In this work, the impact of oil price shocks on the stock exchanges of three countries in the Caspian Basin − Iran, Kazakhstan and Russia − was examined through a structural vector autoregression (SVAR) model. For the research, monthly data from the stock exchanges, the oil price, inflation, industrial production and exchange rates were collected between March 2005 and June 2018. According to the results of variance decomposition, in these three countries, the impact of negative oil price shocks on the stock exchanges was stronger than that of positive shocks, and constituted the largest source of changes in the three stock exchanges. In addition, according to the results of impulse response functions, the response of the stock exchanges in the three countries to negative oil shocks was highly significant. Consequently, these countries should avoid macroeconomic imbalances and falls in their stock exchanges due to the negative impact of the oil price, and should instead focus on industrial production that will contribute to exports. In this way, they can avoid the negative impact of oil price shocks on their stock exchanges.

Introduction

In this work, the main aim was to examine the asymmetric impact of Brent oil price shocks on the stock exchanges of three Caspian Basin countries through structural vector autoregression (SVAR). It is known that Caspian Basin countries are not globally strong in medium- and high-tech industrial production,1 and that they are highly dependent on oil production for export. Moreover, their own economies are susceptible to positive or negative impacts from oil prices. In this research, it is assumed that stock exchanges are largely dependent on fluctuations in the oil price in these countries. Oil price shocks are taken to be one variable that exerts a significant impact on their stock exchanges. In addition, it is assumed that the exchange rate and inflation can also influence the stock exchanges. Hence, the main question is how the oil price shocks, exchange rates, inflation and industrial production influence the stock markets. If there are significant impacts on stock exchanges, which variables show the strongest effects and how great are the impacts in these countries? Furthermore, as it is assumed that the oil price can have a significant impact on stock exchanges, then what about the impact of positive or negative shocks? In other words, which shocks have the stronger impacts on stock exchanges? This work seeks to answer these questions.

Abstract

Purpose – There is an existing relationship among shareholders, boards of directors and management of companies. Corporate governance practices of companies are expected to ensure that this relationship maximises the wealth of shareholders. Differences exist among corporate governance of companies listed on the Ghana Stock Exchange. Companies, for purposes of liquidity, hold cash, but cash holdings also add to the cost of financing, according to working capital theories. The study, thus, sought to examine the relationship between corporate governance practices, ownership structure, cash holdings and firm value. Design/methodology/approach – The study deployed the seemingly unrelated regression to reduce the problem of multicollinearity resulting from the strong relationship between cash reserves and some control variables. Findings – The study found no significant relationship between board size and firm value. Similar findings were also made on the relationship between proportion of non-executive directors on the board and firm value. However, firms audited by the big four audit firms are valued higher by the capital market. Cash holdings of firms negatively affect performance, and this is statistically significant. A positive relationship arises between a firm’s cash holdings and its value as a result of debt financing, even though this is not significant. Originality/value – The study is the first of its kind that deploys Tobin’s Q as a measure of firms’ value to reflect investors’ valuation of firms in Ghana. The study is also the first of its kind to test the interactive effect of debt financing and cash holdings on firm value in Ghana.

Introduction

The value of a company is affected by factors within the control of management and other external factors. Valuation models identify the value of a company as a function of the risk faced by the company. Risk, in the literature, is identified as systematic and unsystematic risk, where systematic risk refers to risk arising from market-related factors, and unsystematic risk arises from entity-specific factors. Entity-specific factors, such as cash holding, corporate governance and ownership structure, can influence a company’s value (Harford et al., 2008). In valuation of equity shares, one approach identified in the literature is the free cash flow method, which values shares based on the free cash flow available. This means that the amount of cash a company holds at any point in time has influence on its value (Isshaq et al., 2009). Corporate governance, the mechanism within which a firm operates and is directed, can also influence its value. Jensen (1993) identified that as a result of the agency problem, the shareholder wealth maximisation goal, which managers are to pursue, is disregarded for personal goals, such as increasing their perks. This phenomenon is not value-adding. Eberhart (2012), in a research based in Japan, showed that companies that adopted the Anglo-American committee system experienced increase in their firm valuation after the adoption, and the reason stated was that the adoption signalled a move to transparency. With transparency, asymmetric agency cost was also expected to be reduced. Other researchers, such as Stiglbauer and Velte (2014), show that soft law regulations governing corporate governance have no impact on the value of firms. The effect of executive ownership on different economic variables has been assessed extensively in current literature (Iona et al., 2017; Iona and Leonida, 2016; Ehikioya, 2009).

