Stock Market Transactions Research Articles

IMPACT OF CORPORATE GOVERNANCE ON INTELLECTUAL CAPITAL EFFCIENCY - EVIDENCE FROM SRI LANKAN BANKING SECTOR

The study explores the intricate link between Corporate Governance (CG) factors and Intellectual Capital (IC) efficiency within the Sri Lankan banking sector. It delves into the impact of variables such as Board Size (BSIZE), Board Activity (BACT), Board Independence Composition (BIND), Audit Committee Size (AUDS), and Frequency of Audit Committee Meeting (AUDM) on Intellectual Capital, treating the latter as the dependent variable. For the five years between 2018 and 2022, the research technique comprises a thorough investigation of banking sector company in Sri Lanka, excluding three banks. The goal of the study is to obtain understanding of the connection between CG variables and IC efficiency through secondary data analysis using data from stock market transactions and audited financial statements. The main analytical tools are descriptive statistics and regression modeling, which enable a detailed comprehension of the relationship between CG dynamics and IC efficiency. Through a detailed examination of these variables across time and across different financial institutions, the study seeks to illuminate the ways in which CG practices impact the creation and application of IC in the banking industry. In the conclusion, the results of this study are important not only for scholars but also for professionals and decision-makers in the banking sector in Sri Lanka. The study provides significant insights that can guide strategic decision-making, governance reforms, and resource allocation within banking institutions, ultimately leading to improved organizational efficiency and long-term sustainability. This is achieved by revealing the complex relationships between Corporate Governance and Intellectual Capital.

Attention of women's liberation and investor herding behavior

In recent decades, the women's liberation movement is garnering attention on a global scale. To investigate the impact of women's liberation attention on herding behavior among male and female investors in the stock market, we obtain a unique account-level data set of retail investor transactions in the Chinese stock market. Our results document that investors in Chinese cities with higher women's liberation attention demonstrate less herding behavior than other investors. Moreover, by facilitating investors' confidence and risk tolerance, women's liberation attention indirectly reduces herding behaviors. Our results remain solid in robustness tests, and additional tests confirm that women's liberation attention helps narrow the gender gap in herding behavior. Moreover, women's liberation attention is particularly significant in reducing investor herding behavior under volatile market conditions. Our paper contributes to the studies of gender equality in the stock market and provides empirical evidence by using account-level data.

Sambut Tahun Baru Belajar Investasi Seru Bersama Siswa/i SMKN Negeri 3 Bengkulu Tengah

Technological changes and developments must be utilized by all groups, not only for consumption but can be used as a tool for investment, investment in this case is stock and capital market investment which can be done by all groups, including students. The problem that occurs at SMKN 3 Central Bengkulu is that students and teachers do not yet fully understand investment in the capital and stock markets. With this problem, the community service team consisting of lecturers from the Faculty of Economics and Business, Dehasen University, Bengkulu, in collaboration with the Indonesian Stock Exchange, representatives of Bengkulu, provided material regarding the capital and stock markets. The solution that the community service team can provide at SMKN 3 Central Bengkulu is to provide assistance in operating the applicationProfits Anywhere which has been downloaded by students and teachers on their respective smartphones. In this case, the method used is preparation, observation and presentation of the FGD. With this activity, students and teachers understand more about capital and stock market transactions and the importance of cultivating an entrepreneurial spirit in life and will later be able to gain profits without just relying on job vacancies from the government and private sectors.

IMPACT OF DIGITAL FINANCE ON STOCK MARKET PERFORMANCE IN NIGERIA (2012M1-2021M12)

Purpose: The stock market has long played a significant role in fostering economic growth in developing countries by providing equity funds for capital formation. Technological advancements, particularly in digital technology, have significantly influenced financial services, becoming a crucial aspect of financial affairs. Notably, digital payment services have gained prominence in Nigeria, offering potential improvements to the Nigerian stock market. Despite this, there appears to be a perceived gap between digital finance transactions and the performance of the Nigerian stock market. Therefore, this study centred on the impact of digital finance on stock market performance in Nigeria. The study specifically aimed to assess the influence of Automated Teller Machine (ATM), Point of Sales (POS), mobile payment (mobilepay), and web-based payment (webpay) digital finance transactions on stock market capitalization ratio in Nigeria. Methodology: This study, conducted for the period from January 2012 to December 2021, utilized Fully Modified Ordinary Least Squares regression to investigate the impact of digital finance on stock market performance in Nigeria. Monthly secondary data were sourced from the Central Bank of Nigeria's Statistical Bulletin, Statistics and Monthly Economic Reports. Findings and Conclusion: Empirical findings revealed that ATM, POS, mobile-based, and web-based digital finance transactions had a positive and significant impact on the stock market capitalization ratio in Nigeria. Consequently, the study concluded that digital finance contributes to enhancing stock market performance in the country Originality/Value: To promote further growth in the stock market, the study recommends expanding the deployment and usage of digital finance channels such as ATM, POS, mobile-based, and web-based channels, encouraging their utilization for stock market transactions by participants.

