Showing papers on "Stock exchange published in 2017"

Why Stock Markets Crash: Critical Events in Complex Financial Systems

Abstract: The scientific study of complex systems has transformed a wide range of disciplines in recent years, enabling researchers in both the natural and social sciences to model and predict phenomena as diverse as earthquakes, global warming, demographic patterns, financial crises, and the failure of materials. In this book, Didier Sornette boldly applies his varied experience in these areas to propose a simple, powerful, and general theory of how, why, and when stock markets crash. Most attempts to explain market failures seek to pinpoint triggering mechanisms that occur hours, days, or weeks before the collapse. Sornette proposes a radically different view: the underlying cause can be sought months and even years before the abrupt, catastrophic event in the build-up of cooperative speculation, which often translates into an accelerating rise of the market price, otherwise known as a "bubble." Anchoring his sophisticated, step-by-step analysis in leading-edge physical and statistical modeling techniques, he unearths remarkable insights and some predictions--among them, that the "end of the growth era" will occur around 2050. Sornette probes major historical precedents, from the decades-long "tulip mania" in the Netherlands that wilted suddenly in 1637 to the South Sea Bubble that ended with the first huge market crash in England in 1720, to the Great Crash of October 1929 and Black Monday in 1987, to cite just a few. He concludes that most explanations other than cooperative self-organization fail to account for the subtle bubbles by which the markets lay the groundwork for catastrophe. Any investor or investment professional who seeks a genuine understanding of looming financial disasters should read this book. Physicists, geologists, biologists, economists, and others will welcome Why Stock Markets Crash as a highly original "scientific tale," as Sornette aptly puts it, of the exciting and sometimes fearsome--but no longer quite so unfathomable--world of stock markets.

Corporate Governance and Blockchains

Abstract: Blockchains represent a novel application of cryptography and information technology to ageold problems of financial record-keeping, and they may lead to far-reaching changes in corporate governance. During 2015 many major players in the financial industry began to invest in this new technology, and stock exchanges have proposed using blockchains as a new method for trading corporate equities and tracking their ownership. This essay evaluates the potential implications of these changes for managers, institutional investors, small shareholders, auditors, and other parties involved in corporate governance. The lower cost, greater liquidity, more accurate record-keeping, and transparency of ownership offered by blockchains may significantly upend the balance of power among these cohorts.

Escaping the iron cage: Liabilities of origin and CSR reporting of emerging market multinational enterprises

Abstract: This article examines the link between the condition of institutional voids in emerging markets and the use of the practice of corporate social responsibility (CSR) reporting by emerging market multinational enterprises (EM-MNEs). Based on neo-institutional theory and in light of the specificity of emerging markets, we propose a positive relationship between institutional voids and CSR reporting. Home-country institutional voids push companies to internationalize as a way to escape the institutional constraints and inefficiencies in their own markets, but at the same time create legitimacy challenges for these companies abroad. In particular, EM-MNEs from less institutionally developed countries are likely to face liabilities of origin – negative perceptions in host countries about these firms’ willingness and ability to conduct legitimate business. CSR reporting is an effective strategy to overcome such liabilities and barriers to legitimation as it conveys to host countries and global stakeholders alignment with global meta-norms and expectations. Internationalization, listing on developed country stock exchanges, and time, further magnify EM-MNEs’ legitimacy challenges and thus the use of CSR reporting to mitigate them. Our hypotheses are supported in a longitudinal study of 157 of the largest EM-MNEs ranked by the United Nations Conference on Trade and Development (UNCTAD) between 2004 and 2011.

Does Integrated Reporting Matter to the Capital Market

Abstract: Integrated reporting ( ) is an emerging international corporate reporting initiative to address limitations to extant corporate reporting approaches, which are commonly criticized for being both voluminous and disjointed. While is gaining in popularity, current momentum has been limited due to a lack of clear evidence of its benefits. Utilizing the most suitable setting currently available, being discretionary disclosures made by listed companies on the Johannesburg Stock Exchange, this study provides evidence that analyst forecast error reduces as a company's level of alignment with the framework increases. Further, the improved alignment is associated with a subsequent reduction in the cost of equity capital for certain reporting companies. The results are obtained after controlling for factors relating to financial transparency and the issuance of standalone non-financial reports, which suggests that is providing incrementally useful information to the capital market over and above existing reporting mechanisms.

