Search

Stock index trend prediction is a very important topic in the finance. The purpose of this paper is to compare six ensemble learning related techniques for stock index direction prediction, including four boosting methods (Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT)), one bagging method (Random Forest (RF)) and one tree-structured machine learning method (Decision Tree (DT)). The Shanghai Composite Index is chosen for experimental evaluation. A factor library of seventy-two technical factors, thirty-five macro factors and seven micro factors are our inputs. Our predictions are one month ahead, and each prediction model is evaluated by the Area Under Curve (AUC). The results indicate that ensemble learning techniques perform well in stock index prediction, with all AUC values above 0.5. RF is considered as the top algorithm with an AUC value of 0.7355 before feature selection and 0.6736 after feature selection. Also, we predict the stock index trend using a comprehensive factor library and three single factor libraries, respectively. The results show that forecasting stock index directions with a complete factor library is of great importance, which could achieve more stable forecasting results. This study contributes to literature in that it is, to the best of our knowledge, the first to make an extensive evaluation of ensemble learning related methods by constructing a comprehensive factor library and three single factor libraries. [ABSTRACT FROM AUTHOR]

In order to evaluate and estimate significant aspects of time series data, various models with varying degrees of variation have been built in the last few years. The overarching goal of study is to examine the variance dynamics of several Indian stock market indexes using Normal and Students-t distributions. The sample data spans a long time period (more than 40 years), from April 1979 to May 2023, and includes dramatic happenings. The econometric method relies heavily on the GARCH models and other statistical investigations. The outcomes contribute to the growing body of knowledge in the field. The possible rewards and risks of an investment are the focus of this empirical study. The results encompass the dynamics of financial series, volatility sketching, a statistical and GARCH model feature assessment, and the fitness of series returns. This opens the door to different assessments of the Indian financial market.

This paper focuses on the examination of the long-run relationship between money supply and selected national and global stock indices. Detailed knowledge of this relationship can be used by analysts, investors and monetary policy makers. Analysis of the relationship was performed using a 2-stage Engle-Granger cointegration. First, the stationarity of the time series was tested, then both the long-term OLS model and the short-term EC model were estimated. Time series were always tested on the longest period for which data were available. The longterm dependence of stock indices on the respective M2 was confirmed for the BOVESPA, FTSE100, S&P/BMV IPC, S&P BSE500, TSX and The 5000 Wilshire Small Cap Price Return indices. In contrast, the dependence between world money supply indicator GlobalM2, the stock index FTSEALL World, and the S&P500 index was not demonstrated. Additionally, no dependence was identified between the respective M2 and the DAX, PX, Nikkei225, KOSPI, SMI, SPCITIC300, Eurostoxx50, Willshire5000PR and ATX indices. Backward dependence of M2 on the stock index was found only for the Chinese SPCITIC300 index.

The paper examines market co‐movement between pairs of financial assets in the time‐frequency domain. Recent finance literature confirms the integration of cryptocurrencies and financial assets, which may bring more investments with the possibility of surplus liquidity in the cryptocurrency segment, leading to financial instability. The novelty of this paper is examining the integration of cryptocurrencies and the indices of equity, sustainability, renewable energy, and crude oil for the daily observations from 2015 to 2021 by using the wavelet coherency method. The empirical results signify no integration in the short‐term scales and grow stronger in the medium‐term scales, especially during the COVID‐19 period, and further exhibit weaker heterogeneous associations in the long‐term scales. However, the sustainability, clean energy indices follow similar dynamics of the equity market and crypto pairs. In contrast, the global crude oil index showcases the minor integration with cryptocurrencies compared with other traditional asset classes. Hence, the cryptocurrency market fails to confirm the safe haven features, especially during the COVID‐19 periods (Medium‐term), which facilitate the domestic and international investors expecting to hedge their price risk in equity markets using cryptocurrencies may have to look for short‐term. The lead–lag heterogeneous effects of the asset‐pairs may pave arbitrage opportunities for investors. [ABSTRACT FROM AUTHOR]

