Stock Exchange Index Research Articles

Based on Baidu Index and GBDT Shanghai Index rise and fall forecast

The stock market reflects the country's economic conditions, and it is of great significance to have a good prediction effect on the stock market. But with the rapid rise of the Internet, big data, and machine learning, the prediction of the stock market trend is not limited to the traditional methods and data sets. The trend of the stock market is not only dependent on itself but also affected by some other factors. Therefore, based on the machine learning model, this paper studies the prediction of investors' attention to the Shanghai Composite Index trend. This paper crawled the relevant index data from the website of Baidu Index based on the selected keywords. The correlation coefficient is used to select the keyword data with the best lag order and data type and as the model's input data. Through the establishment of LSTM, LASSO, RF, and GBDT models, the rise and fall of the Shanghai Composite Index are predicted. That is to say. The paper takes the accuracy of the rise and fall prediction as the judgment standard. GBDT model has the best prediction effect on the Shanghai Stock Exchange Index and can best explain the rise and fall of the Shanghai Stock Exchange Index. So, people can use this research to buy stocks before they rise and sell them before they fall.

Effect of CEO Duality and Board Characteristics on the Choice of Sustainability Report Format of Listed Firms in India

Stakeholders have raised concerns about how business operations affect the environment. Firms have responded through integrated reporting (financial and non-financial), which seeks to provide additional information about firm activities to improve stakeholders’ trust. This study examines the effect of chief executive officer (CEO) duality and board characteristics on the choice of the sustainability report format in India. The study uses an inclusive sample of 800 firm-year observations between 2010 and 2019. The study applies the binary probit to analyze the data from the Indian Stock Exchange. We find that CEO duality increases the likelihood of choosing integrated reporting over stand-alone sustainability reporting. This study suggests that the combined role leads to poor governance and contributes to a choice that presents less information to stakeholders. The second finding shows that independent directors are more likely to choose stand-alone reporting over integrated reporting. The study suggests that the internal policing responsibility of independent directors supports a report format that communicates more information to stakeholders. Finally, the total number of directors is insignificant in terms of the sustainability report format. Our study adds novelty to research because previous studies have only examined CEO and sustainability. However, this study is the first to investigate CEO duality and board characteristics in the choice of a sustainability report format.

ERROR CORRECTION MODEL APPROACH AS A DETERMINANT OF STOCK PRICES

The short-term and long-term effects of ROI, EPS, PER Inflation, SBI, Exchange Rate, and GDP on the stock price are the focus of this study. The study's data came from company financial statements, including the Indonesian Stock Exchange Index LQ45. The stationarity test, the classical assumptions test, the cointegration test, and the error correction model test was utilized in this study's statistical analysis. KURS and SBI had a positive effect on stock prices in the short term, but there is no effect in the long term, and inflation and GDP do not affect the stock price both in the short term and in the long term, according to this study. As a result, investors and businesses can use this study's contribution as a point of reference when considering factors that have a short-term and long-term impact on stock prices.

Deep Learning Based Model to Forecast the Direction of Stock Exchange Market Using social media: A Review

Abstract: A company's stock exchange price is not just a measure of the present worth of the value of its shares, but an indicator of its expected future performance as well. The company is an incorporated organization with the Regulatory Body and shares trading members are registered with SEBI. Given that stock market statistics are extremely time-specific and usually nonlinear in nature, it is very difficult to forecast future stock values. The forecast offers details about the present state of the flow of inventory values. This will also be used to decide whether to purchase or sell the individual shares of the particular stock for consumers. Many scientists were conducted with different data mining techniques to forecast stock market prices. This paper attempts to forecast the stock price of companies in the national stock exchange index LIX15 using Neural Network technology. For designing and training the models, the past data of the chosen stock will be used. To verify the consistency of the model, the effects of the model are used for comparison with actual data.