A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction

Abstract: This study investigates stock market indices prediction that is an interesting and important research in the areas of investment and applications, as it can get more profits and returns at lower risk rate with effective exchange strategies. To realize accurate prediction, various methods have been tried, among which the machine learning methods have drawn attention and been developed. In this paper, we propose a basic hybridized framework of the feature weighted support vector machine as well as feature weighted K-nearest neighbor to effectively predict stock market indices. We first establish a detailed theory of feature weighted SVM for the data classification assigning different weights for different features with respect to the classification importance. Then, to get the weights, we estimate the importance of each feature by computing the information gain. Lastly, we use feature weighted K-nearest neighbor to predict future stock market indices by computing k weighted nearest neighbors from the historical dataset. Experiment results on two well known Chinese stock market indices like Shanghai and Shenzhen stock exchange indices are finally presented to test the performance of our established model. With our proposed model, it can achieve a better prediction capability to Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index in the short, medium and long term respectively. The proposed algorithm can also be adapted to other stock market indices prediction.

Determinants of Corporate Social Responsibility Disclosure: An Empirical Study of Polish Listed Companies

Abstract: In this paper we explore whether a number of elements influence the levels of corporate social responsibility (CSR) disclosure in the annual reports of Polish companies. These elements include the following: company size, profitability, financial leverage, industry environmental sensitivity, board size, women on the board, internationalization, and reputation. We use content analysis to determine the quality of CSR disclosures. We test our hypotheses using a Tobit regression analysis on a sample of 60 reports from Polish companies listed on the Warsaw Stock Exchange. We find industry environmental sensitivity to have significant influence on CSR disclosures. Our research findings also reveal a relationship between company turnover, duration of the stock exchange listing, inclusion in the Respect Index portfolio and foreign capital share, and the level of CSR disclosures. This study extends the scope of previous studies by including non-commonly used independent variables: the company’s internationalization and reputation. To the authors’ knowledge, it is the primary step to investigating CSR reporting practices coupled with the corporate characteristics in a Central and Eastern European country such as Poland. The paper contributes to the understanding of determinants of CSR disclosure and offers findings which are potentially useful for both theory and practice.

Stock market one-day ahead movement prediction using disparate data sources

Abstract: A financial expert system for predicting the daily stock movements.Knowledge base captures both traditional and online data sources.The inference engine uses three artificial intelligence techniques.Prediction accuracy of 85% is higher than the reported results in the literature.The system is hosted online and freely available for investors and researchers. There are several commercial financial expert systems that can be used for trading on the stock exchange. However, their predictions are somewhat limited since they primarily rely on time-series analysis of the market. With the rise of the Internet, new forms of collective intelligence (e.g. Google and Wikipedia) have emerged, representing a new generation of crowd-sourced knowledge bases. They collate information on publicly traded companies, while capturing web traffic statistics that reflect the publics collective interest. Google and Wikipedia have become important knowledge bases for investors. In this research, we hypothesize that combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system. Three machine learning models, decision trees, neural networks and support vector machines, serve as the basis for our inference engine. To evaluate the performance of our expert system, we present a case study based on the AAPL (Apple NASDAQ) stock. Our expert system had an 85% accuracy in predicting the next-day AAPL stock movement, which outperforms the reported rates in the literature. Our results suggest that: (a) the knowledge base of financial expert systems can benefit from data captured from nontraditional experts like Google and Wikipedia; (b) diversifying the knowledge base by combining data from disparate sources can help improve the performance of financial expert systems; and (c) the use of simple machine learning models for inference and rule generation is appropriate with our rich knowledge database. Finally, an intelligent decision making tool is provided to assist investors in making trading decisions on any stock, commodity or index.

An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange

Abstract: Display Omitted We propose three versions of fuzzy TOPSIS for solving group MADM problems.We apply fuzzy set theory to handle the imprecise information in the real-world problems.We take advantage of fuzzy-valued distance and fuzzy ranking method to provide a more rational decision-making process.We apply the proposed methods in the Tehran stock exchange. In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.