Investing in stocks and shares is a common strategy to pursue potential gains while considering future financial needs, such as retirement and children's education. Effectively managing investment risk requires thoroughly analyzing stock market returns and making informed predictions. Traditional models often utilize normal variance distributions to describe these returns. However, stock market returns often deviate from normality, exhibiting skewness, higher kurtosis, heavier tails, and a more pronounced center. This paper investigates the Laplace distribution and its generalized forms, including asymmetric Laplace, skewed Laplace, and the Kumaraswamy Laplace distribution, for modeling stock market returns. Our analysis involves a comparative study with the widely-used Variance-Gamma distribution, assessing their fit with the weekly returns of the S&P 500 Index and its eleven business sectors, drawing parallel inferences from international stock market indices like IBOVESPA and KOSPI for emerging and developed economies, as well as the 20+ Years Treasury Bond ETFs and individual stocks across varied time horizons. The empirical findings indicate the superior performance of the Kumaraswamy Laplace distribution, which establishes it as a robust alternative for precise return predictions and efficient risk mitigation in investments. [ABSTRACT FROM AUTHOR]

Political events significantly impact economic indices, including agricultural commodities. While Granger causality is a well-established method for analyzing interdependencies between time series data, its traditional application can be challenging to interpret across multiple periods. This research enhances the Granger causality method to quantify changes in the interlinkages among variables over time, offering a more intuitive framework for analyzing how political events affect economic indices. The proposed method involves conducting Granger causality tests across different periods, forming vectors from the results to capture transitions from Granger-causing to non-Granger-causing variables. These vector amplitudes provide quantitative measures of changes with explanatory power over time. The dataset includes eight variables over a decade, focusing on the following major geopolitical events: the Russian occupation of Crimea in 2014 and the invasion of Ukraine in 2022, with an intermediate "no-shocks" period as the reference. The results show significant changes in the interlinkages among the variables during crisis periods compared to stable periods. This enhanced method provides valuable insights, informing trading strategies and risk management during periods of geopolitical instability. This innovative approach offers a novel tool for market participants to better understand and respond to economic shocks caused by political events. [ABSTRACT FROM AUTHOR]

The research examines the dynamic correlation between the volatility of the Bombay Stock Exchange (BSE) and the Small- and Medium-enterprises (SM Es) Index of BSE in India. It focuses on analysing the volatility spillover between SME and BSE by utilising the generalised autoregressive conditional heteroskedasticity (GARCH) model. The study extends its scope by examining the impact of the current pandemic, COVID-19, on the volatility of both SME and BSE by utilising the exponential GARCH and threshold GARCH models. Findings suggest that there is volatility spillover between the two indices. The results also highlight that the pandemic significantly affected the volatility of both indices. The findings of the study provide important implications for policymakers, portfolio managers, and investors, particularly when the Indian government has been emphasising on the growth and continuous support of the small and medium enterprises and increasing foreign investments in India's SMEs in recent years. [ABSTRACT FROM AUTHOR]

This article mainly investigates whether the war in Ukraine-Russia induces uncertainties and how this affects the returns of world stock market indices and commodity prices. To do so, we use the Economic Policy Uncertainty index (EPU), 96 stock market indices and the price of 67 commodities worldwide over the period from January 2022 to April 2023. Our empirical results show that the war between Ukraine and Russia increases uncertainties, and which negatively affect the performance of global financial markets and lead to higher commodity prices. These negative shocks tend to be smaller the longer the war persists. We also observe that responses to war-induced uncertainties are stronger in Europe and America. Overall, we provide unique evidence of the impact of war on uncertainties and how this is transmitted to stock market performance and commodity prices. [ABSTRACT FROM AUTHOR]

In order to evaluate and estimate significant aspects of time series data, various models with varying degrees of variation have been built in the last few years. The overarching goal of study is to examine the variance dynamics of several Indian stock market indexes using Normal and Students-t distributions. The sample data spans a long time period (more than 40 years), from April 1979 to May 2023, and includes dramatic happenings. The econometric method relies heavily on the GARCH models and other statistical investigations. The outcomes contribute to the growing body of knowledge in the field. The possible rewards and risks of an investment are the focus of this empirical study. The results encompass the dynamics of financial series, volatility sketching, a statistical and GARCH model feature assessment, and the fitness of series returns. This opens the door to different assessments of the Indian financial market. [ABSTRACT FROM AUTHOR]

This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations. [ABSTRACT FROM AUTHOR]

The main aim of this research study is to examine the behavior of the European developed stock market from France during the period that covers the COVID-19 pandemic and the war between Russia and Ukraine. The empirical analysis is based on selected CAC 40 stock index from the developed stock market in France during the sample period from January 2018 to December 2023. The empirical results provide an overview of the behavior of the French stock market in the selected period.

