Stock Index Movement Research Articles

PERBANDINGAN RETURN PENGGUNAAN MOVING AVERAGE CONVERGENCE DIVERGENCE DAN RELATIVE STRANGE INDEX PADA INDEKS SAHAM SYARIAH INDONESIA

This study compares the return levels from the use of the technical indicators Moving Average Convergence Divergence (MACD) and Relative Strength Index (RSI) applied to the simulation of the movement of the Indonesia Sharia Stock Index (ISSI). This research is a population study using panel data in the form of daily closing values over 12 years of the Indonesia Sharia Stock Index movement. The data was processed using a technical analysis approach with two momentum indicators, MACD and RSI, to obtain buy and sell signals based on the principles of each indicator, applied to the ISSI. The use of the MACD and RSI technical indicators on the ISSI shows that MACD yielded a profit of 207% with a success rate (win rate) of 72%, while RSI provided a profit of 125% with a win rate of 55%.

Prediksi Pergerakan Harga Saham Menggunakan Support Vector Machines Di Indonesia

Predicting stock movements is challenging due to their dynamic nature and influence from various factors. One of these factors is the lack of research considering the use of fundamental analysis regarding currency exchange rates and the use of foreign stock index movements related to technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The results obtained have an average prediction accuracy rate of 65.33%. The inclusion of currency exchange rates and foreign stock index movements as predictors in this study can increase the average prediction accuracy rate by 11.78% compared to predictions without using these two variables, which only result in an average prediction accuracy rate of 53.55%. Keywords: Fundamental Analysis, Sentiment Analysis, Stock Prediction, Support Vector Machines, Technical Analysis.

The Impact of the Declaration of Presidential and Vice Presidential Candidates on Capital Market in Indonesia:

This research aims to analyze the impact of the declaration of the presidential and vice presidential candidates on the dynamics of the capital market in Indonesia. The method used is quantitative analysis with a multiple dummy variable regression approach. The results prove that the declaration of the presidential and vice presidential candidates has a significant impact on the dynamics of the capital market in Indonesia. The movements of conventional stock indices (Composite and LQ45) and sharia stock indices (JII70 and ISSI) are significantly influenced by the declaration of the presidential and vice presidential candidate pairs for the 2024 Election, with quite strong correlations (Composite 0.6242, LQ45 0.6805, JII70 0.7968, and ISSI 0.5181). The declaration of the Anies-Muhaimin pair (Capres1) is positively correlated with the movement of the Composite stock index, LQ45, JII70 and ISSI. Meanwhile, the declaration of the Ganjar-Mahfudz pair (Capres 2) and the Prabowo-Gibran pair had a negative correlation with the movement of the Composite stock index. Research facts show that the capital market responded positively to the declaration of pair number 1 (Anies-Muhainin). On the other hand, the capital market responded negatively to the declarations of pair Number 2 (Prabowo-Gibran) and pair Number 3 (Ganjar-Mahfudz). The capital market was under greater pressure after the Prabowo-Gibran pair was declared. Market pressure is greater on Prabowo-Gibran compared to the Ganjar-Mahfudz pair and the Anies-Amin pair.
 Keywords: presidential and vice presidential candidate declaration, conventional capital market, sharia capital market

Macroeconomic Factors and Jakarta Stock Exchange: A Comparative Analysis Pre- and Until the COVID-19 Pandemic

The COVID-19 pandemic outbreak has caused disruptions in various aspects of life and the economy, including the movement of stock indexes. The objective of this study is to examine the influence of macroeconomic factors on the volatility of the Jakarta Composite Index both before and after the COVID-19 pandemic. Monthly secondary data from June 2007 to December 2019 were analyzed to identify pre-COVID trends, while data from June 2007 to December 2021 were used to identify post-COVID-19 patterns. The analysis incorporated the Granger causality test, Cointegration test, Vector Error Correction Model, and Augmented Dickey-Fuller. The results of the causality analysis revealed variations in the impact of interest rates, global oil prices, inflation, and exchange rates on the Jakarta Composite Index before and following the COVID-19 pandemic. These findings hold significance for investors as they enable the identification of potential risks associated with investments during global crises.