The Firm-Specific Determinants of Corporate Capital Structure: Evidence from Turkish Panel Data

Abstract: The purpose of this study is to carry out an empirical testing, using dynamic panel data methodology, to analyze the impact of firm specific characteristics on the corporate capital structure decisions of Turkish firms The sample covers 123 Turkish manufacturing firms listed on the Istanbul Stock Exchange (ISE) and the analysis is based on the year-end observations of ten consecutive years running from 1993-2002 In this study, the panel data methodology is used and six variables – size, profitability and growth opportunities in plant, property and equipment, growth opportunities in total assets, non-debt tax shields and tangibility – are analyzed as the firm specific determinants of the corporate capital structure This work contributes to the existing body of literature in the way that all of the independent variables of the study are significant determinants for the capital structure decisions of Turkish firms Our analysis shows that variables of size and growth opportunity in total assets reveal a positive association with the leverage ratio, however, profitability, growth opportunities in plant, property and equipment, non-debt tax shields and tangibility reveal inverse relation with debt level

Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market

Abstract: With the development of the social network, the interaction between investors in stock market became more fast and convenient. Thus, investor sentiment which can influence their investment decisions may be quickly spread and magnified through the network, and to a certain extent the stock market can be affected. This paper collected the user comments data from a popular professional social networking site of China stock market called Xueqiu, then the investor sentiment data can be obtained through semantic analysis. The dynamic analysis on relationship between investor sentiment and stock market is proposed based on Thermal Optimal Path (TOP) method. The results show that the sentiment data was not always leading over stock market price, and it can be used to predict the stock price only when the stock has high investor attention.

Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance

Abstract: Supply chain finance (SCF) becomes more important for small- and medium-sized enterprises (SMEs) due to global credit crunch, supply chain financing woes and tightening credit criteria for corporate lending. Currently, predicting SME credit risk is significant for guaranteeing SCF in smooth operation. In this paper, we apply six methods, i.e., one individual machine learning (IML, i.e., decision tree) method, three ensemble machine learning methods [EML, i.e., bagging, boosting, and random subspace (RS)], and two integrated ensemble machine learning methods (IEML, i.e., RS–boosting and multi-boosting), to predict SMEs credit risk in SCF and compare the effectiveness and feasibility of six methods. In the experiment, we choose the quarterly financial and non-financial data of 48 listed SMEs from Small and Medium Enterprise Board of Shenzhen Stock Exchange, six listed core enterprises (CEs) from Shanghai Stock Exchange and three listed CEs from Shenzhen Stock Exchange during the period of 2012–2013 as the empirical samples. Experimental results reveal that the IEML methods acquire better performance than IML and EML method. In particular, RS–boosting is the best method to predict SMEs credit risk among six methods.

Relationships Between Stock Markets and Macroeconomic Variables: an Empirical Analysis of the Istanbul Stock Exchange

Abstract: The purpose of this study is to investigate the relationships between returns in Istanbul Stock Exchange (ISE) and macroeconomic variables of Turkish economy. Employing cointegration tests and vector error correction model (VECM) on a quarterly data set, we find long-term stable relationships between ISE and four macroeconomic variables, GDP, exchange rate, interest rate, and current account balance. As a result of causality tests, we found unidirectional relationships between macro indicators and ISE index. That is, consistent with the existing literature, changes in GDP, foreign exchange rate and current account balance have an effect on ISE index. However, on the contrary to expectations, changes in the stock market index do affect interest rates.

Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand (SET)

Abstract: This article investigates the profitability of candlestick patterns. The holding periods are 1, 3, 5, and 10 days. Two exit strategies are studied. One is the Marshall–Young–Rose (MYR) exit strategy and the other is the Caginalp–Laurent (CL) exit strategy. The MYR applies a prespecified date to exit the market. In contrast, the CL sets an exit price equal to an average holding period closing price, assuming that investors liquidate their positions evenly within this period. The daily data include open, high, low, and close prices of component stocks of the SET50 index (the 50 largest capitalization stocks in the Stock Exchange of Thailand [SET]) for a 10-year period from July 3, 2006, to June 30, 2016. This study tests the predictive power of bullish and bearish candlestick reversal patterns both without technical filtering and with technical filtering (Stochastics [%D], Relative Strength Index [RSI], Money Flow Index [MFI]) by applying the skewness adjusted t test and the binomial test. The statistical a...

