Form Of Investment Research Articles

An Exploratory Study on the Effect of Macroeconomic Indicators on the Stock Market Performance among 6-ASEAN Countries

The stock market's performance has been impacted by the global economy's recovery from the COVID-19 pandemic. This study investigates the impact of various factors on stock market performance, with a focus on the global economic recovery from the COVID-19 pandemic. The stock market has experienced positive growth, driven by low interest rates, ample liquidity, and sustained investor optimism, despite concerns over rising inflation and interest rates. This research aims to analyze stock market performance across six ASEAN countries, using a range of data sources, including historical stock prices, financial reports, and macroeconomic indicators. The findings reveal that the real exchange rate exerts the most significant influence on stock market performance. The insights from this study provide valuable information for investors, policymakers, and government bodies, aiding in strategic decision-making and investment planning in these markets.

Evaluating the Effectiveness of Macroeconomic Determinants on the Performance of the Dhaka Stock Exchange: A Time Series Approach

The present study examines the causal relationships between selected macroeconomic factors and the Dhaka Stock Exchange Broad Index (DSEX) performance from January 2014 to December 2023. Time series econometric techniques include Dickey-Fuller (ADF) and Phillips-Perron (PP) tests to verify stationarity, Granger causality to investigate short-term dynamics, and Johansen's co-integration test to evaluate long-term relationships. The results show that while there is no long-term cointegration, there are significant short-term relationships between macroeconomic variables and the DSEX broad index. GDP growth and unemployment significantly impact stock performance, with two-way causality. While interest rates and the money supply have little impact on the index, inflation forecasts trends in inflation even if it doesn't directly influence it. Overall, the DSEX broad index shows how important it is to Bangladesh's macroeconomic dynamics by accurately predicting changes in GDP growth, unemployment, and inflation. The researcher provides unique insights into the mechanics of the Bangladeshi stock market, filling a vacuum in the literature on emerging countries. It analyzes how macroeconomic conditions affect the DSEX index over time, including the COVID-19 pandemic. The study investigates the influence of several macroeconomic variables on DSEX Index performance, providing significant information for Bangladeshi investors and policymakers looking to increase profits, inform policy choices, and promote economic stability. To have a better knowledge of stock market dynamics, future studies should include other macroeconomic aspects and regional markets.

Asymmetric impact of the COVID-19 on the Moroccan stock exchange

PurposeThis study aims to investigate the asymmetric impact of the COVID-19 pandemic on the stock market returns of companies listed on the Casablanca Stock Exchange.Design/methodology/approachTo achieve this objective, we utilized the SymCovid series, which comprises two sub-series: one representing the deterioration of the pandemic situation (NEG) and the other representing the improvement of the pandemic situation (POS). We employed the Nonlinear Autoregressive Distributed Lag model, incorporating Bounds Testing as proposed by Pesaran and Shin (1999) and Pesaran et al. (2001), to explore the asymmetry of the pandemic’s impact on stock prices listed on the Casablanca stock exchange.FindingsOur analysis using the NARDL econometric model reveals an asymmetric effect of COVID-19 on stock prices. Notably, we observe that stock prices react more strongly to a worsening pandemic situation than to an improvement, on average. Furthermore, our main findings indicate that while the improvement in the pandemic situation has no significant long-term impact on stock prices, it does exhibit a significant positive effect in the short term. Conversely, the deterioration in the pandemic situation has a more pronounced negative effect on stock prices in the long term than the short term.Originality/valueOur study fills a gap in the existing literature by focusing on the impact of the COVID-19 pandemic on stock returns in the context of the Casablanca Stock Exchange, which has been relatively understudied compared to other regions such as Asia, Europe and the Americas. We go beyond previous research by examining whether stock returns exhibit asymmetric responses to changes in the pandemic situation, highlighting potentially unique dynamics in emerging market economies during crises and providing valuable information for investors, policymakers and researchers.

A Collection of Wisdom in Predicting Sector Returns: The Use of Google Search Volume Index

This study investigates whether the aggregate investor information demand for all stocks in a sector demonstrated in the Google search volume index (SVI) can predict the sector’s performance. The evidence shows that a sector’s abnormal SVI can predict the sector’s performance next month, even after controlling for the sector’s contemporaneous standardized unexpected earnings and lagged returns on both the market and the sector. Also found is a partial reversal in the sector’s long-run performance that is not completely consistent with the price pressure hypothesis. This indicates that some fundamental information about a sector can be captured by the sector’s abnormal SVI on a timely basis.