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. [ABSTRACT FROM AUTHOR]

Stock markets have always provided countries with a practical and flexible way to finance their economies. Hence, Islamic finance has embraced the stock market since the early 90s adopting the same framework as an ethical investment. Accordingly, Islamic investors in emerging countries shall have a range of choices when constructing a financial portfolio. However, existing screening methodologies lack flexibility as they are mainly based on rigid ratios and irrelevant thresholds. Consequently, these methodologies lead to an inefficient stock index as they completely ignore the features of each stock market along with the specificities of each industry. Thus, our study will try to propose a new screening methodology based on the optimal financial structure of each industry. The main objective of our study is to propose a methodology that will overcome different loopholes addressed in the literature. The present paper is an explanatory study which needs an empirical confirmation of the proposed methodology in order to measure its performance and efficiency against existing shari’ah-compliant indices. Hence, the main preliminary finding of our research is to enrich the academic debate on shari’ah-compliant screening methodologies through appealing to conventional corporate finance framework to enhance current methodologies. [ABSTRACT FROM AUTHOR]

The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous variables affecting the index's profitability for academics, portfolio managers, and investment professionals. Amid the global ramifications of the S&P 500, particularly in combating the eroding purchasing power caused by inflation, investing in stock indexes emerges as a means to safeguard wealth. The study employs various statistical techniques, emphasizing a methodical approach to uncover influential variables, and using static regression and autoregressive models for immediate and time-lagged effects. In conclusion, the findings have broad practical implications beyond investment strategy, extending to portfolio construction and risk management. Acknowledging inherent uncertainties in financial market forecasts, future research endeavors should target long-term trends, specific influences, and the impact of exchange rate fluctuations on index evolution. Collaboration across regulatory bodies, academia, and the financial industry is underscored, holding the potential for effective risk monitoring and bolstering overall economic and financial market stability. This research serves as a foundational step towards enhancing market understanding and facilitating more efficient investment decision-making approaches. [ABSTRACT FROM AUTHOR]

This paper focuses on the examination of the long-run relationship between money supply and selected national and global stock indices. Detailed knowledge of this relationship can be used by analysts, investors and monetary policy makers. Analysis of the relationship was performed using a 2-stage Engle-Granger cointegration. First, the stationarity of the time series was tested, then both the long-term OLS model and the short-term EC model were estimated. Time series were always tested on the longest period for which data were available. The long-term dependence of stock indices on the respective M2 was confirmed for the BOVESPA, FTSE100, S&P/BMV IPC, S&P BSE500, TSX and The 5000 Wilshire Small Cap Price Return indices. In contrast, the dependence between world money supply indicator GlobalM2, the stock index FTSEALL World, and the S&P500 index was not demonstrated. Additionally, no dependence was identified between the respective M2 and the DAX, PX, Nikkei225, KOSPI, SMI, SPCITIC300, Eurostoxx50, Willshire5000PR and ATX indices. Backward dependence of M2 on the stock index was found only for the Chinese SPCITIC300 index. [ABSTRACT FROM AUTHOR]

Stock index forecasting has always been an interesting subject for investors and related scholars. Accurately stock index forecasting can provide some helpful suggestions for investors and keep financial markets stable. In this study, a new forecasting system, including point prediction and interval prediction, has been proposed to predict the stock index. For obtaining a better predictive effect, multiple influencing variables are also considered in the novel model. More specifically, in the point prediction models, this study applies gradient boosting decision tree (GBDT) to choose some variables related to the stock index by determining their contribution to accurate prediction. Next, an autoencoder (AE) is utilized to reduce the dimensionality of screened factors for the purpose of reducing the effect of noise and improving the efficiency of forecasting. These reconstructed features are all inputted into bidirectional long short-term memory (BiLSTM) to do point prediction. The interval prediction is based on point prediction results and Gaussian process regression (GPR), intended to quantitative uncertainty of the variables. This study chooses the Chinese stock index including the Shanghai Securities Composite Index (SSEC), Shenzhen Composite Index (SZI) and China Securities Index 300 (CSI300) to demonstrate the validity of the innovative hybrid model. Furthermore, this study also selects some other models for comparison. Evaluating the performance of the novel hybrid model, it could be considered as a valid way to do stock index forecasting. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Finance & Financial Law / Finanse i Prawo Finansowe is the property of Wydawnictwo Uniwersytetu Lodzkiego and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The act of modeling and forecasting stock market volatility has become essential to risk management practice; it has become one of the most prevalent subjects in financial econometrics and has been mainly and continuously used in the valuation of financial assets and the Value at Risk, as well as the pricing of options and derivatives. The purpose of this paper is to contrast the FIGARCH model in the prospect of establishing the best algorithm to forecast PSI 20 index (PSI stands for Portuguese Stock Index). This study analyzed PSI 20 index to identify the behavior of the Portugal stock market in terms of volatility and then evaluated the forecasting ability of GARCH family models using Portugal PSI 20 index sample data for the long period from May 2010 - August 2023 (which is more than 13 years in daily data). There are 3396 daily observations. The results indicated that the PSI 20 index’s volatility is asymmetric. The present empirical investigation also seeks to illustrate the potential for investment returns as well as the associated risk. Our findings may have implications for risk management in Portugal, as well as a deeper understanding of the PSI-20 Portugal stock market volatility dynamics, given the scarcity of prior such studies.

