Stock Index Data Research Articles

A novel stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation for modeling and simulating of financial indicators

The main purpose of this study is to develop a new discrete time stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation which follows bouncing autoregressive AR1, with fixed variance and trend for modeling and simulating of international financial time series forecasting.This study offers the four discrete time stochastic models. The first model is determined as autoregressive integrated moving average-Arima1,1,1, and the parameter estimates of this model are found using the maximum likelihood method (MLM). Monte Carlo simulation is applied to the obtained parameters. This model is named MoCArima for short. Second, the MoCArima model whose parameters are estimated with particle swarm optimization (PSO) is determined as Proposed Monte Carlo (MC) Arima1,1,1, and is briefly named as ProMoCArima. The third model is determined as Fixed Variance Stochastic Model which has a bouncing autoregressive AR1 structure with constant variance and trend component. This model is briefly named as FiVaS. The parameter estimates are found using the MLM. The fourth model is determined as the Proposed Fixed Variance Stochastic Model. This model is briefly named as ProFiVaS. Structurally the same as the third model. The parameter estimates of both trend and stochastic parts of the FiVaS model are found by the PSO.Proposed models are applied to forecasting international different financial indicators to show its superiority and applicability. The performance and usage efficiency of the proposed four stochastic models are examined on a total of six different international financial data types, five of which are stock index data BIST100 (Türkiye), S&P500 (USA), DAX (Germany), FTSE100 (UK), NIKKEI225 (Japan), and one of which is the exchange rate EURO/USD. The ProFiVaS among all models provides the best performance on the all indices. The ProFiVaS can easily be extended to any real-world time series prediction requirements.The proposed models can be extended to all stationary and non-stationary linear or nonlinear autoregression models opening the door to modeling data types obtained from different sector fields. A part of the future plan of this work is to design a decision support system software that assigns a value of 1 when an investor needs to invest in a financial indicator, and 0 otherwise.

ANALISIS HUBUNGAN HARGA EMAS DAN PASAR SAHAM MENGGUNAKAN MIXED-COPULAS

Gold is considered as a reliable investment tool for long-term savings and/or investment portfolios. Investors who do not like high risks trust gold to be a safe haven commodity that can mitigate the impact of any financial crisis. Gold and stocks are often used as substitutes for each other, where the two have an inverse relationship. Copula is used to capture the dependence relationship between world gold prices and the stock indexes. The data used are the stock index data for the JKSE Indonesia), PSE (Phillipines), Nikkei 225 (Japan), HSI (Hong Kong), and world gold prices (XAU) from January 1, 2014 to December 31, 2019. From the data, the ARMA-GARCH model is made to solve the problem of autocorrelation and heteroscedasticity. Then, the correlation between assets is calculated using the rank correlation. Furthermore, four pairs of data are made from each stock index with the price of gold. Next, the best copula and the estimated Value-at-Risk (VaR) are sought for each portfolio. From the results of the selecting of the best copula for each pair of data, it is found that gold can be a safe haven asset in the Hong Kong's stock market. The VaR results show that the biggest loss is in the Japanese market.

Research on stock trend prediction based on grey wolf optimized RF and SVM models

The volatility of the stock market has a profound impact on the stability of the financial market, and then has a significant effect on the overall economic situation, so the accurate prediction of stock trend has always been the focus of research in the financial field. In this paper, the raw minute-level stock data of SSE 50 and CSI 300 in 2021 are normalized, and the historical training and test sets of daily stocks for model training and testing are established based on the datasets. The RF-SVM model based on the gray wolf optimization algorithm is constructed to compare the traditional support vector machine and random forest model, and the relevant performance indexes are calculated. In this study, the RF-SVM model based on gray wolf optimization is highly correlated with the characteristics of stock data, and the prediction accuracy of stock index data is more than 85%. The cross-validation score is over 0.77 and the overall weighted score is as high as 0.98. Compared with the SVM and random forest models under the default parameters, the RF-SVM model based on gray wolf optimization has an average advantage of 25% in performance indicators. The generalization ability is increased by about 26%; The fitting error was reduced by about 51.15%; The overall average score advantage is about 34%. These results prove that the RF-SVM model of gray wolf optimization has superior performance and significant optimization effect in stock prediction, and compared with the traditional grid search optimization algorithm, gray wolf optimization has excellent adaptability and can gradually obtain the global optimal parameter configuration.