Volatility spillover between carbon market and related markets in time-frequency domain based on BEKK-GARCH and complex network analysis

With rising public attention to climate issues and sustainable development, the connection between carbon market and its related markets has become closer. Based on BEKK-GARCH method and complex network theory, this study tries to explore the volatility spillover effects and network topology among China's carbon market, non-renewable energy market, renewable energy market, high-tech market and climate policy uncertainty index in time-frequency domain. It is found that, firstly, there are significant asymmetric volatility spillover effects among the markets. Secondly, the spillover effects between markets are time-varying, especially during crisis periods in which they are significantly higher than that during the stable period. Thirdly, with time increasing, the closer the connection between markets. In the long run, the closeness of the connection is directly proportional to the spillover effect. Finally, climate policy uncertainty is the main risk source. For full sample, from perspective of frequency domain, non-renewable energy and carbon markets are the main risk recipients. Based on the findings, it should step up efforts on market monitoring and intervention during crisis periods, strengthen cross-market risk management, pay attention to climate uncertainty and time-frequency varying characteristics of volatility spillovers, so as to provide useful information for policy makers and investors.

Analysis of the Effect of Corporate Social Responsibility on Company Management Performance

General Background: Corporate Social Responsibility (CSR) significantly influences company performance in today’s business environment. Specific Background: Inconsistent findings exist regarding CSR's impact on management performance, particularly in Indonesia’s state-owned enterprises (BUMN). Knowledge Gap: This study addresses this gap by examining the effects of environmentally and socially oriented CSR on the management performance of BUMN companies listed on the Indonesia Stock Exchange (IDX) from 2019 to 2022. Aims: The study aims to empirically assess how CSR influences management performance using quantitative data from annual reports. Results: The findings reveal that both environmentally and socially oriented CSR positively impact management performance, with implications for financial outcomes. Novelty: This research offers new insights into the relationship between CSR initiatives and firm performance. Implications: The results provide valuable information for investors and corporate managers, highlighting the necessity of effective CSR strategies to enhance operational performance and build investor confidence. Highlights: Positive Impact: Both environmentally and socially oriented CSR significantly enhance management performance in BUMN companies. Investment Insights: Effective CSR strategies are essential for building investor confidence and improving financial outcomes. Research Contribution: This study fills the knowledge gap by empirically linking CSR initiatives to firm performance in Indonesia. Keywords: Corporate Social Responsibility, Management Performance, State-Owned Enterprises, Indonesia Stock Exchange, Financial Outcomes

The Effect Of Gender Diversity And Free Cash Flow On Dividend Policy With Firm Size As A Moderating Variable

The purpose of this study was to determine the effect of gender diversity and free cash flow on dividend policy with firm size as a moderating variable. The population in this study are mining companies listed on the Indonesia Stock Exchange in 2020-2023. This study used purposive sampling technique, so 138 data were obtained from 87 mining companies listed on the Indonesia Stock Exchange. The results showed that gender diversity has no effect on dividend policy, free cash flow has a significant positive effect on dividend policy. Firm size cannot moderate the effect of gender diversity and free cash flow on dividend policy. The implications of the results of this study are expected to provide information for consideration by stakeholders such as the board of directors and as information for investors in making investment decisions.

Do Investors’ Site Visits Improve Bond Market Pricing Efficiency? —An Empirical Study from the Secondary Bond Market

ABSTRACT Taking bonds issued by A-share companies listed on the Shenzhen Stock Exchange from 2014 to 2020 as a sample, this paper explores the effect of corporate site visits on bond trading spreads. The study finds that the more investors visit a given firm, the lower the trading spreads of bonds issued by that firm, and this phenomenon is more pronounced in companies with greater information asymmetry or agency conflicts. Further research shows that investors evaluate the financial status of the company more accurately through site visits and reduce irrational decisions caused by information uncertainty. Consequently, site visits reduce the financing gap caused by macroeconomic factors. The results show that personal communication between investors and listed companies could provide effective information for bond investors and reduce their information risk. This paper expands the existing research on issues related to secondary bond market pricing and investors’ site visits and provides important empirical evidence for regulators to use to further optimize the bond market disclosure system to reduce information asymmetry.

The Use and Usefulness of Big Data in Finance: Evidence from Financial Analysts

Alternative data have become a new source of information for investors. This paper examines how this development is reshaping the role of sell-side analysts. Employing textual analysis of analysts’ written reports, we show that analysts themselves have begun to adopt alternative data in their analyses. Furthermore, we find that when analysts report drawing from alternative data they generate more accurate earnings forecasts, and their brokerages subsequently receive higher amounts in trading commissions from investors, suggesting that investors value the adoption of alternative data by analysts. This paper was accepted by Kay Gieseke, finance. Funding: This research is supported by NTU-Singapore under its startup grant program. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02659 .