The main aim of this research study is to examine the behavior of the European developed stock market from France during the period that covers the COVID-19 pandemic and the war between Russia and Ukraine. The empirical analysis is based on selected CAC 40 stock index from the developed stock market in France during the sample period from January 2018 to December 2023. The empirical results provide an overview of the behavior of the French stock market in the selected period. [ABSTRACT FROM AUTHOR]

This research investigates the effectiveness of various machine learning models, including Random Forest, Neural Networks, Adaboost, Discriminant Analysis, Logit Model, Support Vectors, and Kernel Factory. The study aims to forecast fluctuations in the ASEAN-5 stock index prices within an eleven-year period. The study provides useful information about how well machine learning techniques can predict changes in the stock market, with potential implications for both academic researchers and market participants. The findings imply that Adaboost consistently outperforms all others in predicting price changes accurately. This shows that machine learning algorithms are capable of accurately forecasting the movement of the ASEAN-5 stock index values. This study contributes to the growing body of research on the use of machine learning techniques in finance and provides investors with information to make informed decisions about investments in the ASEAN-5 region, ultimately leading to increased returns and improved portfolio performance. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic created unprecedented challenges for communities and economies around the world. Based on 13 leading global stock indices, the event study method is adopted in this research to explore the impact of the COVID-19 pandemic on the performance of the stock market indices in the short term. Regression results show that the global stock markets performed poorly in response to the COVID-19 pandemic. The findings of the event study imply that the stock markets reacted rapidly and negatively to the COVID-19 pandemic when lockdown restrictions were announced to contain the spread of the novel coronavirus. The Asian stock indices experienced more negative abnormal earnings than the stock indices of the countries outside Asia. Moreover, investor sentiments act as a wedge between financial investment decisions, returns, and fear of uncertainty caused by the pandemic. Furthermore, the panic experienced by investors may be an effective transmission channel through which the COVID-19 outbreak affects the returns on the stock market indices. [ABSTRACT FROM AUTHOR]

The Karachi Stock Exchange (KSE) is the largest stock exchange in Pakistan and one of the largest in South Asia. The KSE operates as an automated trading platform and offers a range of products, including shares of companies listed on the exchange, bonds, and other securities. The stock market in Karachi is an important indicator of the health of the Pakistani economy and is often used as a barometer of investor sentiment. The performance of the KSE can be influenced by a number of factors, including economic growth, inflation, interest rates, and political stability. Overall, the KSE has been performing well in recent years, with the benchmark KSE 100 Index reaching record highs and attracting increasing amounts of foreign investment. [ABSTRACT FROM AUTHOR]

As an emerging development model, green development requires the participation of the government, the market, and the public. It needs the formation of a positive interaction between policy guidance, market demand, and public will in order to realize a win–win situation comprising sustainable economic development and environmental protection. Thus, we explored green-development-related policies in China and defined four areas: industrial energy preservation, petrochemicals, agriculture, and the integration of the Yangtze River Economic Belt. Then, we analyzed how public attention relative to green development policies impacted the return of stock indices in the green industry using the Baidu search index. The research study was conducted on all trading days of A-shares in China from 1 January 2013 to 1 January 2023. In total, 2430 valid datasets were observed and curated over these 10 years. The public attention to green development policies was classified into four categories: industrial energy efficiency, petrochemicals, agriculture, and the Yangtze River Economic Zone. The attention focused on a category was measured by the number of searches of keywords that were the category's names on the Baidu platform. The number of searches was considered the variable representing the public's attention relative to each area of green development policies. The results showed that the increase in public attention to green development policies was not correlated with the increase in the return on investment (ROI) of green and carbon-neutral industries. In addition, information on green development policies had no significant effect on encouraging individuals to participate in investments in the green industry and had little effect on the financing of the carbon-neutral industry. The lack of details for green development policies and effective guidelines for potential investors needs to be overcome in order to encourage investment and financing. [ABSTRACT FROM AUTHOR]