Herding behaviour towards high order systematic risks and the contagion Effect—Evidence from BRICS stock markets

This paper investigates the existence of herding movements towards several systematic risk factors derived from the Capital Asset Pricing Model (CAPM) and its extensions. The measure of herding is estimated using the dispersion of the risk factor loadings. The state space model is employed to extract time series of herding dynamics. We empirically survey the herding behaviors in the BRICS stock markets (i.e., Brazil, Russia, India, China, and South Africa) using monthly stock index data from 2006 to 2022, and identify various herding patterns towards specific factors. We also examine the impact of unanticipated shocks in crucial macroeconomic variables on the degree of herding measure in these countries. Lastly, we test the contagion hypothesis of herding across markets using correlation analysis. The results show that the level of herding linkages increases significantly in periods of market stress, casting doubt on the effectiveness of asset allocation in these markets for the sake of diversity.

Testing the goodness-of-fit of the stable distributions with applications to German stock index data and Bitcoin cryptocurrency data

Testing the goodness-of-fit of the stable distributions with applications to German stock index data and Bitcoin cryptocurrency data

Adjustment speed of psychological anchors: a comparative study of the G7 and BRIC stock markets

ABSTRACT This paper proposes a model to estimate the dynamic psychological anchor and adjustment speed of investors in stock markets. By employing covariate-dependent threshold settings in regression kink models, we identify the changing psychological anchor and how quickly investors adapt to a target level. Analysing stock index data from the G7 and BRIC countries, we find evidence of time-varying psychological anchors in most BRIC countries. Additionally, our results indicate that the BRIC countries exhibit slower adjustment to target anchors compared to the G7 countries.

Impact of financial contagion on developed and emerging economies in the wake of the covid-19 pandemic and the Russia-Ukraine conflict

The world economy has been experiencing two double difficulties, including the COVID-19 pandemic and the war between Russia and Ukraine. The Russia-Ukraine war, accompanied by strong Western economic sanctions as well as Russia’s reactions, has had a comprehensive and profound negative impact on the world economy. With the openness of the economy as high as it is today, the influence between different economies is understandable. The research results focus on analyzing the financial contagion between mature and emerging markets in the post-COVID pandemic and the war of Russia and Ukraine. The study collected random stock index data from 5 developed and 5 developing countries from 2015 to the end of January 2023. The study uses the GARCH(1,1)-M model to find the financial spread between selected countries. Research results show that developed countries have a larger financial spread than developing countries, especially during the post-COVID pandemic and the war of Russia - Ukraine period.

Time-Series Analysis and Forecasting of S&P500 Index Based on ARIMA and ETS Model

The study embarks on an insightful journey into the world of stock indices through the deeper understanding of time series analysis application, especially during the special period of oil price volatility. The goal of this paper is to utilize sophisticated statistical models, including Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS), to extract meaningful information from historical stock index data of S&P500 by Yahoo Finance, enhancing predictive accuracy and operating forecasts to inform strategic decision-making. In addition to the prediction results, the study also compares the forecasts of the two models through some values, and concludes that the prediction effect of the ARIMA model is better than that of the ETS model. The reason behind this result has a lot to do with the processing of the data itself and the fit of the models. For S&P500 during the period of the study, the ARIMA model’s prediction result is better. The insights derived from this analysis are expected to empower investors, researchers, and market analysts with a deeper understanding of the stock index’s past behavior and its implications for future performance.