Impact of The Coronavirus Covid-19 On Enterprises Sector In Poland - Evidence From The Warsaw Stock Exchange Index

2. Ashraf, B.N. 2020. Stock markets’ reaction to COVID-19: Cases or fatalities? Research in International Business and Finance 54. Google Scholar

Through a Glass Cliff Darkly

Abstract: After breaking through the glass ceiling, women often obtain precarious or risky leadership positions in crisis-ridden organizations ( the glass cliff; Ryan & Haslam, 2005 ). Due to women’s minority status, their appointment in a crisis can signal important changes to organizational stakeholders indicating the use of new strategies for overcoming the crisis (signaling theory; Spence, 1973 ). Our study examines whether the media visibility of organizations moderates these signaling effects in ways that either strengthen or weaken glass cliffs. We augmented the archival dataset used by Haslam et al. (2010 ) in which the glass cliff phenomenon was discovered by including data on the media coverage that the Financial Times Stock Exchange index 100 companies received between 2001 and 2005. Our analysis shows that glass cliffs were more pronounced in companies with low media visibility. This suggests that the media visibility of organizations can contribute to increased accountability regarding their personnel decisions in ways that expose women leaders to less discrimination.

NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange

This is an effort to analyze the reaction of stock prices of Indian public and private banks listed in NSE and BSE to the announcement of seven best case news events. Several recent studies have analyzed the correlation between stock prices and news announcements; however, there is no evidence on how private and public sector Indian bank stocks react to important news events independently. We examine these features by concentrating on a sample of banking and government news events. We classify these news events to create a group of negative and a group of positive tone of announcements (sentiments). The statistical results show that the negative banking news announcements had a one-month impact on private banks, with statistically significant negative mean CARs. However, with highly statistically substantial negative mean CARs, the influence of the negative banking news announcements on public banks was observed for two months after the news was published. Furthermore, the influence of the positive banking news on private banks persisted a month after the news was published. Positive banking news events had an influence on public banks for five days after they were published. The study concludes that public bank stocks react more to negative news announcements than positive news announcements in the same manner as the sentimental polarity of the news announcements as compared to private bank stocks. First, we retrieved the news articles published in prominent online financial news portals between 2017 and 2020, and the seven major news events were extracted and classified using multi-class text classification. The Random Forest classifier produced a significant accuracy of 94% with pre-trained embeddings of DistilBERT, a neural network model, which outperformed the traditional feature representation technique, TF-IDF. The training data for the classifier were balanced using the SMOTE sampling technique.

Mandatory CSR expenditure and firm performance in lag periods: Evidence from India

This study examines whether mandatory CSR expenditure has the same effect on firm performance after years of implementation. The data set consists of 80 firms with 640 firm-year observations on the Bombay Stock Exchange in India from 2013 to 2020 that practised sustainability reporting. This study used panel regression with random effect & fixed effect, and generalized method of moments (GMM) assumptions. The first findings show that mandatory CSR expenditure in the current year negatively affects Tobin q. The second findings show that mandatory CSR expenditure in the lag years (1 to 2 years) has no association with return on assets or Tobin q. The third findings show that mandatory CSR expenditure in the lag years (3 and above) has no association with return on assets or Tobin q. The re-alignment between voluntary CSR investment and associated benefit mostly happens in lag 1 and 2, but mandatory CSR shows insignificant results. Our results are robust to controlling firm-level variables. Previous studies examined mandatory CSR expenditure on firm performance, but this new study addresses the shorter and longer lag of mandatory CSR expenditure on firm performance. The study’s implication suggests the possible acceptability of allowing firms to write CSR expenditure as tax-deductible until the CSR expenditure and the associated benefit are aligned. However, until then, managers must be authorised to spend on CSR activities, knowing there is a safety net when CSR expenditures do not produce positive firm performance in subsequent years.

ANALISIS PENGARUH SENTIMEN INVESTOR, TINGKAT SUKU BUNGA, NILAI TUKAR DAN CONTAGION EFFECT TERHADAP IHSG

Investment is an activity to delay consumption, with the aim of getting added value in the future. Currently, the development of investment in Indonesia has increased greatly, as seen from the movement of the Jakarta Stock Exchange Composite Index (JKSE) which has occurred during the last 5 years has experienced a significant increase. The movement of the Jakarta Stock Exchange Composite Index (JKSE) is influenced by several factors. This study aims to determine how the influence of investor sentiment, exchange rate, BI Rate and Contagion Effect on the Jakarta Stock Exchange Composite Index (JKSE). The research method used in this research is Multiple Regression Analysis and Vector Error Correction Model. The results of this study indicate that there is a positive influence between Investor Sentiment and Jakarta Stock Exchange Composite Index (JKSE), Exchange Rates have a negative effect on Jakarta Stock Exchange Composite Index (JKSE), while the BI Rate has no effect on the Jakarta Stock Exchange Composite Index (JKSE). The ASEAN Stock Exchange Index has a long-term relationship with the Jakarta Stock Exchange Composite Index (JKSE) and is integrated with each other.