Indikator Makro Ekonomi dan Pengaruhnya pada Indeks Saham Sektor Infrastruktur Indonesia

This research examines the influence of macroeconomics as measured by exchange rates, interest rates, and inflation on the movement of the Infrastructure industry stock indeX in Indonesia (IDXINFRA).

Dampak Deklarasi Capres-Cawapres terhadap Dinamika Pasar Modal di Indonesia:

This research aims to analyze the impact of the declaration of the presidential and vice presidential candidates on the dynamics of the capital market in Indonesia. The method used is quantitative analysis with a multiple dummy variable regression approach. The results prove that the declaration of the presidential and vice presidential candidates has a significant impact on the dynamics of the capital market in Indonesia. The movements of conventional stock indices (Composite and LQ45) and sharia stock indices (JII70 and ISSI) are significantly influenced by the declaration of the presidential and vice presidential candidate pairs for the 2024 Election, with quite strong correlations (Composite 0.6242, LQ45 0.6805, JII70 0.7968, and ISSI 0.5181). The declaration of the Anies-Muhaimin pair (Capres1) is positively correlated with the movement of the Composite stock index, LQ45, JII70 and ISSI. Meanwhile, the declaration of the Ganjar-Mahfudz pair (Capres 2) and the Prabowo-Gibran pair had a negative correlation with the movement of the Composite stock index. Research facts show that the capital market responded positively to the declaration of pair number 1 (Anies-Muhainin). On the other hand, the capital market responded negatively to the declarations of pair Number 2 (Prabowo-Gibran) and pair Number 3 (Ganjar-Mahfudz). The capital market was under greater pressure after the Prabowo-Gibran pair was declared. Market pressure is greater on Prabowo-Gibran compared to the Ganjar-Mahfudz pair and the Anies-Amin pair.
 Keywords: presidential and vice presidential candidate declaration, conventional capital market, sharia capital market

Do financial ratios affect stock prices in the consumer non-cyclical sector?

<p>Maintaining a company's position among its competitors requires significant investment funds. The capital market is an alternative for companies to overcome investment funding constraints. Capital market performance is reflected in the Stock Index, which describes stock price movements. Fluctuating stock index movements indicate that there are things that influence the direction of the index. This research method uses secondary data sourced from the official website of the Indonesian Central Securities Depository (<em>Kustiodian Sentral Efek Indonesia-KSEI</em>), the Indonesian Stock Exchange (<em>Bursa Efek Indonesia-BEI</em>), the Financial Services Authority (<em>Otoritas Jada Keuangan-OJK</em>), and www.investing.com. Thirty-three companies operating in the non-cyclical consumer sector and listed on the Indonesian Sharia Stock Index (ISSI) for the 2018-2022 period are the sample in this research. Furthermore, the approach used in this research is a quantitative method with multiple linear regression analysis tools processed using the SPSS 25.00 program. The results have a positive and insignificant effect, namely the Current Ratio (CR) and Debt to Equity Ratio (DER). On the contrary, Earning Per Share (EPS) and Return On Equity (ROE) are factors that have a positive and significant influence on share prices in the non-cyclical consumer sector recorded in the Indonesian Sharia Stock Index (ISSI) for the 2018-2022 period.</p>

The Effect of Inflation, Exchange Rate, and BI Rate on the Indonesian Sharia Stock Index

Islam strongly advises against investment, as mentioned in lines 46–49 of the Surah Yusuf. The Islamic stock market, which offers Islamic stocks as one of its components, is one place where somebody can invest. Several macroeconomic and microeconomic factors, as well as equity investment, have an impact on Islamic stocks. This study examined the impact of inflation, exchange rates, and the BI Rate on the Indonesian Sharia Stock Index (ISSI) throughout the years 2017–2021. Statistical data on the market capitalization of the Indonesian Sharia Stock Index (ISSI) given at the Financial Services Authority (OJK), as well as other variables like inflation, currency rates, and the BI, were used in the quantitative research technique. Analysis of the research data was carried out through multiple linear regression modeling. The results showed that the Inflation variable (X1) had no effect on the Indonesian Sharia Stock Index (ISSI), the Exchange Rate variable (X2) had a significant negative effect on the Indonesian Sharia Stock Index (ISSI) and the BI Rate variable (X3) had an effect significantly positive to the Indonesian Sharia Stock Index (ISSI). The macroeconomic variables used in this study have an impact on the movement of the Islamic stock index, which causes the movement of the ISSI to vary quite a bit.