1- Introduction

2- Equity crowdfunding in Germany

3- Hypotheses

4- Data and methods

5- Results

6- Conclusions

References

Abstract

Equity crowdfunding is a new form of entrepreneurial finance, in which investors do not receive perks or engage in pre-purchase of the product, but rather participate in the future cash flows of a firm. In this paper, we analyze what determines individual investment decisions in this new financial market. One important factor that may influence the behavior of investors is the way the portal allocates securities. We use unique data from four German equity crowdfunding portals to examine how the allocation mechanism affects funding dynamics. In contrast with the crowdfunding campaigns on Kickstarter, on which the typical pattern of project support is U shaped, we find that equity crowdfunding dynamics are L shaped under a first-come, first-served mechanism and U shaped under a second-price auction. The evidence also shows that investors base their decisions on information provided by the entrepreneur in the form of updates as well as by the investment behavior and comments of other crowd investors.

Introduction

In recent years, crowdfunding has become a mainstream source of funding for early-stage firms. Equity crowdfunding (also referred to as investment-based crowdfunding, securitiesbased crowdfunding, and crowdinvesting) is a sub-category of crowdfunding in which firms issue financial securities to satisfy their capital needs. At this point, empirical research on equity crowdfunding is still scarce, because this market segment was not available until only recently to the general crowd in jurisdictions such as the United States or lacked specific regulation elsewhere to facilitate its development (Hornuf and Schwienbacher, 2017b). Some seminal articles on equity crowdfunding have described the size, growth, and geographic distributions of the market (Vulkan et al., 2016; Günther et al., 2017), have analyzed the static factors that affect funding success based on ultimate campaign outcome (Ahlers et al., 2015; Hornuf and Schwienbacher, 2017a), and have provided first insights into the returns on investment (Hornuf and Schmitt, 2016; Signori and Vismara, 2016). Agrawal et al. (2016) show that syndicates help well-informed lead investors in equity crowdfunding extend their knowledge to a less well-informed crowd that is consequently willing to invest in a company. In a similar vein, Vismara (2017) evidences that founders have a greater chance of reaching the funding goal in an equity crowdfunding campaign if they possess more social capital as measured by the number of the founder’s LinkedIn connections. More recently, scholars have focused on the dynamic effects of equity crowdfunding. As the first researcher to assess dynamics in equity offerings, Vismara (2017) shows that information cascades among individual investors are an important factor in the equity crowdfunding investment process. By running a cross-sectional analysis on the portal Crowdcube, he finds that investments in the early days of a campaign are important in attracting investors later on and consequently increase the probability of funding success. These findings highlight the existence of dynamic effects within campaigns that are generally overlooked in other studies. Another recent article investigates whether information disclosure in the form of updates affects the investment dynamics on Companisto and Seedmatch (Block et al., 2017). The authors find that startups can provide credible information about their quality when posting updates about their new developments, such as funding events, business developments, and cooperation projects.

1- Introduction

2- ARTIFICIAL NEURAL NETWORK

3- Methodology

4- RESULTS AND DISCUSSION

5- CONCLUSION

References

Abstract

The neural network, one of the intelligent data mining technique that has been used by researchers in various areas for the past 10 years. Prediction and analysis of stock market data have got an important role in today’s economy. The various algorithms used for forecasting can be categorized into linear (AR, MA, ARIMA, ARMA) and non-linear models (ARCH, GARCH, Neural Network). In this paper, we are using four types of deep learning architectures i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available. Here we are using day-wise closing price of two different stock markets, National Stock Exchange (NSE) of India and New York Stock Exchange (NYSE). The network was trained with the stock price of a single company from NSE and predicted for five different companies from both NSE and NYSE. It has been observed that CNN is outperforming the other models. The network was able to predict for NYSE even though it was trained with NSE data. This was possible because both the stock markets share some common inner dynamics. The results obtained were com- pared with ARIMA model and it has been observed that the neural networks are outperforming the existing linear model (ARIMA).

Introduction

Stock market is a place where shares or stocks of a firm are traded. It can be split into two components: • primary market • secondary

market Primarymarket is where new issues are introduced to the market through Initial Public Offerings. Secondary market is where investors trade securities that they already own. Stock market is having a highly fluctuating and non-linear time series data. A time series is a set of data measured over time to acquire the status of some activity [6]. Linear models like AR, ARMA, ARIMA [9][10] have been used for stock market forecasting. The only problem with these models are, that they work only for a particular time series data, i.e the model identified for a particular company won’t perform well for another. Due to the equivocal and unforeseeable nature of stock market, stock market forecasting takes higher risk compared to other sectors. It is one of the most important reason for the difficulty in stock market prediction. Here is where the application of deep-learning models in financial [4] forecasting comes in. Deep neural network got its name due to the use of neural network architecture in DL models. It is also called as ANN. ANNs are good approximators and they are capable to learn and generalize from experience. Practical application of ANN in forecasting problems is very successful due to the following characteristics:

• ANN’s are good function approximators, so the input and output relationship can be examined by them even if the data set is very complex. • ANN’s can identify new test samples even if they have not been used during the training of network.