Exogenous and endogenous factors affecting stock market transactions: A Hawkes process analysis of the Tokyo Stock Exchange during the COVID-19 pandemic.

Transactions in financial markets are not evenly spaced but can be concentrated within a short period of time. In this study, we investigated the factors that determine the transaction frequency in financial markets. Specifically, we employed the Hawkes process model to identify exogenous and endogenous forces governing transactions of individual stocks in the Tokyo Stock Exchange during the COVID-19 pandemic. To enhance the accuracy of our analysis, we introduced a novel EM algorithm for the estimation of exogenous and endogenous factors that specifically addresses the interdependence of the values of these factors over time. We detected a substantial change in the transaction frequency in response to policy change announcements. Moreover, there is significant heterogeneity in the transaction frequency among individual stocks. We also found a tendency where stocks with high market capitalization tend to significantly respond to external news, while their excitation relationship between transactions is weak. This suggests the capability of quantifying the market state from the viewpoint of the exogenous and endogenous factors generating transactions for various stocks.

The Impact of Commercial Sectors on Environmental Quality: A Case Study of Tabriz's Ecosystem and Financial Landscape

Commercial sectors play a crucial role in the national economy by facilitating resource accumulation through both small and large savings, optimizing the distribution of financial resources, and directing them towards the costs and investment requirements of productive sectors. Most research exploring the link between economic growth and environmental factors is grounded in the Kuznets hypothesis. The findings indicate a unidirectional relationship between financial development and gas emissions. Specifically, the ratio of domestic credit to the private sector, expressed as a percentage of GDP, positively influences financial markets and significantly impacts biogas emissions in selected middle-income nations. Consequently, the hypothesis asserting a significant relationship between domestic credit to the private sector (as a percentage of GDP) and environmental gas emissions cannot be dismissed. Additionally, the ratio of traded stocks to the volume of stock market transactions, serving as a capital market indicator, has a positive and significant effect on emissions, reinforcing its role as an environmental quality indicator in the co-gas group of the two selected middle-income countries. Thus, the hypothesis regarding the significant relationship between the ratio of traded stocks and gas emissions as an environmental quality indicator remains valid. Based on the findings of this study, several recommendations are proposed: Reducing government size to foster greater non-governmental sector participation, enhancing public awareness across various demographics through media to mitigate environmental pollution, discouraging foreign investment in polluting industries, and promoting the establishment and development of environmental protection organizations through training and resource allocation.

Impact Analysis of Price War on the New Energy Automobile Industry Based on BYD and GWM

The price war that broke out in March in the first half of this year just ended in July. This is a knockout competition for auto companies, and companies that win the price war can take the lead in the market in the next few years. It turned out that BYD won the price war, while Great Wall Motors semi-annual performance dropped sharply. This article analyzes the strategies of BYD and Great Wall Motors in the price war and explores the various measures that new energy car companies should take in the price war. In-depth comparison of the data of the two companies, through data comparison, data analysis and event analysis, a relatively comprehensive analysis of the sales, financial and stock market transaction data of the two companies was carried out to explore the results of the price war. This article focuses on the impact of price wars on car companies and explores what strategies new energy car companies should adopt in the future.

The Impact of Information Asymmetry on Investment Behavior in the Stock Market

The stock market is the main component of the capital market and plays an important role in promoting the development of the national economy. Information asymmetry, as a typical issue in China’s stock market, triggers stock price fluctuations, increases stock market risks and largely influences investors’ investment behavior in stock market. Therefore, improving the information asymmetry problem is of great significance for the stable development of the stock market. This paper depends on the nature of information asymmetry, explores its specific manifestation in the stock market, and then analyses the process of information asymmetry issues on investment behavior. Studies indicate that information asymmetry leads to misdirected investment from three aspects, which include judgment on individual stock, assessment of the market value of the company and expectations of stock market developments, resulting in investor losses. Based on the above conclusions, this paper puts forward recommendations for improvement in terms of market regulation and the legal system: to improve the transparency of stock market transactions through the disclosure of information by regulators and companies. To complete the legislation of the stock market and establish strict punishment mechanisms to ease the problem of asymmetric information and maintain the stable operation of the stock market.