Co-evolution of neural architectures and features for stock market forecasting: A multi-objective decision perspective

In a multi-objective setting, a portfolio manager’s highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of non-dominated neural network models for further selection by the decision-maker. A new co-evolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the co-evolution is posed as a multi-criteria problem to evolve sparse and efficacious neural architectures. The well-known dominance and decomposition based multi-objective evolutionary algorithms are augmented with a non-geometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the co-evolution is augmented to accommodate the data-based implications of distinct market behaviors prior to and during the ongoing COVID-19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized co-evolution approach. The results on three market indices (NASDAQ, NYSE, and S&P500) in pre- and peri-COVID time windows convincingly demonstrate that the proposed co-evolution approach can evolve a set of non-dominated neural forecasting models with better generalization capabilities.

Unmasking the Effect of News Media on Islamic Stock Market: Are ASEAN-3 Countries Vulnerable to Covid-19 News?

This study provides a novel viewpoint by clarifying the effect of the COVID-19 news on the fluctuations of the Islamic capital market in the ASEAN Region. It aims to explore the effect of news related to the COVID-19 pandemic on the movement of the Islamic stock index in ASEAN-3 countries (Indonesia, Malaysia, Thailand). This research examined five news-related indices: panic emotion index, media hype index, sentiment index, fake news index, and contagion index. The research employed long-term daily secondary data covering one year during the COVID-19 pandemic to test the hypothesis. A total of 1086 data were analyzed using the panel regression method through EViews 9. The findings indicated that news media and the resulting sentiment have the potential to negatively affect the Islamic stock index movement through the panic and media hype index. The excitement and panic caused by the online news media negatively affected the movement of Islamic stock prices in the three ASEAN Islamic stock indexes, whereas fake news and contagion index were found to have no significant effect on the stock index movement.

Conducting stock market index prediction via the localized spatial–temporal convolutional network

The stock index movement prediction is a fundamental research issue in financial investment. The high-noise and dynamic of stock data make this problem challenging. Efficiently modeling the underlying spatial–temporal correlations between indices is a potential study area for raising the prediction performance. Existing methods usually use separate modules to capture spatial and temporal correlations, which lose sight of local spatial–temporal correlation. In this paper, we propose a brand-new prediction model named the localized graph convolutional network (LoGCN) for stock index forecasting. This model can represent the intricate local spatial–temporal connections via a well-designed convolution mechanism. First, the localized graph construction method is proposed to model the local spatial–temporal correlations. Then, the localized graph convolutional module is presented to fuse the impact of different indices. Finally, the fully connected network is put forward to transform the features into the expected index trends. 42 Chinese stock market indices were selected to conduct extensive experiments. Three evaluation metrics including the increase rate regression, further trends classification, and stock market back-testing were adopted to the experimental comparison. The experiments show that our method is 4.2%, 3.1%, and 40% better than conventional methods under these three metrics.

Stock Market Prediction Compliance Using AI and ML

In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this project the prediction of stock market is done by the Support Vector Machine (SVM). In the project, we proposed the use of the data collected from different global financial markets with machine learning algorithms in order to predict the stock index movements. SVM algorithm works on the large dataset value which is collected from different global financial markets. Various machine learning based models are proposed for predicting the daily trend of Market stocks. The model generates higher profit compared to the selected benchmarks. Keywords: Stock Market, Machine Learning, Predictions, Support Vector Machine.