Big-Data Technology in Finance: Quality of Stock-Trading Information in Vietnamese Stock Market

Quantitative finance is a field of applied research where the data is important information investors’ profitability and financial risk of financial assets in the processes of decision-making. And financial institutions and securities have applied big-data technologies in stock market to exploit useful information for investors using mobile stock-trading apps. The standardization of database from the raw data of companies’ financial statements, statistical reports and stock-trading data support to enhance data mining analysis by predictive patterns with high feasibility for useful information for investors’ decision-making. And quantitative tools of predictive patterns for financial advanced indexes test expected market return and favourable market timing for buying or selling stocks in stages of market cycle and prediction of market risk before market-trend crash. On the development of securities’ mobile stock-trading apps, the addition of new advanced indexes increases the quality of stock-trading information in Vietnamese stock market generally, seeks competitive advantages of market segments for securities particularly when they change big-data technology in mobile stock-trading apps. This contribution has a better using FinTech combined with stock-trading algorithms to provide more useful and confident information of optimal profitability and financial risk management that individual and institutional investors catch up the trend of big-data technology.

Regulatory framework for the formation of sustainability reporting

This article is dedicated to the study of the regulatory framework for the formation of sustainability reporting, the relevance of which is determined by the increasing impact of enterprisesʼ activities on the environment, society, and the economy. To date, the formation of sustainability reporting for Ukrainian enterprises is not mandatory, but in the context of enhanced European integration, understanding international and European reporting standards becomes critically important. The aim of the research is to review the current regulatory framework for the formation of sustainability reporting and to develop proposals for its implementation in the activities of Ukrainian business entities. The article analyzes the standards of the Global Reporting Initiative (GRI), European Sustainability Reporting Standards (ESRS), and the standards of the International Sustainability Standards Board (ISSB). GRI offers universal, thematic, and sectoral standards that provide a comprehensive approach to disclosing information about the impacts of an enterprise on the economy, society, and the environment. The ESRS standards, which have become mandatory for large companies and enterprises whose shares are listed on EU stock exchanges, aim at harmonizing sustainability reporting within the framework of the European Green Deal. ISSB standards, which include recommendations from SASB, TCFD, and CDSB, ensure the disclosure of financially significant information for investors. The analysis showed that all three systems of standards are largely aligned with each other, which facilitates the process of transition between them for enterprises. The use of GRI standards in Ukraine can serve as a basis for further transition to ESRS and ISSB standards in the event of Ukraineʼs accession to the EU. The article emphasizes the importance of developing methodological recommendations for the implementation of GRI for Ukrainian enterprises. The conclusions of the study indicate the necessity of actively implementing international sustainability reporting standards in Ukraine to enhance transparency, stakeholder trust, and strategic enterprise management.

The Impact Of Daftar Efek Syariah (DES) Announcement To The Asii’s Abnormal Returns And Trading Volume

DES announcement can be an important information for investors in making investment decisions. Islamic investors have to sell their stock when their stocks are excluded from DES. On the other hand, investors who want to invest in Islamic stocks will buy stocks that are included in the DES. The purpose of this study is to examine the impact of ASII stock reentry and exit from and into DES to the abnormal return and trading volume activity. This study uses secondary data obtained from investing.com. The data used in this study are daily stock closing prices, IHSG index and daily stock trading volume. This study follows event study methods. The first event window used in this study is 7 days before November,23,2020 and 7 days after. The second event window used is 7 days before May 24, 2023 and 7 days after. Descriptive statistic and Wilxocon sign ranks is used to analysis the data. The results showed that there was no significant difference in abnormal returns both at the time of ASII exit and reentry. However, trading volume of ASII stock significant difference before and after exit announcement. But when ASII stock reentry to the DES there was no significant difference.

Suitability Analysis of Solar Photovoltaic Farm Locations Using GIS: A Case Study of Nakuru County, Kenya

