Stock Exchange Market Research Articles

The Moderating Effect of Accounting Conservatism on Managerial Ownership and Growth Opportunity Relationship

The application of the accounting conservatism method in the company's financial statements is essential for company managers in their consideration of strategy development. In uncertain global economic conditions, the application of the precautionary principle or accounting conservatism in financial statements is needed to maintain the stability of the company's financial condition. This study aims to test and analyze the effect of managerial ownership and growth opportunities on accounting conservatism with leverage as a moderating variable. This study uses secondary data in the form of annual financial reports on property and real estate sector companies listed on the Indonesia Stock Exchange (IDX) and Indonesian Capital Market Directory. This study used a purposive sampling technique. The results showed that managerial ownership affects accounting conservatism, while growth opportunity does not affect accounting conservatism. For leverage used as a moderating variable, it is unable to moderate the effect of managerial ownership and growth opportunity on accounting conservatism. This study can help investors and regulators understand the role of managerial incentives in shaping financial reporting behavior. Companies and regulators may want to consider ownership structure when evaluating financial transparency and risk.

How has the relationship between major financial markets changed during the Russia–Ukraine conflict?

Geopolitics events have a significant impact on financial markets in the process of global economic integration and financial liberalization. Focusing on the 2022 Russia-Ukraine conflict, this paper utilizes the TVP-VAR network connectedness approach to investigate its specific impacts on the connectedness of major global financial markets. It examines the dynamic evolution of high-frequency and low-frequency shocks on market correlations during the pre-conflict, initial, and stalemate stages, revealing the changes in risk transmission paths. Findings show that the conflict led to increased market interconnectedness. Low-frequency became a major part of the total spillover, signaling long-term structural changes. The stock and foreign exchange market initially responded swiftly, serving as starting points for risk diffusion. The commodity market has been risk receivers throughout the conflict period. Intra-market and cross-market linkages have also changed. The German stock market was the main source of risk spillovers in the conflict, influencing international market linkages. In the global financial market network, several stock markets were the primary risk transmitters in the early stage of the conflict, while foreign exchange markets took on this role during the stalemate period. In addition, gold continues to play a safe-haven role. Furthermore, the TVP-VAR model is used to quantitatively assess the extent of the impact of the conflict on the spillover effects of major global financial markets at different time points and lags. Based on these findings, this paper constructs diversified portfolios and proposes a series of targeted risk management recommendations, which provide a theoretical basis for future policy formulation under similar crises.

Revisiting the impact of ESG on financial performance: Empirical evidence from the Italian Stock Exchange

Purpose: This paper re-examines the overall and pillar-level impact of the envi-ronmental, social, and governance (ESG) performance on the financial perfor-mance of companies listed in the Italian Stock Exchange market. By using the seminal paper by Velte (2017) – which focused on the German context – as the base comparison, we also test the moderating role of a firm's characteristics on that association. Design/methodology/approach: We analyze data on companies listed in the Ital-ian Stock Exchange from 2010 to 2022, sourced from the Refinitiv Eikon plat-form, for a total of 650 firm-year observations. Correlation and regression anal-yses are conducted to evaluate the link between ESG and a series of financial per-formance indicators including the return on assets (ROA) and Tobin's Q ratios. Findings: While neutral results are found for ROA, this study reveals that ESG per-formance has a positive impact on Tobin's Q, in contrast to what was found by Velte (2017). However, we confirm that the governance pillar exerts the most sig-nificant effect on Tobin's Q. Furthermore, the analysis of the firms' characteristics as moderating factors reveals that the risk and financial leverage of a company negatively influence the relationship between ESG performance and financial per-formance. Originality/value: This study contributes to the empirical literature on the associ-ation between ESG performance and financial performance by re-examining the study by Velte (2017) for a different period and different context. Moreover, we test for the moderating effect of the firms' characteristics on the relationship be-tween ESG and financial performance.