Political uncertainty and stock market liquidity, size, and transaction cost: The role of institutional quality

AbstractUsing panel data of 42 countries from 2001 to 2019, we examine whether political uncertainty (caused by uncertainty about national elections) explains variations in cross‐country liquidity, market size, and transaction cost. We also investigate whether institutional quality moderates the negative effects of political uncertainty on stock market development. We show that political uncertainty reduces stock market size, liquidity, and increases transaction costs. Our results indicate that institutional quality moderates the negative effects of political uncertainty on stock market development. However, we find no effects in emerging markets arising from the high prevalence of weak institutions. We confirm the robustness of our findings using alternative financial development measures and endogeneity. This study enhances our understanding of the salient role of political uncertainty in the development of the stock market, with important implications for market regulators, corporations, and investors.

The Different Dividend Signaling Effect under Tax Deduction around Ex-Right Day: Evidence from Taiwan Stock Exchange

Dividend tax policy is one of the important tools of government taxation. Observing the dividend tax policy and the behavior of stock prices around ex-rights will not only shed light on investment strategies, but also give us a clearer understanding of the microstructure of the capital market. Taiwan went through dividend tax policy and National Health Insurance (NHI) supplementary premium changes from 2014 to 2016. Therefore, this paper adopts the event study method to conduct empirical research on this major event period. The research conclusion points out: (1) During the research period, the company studied had a positive cumulative abnormal return before and on the ex-right day, and there was a negative cumulative abnormal return after the ex-right day. (2) When the tax reduction effect is more favorable to investors, there will be only a positive relationship with positive abnormal returns. (3) There is no statistical significance between the dividend tax reform policy and the negative abnormal return after ex-rights. The empirical results of this paper can help to better understand the pricing process of stocks by market microstructure systems such as dividend tax policies and help build a more stable stock market transaction structure. On the other hand, investors and companies can also gain their own investment or dividend policy inspiration from this research.

Stock Market Prediction Based on Financial News Text Mining and Investor Sentiment Recognition

The stock market is usually regarded as the bellwether of the economy, which can reflect the economic operation of a country or region. As a significant part of the financial market, the equity market plays a critical role in the financial sector. Whether in academia or investment field, stock market forecasts always excite great interest. Financial news is an important source of information in the financial market, which reflects the mood swings of investors and often goes hand in hand with the market trend. However, due to the unstructured and professional characteristics of financial news, there are challenges in accurately quantifying their emotional tendencies. This research is based on Hidden Markov Model (HMM) to segment financial news text. The recognition and classification of news emotion is carried out by bidirectional long short-term memory (BI-LSTM) algorithm, and long short-term memory(LSTM) model is trained with text emotion index and stock market transaction data to realize the prediction of stock market. The results show that BI-LSTM algorithm performs better than the emotional dictionary algorithm in emotional recognition. And the emotional index of financial news text can enhance the accuracy of stock market prediction to a certain extent. Compared with using stock market technical index and news text vector only, the prediction accuracy can be improved by about 2%.

ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market

In today’s world, people study and evaluate trading stocks to make informed decisions, based on available financial data and market information. Previous researchers relied on trend identification before making any decision to buy or sell stocks but fail to make accurate decisions due to complex systems. Some studies showed analysis to apply to stop loss on every stock transaction that got wrong levels due to limited features scaling that relied on single indicators without checking the performance metrics such as mean, standard deviation, and value at risk. Some existing models are based on theoretical implementation and they possess inaccurate success in real-time stock market transactions. Earlier risk management techniques were based on fundamental statistics of the company performance based on specific quarters that propose the future expects in the positive direction that is not every true which results in huge financial loss. Previous researchers failed to consider dynamic risk management parameters to ensure minimum loss for decision-making in fast-moving stock variations. Machine learning simply refers to learning about computers and making predictions from data. Identifying and analyzing the risk factors in the stock market are the major and crucial stage for predicting the company stock values at the national and international levels. In existing research, all risk management-related factors are analyzed based on fundamental statistics of the company performance which are measured as quarterly results, which will not give long-term true predictions and will not provide positive directions to invest in further stocks. This research majorly focused on risk management for national stock companies using the machine learning methodology and algorithms. The objective is to determine if stock market indicators are suitable decision-aid tools within the context of intraday risk management. The review of the literature revealed that while there are many studies looking to foresee changes in the stock market, there are few studies looking to improve stock market risk management methods using machine learning algorithms. The goal of this study was to fill this gap by utilizing the body of existing research on stock index forecasting combined with machine learning techniques for both short- and long-term risk managements. It has described the association between machine learning models and implicated the data with respect to discrete models based on supportive, dependable, nondependable parameters along with the name and type of the stock. This research has integrated a few crucial dependable parameters such as oil prices, on-hand projects, and future projects. It has integrated with the simple, multiple linear regression models to generate a signal for SPY growth. The proposed ML-based model has been evaluated by comparing two states of training and testing and achieved 96.3% of accuracy. The parameters used for evaluation are closing price, price differences, and daily return. The performance range of the proposed multiple regression model lies along the maximum drawn down which is 0.04411 for test cases and 1.2533 for training cases. Compare the performance of the proposed approach with that existing models with respect to the number of keys and methods associated with training and testing the data.

ASSESSMENT OF NIGERIA STOCK MARKET DEVELOPMENT ON GROSS DOMESTIC PRODUCT

The study looked at the evolution of the Nigerian stock market from 1990 to 2020 to determine whether there was any correlation and how it could aid in economic development. Expost facto research was employed for this study. From 1990 through 2020, a sample of stock market capitalisation and GDP at year-end is chosen. Stock market turnover ratio and stock market value traded ratio versus GDP were employed as measures of stock market development in the study. The R-squared value (0.705770) suggests that the exogenous variables (Stock Market Value Traded Ratio (VTR) and Stock Market Turnover Ratio (TOVR) in the model explain 71 percent of the total fluctuations in Gross Domestic Product. The Fstatistic (6.853455) shows that the entire model correctly and significantly explains the phenomena. The evidence from the long-run regression estimate demonstrates that Gross Domestic Product has a considerable positive effect on the Stock Market Value Traded Ratio (VTR) and Stock Market Turnover Ratio (TOVR). The Nigerian stock market is hence illiquid and has a high transaction cost. Low liquidity means that investors will have a harder time converting their stocks to cash. It is recommended that, in order to improve liquidity, the cost of transactions in the Nigerian stock market and the methodology used to determine stock prices be reviewed, among other things, to make raising capital more affordable.

Economic policy uncertainty and the US stock market trading: non-ARDL evidence

The present study investigates the impact of economic policy uncertainty, and economic factors on the stock market index in the USA using Non-ARDL and Quantile models. The findings reveal that declining economic and economic-political factors will increase the stock market index in the US. The results indicate that the effect of inflation and GDP variables follows a nonlinear pattern. Similar results using quantitative regression showed asymmetric impacts of inflation and GDP on stock market transactions.

The Formulation of Stock Trading Strategy Summary

Stock traders can maximize their total income by making scientific and reasonable transactions. According to the daily price flow, this paper determines whether traders should trade the assets in their portfolios. By establishing prediction models of future output value of different investment financial projects and quantitative analysis and sensitivity analysis of losses, we can combine the investments of financial products and evaluate their future value .Finally, the sensitivity analysis, advantages and disadvantages evaluation, error analysis and improvement of the model are carried out in this paper, so that the whole model can better provide guidance and practice when dealing with stock market transactions.

Research on the Application of Generative Adversarial Networks in the Generation of Stock Market Forecast Trend Images

Investors make capital investment by buying stocks and expect to get a certain income from the stock market. When buying stocks, they need to draw up investment plans based on various information such as stock market historical transaction data and related news data of listed companies and collect and analyze these data. The data are relatively cumbersome and require a lot of time and effort. If you only rely on subjective analysis, the reference factors are often not comprehensive enough. At the same time, Internet social media, such as the speech in stock forums, also affect the judgment and behavior of investors, and investor sentiment will have a positive or negative effect on the stock market. This has an impact on the trend of stock prices. Therefore, this article proposes a stock market prediction model that uses data preprocessing technology based on past stock market transaction data to establish a stock market prediction model, and secondly, an image description generation model based on a generative confrontation network is designed. The model includes a generator and a discriminator. A time-varying preattention mechanism is proposed in the generator. This mechanism allows each image feature to pay attention to the image features of other stock markets to predict stock market trends so that the decoder can better understand the relational information in the image. The discriminator is based on the recurrent neural network and considers the degree of matching between the input sentence and the 4 reference sentences and the image features. Experiments show that the accuracy of the model is higher than that of the stock pretrend forecast model based on historical data, which proves the effectiveness of the data used in this paper in the stock price trend forecast.