Motivation: Under contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the possible nonlinear relations between the two indicators. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated. Objectives: To contribute to filling the gaps identified in existing research, this study models relationship between the stock market index and coronavirus pandemic by leveraging volatility in the stock market and covid data through time delay and best degree in a polynomial environment. The resultant optimal time delay and best degree model is used to derive a high-accuracy prediction of stock market index. Novelty: In line with the possible relations, the novelty of this study is that it proposes, validates and implements polynomial regression with time delay to model nonlinear relationship between the stock index and covid. Methods: This study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment. Findings: The empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation of the nonlinear relationship such predictors have with stock index for China. Relative to results from the polynomial regression without delay, the empirical evidence from the model with delay show that an optimal time delay of 17 weeks makes it possible to predict the stock index at high accuracy and record improvements of 16-fold or higher. The representative delay model is used to project for up to 17 weeks for future trends in the stock index. Implication: The implication of the findings herein is that the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases to achieve better results.

Multi-step stock index forecasting is vital in finance for informed decision-making. Current forecasting methods for this task frequently produce unsatisfactory results due to the inherent randomness and instability of the data, thereby underscoring the demand for advanced forecasting models. Given the superiority of the capsule network (CapsNet) over CNNs in various forecasting and classification tasks, this study investigates the potential of integrating a 1D CapsNet with an LSTM network for multi-step stock index forecasting. To this end, a hybrid 1D-CapsNet-LSTM model is introduced, which utilizes a 1D CapsNet to generate high-level capsules from sequential data and an LSTM network to capture temporal dependencies. To maintain stochastic dependencies over different forecasting horizons, a multi-input multi-output (MIMO) strategy is employed. The model's performance is evaluated on real-world stock market indices, including S&P 500, DJIA, IXIC, and NYSE, and compared to baseline models, including LSTM, RNN, and CNN-LSTM, using metrics such as RMSE, MAE, MAPE, and TIC. The proposed 1D-CapsNet-LSTM model consistently outperforms the baseline models in two key aspects. It shows notable reductions in forecasting errors when compared to the baseline models. Additionally, it displays a slower rate of error escalation as forecast horizons lengthen, suggesting enhanced robustness for multi-step forecasting tasks.

We forecast realized volatilities by developing a time-varying heterogeneous autoregressive (HAR) latent factor model with dynamic model average (DMA) and dynamic model selection (DMS) approaches. The number of latent factors is determined using Chan and Grant's (2016) deviation information criteria. The predictors in our model include lagged daily, weekly, and monthly volatility variables, the corresponding volatility factors, and a speculation variable. In addition, the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models, including size, inclusion probabilities, and coefficients, are examined. We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts. Furthermore, the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.

Considering the upward trend of the inflation rate and the relative stabilization of the nominal interest rate in Iran, this question becomes important: how have the stock returns of different industries reacted to these two macroeconomic variables? In this article, the effect of inflation and interest rate on stock returns of different industries in Iran is investigated empirically according to monthly data of the period April 2010- March 2022 using Panel-ARDL model. First, to make the comparison between stock indices of different industries possible, these indices were homogenized based on a base month and then the model was estimated. Empirical results show that inflation in the short and long term has a positive and significant effect on the nominal returns of stocks of various industries for the period, but inflation has a negative and significant effect on the real returns in the long term. The nominal interest rate in the short and long term causes a decrease in the nominal and real returns of stocks of various industries. In addition, the variables of exchange rate, global oil price, and liquidity were also considered as control variables. These results show that stocks have not been able to protect against inflation in the long term, and monetary authorities should pay attention to its effects on the stock market when managing interest rates.