Renewable energy sources play a crucial role in reducing global reliance on fossil fuels. Advancements in technology has en- abled harnessing of renewable energy from solar, wind, and ocean tides to be viable. Solar energy, in particular, has gained significant global recognition as a renewable energy alternative. This study integrates Multi-Criteria Evaluation (MCE) and Geographic Information Systems (GIS) to assess Solar Photovoltaic Farms (SPVFs) suitability in Nakuru County, Kenya. The study considered seven criteria including; slope, solar radiation, aspect, land use land cover, proximity to roads, power transmission lines, and settlements. These were evaluated using the Analytical Hierarchy Process (AHP) to generate weights for each decision criterion. The weights were used to overlay independent criteria maps that were formed as a result of the re- classification of each criterion from which a composite rated map was developed. Similarly, a composite restriction map was created by leveraging on constraint criteria including; protected ecosystems, water bodies, settlement areas, slope over 20%, proximity to roads, proximity to the transmission line, and land use land cover. Results obtained from overlaying the composite rated and restricted maps reveal Nakuru County’s general suitability for SPVFs, except for Kuresoi North and Kuresoi South divisions which exhibit low solar radiation. Extremely-suitable areas ac- counted for 3.00% (224.14 km²), very-suitable areas 34.05% (2541.45 km2), moderately-suitable areas 7.76% (579.55 km2), marginally-suitable areas 1.47% (109.77 km2) while least-suitable areas covered 0.02% (1.39 km2). The study provides valuable data and information for government agencies and investors to identify potential Photovoltaic (PV) system sites. Furthermore, the government is encouraged to establish a favorable framework for solar PV exploration and provide incentives to the private sector to facilitate the establishment of SPVFs.

Do short-sale constraints inhibit information acquisition? Evidence from regulation SHO

We investigate how short-sale constraints influence investor information acquisition. Leveraging the U.S. Securities and Exchange Commission's (SEC's) Regulation SHO pilot program, we find that relaxed short-sale constraints lead to increased public information acquisition by investors. Investors acquire and process valuable information and utilize it to improve their short-selling decisions after the relaxation of short-sale constraints. We obtain similar findings using a quasi-natural experiment in China that lifted short-sale bans, supporting the external validity of our results. Collectively, our evidence demonstrates that reduced trading frictions promote information acquisition and contribute to price discovery.

기업의 부가가치정보와 ESG평가정보가 외국인지분율에 미치는 영향

Foreign investors are expected to assume that companies with high value-added information and ESG evaluation information are sound companies and to invest their funds. Therefore, we would like to verify this. The purpose of this study is: 1) Companies with high added value information will have a high foreign ownership ratio. 2) Companies with high ESG evaluation information will have a higher foreign ownership ratio. And 3) we conducted an empirical analysis predicting that value-added information would have a higher positive influence and information power on the foreign ownership ratio than ESG rating information. The research method is based on the company's added value information and foreign ownership ratio data from 2016 to 2020 provided by Fn-guide and ESG evaluation information published by the Korea ESG Research Institute, which is a statistical processing method such as basic statistical analysis, correlation analysis, Regression analysis was performed. To summarize the research results, the correlation analysis results show the dependent variable foreign ownership ratio and variables of interest: 1) added value information and 2) ESG evaluation information (rating (R), environment (E), society (S), and governance (G) sectors) The results showed that there was a significant positive (+) correlation. And the regression analysis results show that both value-added information and ESG evaluation information (rating (R), environment (E), society (S), and governance (G) sectors) have a significant positive (+) effect on the foreign ownership ratio. appear. In addition, ESG rating information was found to have greater influence and information power on the foreign ownership ratio than value-added information. The implications of these research results suggest that foreign investors are interested in and tend to invest funds in companies with high added value information and ESG evaluation information. Therefore, it is expected that added value information and ESG evaluation information serve as useful accounting information for foreign investors in the capital market.

The Effect of Exchange Rate, Interest Rate, and Inflation on Jakarta Composite Index (IHSG) during COVID-19 Pandemic in Indonesia

This study aims to test and analyze the effect of macroeconomic factors on the Jakarta Composite Index during the Pandemic of COVID-19 in Indonesia. The macroeconomic factors used in this study as independent variables are Exchange Rate, Interest Rate, and Inflation. While Indeks Harga Saham Gabungan (IHSG) or Jakarta Composite Index is used as the dependent variable. This study uses daily data during the pandemic period in Indonesia, from April 2020 to June 2023. The secondary data are obtained from several official websites, such as www.investing.com and www.bi.go.id. The analysis technique used is descriptive analysis and hypothesis testing with STATA tools. The results obtained show that all independent variables have a significant effect on the dependent variable. Exchange rate and interest rate have a negative significant impact on IHSG. Conversely, the inflation effect is positively significant to IHSG. This research adds to our understanding of the financial dynamics in difficult times and provides insightful information for investors, analysts, and policymakers attempting to manage the intricacies of the stock market in the context of COVID-19.