Effect of ownership structure and board structure on firm value: evidence from India

Abstract: This paper aims to explore the relationship of promoter ownership and board structure with firm performance for Indian companies.,Corporate governance structures of 391 Indian companies out of CRISIL NSE Index (CNX) 500 companies listed on national stock exchange (NSE) have been studied for their impact on performance of companies. Panel data regression methodology has been used on data for five financial years from 2010 to 2014 for the selected companies. Performance measures considered are market-based measure (Tobin’s Q) and accounting-based measure (return on assets [ROA]).,The empirical findings indicate that market-based measure (Tobin’s Q) is more impacted by corporate governance than accounting-based measure. There is significant positive association between promoter ownership and firm performance. It is also indicated that the relationship between promoter ownership and firm performance is different at different levels of promoter ownership. Board size is found to be positively related to ROA; however, board independence is not found to be related to any of the performance measures.,Limitations of the study are in terms of data methodology and possible omission of some variables. It is felt that endogeneity and reverse causality might be better addressed using simultaneous equation methodology.,The paper adds to the emerging body of literature on corporate governance performance relationship in Indian context using a reasonably wider and newer data set.

The effects of ownership structure on likelihood of financial distress: an empirical evidence

Abstract: The purpose of this paper is to explore the role of corporate governance proxies by ownership structure on the likelihood of firms’ financial distress for a sample of 146 Pakistani public-limited companies listed at the Karachi Stock Exchange over the period of 2003-2012.,The dynamic generalized method of moments (GMM) estimator and panel logistic regression (PLR) are used to determine the impact of corporate governance on the financial distress. The ownership structure is used as a determinant of corporate governance, while the Altman Z-score is utilized as an indicator of financial distress, as it measures financial distress inversely. The smaller the values of the Z-score, the higher will be the risk of financial distress.,The authors find insignificant impact of ownership structure on firms’ likelihood of financial distress based on the dynamic GMM method. However, the PLR results indicate that foreign shareholdings have a significant negative association with firms’ likelihood of financial distress, in the case of Pakistan. An evidence of a negative and insignificant relationship between institutional ownership and financial distress was observed, which indicates the passive role of institutional investors in Pakistan. The results also reveal a positive and significant relationship between insider’s ownership and likelihood of financial distress. This finding is consistent with the entrenchment hypothesis which predicts that insiders are more aligned with their self-interest than outside shareholders’ interest when their shareholding increases in the business. Furthermore, the results also reveal insignificant association between government shareholdings and the probability of financial distress. The reason could be the social welfare objective of the government entities rather than profit maximization.,The findings of this study provide more insight to corporate managers and investors about the association between the quality of corporate governance and the degree of financial distress, with respect to Pakistani firms. Furthermore, this study contributes to the existing literature by adding new evidence from developing countries like Pakistan which are helpful for regulatory bodies and policymakers in the formulation of long-term corporate governance strategies to manage the financial distress. It is well established that strengthening the quality of corporate governance practices enhances the efficiency of capital markets and reduces the probability of financial distress.,The study extends the body of existing literature on corporate governance and the likelihood of financial distress with reference to Pakistan. The results suggest that policymakers may pay special attention to the quality of corporate governance, specifically ownership structure, while predicting corporate financial distress.

Short term stock price prediction using deep learning

Abstract: Short — term price movements, contribute a considerable measure to the unpredictability of the securities exchanges. Accurately predicting the price fluctuations in stock market is a huge economical advantage. The aforementioned task is generally achieved by analyzing the company, this is called as fundamental analysis. Another method, which is undergoing a lot of research work recently, is to create a predictive algorithmic model using machine learning. To train machines to take trading decisions in such short — period of time, the latter method needs to be adopted. Deep Neural Networks, being the most exceptional innovation in Machine Learning, have been utilized to develop a short-term prediction model. This paper plans to forecast these short — term prices of stocks. 10 unique stocks recorded on New York Stock Exchange are considered for this review. The review essentially focuses on the prediction of these short — term prices leveraging the power of technical analysis. Technical Analysis guides the framework to understand the patterns from the historical prices fed into it, and attempts to probabilistically forecast the fleeting future prices of the stock under review. The paper discusses about two distinct sorts of Artificial Neural Networks, Feed Forward Neural Networks and Recurrent Neural Networks. The review uncovers that Feed Forwards Multilayer Perceptron perform superior to Long Short-Term Memory, at predicting the short — term prices of a stock.