This paper investigates the impact of foreign fund' flow on the Indonesian stock index incorporating other variables, namely the international stock market, gold price, foreign exchange rate, and the oil price. GJR-GARCH (1,1) model is used to analyze daily time-series data on IDX, foreign fund flows, the S&P 500, and gold, currency, and oil prices from 2014 to 2019. There is an evidence of leverage effect. It means that there is an asymmetric news impact on the conditional variances. Currency and oil prices are the only variables to have an impact on the Indonesian stock market index, while the rest of the variables do not influence the index. The government may provide infrastructures to attract foreign investors. At the same time, the government has to issue the policy that will protect the economy from stock market shocks. Finally, investors may include gold in their portfolio to diversify their investments. JEL Classification: G120, G10, G40 [ABSTRACT FROM AUTHOR]

In this paper we implement a combination of data-science and fuzzy theory to improve the classical Barndorff-Nielsen and Shephard model, and implement this to analyze the S&P 500 index. We preprocess the index data based on fuzzy theory. After that, S&P 500 stock index data for the past 10 years are analyzed, and a deterministic parameter is extracted using various machine and deep learning methods. The results show that the new model, where fuzzy parameters are incorporated, can incorporate the long-term dependence in the classical Barndorff-Nielsen and Shephard model. The modification is based on only a few changes compared to the classical model. At the same time, the resulting analysis effectively captures the stochastic dynamics of the stock index time series. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic might accelerate Fourth Industrial Revolution such as FinTech, AI and Robotics. This study explores impact of the COVID-19 on Fourth Industrial Revolution by quantifying such an impact on stock market. Stock indices and ETFs are handled to find log-return distributions via kernel estimation. Before, during and after outbreak of the COVID-19, distributions and related measures are investigated. Results reveal that E-commerce and Fourth Industrial Revolution have grown 1.5-1.7 times in increase rates of daily net returns, compared to main stock indices. A statistical test supports that AI & Robotics sector has grown under influence of the COVID-19. [ABSTRACT FROM AUTHOR]

The act of modeling and forecasting stock market volatility has become essential to risk management practice; it has become one of the most prevalent subjects in financial econometrics and has been mainly and continuously used in the valuation of financial assets and the Value at Risk, as well as the pricing of options and derivatives. The purpose of this paper is to contrast the FIGARCH model in the prospect of establishing the best algorithm to forecast PSI 20 index (PSI stands for Portuguese Stock Index). This study analyzed PSI 20 index to identify the behavior of the Portugal stock market in terms of volatility and then evaluated the forecasting ability of GARCH family models using Portugal PSI 20 index sample data for the long period from May 2010 - August 2023 (which is more than 13 years in daily data). There are 3396 daily observations. The results indicated that the PSI 20 index's volatility is asymmetric. The present empirical investigation also seeks to illustrate the potential for investment returns as well as the associated risk. Our findings may have implications for risk management in Portugal, as well as a deeper understanding of the PSI-20 Portugal stock market volatility dynamics, given the scarcity of prior such studies. [ABSTRACT FROM AUTHOR]

The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid development of data engineering, a situation has arisen wherein extensive amounts of information must be processed at finer time intervals. Addressing the prevalent issues of difficulty in handling multivariate high-frequency time-series data owing to multicollinearity, resource problems in computing hardware, and the gradient vanishing problem due to the layer stacking in recurrent neural network (RNN) series, a novel algorithm is developed in this study. For financial market index prediction with these highly complex data, the algorithm combines ResNet and a variable-wise attention mechanism. To verify the superior performance of the proposed model, RNN, long short-term memory, and ResNet18 models were designed and compared with and without the attention mechanism. As per the results, the proposed model demonstrated a suitable synergistic effect with the time-series data and excellent classification performance, in addition to overcoming the data structure constraints that the other models exhibit. Having successfully presented multivariate high-frequency time-series data analysis, this study enables effective investment decision making based on the market signals. [ABSTRACT FROM AUTHOR]

This study attempts to estimate the impact of the announcements of hosts of large sporting events on domestic stock markets. The research problem is to establish a connection between the uniqueness of a sporting event and investors' beliefs through stock price behavior. Using appropriate estimation windows, 13 different sporting events classified as large, including mega and major events, were tested. The obtained results show that, in principle, one day after the announcement of the host of a large sporting event, an average positive reaction of 0.22% is observed on national stock exchanges. The analyzed events were also classified as being neutral for capital markets or generating positive or negative reactions when the host country is announced to the public. [ABSTRACT FROM AUTHOR]

The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous variables affecting the index’s profitability for academics, portfolio managers, and investment professionals. Amid the global ramifications of the S&P 500, particularly in combating the eroding purchasing power caused by inflation, investing in stock indexes emerges as a means to safeguard wealth. The study employs various statistical techniques, emphasizing a methodical approach to uncover influential variables, and using static regression and autoregressive models for immediate and time-lagged effects. In conclusion, the findings have broad practical implications beyond investment strategy, extending to portfolio construction and risk management. Acknowledging inherent uncertainties in financial market forecasts, future research endeavors should target long-term trends, specific influences, and the impact of exchange rate fluctuations on index evolution. Collaboration across regulatory bodies, academia, and the financial industry is underscored, holding the potential for effective risk monitoring and bolstering overall economic and financial market stability. This research serves as a foundational step towards enhancing market understanding and facilitating more efficient investment decision-making approaches.