Mechanisms of Stock Selection and Its Capital Weighing in the Portfolio Design Based on the MACD-K-Means-Mean-VaR Model

When designing a stock portfolio, investors must select stocks with different characteristics and increasing price trends and weigh each capital. Both are fundamental to diversifying loss and profit. Therefore, the mechanisms that accommodate both are needed. Based on this, this research aims to design a stock selection and capital weighing mechanism using the MACD-K-means-Mean-VaR model. The moving average convergence–divergence (MACD) is used to analyze stock buying time, providing trend, momentum, and potential price reversal insights. Then, stocks with increasing price trends are clustered using K-means, a grouping simple pattern data method based on specific characteristics. The best stocks from each cluster are capital weighted using the mean value at risk (mean-VaR), a portfolio optimization model adjusting loss possibility to the investor’s acceptance tolerance. The mechanism is then applied to Indonesia’s 100 stock index data to analyze variable sensitivities and compare it with another model. The application reveals that all variables significantly impact portfolio return mean and VaR, suggesting the need for clustering and analyzing stock price movements in stock portfolio design. This research academically develops a portfolio design mechanism by clustering stocks and analyzing price movement trends. It enables investors to practically diversify and choose stocks with increasing price trends, reducing losses and increasing profit opportunities.

A novel CBAMs-BiLSTM model for Chinese stock market forecasting

The convolutional block attention module (CBAM) has demonstrated its superiority in various prediction problems, as it effectively enhances the prediction accuracy of deep learning models. However, there has been limited research testing the effectiveness of CBAM in predicting stock indexes. To address this gap and improve the prediction accuracy of stock indexes, we propose a novel model called CBAMs-BiLSTM, which combines multiple CBAM modules with a bidirectional long short-term memory network (BiLSTM). In this study, we employ the standard metric evaluation method (SME) and the model confidence set test method (MCS) to comprehensively evaluate the superiority and robustness of our models. We utilize several representative Chinese stock index data sets, namely, the Shanghai (Securities) Composite Index and the Shenzhen Composite Index, as our experimental data. The numerical results demonstrate that CBAMs-BiLSTM outperforms BiLSTM alone, achieving average reductions of 13.06%, 13.39%, and 12.48% in MAE, RMSE, and MAPE, respectively. These findings confirm that CBAM can effectively enhance the prediction accuracy of BiLSTM. Furthermore, we compare our proposed model with other popular models and examine the impact of changing data sets, prediction methods, and the size of the training set. The results consistently demonstrate the superiority and robustness of our proposed model in terms of prediction accuracy and investment returns.

Analysis of Influencing Factors of Stock Return Rate

The people who are investors in the Chinese stock market always seek sequences or patterns, like the periodicity of the stock market, to help them for making a better return. Now some of the patterns have been proven. The periodicity of stocks and investor sentiment will affect investors' decisions and have a huge impact on China's stock market. This article aims to find out the influencing factors on different aspects of the stock market’s periocity. The article shows the impacts on China’s stock market caused by the periodicity of factors like the rate of return, investor sentiment, macroeconomic indicators, and economic cycles. From the method of calculating the past five years’ stock index data and comparing the fluctuation every single month can find out the proper time for investment. From other aspects, the firm report has been an example of an impact on investor sentiment. And all in all, these aspects correlate with the return to the stock market. Every factor has been unconsciously altering the stock market to a certain extent.

Volatility and Liquidity Comparison of Indonesian and Singapore Stock Market in COVID-19 Mobility Restrictions Era

The COVID-19 case found at the end of December 2019 became a pandemic in March 2020. The research aimed to see and understand the differences in the performance of the Indonesian and Singapore stock indices represented by the Indeks Harga Saham Indonesia (IHSG) and Straits Times Index (STI) before and after the implementation of community mobility restrictions (Pembatasan Sosial Berskala Besar (PSBB) in Indonesia and Circuit Breaker in Singapore). The stock index data were stock index prices at closing and stock trading volume. The stock index performance was measured by its volatility and liquidity. Meanwhile, data volatility with heteroscedasticity symptoms was measured using the GARCH (1,1) model. Meanwhile, the standard deviation was used to measure homoscedastic data. The results show differences in return volatility and stock index liquidity before and after restrictions on community mobility. The return volatility of the IHSG and STI is higher before the community mobility restrictions compared to the period after. IHSG experiences liquidity after PSBB I and before PSBB II. The conclusion that can be drawn from these results is that liquidity in Indonesia does not improve when PSBB I is implemented, but liquidity improves in PSBB II. Meanwhile, STI’s liquidity is higher in the period after the implementation of Circuit Breaker. These results indicate that implementing the Circuit Breaker helps to improve the stock index’s performance in Singapore because volatility decreases when the policy is implemented. The policy also reduced the liquidity of the Singapore stock index.