Impact of Macroeconomic Factors towards Price Index Performance in New York Stock Exchange Market

The NYSE is the world's biggest exchange market. Performance of the stock market has a big impact on how wealthy the economy is and how people live their daily lives. The stock price index, which serves as a benchmark for stock market performance, is a crucial tool for assessing each nation's economic situation. As a result, a significant amount of study has been done to determine how macroeconomic factors affect the performance of stock price indices. However, the literature does not support a consistent conclusion, but rather offers contradictory results. This study's objective is to dispel uncertainty in the literature by presenting fresh ideas that can improve descriptions of and understandings of stock price index performance. The aim of this research is to examine the New York Stock Exchange index for a span of four years that is between 2017 and 2021. Also, to examine stock price index performance and macroeconomics indicators. The NYSE Composite Index (NYA), which measures stock price index performance over the Nyse period 2017 to 2021, serves as the study's dependent variable (DV), and the GDP, rate of interest, inflation rate, and exchange rate serve as its independent variables (IVs). All of the companies listed and operating on the New York Stock Exchange (NYSE), as represented by NYA, are the study's target population. Secondary data were employed in this investigation. The DV data was acquired from NYSE websites, whilst the IVs were taken from reliable sources like the World Bank and International Money Fund. Regression analysis, descriptive statistics, and the reliability test were the only data analysis techniques used in this study.

Predictive Powers of Carbon Emissions and Oil Price on Stock and Foreign Exchange (FX) Markets Using Multivariate Recurrent Neural Networks (RNN) Model

The predictive power of environmental factors such as CO2 levels and Brent oil price was evaluated on the stock market (S&P 500) and foreign exchange (FX) rates for market currency pairs (EUR/USD) and emerging market currency pairs (MXN/USD) with recurrent neural networks (RNN). The S&P 500 is a collection of well-established companies in the United States representing the macroeconomic health of the nation and the global economy. The FX market is a global decentralized over-the-counter (OTC) market used to determine the spot price of currency pairs. A highly leveraged market usually trades within a specific price range. Since stocks and FX pairs are highly correlated to macroeconomic factors, it was hypothesized that environmental factors such as CO2 levels and oil prices also have predictive power due to their close causal relationship with anthropological economic activities. To verify the predictive power, an RNN model was built, and a bi-directional neural network with an internal state was used to process data sequences. The performance of RNN was quantified by measuring the residual prediction from the true value. Although the study at its current state might need further statistical rigor, it concluded that environmental factors increased the predictive power for the S&P 500 while decreasing it for the DM FX pair and the EM FX pair showed mixed results.

The determinants of volatility connectedness of South African equity super sectors

The paper aims to explore the determinants of total volatility connectedness of nine super sectors on the Johannesburg Stock Exchange (JSE) market from 3rd January 2006 to 31st December 2021. These sectors are Automobile and Parts, Chemical, Telecommunication, Technology, Energy, Health, Finance, Insurance, and General Industrials. The paper applied Diebold and Yilmaz connectedness matrix and the time-varying parameter – vector autoregressive (TVP-VAR) model to determine the sectorial total volatility connectedness index (STVCI). After that, the nonlinear autoregressive distributed lag model (NARDL) was used to determine the asymmetric effects and the drivers of STVCI. It was found that the partial sum decomposition of the South African volatility index (SAVI) and Economic Policy Uncertainty Index (EPU) are the key determinants of the STVCI both in the long and short run. However, domestic market return (DMR) shows no significant asymmetric effect on STVCI. The study concluded that SAVI and EPU are the key determinants of volatility connectedness among the JSE super-sectors. The results unveil important implications for sectorial investors and policymakers on potential regulations and stability of the significant determinants of spillover risk.