Analysis of the macroeconomic factors influence on the performance of financial markets

"This paper represents empirical research of the theory according to which there is an influence between macroeconomic factors and the financial performance of different states, depending on the success of their companies in the stock market. Similar results have been observed in research by various economists over the past decades. The most relevant companies are included in the group stock market indicators that have major effects on national, regional and global economies. Thus, we verified the effects of inflation, unemployment and GDP (Gross Domestic Product) per capita on their value variation by multiple linear regression, by checking the direction of the link between factors and the possibility of its existence. We analysed the evolution of macroeconomic factors over time and how they could influence stock market transactions and the value of their quotations. Considering the differences between the levels of development of the categories of countries according to the human development index, we performed a comparative analysis on developed and emerging states, a total of 6 states equally distributed between the two categories. "

Studying Changes on Stock Market Transactions Using Different Techniques for Multivariate Time Series

There are many studies dealt with univariate time series data, but the analysis of multivariate time series are rarely discussed. This article discusses the theoretical and numerical aspects of different techniques that analyze the multivariate time series data. These techniques are ANN, ARIMA, GLM and VARS models. All techniques are used to analyze the data that obtained from Egypt Stock Exchange Market. R program with many packages are used. These packages are the "neuralnet, nnet, forecast, MTS and vars". The process of measuring the accuracy of forecasting are investigated using the measures ME, ACF, MAE, MPE, RMSE, MASE, and MAPE. This is done for seasonal and non-seasonal time series data. Best ARIMA model with minimum error is constructed and tested. The lags order of the model are identified. Granger test for causality indicated that Exchange rate is useful for forecasting another time series. Also, the Instant test indicated that there is instantaneous causality between Exchange rate and other time series. For non-seasonal data, the NNAR() model is equivalent to ARIMA() model. Also, for seasonal data, the NNAR(p,P,0)[m] model is equivalent to an ARIMA(p,0,0)(P,0,0)[m] model. For these data, we concluded that the ANN and GLMs of fitting multivariate seasonal time series is better than multivariate non-seasonal time series. The transactions of Finance, Household and Chemicals sectors are significant for Exchange rate in non-seasonal time series case. The forecasts that based on stationary time series data are more smooth and accurate. VARS model is more accurate rather than VAR model for ARIMA (0,0,1). Forecasts of VAR values are predicted over short horizon, because the prediction over long horizon becomes unreliable or uniform.

Myopic Loss Aversion and Investment Decisions: From the Laboratory to the Field

Whether, and to what extent, behavioral anomalies uncovered in the lab manifest themselves in the field remains of first order importance in finance and economics. We begin by examining behavior of retail traders/investors making investment decisions in constructed laboratory markets. Our results show that the behaviors of the traders are consistent with myopic loss aversion. We combine the lab results with a unique individual-level matched dataset on daily stock market transactions and portfolio positions over a two year period. We find that lab behaviors help to predict, but do not fully capture, the essential real-world trading analogs of retail traders. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

The effect of insider trading laws and enforcement on stock market transaction cost

Theoretical arguments suggest that as countries enact insider trading laws and complement them with enforcement, stock market information risk reduces and investor participation increases, and this will therefore have a negative effect on liquidity trading cost. Consistent with this expectation, based on panel data comprised of 32 countries for the period 2001–2015, we find that stringent insider trading laws and enforcement reduce stock market transaction cost. However, in countries where investor protection is poor, our results show that stringent insider trading laws have no effect on liquidity trading cost. We further find that stringent insider trading laws interact with institutional quality to reduce liquidity trading cost. Our findings are robust to difference-in-differences based on the 2008 global financial crises. The overall evidence implies that market participants will experience lower liquidity trading cost if insider trading laws are enforced.