Powerful Backtests for Historical Simulation Expected Shortfall Models

Since 2016, the Basel Committee on Banking Supervision has regulated banks to switch from a Value-at-Risk (VaR) to an Expected Shortfall (ES) approach to measuring the market risk and calculating the capital requirement. In the transition from VaR to ES, the major challenge faced by financial institutions is the lack of simple but powerful tools for evaluating ES forecasts (i.e., backtesting ES). This article first shows that the unconditional backtest is inconsistent in evaluating the most popular Historical Simulation (HS) and Filtered Historical Simulation (FHS) E S models, with power even less than the nominal level in large samples. To overcome this problem, we propose a new class of conditional backtests for E S that are powerful against a large class of alternatives. We establish the asymptotic properties of the tests, and investigate their finite sample performance through some Monte Carlo simulations. An empirical application to stock indices data highlights the merits of our method.

Stock Index Prediction Based on ARIMA Model

Stock index price forecasting is a very important thing for financial markets. Stock indices select the most representative stocks in the stock market, which are the most favorable representatives of the industry, sector or market. Successful prediction of them can guide investors to a good payoff and allow researchers to understand the workings of the market economy. Time series models are widely used in stock price forecasting precisely because they have the advantage of being able to predict in complex environments such as large shocks. Therefore, this paper introduces the process of using time series model - ARIMA model to establish the prediction of index price fluctuation. And the five-year-long stock index data from May 2017 to April 2022 of the CSI 300 index is used as the research sample to successfully predict the volatility trend of the CSI 300 index in May 2022. The outcomes demonstrate that the ARIMA model has excellent capability for short-term forecasting. and is capable of forecasting complex situations that cannot be accomplished by linear models.

Hesitant fuzzy time series forecasting model of higher order based on one and two-factor aggregate logical relationship

Fuzzy time series forecasting is a methodology that incorporates fuzzy logic to handle uncertainty in time series data. It allows for more flexible and accurate predictions by considering the linguistic terms and membership functions associated with each data point, enabling a better representation of complex patterns and trends within the time series. A time series forecasting model examines the relationships between present and past observations for future measurement. This article proposes a hesitant fuzzy time series forecasting (FTSF) technique for higher order based on one and two-factor aggregate fuzzy relationships. The proposed method presents a new approach to partitioning intervals into an unequal and equal length. The hesitant fuzzy sets are defined for each observation, and fuzzification is based on their maximum score function. Further, aggregated triangular fuzzy numbers are defined using an aggregation operator corresponding to the fuzzified datum. In order to enhance forecasting performance, this work also introduces a novel defuzzification rule based on aggregated fuzzy logical relationship groups (AFLRGs) for higher order one and two-factor fuzzy time series. The implementation of the proposed approach is verified on two data sets to the forecasting of enrollment data and TAIFEX and TAIEX stock index data sets, and the forecasting error is tested in term of different statistical parameters. Thus, the model validates the effectiveness of the proposed method with higher forecasting accuracy rate in the environment hesitant fuzzy information.

PENERAPAN METODE FUZZY TIME SERIES MENGGUNAKAN PARTICLE SWARM OPTIMIZATION ALGORITHM UNTUK PERAMALAN INDEKS SAHAM LQ45

Stocks have a volatile nature and it is difficult to predict the ups and downs. Therefore, stock data forecasting is done by investors to get a picture of future results. Fuzzy Time Series is a time series method that is suitable for forecasting fluctuating stock data because it does not require the fulfillment of assumptions such as normality and stationarity, but the Fuzzy Time Series method has weaknesses in determining intervals. So that in this study, interval optimization will be carried out on Fuzzy Time Series with Particle Swarm Optimization algorithm to predict LQ45 stock index data, Particle Swarm Optimization algorithm is used because it produces more optimal interval values compared to other optimization methods such as Genetic Algorithm. The data to be used is the closing price of the LQ45 stock index on January 5, 2020 to December 26, 2021. Forecasting using the Fuzzy Time Series method produces a SMAPE value of 1.53%, then after optimization using the Particle Swarm Optimization algorithm, the SMAPE value decreases to 1, 27%. Therefore, it can be concluded that optimization using Particle Swarm Optimization on Fuzzy Time Series produces a more optimal forecasting value.