Directional Dynamics of Stock and Equity Index Movements in the Case of Bombay Stock Exchange (BSE Sensex), and the Hang Seng (Hong Kong)

Directional Dynamics of Stock and Equity Index Movements in the Case of Bombay Stock Exchange (BSE Sensex), and the Hang Seng (Hong Kong)

Are Sustainability Indices Infected by the Volatility of Stock Indices? Analysis before and after the COVID-19 Pandemic

Considering the growing importance of sustainable investments worldwide, we explore the volatility transmission effects between the EURO STOXX Sustainability Index and the stock market indexes of its stocks. Using daily index return data, during 2000–2022, covering the COVID-19 pandemic, Multivariate Generalized Auto-Regressive Conditional Heteroskedasticity (MGARCH) models are used to explore if volatility effects of the stock indices felt during the pandemic implied any evolution in the effects already felt between the volatilities existing in these stock indices and the effects of stock market indices’ volatility over the sustainability index. Results point to the great dependence that the sustainability index has on stock index movements. The volatility felt in stock indices during the pandemic period did not become decisive in reversing a previous correlation trajectory between the stock market and sustainability indexes. Provided that sustainability is not observed exclusively in financial and economic terms, but in a triple bottom line context (including the social and environmental sides), we should not verify a high influence of stock market indexes over the sustainability index, as the results point out. Policymakers and investors should be aware of the high influence and take measures to turn the sustainability index more independent.

Dampak Pandemi Covid-19, Kurs dan Inflasi terhadap Pasar Modal Syariah:


 
 The objectives of this study are: (1) To find out whether the Covid-19 pandemic, exchange rate and inflation have a partial effect on the movement of JII stock indexes. (2) To find out whether the Covid-19 pandemic, exchange rate and inflation have a partial effect on the movement of JII70 stock indexes. . (3) To find out whether the Covid-19 pandemic, exchange rate and inflation have a partial effect on the movement of ISSI stock indexes. As for the research conducted using quantitative methods. Data collection techniques are carried out by observation, documentation, literature study. Data analysis techniques use classical assumption tests including normality tests, multicolenearity tests, autocorrelation tests, heterochedasticity tests and hypothesis tests including partial tests (t tests), coefficient of determination (R) tests, F tests. . The results showed that simultaneously covid-19 cases, exchange rates, and inflation had a significant effect on the JII, JII70, and ISSI stock indices. . Partially, the exchange rate has a significant effect on the JII and JII70 stock indices are negatively correlated. Meanwhile, covid cases and inflation have an insignificant effect despite the trend. Meanwhile, partially covid cases, exchange rates and inflation have a significant effect on the ISSI Sharia stock index.
 Keywords : Covid-19 cases, exchange rates, inflation, and composite stock indices (JII, JII70, and ISSI)

Pengaruh Jangka Pendek dan Jangka Panjang Saham Global terhadap Indeks Harga Saham Gabungan (IHSG) Periode 2015:M01 - 2020:M12

Abstract: Purpose: This study aims to analyze the external and internal factors that affect the movement of the Composite Stock Price Index (IHSG) in Indonesia in the short and long term. External influences of global stock indices are DJIA, NIKKEI225 and SSEC, as well as the exchange rate (USD/IDR). The most influential internal influence is inflation. Research methodology: This study uses the ECM (Error Correction Model) method. ECM aims to determine the long-term and short-term effects of variables that affect the JCI in Indonesia. In addition, this study uses monthly time series data from January 2015 to December 2020. Results: The results of this study show that the DJIA index and Inflation have a positive and significant influence in the long and short term on the movement of IHSG in Indonesia. The SSEC and Exchange Rate Index (USD/IDR) have a negative and significant relationship in the long and short term to the movement of IHSG in Indonesia, while the NIKKEI225 index has no long-term or short-term influence on the movement of IHSG in Indonesia. Limitations: The limitation of this research is that there are many variables outside the model that are not included in the study Contribution: For investors who will start investing in the Indonesia Stock Exchange, before investing, they should pay attention to the movement of global stock indexes (DJIA and SSEC). Keywords: 1. Globalization 2. KOF Globalization Index 3. Economic Growth

Predicting stock index movement using twin support vector machine as an integral part of enterprise system

AbstractIn order to improve the predicting performance of stock index movement, this study proposes a new predicting model called Twin Support Vector Machines (TWSVM), which will be used to predict the trend of Shanghai Securities Composite Index (SSCI) and Standard and Poor's 500 Index (S&P500 Index), respectively. Thirteen indicators constructed by stock index historical data are selected as input features of the predicting model. The predicting target is the stock index daily movement, up or down. The decision tree (DT), Naive‐Bayes (NB), random forests (RF), probabilistic neural network (PNN) and support vector machine (SVM) are set as contrast experiments. The experiment results indicate that the TWSVM predicting model has a better predicting performance on both stock price and index daily movement.