Behavioral economics: who are the investors with the most sustainable stock happiness, and why? Low aspiration, external control, and country domicile may save your lives—monetary wisdom

Slight absolute changes in the Shanghai Stock Exchange Index (SHSE) corresponded to the city’s immediate increases in coronary heart disease deaths and stroke deaths. Significant fluctuations in the Shenzhen Stock Exchange Index (SZSE) corresponded to the country’s minor, delayed death rates. Investors deal with money, greed, stock volatility, and risky decision-making. Happy people live longer and better. We ask the following question: Who are the investors with the highest and most sustainable stock happiness, and why? Monetary wisdom asserts: Investors apply their deep-rooted values (avaricious love-of-money aspiration and locus of control, Level 2) as a lens to frame critical concerns in the proximal-immediate (Shanghai Stock Exchange Index changes, Level 1) and the omnibus-distal contexts (domicile: city vs. country, Level 2) to maximize expected utility (portfolio changes, Level 1) and ultimate serenity (stock happiness, Level 1). We collected multilevel data—the longitudinal SHSE and 227 private investors’ daily stock happiness and portfolio changes for 36 consecutive trading days in four regions of China. Investors had an average liquid asset of $76,747.41 and $54,660.85 in stocks. This study is not a “one-shot” game with “nothing at stake.” We classified Shanghai and Beijing as the city and Shenzhen and Chongqing as the country. Our cross-level 3-D visualization reveals that regardless of SHSE volatility, investors with low aspiration, external control, and country domicile enjoy the highest and most sustainable stock happiness with minimum fluctuations. Independently, investors with low aspiration, external control, and country domicile tend to make fewer portfolio changes than their counterparts. Behaviorally, less is more, debunking the myth—risky decisions excite stock happiness. Our longitudinal study expands prospect theory, incorporates attitude toward money, and makes robust contributions to behavioral economics and business ethics. We help investors and ordinary citizens make happy, healthy, and wealthy decisions. Most importantly, the life you save may be your own.

Pre And Post Vaccination Effect On Selected Sectoral Indices Of National Stock Exchange: A Critical Study

This study is an attempt to explore the effect of the COVID-19 pandemic on selected sectoral indices of the national stock exchange and the relationship among themselves also examined. COVID-19 pandemic was first identified in China during December 2019. To prevent the spread of pandemics various measures were taken by countries around the world. All commercial activities were restricted and people had to remain in their houses which affected the economy of countries. The stock market was also affected by this pandemic. For exploring the relationship and effect of pandemic various statistical tools have been applied. Secondary data of all sectoral indices price from 24th Jan to 24th July 2020 have been taken for the analysis. findings suggest that there is a significant impact of the Lockdown period on the performance of the stock indices.

Cointegration and causality relationship of Indian stock market with selected world markets

Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

Predicting the NSE stock index trends considering global financial variables and ARIMA model

With the advent of technology and advancements in storage capacities, one can get the historical data for any of the major stock markets of the world. In the last few decades, the Indian stock market grew very fast. The investment capacity and frequency for Indian stock markets increased drastically recently. More and more people are investing in stock markets and mutual funds nowadays and as a result of that there have been numerous attempts to forecast the stock market index so as to gain maximum profit. The proposed work forecasts the opening value of the National Stock Exchange index using the ARIMA model.

IPOs in Indian Stock Market: Analyzing Pricing and Performance of IPO Listed in 2021

The covid-19 pandemic has had unprecedented effects on the world economy. All countries, including India, have gone through this crisis. The coronavirus reported in India in March 2020 has spread across the nation, affecting all industries. Like other sectors of the economy, the pandemic has adversely affected the Indian stock market. In March 2021, the value of the Nifty and Sensex, two important indices, fell from 12000 points to 8000 points and 32000 points to 17000 points, respectively. The initial wave continued till the end of the year. However, 2021 has been a bumper year for the stock market, with businesses generating $1.2 trillion through Initial Public Offering (IPOs). The year started with an IPO and ended with one, and sixty-six IPOs were listed on the Indian stock exchange despite the adverse environment. It is more than twice as many as the previous year. Some IPOs received more public attention and could not perform as expected. This study aims to examine the performance of IPOs on the day of listing and their current status. The findings of this study will help those new to the field of trading and researchers to understand the process of IPO listing and its working.