The article proposes a complex of economic and mathematical models of assessment, analysis, and forecasting of the world stock indices development state for effective management of investment flows. The modern concept and strategy of European countries’ stock market development were considered, and the existing methods of diagnosing the stock market development level were analyzed. ARIMA models were built and spectral analysis of the main world indices was carried out. The possible trends in the stock indices development for the future period were analyzed using predictive models. It makes it possible to determine directions for improving the stock market efficiency and investment strategies in modern conditions. The proposed set of models for evaluation, analysis, and forecasting of the world stock indices state can be used to test various trading strategies and to test various hypotheses in relation to the entire stock portfolio of a particular company or to one stock index. The results of the research can be used in the development of theoretical provisions and methodological tools for evaluating the global stock market effectiveness in modern conditions and development trends in local markets.

Stocks play a crucial role in the stock market, which is at the very core of every nation's economic growth. Investors, analysts, and others in related fields have turned their attention to stock price analysis. This study empirically investigates the conditional variance (volatility) or empirically estimates the price volatility spillover transmission in the daily returns of IPC Mexico index from Mexico stock market, for the long period January 1993 - July 2023 (which is more than 30 years daily data) using the GJR- GARCH model. There are 7661 daily observations included in the study. The recurrence of outcomes had leptokurtosis, were skewed to the left, and were not normally scattered; there was also confirmation of ARCH effects. The insights of the symmetric GARCH model showed confirmation of volatility clustering and endurance; the results of the GARCH-GJR model demonstrated the leverage impact and volatility clustering in the sample index. Favorable events alter the conditional variance (volatility), resulting in significant asymmetric GJR-GARCH values. This leads to the conclusion that favorable information has greater effects on index return volatility than adverse information. The results of this research constitute vital information for financiers, risk analysts, and regulators. [ABSTRACT FROM AUTHOR]

The article proposes a complex of economic and mathematical models of assessment, analysis, and forecasting of the world stock indices development state for effective management of investment flows. The modern concept and strategy of European countries’ stock market development were considered, and the existing methods of diagnosing the stock market development level were analyzed. ARIMA models were built and spectral analysis of the main world indices was carried out. The possible trends in the stock indices development for the future period were analyzed using predictive models. It makes it possible to determine directions for improving the stock market efficiency and investment strategies in modern conditions.The proposed set of models for evaluation, analysis, and forecasting of the world stock indices state can be used to test various trading strategies and to test various hypotheses in relation to the entire stock portfolio of a particular company or to one stock index. The results of the research can be used in the development of theoretical provisions and methodological tools for evaluating the global stock market effectiveness in modern conditions and development trends in local markets. [ABSTRACT FROM AUTHOR]

Copyright of Central Asian Economic Review is the property of Narxoz University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this paper is to identify the long-run and short-run relationship between the values of the Macedonian Stock Exchange Index composed of 10 most liquid listed stocks (MBI10), the Zagreb Stock Exchange Index (CROBEX) composed of the most liquid listed stocks, the Sarajevo Stock Exchange Index (SASX-10) composed of 10 most liquid listed stocks and the Belgrade Stock Exchange Index composed of 15 most liquid listed stocks (BELEX 15) and the selected macroeconomic variables. In order to identify the macroeconomic variables that affect the values of the selected stock indices, the analytical-synthetic method and the statistical method are applied. The statistical method uses econometric models for data analysis and interpretation and includes the application of the following econometric tools: Panel unit root test, Fisher -Johansen cointegration test, application of the panel vector error correction model (PVECM) and the Wald test statistics. The results of PVECM between the values of the selected stock indices and independent variables such as industrial production index 2015=100, average monthly gross wages, shows existence of conditionality or causal relationship on the long-run, when independent variable Harmonized Index of Consumer Prices (HICP) according to the COICOP classification 2015=100 is exluded from the model. By applying PVECM, it can be concluded that there is a long run causality running from independent variable to dependent variable, meaning that between the values of the selected stock indices and industrial production and average gross wages there is speed of adjustment towards long run equilibrium.

