared to order 3 of 67%. Based on the comparison results of using orders 2 and 3, it can be an important factor for investors to help make investment decisions in terms of forecasting the next production of NVIDIA shares.