S&P 500 Index and Volatility Forecast of Chinese Stock Market

The main purpose of this article is to examine the role of the S&P 500 index in predicting the volatility of China's stock market. Our work is based on the autoregressive model (AR). We further extend this simple benchmark model by adding the volatility of the S&P 500 index. Intrasample regression shows that after adding this indicator, the overall goodness of fit of the model is rising, the explanatory ability is enhanced, and the added variables are also very significant. The out of sample prediction shows that, in terms of statistical test, the extended model has a positive out of sample R-square compared with the benchmark model, and has passed the CW test; In terms of economic test, we find that the extended model has positive CER and Sharp Ratio (SR) compared with the benchmark model. The out of sample predictions of these two aspects show that the newly added S&P 500 index has a good prediction effect. In addition, we also conducted various robustness tests, such as replacing the previous dependent variable (Shanghai Stock Exchange Index) with the CSI 300 index, replacing the previous extended window with a rolling window for prediction, and extending the previous single period prediction to multi period prediction. In the multi period forecast, we found that the S&P 500 index is only effective in a short period, for example, within 3 months, but cannot play a predictive role when it is extended to 6 months.

A novel evolutionary optimization algorithm based solution approach for portfolio selection problem

<span>The portfolio selection problem is one of the most common problems which drawn the attention of experts of the field in recent decades. The mean variance portfolio optimization aims to minimize variance (risk) and maximize the expected return. In case of linear constraints, the problem can be solved by variants of Markowitz. But many constraints such as cardinality, and transaction cost, make the problem so vital that conventional techniques are not good enough in giving efficient solutions. Stochastic fractal search (SFS) is a strong population based meta-heuristic approach that has derived from evolutionary computation (EC). In this paper, a novel portfolio selection model using SFS based optimization approach has been proposed to maximize Sharpe ratio. SFS is an evolutionary approach. This algorithm models the natural growth process using fractal theory. Performance evaluation has <span>been conducted to determine the effectiveness of the model by making comparison with other state of art models such as <a name="_Hlk101168095"></a>genetic algorithm (GA) and simulated</span> annealing (SA) on same objective and environment. The real datasets of the Bombay stock exchange (BSE) Sensex of Indian stock exchange have been taken in the study. Study reveals the superior performance of the SFS than GA and SA.</span>

Going concern, COVID and bankruptcy prediction: in Italy identified valid forecast ratios of bankruptcy prediction

In this paper, a theoretical and operational analysis was carried out on the issue of going concern. First of all, from a theoretical point of view, the subject of going concern has been examined in-depth to explain how the Covid-19 health emergency has impacted the companies' ability to carry out their activities shortly. It also achieves this aim by implementing operational research having as its object forty companies belonging to three macro-sectors: industrial, financial services and non-financial services, whose stocks make up the Italian stock exchange index Ftse Mib (Financial Times Stock Exchange Milan. The FTSE MIB is the benchmark stock index in Italy. It consists of a basket of 40 stocks on the Euronext Milan and Euronext MIV Milan markets identified by capitalisation, trading volume and sector). The theoretical and operational analysis of the concept of going concern was carried out considering, also, the bankruptcy alert indices identified by the Italian National Council of Chartered Accountants and Accounting Experts. These indicators were introduced by Italian legislation with the entry into force of the new Code of Corporate Crisis to identify possible economic and financial instability situations before the state of crisis. The research, as mentioned above, was carried out using the alert indices recognised by the National Council of Chartered Accountants and Accounting Experts. This research emerged that these indices are actually predictive of a company crisis. It also completed the study illustrated in this paper by comparing the results of the bankruptcy's predictive alert indices and Altman's Z-score. It reached the trends identified by the alert indices with the results obtained from Altman's Z-score, and from this comparison, it can see that the final results are similar. The comparison between the alert ratios illustrated by the National Council of Chartered Accountants and Accounting Experts and the results of Altman's Z-score allows us to affirm two observations: undoubtedly, the alert ratios are characterised by a high predictive validity regarding the bankruptcy of companies and, in addition, that these ratios lead to similar results to those that can obtain by applying Altman's Z-score. In this paper, a theoretical and operational analysis was carried out on the issue of going concern. First of all, from a theoretical point of view, the subject of going concern has been examined in-depth to explain how the Covid-19 health emergency has impacted the companies' ability to carry out their activities shortly. It also achieves this aim by implementing operational research having as its object forty companies belonging to three macro-sectors: industrial, financial services and non-financial services, whose stocks make up the Italian stock exchange index Ftse Mib (Financial Times Stock Exchange Milan. The FTSE MIB is the benchmark stock index in Italy. It consists of a basket of 40 stocks on the Euronext Milan and Euronext MIV Milan markets identified by capitalization, trading volume and sector). The theoretical and operational analysis of the concept of going concern was carried out considering, also, the bankruptcy alert indices identified by the Italian National Council of Chartered Accountants and Accounting Experts. These indicators were introduced by Italian legislation with the entry into force of the new Code of Corporate Crisis to identify possible economic and financial instability situations before the state of crisis. The research, as mentioned above, was carried out using the alert indices recognized by the National Council of Chartered Accountants and Accounting Experts. This research emerged that these indices are actually predictive of a company crisis. It also completed the study illustrated in this paper by comparing the results of the bankruptcy's predictive alert indices and Altman's Z-score. It reached the trends identified by the alert indices with the results obtained from Altman's Z-score, and from this comparison, it can see that the final results are similar.