Asymmetric Effect of Oil Prices on Kazakhstan’s Stock Market Index and Exchange Rate

In this study, the asymmetric effect of oil prices in Kazakhstan on the exchange rate (USD/KZT) and Kazakhstan stock market index (KASE) returns was analyzed using the non-linear ARDL cointegration (NARDL) method with monthly data for the period January 2010-February 2024. As a result of the findings of the study, both positive and negative changes in oil prices statistically affect stock returns and exchange rates in the long term. The estimated long-term positive (0.115982) and negative (0.111234) coefficients of oil prices on KASE returns were found to be statistically significant at the 5% significance level. Thus, in the examined period, the KASE variable gave different reactions to positive or negative shocks in the oil price. In the model where the exchange rate is the dependent variable, the estimated long-term positive (-0.093875) and negative (-0.092918) coefficients of oil prices were found to be statistically significant at the 95% reliability level.

A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence

This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial time-series dataset. These models include Convolutional RNNs, Convolutional LSTMs, Convolutional GRUs, Convolutional Bi-directional RNNs, Convolutional Bi-directional LSTMs, and Convolutional Bi-directional GRUs. Our main objective is to utilize deep learning techniques for simultaneous predictions on multivariable time-series datasets. We utilize the daily fluctuations of six Asian stock market indices from 1 April 2020 to 31 March 2024. This study’s overarching goal is to evaluate deep learning models constructed using training data gathered during the early stages of the COVID-19 pandemic when the economy was hit hard. We find that the limitations prove that no single deep learning algorithm can reliably forecast financial data for every state. In addition, predictions obtained from solitary deep learning models are more precise when dealing with consistent time-series data. Nevertheless, the hybrid model performs better when analyzing time-series data with significant chaos.

Audit Quality, Auditor Specialization, and Sustainability Reporting: Their Impact on Cost of Capital Moderated by Company Size of the Kompas 100 Index in the Indonesia Stock Exchange Market

The capital market has become an attractive instrument for both local and foreign investors. This study aims to analyze and provide empirical evidence regarding the influence of audit quality, auditor specialization, and sustainability reporting on the cost of capital. Additionally, it investigates how company size moderates the relationship between these variables and the cost of capital. The study focuses on companies listed in the Kompas 100 Index on the Indonesia Stock Exchange (IDX) from 2018 to 2022. A quantitative approach is employed, utilizing secondary data available from the IDX (www.idx.go.id), specifically from company annual reports. Data collection involved recording relevant information from the annual reports of each company. The population of the study comprises companies included in the Kompas 100 Index, with a sample selected through non-probability sampling (purposive) with the criteria of being listed in the Kompas 100 Index for the consecutive periods of 2018 to 2022, sample resulted 54 companies. The data analysis method applied is Moderated Regression Analysis (MRA). The results reveal that audit quality has a negative effect on the cost of capital, while auditor specialization shows no significant effect. Furthermore, sustainability reporting also has a negative effect on the cost of capital. As for the moderating variable, company size successfully moderates the relationship between audit quality and the cost of capital, but it does not moderate the effect of auditor specialization or sustainability reporting on the cost of capital.

Seasonality Effects, Stock Exchange and Foreign Exchange Markets: Comparative Analysis of Volatility Behavior During Covid-19