Performance Evaluation of Machine Learning Algorithms for Stock Price and Stock Index Movement Prediction Using Trend Deterministic Data Prediction

This experimental study addresses the problem of predicting the direction of stocks and the movement of stock price indices for three major stocks and stock indices. The proposed approach for processing input data involves the computation of ten technical indicators using stock trading data. The dataset used for the evaluation of all the prediction models consists of 11 years of historical data from January 2007 to December 2017. The study comprises four prediction models which are Long Short-Term Memory, XGBoost, Support Vector Machine ( and Random forests. Accuracy scores and F1 scores for each of the prediction models have been evaluated using this input approach. Experimental results reveal that a continuous data approach using ten technical indicators gives the best performance in the case of the Random Forest classifier model with the highest accuracy of 84.89% (average wise 83.74%) and highest F1 score of 89.33% (average wise 83.74%). The experiments also give us an insight into why a Naïve Bayes Classification model is not a suitable prediction model for the above task.

Analysis Of The Effect Of The Rupiah Exchange, World Oil Price, World Gold Price On The Joint Stock Price Index In The Indonesia Stock Exchange

Stock price fluctuations that could be traded on the stock exchange can be seen from JCI Indonesia so that we can know whether the market is bullish (strong) or bearish (weak). All in result by 2 factors, internal factors and eksternal. Many theory of past research revealed that the composite stock price index is influenced by several factors. Three factors including the value of the rupiah, world gold prices, and world oil prices. Because other than gold commodity rupiah exchange rate and the oil will affect the movement of stock indices on the StockExchange. The study population was the entire data rupiah exchange rate, oil prices and gold prices on the world in 2011-2014 and since such data exist today (historical price). This study uses statistical analisisis, Multiple linear regression, and the F test and T and test (coefficient of determination (R2)), Normality Test, Test Heteroskidastity. Based on hypothesis testing shows that world oil prices and world gold prices have a negative effect and no significant effect on the JCI while the rupiah / US $has a positive and significant impact on JCI.

FORECASTING STOCK MARKET FOOD INDEX USING COMMODITY FUTURES QUOTES

This article is devoted to testing whether commodity futures can act as tools to predict price fluctuations of commodity indices. We decided to choose WIG-food index and leading commodity futures since they are interconnected. Companies included in our index use commodities in their operations. While we managed to find comparable articles regarding financial market forecasts using different indices, we did not find any opportunities to predict niche indices like WIG-food in our example. Therefore, our article may act as clarification of whether it is possible to use machine learning algorithms combined with basic regression to predict commodity indices price fluctuations. The prediction of stock price movements has long been an intriguing topic of financial research. Particularly prominent ways of predicting a stock price trend include using stocks past performance or using sentiment analysis. However, a certain level of criticism must be applied to these methods. Using past performance creates a certain degree of isolation that leaves out important information carried out by other entities and makes the prediction result vulnerable to local perturbations. Before, we tested whether the relationship between these variables exists to implement machine learning algorithms in price forecasting. Our study consists of Introduction, where were familiarize the reader with the topic; a Methodology, where we describe data collection principles and reasoning for conducting the research, as well as describing machine learning algorithms behind our analysis; Results, where we present the outcomes of our models and a Conclusion. We proposed the use of data collected from different global financial markets with machine learning algorithms to predict stock index movements. In this project, we have attempted to forecast stock price trends using machine learning techniques LSTM. Before we ran basic regression using Keras to derive the reasonability of applying the LSTM model. Regression results showed that commodities included in the model were far from perfection and we managed to achieve 48% accuracy of regression predictive power using commodity futures as features. Therefore, we excluded them from the LSTM model since they turned out to be not credible variables to apply in the machine learning algorithm.