Linear budget management in internet of things and 6G network environments: Linear regression and time series analysis

AbstractBudget, as an important component of management accounting, is an effective means for companies to achieve functions such as planning, coordination, and control. It is a bridge and link connecting different units and departments within the company and economic operations. However, current budget management pays less attention to temporal characteristics, leading to budget ambiguity. Taking Company A as an example, the long short‐term memory (LSTM) algorithm was used to collect and process historical data and predict its future budget and revenue situation. It was found that the budget management of Company A was relatively chaotic, with insufficient investor information, and the predicted results were close to the actual situation, proving the effectiveness of the model proposed in this paper.

An insight into the signaling role of Sharia status: a case from an emerging IPO market

Purpose This study aims to examine the potential of Sharia status as ex ante information to signal the quality of an issuing firm by improving the decision-making process of potential investors when assessing initial public offerings (IPOs) in an environment where information asymmetry is pronounced. Potential investors face challenges in evaluating and determining the true value of IPO issues, which inherently influences their decision-making. Consequently, this results in pronounced price fluctuations in IPO shares, leading to higher underpricing. Design/methodology/approach This study uses a sample of 350 IPOs listed on the Kuala Lumpur Stock Exchange (KLSE) between 2004 and 2021 to examine the signaling role of Sharia-compliance status. A three-model approach is used to ensure that the study's objectives are met. The first model investigates the effect of Sharia status on underpricing to determine whether the main beneficiary of such a signal is the investor or the issuer. The second model examines the effect of Sharia status on investor demand to determine if such a signal influences prospective investors' investment decision-making processes. The third model inspects the effect of Sharia status on investor divergence of beliefs to measure the signal's ability to reduce information asymmetry within the Malaysian IPO market. Findings The Malaysian IPO market relies heavily on the fixed-price mechanism, which exacerbates high information asymmetry, affecting potential investors' behavior, asset price formation and return generation on the first day of listing. The study results indicate that Sharia status does not have any signaling role in the Malaysian IPO market. This is because investors in the Malaysian market are driven by ex ante information that helps unveil relevant information that leads to capital gains. Furthermore, most new issues in the Malaysian IPO market fall under Sharia status, diluting the relevance of such information for prospective investors in determining profitable investments. Practical implications The findings highlight the challenges faced by issuing firms in estimating market demand due to limited premarket insights and the difficulties prospective investors face in identifying the quality of issuing firms. Efforts to provide more information on investor demand can reduce uncertainty and facilitate more informed decision-making. Originality/value This research stands as one of the pioneering efforts to provide an empirical explanation of the potential signaling influence of Sharia status in an emerging IPO market.

Integrating PCA with deep learning models for stock market Forecasting: An analysis of Turkish stocks markets

Financial data such as stock prices are rich time series data that contain valuable information for investors and financial professionals. Analysis of such data is critical to understanding market behaviour and predicting future price movements. However, stock price predictions are complex and difficult due to the intense noise, non-linear structures, and high volatility contained in this data. While this situation increases the difficulty of making accurate predictions, it also creates an important area for investors and analysts to identify opportunities in the market. One of the effective methods used in predicting stock prices is technical analysis. Multiple indicators are used to predict stock prices with technical analysis. These indicators formulate past stock price movements in different ways and produce signals such as buy, sell, and hold. In this study, the most frequently used ten different indicators were analyzed with PCA (Principal Component Analysis. This study aims to investigate the integration of PCA and deep learning models into the Turkish stock market using indicator values and to assess the effect of this integration on market prediction performance. The most effective indicators used as input for market prediction were selected with the PCA method, and then 4 different models were created using different deep learning architectures (LSTM, CNN, BiLSTM, GRU). The performance values of the proposed models were evaluated with MSE, MAE, MAPE and R2 measurement metrics. The results obtained show that using the indicators selected by PCA together with deep learning models improves market prediction performance. In particular, it was observed that one of the proposed models, the PCA-LSTM-CNN model, produced very successful results.

Legality of Bitcoin in the Perspective of Fiqh Muamalah

This study investigates the legality of Bitcoin from the perspective of Fiqh Muamalah through qualitative analysis and literature review. Fiqh Muamalah, an Islamic jurisprudential framework, regulates economic transactions according to Sharia law. The research examines whether Bitcoin, as a digital currency, aligns with Islamic legal and ethical standards by analyzing scholarly articles, religious texts, and fatwas issued by Islamic scholars. Key considerations include Bitcoin's nature as a currency, its speculative role, and potential for illicit activities. Findings reveal diverse scholarly opinions, with some viewing Bitcoin as permissible under specific conditions, while others raise concerns about its speculative nature and lack of intrinsic value. This study aims to provide comprehensive insights into Bitcoin's legality within Fiqh Muamalah, offering valuable information for policymakers, financial institutions, and Muslim investors.