This study investigated the effects of seasonality on stock exchange and foreign exchange markets of two WAMZ and two BRICS countries which include Nigeria, Ghana, and Brazil and China. The Auto Regressive Integrated Moving Average (ARIMA) regression approach and the Markov-regime switching methodologies were executed. The parsimonious ARIMA estimates reported Nigeria’s stock returns demonstrated lower volatility (SIGMASQ = 0.000141) than Ghana’s stock returns (SIGMASQ = 0.004003). Similarly, Nigeria had a lower returns volatility than Ghana (SIGMASQ = 0.001829 < 0.07727) in the foreign exchange market of their respective local currencies in relation to the US dollar during the covid-19 era. Comparatively, Nigeria’s stock and foreign exchange market performance showed lower returns volatility behaviour than Ghana’s markets during the covid-19 period. Brazil’s stock returns and the foreign exchange market return on the Brazilian Real had higher volatility compared to the Chinese stock market returns and the return on the Chinese Yuan exchange rate vis-à-vis the US dollar during the covid-19 era. Comparatively, Brazil’s stock and foreign exchange performance showed higher returns volatility behaviour than China during the covid-19 period. The ARIMA estimates were upheld by the Markov-switching regression results which reveal highest volatility effect of the foreign exchange market in Ghana and Brazil. Ghana's stock market volatility reverted more quickly than that of Nigeria, while in the second and third regimes, volatility of the Nigeria's stock exchange reverted more speedily than that of Ghana. The Markov estimates also reveals that China had more time than Brazil for stock market volatility to be repaired in the first regime, 40.43 months as against 36.77 months but a much lower time for China’s stock volatility to be restored in the second and third regimes, during and after the covid-19 pandemic. The expected durations in the regimes for the Brazilian forex market are 40.96, 47.98, and 33.99 months compared to 29.78, 27.55, and 21.65 months in the first, second, and third regimes. The study thus established that volatility on the returns of the Brazilian foreign exchange market before, during, and after the covid-19 pandemic takes longer time to die off unlike in the case of China’s forex market. The empirical findings validate the significance of the business cycle theory in these markets. Accordingly, our findings explicate the presence of sizable effects of cyclical swings on the stock and foreign exchange markets of Ghana, and Brazil compared to that of Nigeria and China respectively.

The Dynamic Relationship Between Exchange Rate Volatility and Stock Prices in the Egyptian Real Estate Market and the Moderating Effect of Interest Rates

Among different channels of the financial market, the exchange rates and stock markets play a vital role in the domestic and international markets. This study aims to explore the dynamic relationship between the exchange rate (USD/EGP) and the real estate sector listed companies of the Egyptian Stock Exchange (EGX) from 2013 to 2023. Additionally, the study examines the moderating effect of interest rates on the relationship. To achieve this, the study employs several econometric methods such as Augmented Dicky Fuller (ADF) test, Granger Causality test to determine the direction of the relationship, Vector Autoregressive (VAR) model, and more. The results demonstrate that a long-run relationship exists between the variables. Furthermore, there is a significant unidirectional causality that runs from the exchange rate to real estate stock prices; thus, fluctuations in the exchange rate can significantly affect the profitability of investments in real estate stocks. Therefore, investors should closely monitor the movements in the exchange rate level to predict changes in real estate stock prices. When including the interest rate as a moderator, the outcome changes to bidirectional causality, meaning that the exchange rate and real estate stock price movements affect each other at a 1% significance level. Hence, interest rate could be used as an instrument to control demand in both the stock market and foreign exchange market. Finally, it is recommended that policymakers use the monetary policy while considering this bidirectional relationship in the presence of interest rate as a moderating variable.

Towards Corporate Financial Performance: What is the Role of Board Gender Diversity?

This study seeks to analyse the influence of board gender diversity in influencing the firm financial performance using the agency theory, resource dependency theory and human capital theory in the study. The study population is the forty-nine (49) banks licensed by Bank of Tanzania (BOT) as at December 2022. The study used purposive method of sampling technique and only listed banks were selected as study sample. The study used panel data from listed banks in the Dar es salaam Stock Exchange (DSE) market for the period of ten (10) years from 2013 to 2022. Our study has used secondary data through bank’s annual audited reports extracted from their specific websites and DSE website. Our study used both descriptive and inferential analysis where data collected were analysed by using the STATA 15. The study used return to asset (ROA) as dependent variable and female director education, female director tenure and female director leadership as independent variables. Our results show that female directors on the boardroom with consideration of demographic features of education, tenure and leadership does not significantly influence financial performance of firms. Furthermore, our study recommends further researchers to measure the non-financial influence that female directors may have on firms.