This study makes use of a very long time series of the S&P Composite Index, checking once more that the rates of return benefit from aggregational normality. It performs unit root tests as well as elementary statistical tests that take advantage of normality. It finds that mean blur is not consistent with the hypothesis of random walk with constant parameters, because the means of the annual real rates of linear return can be estimated as usual. It gives further evidence that the rates of return on the S&P Composite Index are mean-reverting. [ABSTRACT FROM AUTHOR]

As an emerging development model, green development requires the participation of the government, the market, and the public. It needs the formation of a positive interaction between policy guidance, market demand, and public will in order to realize a win–win situation comprising sustainable economic development and environmental protection. Thus, we explored green-development-related policies in China and defined four areas: industrial energy preservation, petrochemicals, agriculture, and the integration of the Yangtze River Economic Belt. Then, we analyzed how public attention relative to green development policies impacted the return of stock indices in the green industry using the Baidu search index. The research study was conducted on all trading days of A-shares in China from 1 January 2013 to 1 January 2023. In total, 2430 valid datasets were observed and curated over these 10 years. The public attention to green development policies was classified into four categories: industrial energy efficiency, petrochemicals, agriculture, and the Yangtze River Economic Zone. The attention focused on a category was measured by the number of searches of keywords that were the category’s names on the Baidu platform. The number of searches was considered the variable representing the public’s attention relative to each area of green development policies. The results showed that the increase in public attention to green development policies was not correlated with the increase in the return on investment (ROI) of green and carbon-neutral industries. In addition, information on green development policies had no significant effect on encouraging individuals to participate in investments in the green industry and had little effect on the financing of the carbon-neutral industry. The lack of details for green development policies and effective guidelines for potential investors needs to be overcome in order to encourage investment and financing.

Abstract The multi-fractal analysis has been applied to investigate various stylized facts of the financial market including market efficiency, financial crisis, risk evaluation and crash prediction. This paper examines the daily return series of stock index of NASDAQ stock exchange. Also, in this study, we test the efficient market hypothesis and fractal feature of NASDAQ stock exchange. In the previous studies, most of the technical analysis methods for stock market, including K-line chart, moving average, etc. have been used. These methods are generally based on statistical data, while the stock market is in fact a nonlinear and chaotic system which depends on political, economic and psychological factors. In this research we modeled daily stock index in NASDAQ stock exchange using ARMA-GARCH model from 2000 until the end of 2016. After running the model, we found the best model for time series of daily stock index. In next step, we forecasted stock index values for 2017 and our findings show that ARMA-GARCH model can forecast very well at the error level of 1%. Also, the result shows that a correlation exists between the stock price indexes over time scales and NASDAQ stock exchange is efficient market and non-fractal market.

The act of modeling and forecasting stock market volatility has become essential to risk management practice; it has emerged as one of the most popular themes in financial econometrics and has been mainly and constantly used in the valuation of financial assets and the Value at Risk, as well as the pricing of options and derivatives, are all part of the process. The purpose of this paper is to contrast the GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) family models -- GARCH, PGARCH, EGARCH, GJR-GARCH, MGARCH, and IGARCH -- with the EWMA (Exponentially Weighted Moving Average) models in the prospect of establishing the best algorithm to forecast TSEC volatility weighted index from Taiwan stock market. We employ daily returns for the range between July 1997 and July 2023. The sample data spans a long time period i.e. 26 years of data with 6375 observations, which includes dramatic events such as the financial crisis of 2008, the COVID-19 epidemic, and the war between Russia and Ukraine. We observe that the asymmetric model PGARCH with a Student's t distribution develops the best model for assessing the volatility of the chosen index, but PGARCH with a normal error distribution produces the best estimating performance outcomes and hence outperforms the EWMA model. The present empirical investigation also seeks to illustrate the potential for investment returns as well as the associated risk. Our findings may have implications for risk management in Taiwan, together with a deeper exploration of the TSEC weighted index from Taiwan stock market volatility dynamics, given the scarcity of prior such studies. [ABSTRACT FROM AUTHOR]

The paper aims to present aspects regarding stock indices on the national capital market (Bucharest Stock Exchange, BVB). The pages of this article contain information on the importance of stock indices, the ways of forming the twelve stock indices, as well as the impact of the COVID-19 pandemic and the war in Ukraine on their evolution. [ABSTRACT FROM AUTHOR]