Forecasting European Union allowances futures: The role of technical indicators

With the aim of studying the prognostic power of monthly European Carbon futures prices, we employ a wide variety of potential forecasting indicators, including economic variables like uncertainty indicators, energy prices, and stock index data, in addition to exploring the influence of technical indicators on forecasting for the first time. In both in-sample and out-of-sample results, technical indicators display statistically significant ability, exceeding that of economic variables. To gain valuable insight into which indicators are comparatively relevant to the price of EUAs, three types of forecasting methods: univariate predictive regression, dimensionality reduction techniques, and ensemble learning algorithms are applied. Results show that the first diffusion index in the principal component regression and partial least squares model is greatly influenced by the changes of technical indicators, while only having a weak association with economic variables. With respect to ensemble learning algorithms like Xgboost, greater importance is given to technical indicators compared to economic variables when making predictions. Furthermore, technical indicators demonstrate stable economic value to investors in the form of positive CER gains. Our findings present novel evidence for the predictive capacity of technical indicators and can assist policymakers in formulating sustainable and effective energy policies.

OPTIMALISASI RETURN SKEMA INTRADAY TRADING SAHAM SYARIAH INDONESIA MELALUI PEMILIHAN HARI DAGANG

Traders on the stock exchange under any circumstances try to minimize risk and maximize returns, including when the stock market is affected by Covid-19. At that time the Indonesian stock market experienced an extreme decline in stock prices in general. It happened gradually until it formed a downtrend over a fairly long period. Investors and active traders bear the loss of this condition. Wait and see until the downtrend ends is not always a solution. This study provides input for traders by observing in more detail the fluctuations that occur in the daily period which is referred to as the intraday scheme. Assuming whether there are certain days with a more positive tendency for returns than other days. The research was conducted using quantitative methods with a non-experimental approach with descriptive statistical data processing techniques. The results of observations on the 2017-2022 Indonesian Sharia Stock Index (ISSI) data show that Friday is the day that has the most tendency for prices to rise, the biggest returns, and the highest performance index (based on the Sharpe and Treynor Index) than other days. Keywords: return, Covid-19, intraday trading

Stock index time series prediction based on ensemble learning model

The accuracy of stock index prediction is of great significance to national economic development. However, because of the nonlinearity and long-term dependence of stock index data, effective prediction of future stock index price becomes a challenge. In order to solve the above problems, this paper proposes a research method of stock index time series prediction based on ensemble learning model. This method first uses an Adaboost.R2 algorithm to iteratively train multiple LSTM models and then integrates these LSTM models based on the parameters obtained by iterative training. Finally, it uses the ensemble model to predict stock index time series data. This paper uses the Shanghai Composite index, CSI 300 index and Shenzhen Composite index as experimental data sets, and uses the BP model, CNN model and LSTM model as comparative models to conduct an experimental analysis. The experimental results show that the new ensemble learning model proposed in this paper has certain advantages in the research of stock index time series prediction.

Volatility and risk contagion of international stock market in the context of COVID-19

In order to reveal the impact of the outbreak of COVID-19 epidemic on risk contagion in the international stock market, this paper establishes an ARMA-GARCH-Copula model based on the R-Vine structure to empirically analyze the stock index data of 19 important countries(regions) around the world, and uses complex network to display the risk contagion path. This research shows that the risk contagion correlation of the global stock market has significantly improved due to the impact of COVID-19; The correlation of risk transmission among European countries is significantly higher than that of other regions, reflecting a closer integration connection between EU countries; Finally, unlike previous literature, this article finds that stock market of Hong Kong in China has become an important node in the international stock market, playing an important role in the risk network before and after the epidemic.