Board of directors characteristics and performance in family firms and under the crisis

Abstract: This paper aims to examine the relationship between board of directors’ characteristics and performance in family businesses. It offers evidence to the question of whether a family firm (FF) differs from a non-family firm and looks at the possibility of asymmetrical effects between periods of stability and economic adversity.,A panel data approach was applied to a sample of Portuguese firms listed the on Euronext Lisbon exchange between 2002 and 2013.,The results show that FFs are likely to have a lower proportion of independent members and higher gender diversity on their boards than non-family firms. FF performance is positively related to ownership concentration and gender diversity. There are performance premiums for family businesses, which have more gender diversity than their counterparts. These effects also depend on whether the economy is in recession. The evidence suggests that the presence of women on the board and the leverage and size of the FFs have a more significant impact on the performance in periods of economic adversity.,One limitation of this study is the small size of the sample as it was drawn from the Euronext Lisbon exchange, a small stock exchange market.,This study provides input into the academic discussion on corporate governance and FF, an area which is in need of research. In addition, the authors examine this issue in conjunction with generalised economic adversity, focusing on the possible asymmetrical effects that the nature of the board of directors may have on performance in periods of stability and those of economic adversity. The role of board of directors is crucial to the understanding of corporate behaviour and the setting of the policy that regulates corporate activities.

Impact of Corporate Social Responsibility Dimensions on Firm Value: Some Evidence from Hong Kong and China

Abstract: There has been significant interest and debate on the impact that a firm’s investments in corporate social responsibility (CSR) practices and initiatives have on its market value. In this paper, we target an area that is relatively under-researched: the relevance of CSR practices and initiatives for firms in the emerging economic region of mainland China and Hong Kong, where market development and the institutional environment lag that of developed economies. Using independent CSR assessment data on a sample of large mainland Chinese and Hong Kong firms listed on the Hong Kong Stock Exchange, we evaluate the impact of six CSR dimensions on the firms’ adjusted stock market value over a three-year period. We found support for the influence of only two of the six dimensions considered, namely, the CSR practices and initiatives focused on community investment through philanthropy and, to a lesser extent, the CSR practices and initiatives focused on enhancing workplace quality, to be significant predictors of firm value. This suggests that social and people-centric dimensions of CSR are more relevant than technical and process-centric dimensions of CSR for mainland Chinese and Hong Kong firms. Furthermore, we found support for the hypothesis that the impact of CSR practices and initiatives on firm value follows an inverted U-shaped relationship over time, suggesting that the effect of these initiatives on firm value steadily increases during the initial years after their adoption to reach a maximum and then gradually fades away in subsequent years. To this end, this study advances our knowledge of the specific CSR dimensions that contribute to firm value and their relevance for Chinese and Hong Kong firms.

Measuring the intellectual capital of Italian listed companies

Abstract: Purpose The purpose of this paper is to investigate the relationship between intellectual capital (IC), measured in terms of the market to book (MTB) ratio, and potential key determinants of IC value such as intangible assets (IA) and a range of other factors. Design/methodology/approach The study is conducted for a sample of 140 Italian corporations over the period 2009-2013. Applying a holistic market-based approach, the relationship between IC value and selected determinants from the extant literature is tested. Five hypotheses are tested using a pooled OLS regression model, while controlling for time. ROE is employed as a useful firm profitability indicator from the perspective of an equity investor. Moreover, four robustness tests are undertaken. Findings The results show that IA, profitability, leverage, industry type, auditor type, and family ownership positively affect IC value, whereas SIZE and AGE negatively affect IC value. Moreover, the findings of the robustness tests suggest that all firms, and not just knowledge-intensive business service industry firms, manage knowledge. Research limitations/implications The validity of the findings is limited to the Italian context, as the study focuses on a sample of companies listed on the Milan Stock Exchange, all of which prepare their individual financial statements according to IFRS. Further limitations are related to the use of market value in the short term, as it is influenced by market volatility. The study may allow academic researchers to investigate the impact of other non-accounting sources of information on market value within a multidisciplinary perspective. Practical implications This paper also has implications for managers and practitioners. The findings suggest that managers should not take for granted that firm growth (an increase in SIZE) alone will lead to an increase in IC value, in the absence of a consistent IC-oriented investment strategy. Managers should also avoid smoothing their IC investment as the company grows, in order to maintain a stable MTB ratio. Further, standard setters should seek to explore better means of disclosing non-accounting information relating to IC value. Originality/value This paper contributes to the IC literature as it is the first study which applies the market capitalization approach to analyze IC value determinants in the Italian context, within the framework of IFRS. The findings reveal some interesting relationships between the MTB ratio and recognized intangible investments, which are found to be insignificant in previous studies, confirming that, through the holistic effect, the MTB ratio may be a good proxy for IC.