The effect of Board Independence on Environmental disclosure; the moderating role of Gender Diversity in Amman stock exchange

Depending on the agency theory view and response to the modern invitations to speculate gender diversity role. How it can enhance the relationship between board independence and environmental disclosure due to prior studies focus on direct relationship between them. This study seeks to ascertain how gender diversity moderates the association between board independence and environmental disclosure among non-financial firms listed on the emerging Amman stock exchange market (ASE) between 2014 and 2019. The direct and indirect relationships were examined using multiple regressions on panel data analysis; the empirical findings indicate that board independence and environmental disclosure have a positive and substantial direct link. Additionally, the findings demonstrated that gender diversity is a significant moderator in this relationship. However, this study provides managers, owners and policymakers with a set of main recommendations related to the major need for a more board independence and gender diversity in order to enhance environmental disclosure in emerging markets. Our results are useful to policymakers interested for an important framework on environmental disclosure.

경제정책 불확실성이 한국 금융 불안에 미치는 영향 분석

Korea is a small open economy and is significantly affected by macroeconomic shocks in major countries. Previous studies have shown that financial markets such as the stock market and foreign exchange market are sensitive to financial instability and economic policy uncertainty in major developed countries. However, these studies have typically focused on individual financial markets and have not comprehensively analyzed how financial stress in Korea is affected by uncertainty in major developed countries from the perspective of financial stability. In this study, we use the spillover index proposed by Diebold and Yilmaz(2009, 2012) to analyze the impact of economic policy uncertainty (EPU) in the United States, Europe, Japan, and Korea on Korea's financial stress index (FSI). The study examines the influence of economic policy uncertainty in these major developed countries on Korea's financial stress over time. Furthermore, the findings indicate that during periods of heightened uncertainty, major developed countries have a relatively large impact on Korea's financial stress.

EXCHANGE RATES AND STOCK MARKET DYNAMICS: ISLAMIC VERSUS CONVENTIONAL FINANCIAL SYSTEMS

This study investigates the changes and persistence in dynamic connectedness between stock market performance and exchange rate fluctuations, comparing conventional and Islamic financial systems. With an eye on a global financial landscape characterized by the integration of capital markets and the adoption of floating exchange rate regimes, it examines the effects of exchange rate variations on the dynamics of the stock market in nine different countries - the United Kingdom, Australia, Japan, Singapore, Canada, China, India, Korea, and South Africa. The study employs daily data spanning from November 2015 to July 2023 and uses a comprehensive analysis of three-step methodology, including nonparametric causality-in-quantiles tests, asymmetric slope Conditional Autoregressive Valueat-Risk (CAViaR), and Time-Varying Parameter Vector Autoregressive (TVP-VAR) Connectedness measure. Our results underline the asymmetric impact of exchange rate fluctuations on stock markets and highlights the distinctive characteristics of Islamic financial markets. Comparing Islamic and conventional stock markets in the context of exchange rate fluctuations, this study not only serves to fill a gap in the existing literature but also emphasizes the significance of currency exchange rate swings for global investors, policymakers, and practitioners trying to understand the intricacies of global financial markets.

Analysis of Problems and Countermeasures in Chinese Stock Market

Chinas stock is one of the most popular trading platforms with high global attention, it plays an important role in Chinese economy and global economy. With the rapid development of Chinese economy, Chinas stock market has also achieved significant achievements. It not only provides a vital platform to finance, but also optimize resource allocation. Whats more, it decreases the investors risk of the investment, and makes it is possible for government to carry out macro-economic policies through market stock. it boosts the development of Chinas financial system, and increases the ability of the Chinas financial system to anti-risks. However, there are also some problems existing in Chinese stock market, for example, the less reasonable structure of investors, serious disadvantages in the small and medium-sized GEM market governance, and frequent problems occur in the stock market trading mechanism and exchange system. This paper will as these three problems basis of starting the topic, and put forward countermeasures and strategies, which will have significant role in solving the existing problems efficiently in Chinese stock market.