Solving constrained portfolio optimization model using stochastic fractal search approach

PurposeOptimum utilization of investments has always been considered one of the most crucial aspects of capital markets. Investment into various securities is the subject of portfolio optimization intent to maximize return at minimum risk. In this series, a population-based evolutionary approach, stochastic fractal search (SFS), is derived from the natural growth phenomenon. This study aims to develop portfolio selection model using SFS approach to construct an efficient portfolio by optimizing the Sharpe ratio with risk budgeting constraints.Design/methodology/approachThis paper proposes a constrained portfolio optimization model using the SFS approach with risk-budgeting constraints. SFS is an evolutionary method inspired by the natural growth process which has been modeled using the fractal theory. Experimental analysis has been conducted to determine the effectiveness of the proposed model by making comparisons with state-of-the-art from domain such as genetic algorithm, particle swarm optimization, simulated annealing and differential evolution. The real datasets of the Indian stock exchanges and datasets of global stock exchanges such as Nikkei 225, DAX 100, FTSE 100, Hang Seng31 and S&P 100 have been taken in the study.FindingsThe study confirms the better performance of the SFS model among its peers. Also, statistical analysis has been done using SPSS 20 to confirm the hypothesis developed in the experimental analysis.Originality/valueIn the recent past, researchers have already proposed a significant number of models to solve portfolio selection problems using the meta-heuristic approach. However, this is the first attempt to apply the SFS optimization approach to the problem.

Intraday and interday features in the high-frequency data: Pre- and post-Crisis evidence in China’s stock market

Extreme events—statistically improbable events with profound consequences—happen more often than expected in stock markets and lead to an urgent need to investigate market behavior, in particular in the context of stock market crashes. We propose a methodology that takes into account both intraday (or within-day) patterns and interday (or day-to-day) dynamics of the stock market, with an exclusive focus on the data around the 2015 stock market crisis in China. In the first stage, a quantitative analysis of our methodology accounts well for the differences in intraday and interday dynamics between the pre-Crisis and post-Crisis periods; in the second stage, with the interday features as input to machine learning methods, we predict directional movements of the daily closing price in the Shanghai stock exchange index and develop trading strategies. Empirical results evince the predictability of the market and the informativity of the interday features when they are included in the machine learning methods. Decision-tree-based ensemble methods produce relatively better classification and trading performance than the other methods. The gradient-boosting decision tree deals best with the information derived from the input features, in which the average price is the most important feature.

Inching Closer to the Agenda 2030 : Impact Investment and Potential of Social Stock Exchange in India

The concept of impact investment has gained significant momentum in India in the past decade. The article seeks to analyse the existing green investment options available in India, and its scope against the backdrop of the importance of the Sustainable Development Goals (SDGs). The proposed Social Stock Exchange in India is considered to be a low hanging fruit and can bring in multidimensional opportunities, for the conscious investors to have a safe haven to invest in, as well as for the social organisations, a platform for showcasing the impact they make in the world to raise investors. The article also explores the constructive role a CMA can play in making the Social Stock Exchange “a successful business decision” by India Inc.