Corporate governance implementation rating in Indonesia and its effects on financial performance

Abstract: Purpose The purpose of this paper is to investigate the effect of corporate governance (CG) implementation rating conducted by the Indonesian Institute for Corporate Governance (IICG) on the financial performance of the selected companies. Design/methodology/approach This paper is a hypothesis testing study to analyze CG implementation of 88 firms listed on the Indonesian Stock Exchange. The samples are companies that participated in the Corporate Governance Perception Index (CGPI) Awards in 2008-2012. A panel data regression analysis is conducted on the data collected from IICG reports and its financial statements. Findings The awareness regarding good corporate governance (GCG) enforcement in Indonesian companies has already increased. The listed companies that participated in CGPI Awards during 2008-2012 always experience an increase in both quantity and quality. CG rating of go-public companies in Indonesia affects their accounting-based financial performance, such as return on assets, return on equity and earnings per share. However, CG implementation rating is not directly responded by the Indonesian stock market and has not yet been able to increase the company’s growth in the short term. Research limitations/implications In this study, CGPI rating in a related year is linked to market performance in the same year. Thus, further research may link CGPI rating to market performance in the next year, as the findings of this study show that GCG implementation is not directly responded by the market. Practical implications GCG implementation is required by stakeholders, as it may give a long-term positive impact. Thus, the government needs to stipulate regulations to increase the commitment of the company in implementing GCG. The company can improve the internal factors of the organization that does not support the establishment of GCG based on the findings during the survey of CGPI. Finally, investors and creditors may consider the CGPI rating for their investment decisions. Originality/value This study contributes to the literature in two ways. First, this study uses the comprehensive CG rating in Indonesia. Previous studies on CG rating focused on internal mechanism; in this study, the rating was assessed using four stages of continuous assessment: self-assessment, document evaluation, paper assessment and company visit, which was conducted by an independent team. Second, this study uses the CG index (compliance, conformance and performance) associated with a variety of accounting-based and market-based performance variables: financial performance, market value and growth.

Corporate social responsibility disclosure—choices of report and its determinants: Empirical evidence from firms listed on the Stock Exchange of Thailand

Abstract: This research investigated the report choices used for corporate social responsibility (CSR) disclosure and the determinants of CSR disclosure of firms listed on the Stock Exchange of Thailand (SET). Since 2014, firms listed on the SET have been required to disclose CSR in either an annual registration statement or a separate report called a sustainability report. It was, therefore, noteworthy to examine the choices these firms chose in the first year of disclosure. The independent variables were hypothesized under three dimensions—shareholder power (government ownership), corporate visibility (firm size and age), and economic performance (profitability and leverage). The results revealed that government-owned firms or large firms are more likely to prefer the sustainability report. In addition, content analysis of CSR disclosure was conducted in three industries: resources, technology and industrial products. Nine CSR components with 43 indices were developed and used to score the disclosure of firms in the three industries. The three highest CSR disclosure items found were declaring concerns of human rights and equality, having a policy of anti-corruption, and generous giving. Moreover, this study found a positive relationship between the number of CSR disclosure items and government ownership; however, neither firm age nor economic performance in the year before was related to the CSR disclosure. These research findings support the proposition of the stakeholder theory affirming that firms carry out CSR activities because of their stakeholders' influence, and regardless of economic performance. In Thailand, stakeholders' influence and corporate visibility are significant determinants of the CSR disclosure.