Managerial Performance and Economic Performance in the Technology Sector

The technology (tech) sector outperformed all other sector stock price averages in 2023. This paper addresses two issues regarding the tech sector. First, relative to other firms, are there any managerial areas in which the tech sector outperforms to explain their superior performance relative to all other firms? Second, within the tech sector, what managerial practices lead to higher profits? Four areas of managerial performance are considered: customer satisfaction, employee engagement and development, innovation, and social responsibility. This paper utilizes Bloomberg data and takes the novel approach of utilizing the Drucker Institute Indexes to assess performance in the tech sector. The Drucker Institute has computed a Corporate Effectiveness Index since 2017. The overall effectiveness index is based on performance regarding five dimensions: customer satisfaction, employee engagement and development, innovation, social responsibility, and financial strength. The firms they track are United States companies whose shares are traded on the New York Stock Exchange or Nasdaq Stock Market and meet certain size requirements. Among the four dimensions tested, customer satisfaction is the one dimension that is positive and significant when comparing across tech firms. Second, employee engagement is more important for tech firms than for other firms. The major contribution of this paper’s findings is that while innovation may be the hallmark feature of tech firms, innovation for its own sake does not drive firm performance. A focus on people (customers and employees) is the key to earning higher profits in the tech sector.

Impact of Covid-19 on companies’ performance and financial resilience: Evidence from Moroccan listed companies

The study aims to evaluate the impact of Covid-19 on company performance and explore the moderating effect of companies’ financial resilience. Data were retrieved from 312 firm-year observations corresponding to 76 companies listed on the Moroccan Stock Exchange Market throughout 2018–2021. Five regression models are used to examine the overall impact of the Covid-19 pandemic on corporate performance, as well as the specific effect on corporate performance of each of the financial indicators of corporate resilience, namely sales, leverage, liquidity, and financial autonomy. The results show that although there is a decrease in the mean of both ratios of financial and operational performance of Moroccan listed companies, statistical tests confirm only a significant negative effect of Covid-19 on operational performance. The results also show that companies with high sales, low debt, high liquidity, and financial autonomy are more resilient to the negative impact of the Covid-19 pandemic. Furthermore, given the significant size and the sectorial concentration of Moroccan listed companies, there is no statistical evidence that the negative impact of Covid-19 pandemic varies according to business sector or size. Based on the results, a number of recommendations are made for both governments and companies. Governments should maintain, despite the crisis, both public and private investment in order to sustain the growth of companies’ business sales. It is also important to implement solutions for rescheduling social and tax debts to safeguard the liquidity of companies and limit their recourse to costly debt.

Extreme Value Stable Mixture Modelling with applications to South African stock market indices and exchange rate

Extreme Value Stable Mixture Modelling with applications to South African stock market indices and exchange rate

Comparative Analysis of GARCH-Based Volatility Models of Financial Market Volatility: A Case of Nairobi Security Market PLC, Kenya

This paper conducted a comprehensive comparative analysis of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models to forecast financial market volatility, with a specific focus on the Nairobi Stock Exchange Market. The examined models include symmetric and asymmetric GARCH types, such as sGARCH, GJR-GARCH, AR (1) GJG-GARCH, among others. The primary objective is to identify the most suitable model for capturing the complex dynamics of financial market volatility. The study employs rigorous evaluation criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Error (ME), and Root Mean Absolute Error (RMAE), to assess the performance of each model. These criteria facilitate the selection of the optimal model for volatility forecasting. The analysis reveals that the GJR-GARCH (1,1) model emerges as the best-fit model, with AIC and BIC values of −5.5008 and −5.4902, respectively. This selection aligns with the consensus in the literature, highlighting the superiority of asymmetric GARCH models in capturing volatility dynamics. The comparison also involves symmetric GARCH models, such as sGARCH (1,1), and other asymmetric models like AR (1) GJG-GARCH. While these models were considered, the GJR-GARCH (1,1) model demonstrated superior forecasting capabilities. The study emphasizes the importance of accurate model selection and the incorporation of asymmetry in volatility modeling. The research provides essential insights into financial market volatility modeling and forecasting using both asymmetric and symmetric GARCH models. These findings have significant implications for government policymakers, financial institutions, and investors, offering improved tools for risk assessment and decision-making during periods of market turbulence.