1- Introduction

2- Exchange-regulated markets

3- Voluntary IFRS adoption

4- Data and research design

5- Results

6- Conclusion

References

Abstract

This research note aims to enrich our understanding of reporting incentives of firms listed in European exchange-regulated markets. Many initial public offerings (IPOs) in Europe are within exchange-regulated markets where firms are allowed to choose between local GAAP and IFRS. Therefore, this research note describes the regulatory environment and investigates the choice to voluntarily adopt IFRS within European exchange-regulated markets. Overall, less than 20% of the firms voluntarily adopt IFRS and voluntary IFRS adoption upon IPO is positively associated with firm size, foreign firms, stocks offered to institutional investors prior to the IPO, and a future migration to an EU-regulated market.

Introduction

Regulation 2002/1606/EC (European Commission, 2002) requires publicly listed European firms to prepare their consolidated financial statements in accordance with IFRS only if they are listed in an EU-regulated market. Hence, this does not apply to exchange-regulated markets (2002/1606/EC, Article 4) and firms can choose between local GAAP and IFRS.1 Generally, the literature finds positive capital market effects of IFRS adoption (see Brüggemann et al. (2013) for a review). In turn, this leads to the question why the vast majority of firms did not voluntarily adopt IFRS (Christensen, 2012). This study contributes to the question to what extent and why firms voluntarily adopt IFRS in an unexplored setting. Prior evidence of voluntary IFRS adoption cannot be generalized to the exchange-regulated markets because firms that perform an IPO in the exchange-regulated markets are different from firms that perform an IPO in a EU-regulated market: They are more likely to be owned by private investors prior to the IPO, place relatively few shares within the IPO, and are smaller than their counterparts from EU-regulated markets. Therefore, the aim of this research note is to describe the regulatory environment and to investigate (i) the ratio of voluntary adopters and (ii) the determinants of voluntary adoption of IFRS in the European exchange-regulated markets of Amsterdam, Brussels, Lisbon, Frankfurt, and Paris. To the best of my knowledge, this research note is the first to examine voluntary IFRS adoption of public firms after IFRS became mandatory for most European public firms in 2005. First, I find that overall less than 20% of the firms listed in these exchange-regulated markets voluntarily adopt IFRS.

Introduction

Investment decisions differ from other decision types by the level of risk associated (Dorn and Huberman 2010). They usually involve higher levels of risk than other kinds of purchase decisions (Noussair et al. 2014). This risk factor in financial decisions makes return and loss an issue for investors more than for other consumer types. The factors which have important implications on financial decisions at a microlevel include heuristics, which are the cognitional or emotion-based biases or simplifying decision rules for investment decisions (Hauser 2014; Mousavi and Gigerenzer 2014). Empirical studies indicate that about 50% of people intuitively rely on heuristics (Huberman and Wei 2006). Examples of these heuristics include availability bias, overconfidence, endowment bias, cognitive dissonance and framing bias (Pompian 2006). At a macrolevel, on the other hand, corporate brands and such brand-related variables as brand equity, reputation, performance and trust govern financial decisions as brands gain increasingly more importance not only for customers but also for stakeholders including investors (Aspara 2013; Fischer and Himme 2016). Besides these corporate brands, stock markets, or stock exchanges as a manifestation of the financial market, are considered to have significant impacts on investor decisions by means of the overall brand equity that they resonate for their actual and potential investors. Thus, it is suggested that brand equity of a stock exchange might add to or detract from investors’ risk perception on the way to influence their investment intention. To the best of our knowledge, there has been little research on this perspective, i.e., the brand equity of stock exchange for individual investors as an important influencer in the relationship between risk and investment intention, and thus is worth investigating. The first section opens with the theoretical background. It later develops into the method used in the study. The last section includes the discussion of the results obtained, conclusion and practical implications and ends with the limitations and further research.

Long-term monitoring the key indicators of the securities market could detect market risk structure and risk behaviors in real time and ensure the safe and smooth operation of the securities system. In view of the large number monitoring indicators, high frequency sampling data acquisition in the online monitoring system, it is necessary to use advanced information management tools to efficiently manage and utilize the massive data, as the scale of securities market grows geometrically. Based on the online monitoring of BP neural network, this paper develops a management information system for the monitoring data of the securities market of China to realize the structure analysis and prediction of market performance and market risk structure and carry out automatically early warning and alarm with reference to pre-set threshold parameters, which provides a technical platform for real-time query, analysis statistics, prediction and alarm of market risk structures.

Introduction

Securities market is a component of the wider financial market where securities can be bought and sold between subjects of the economy. By monitoring key indicators’ data reflecting the operating status of the securities market, one can understand the flow direction and configuration of social capital, and find out whether the role of the securities market allocating social funds is valid and reasonable. Monitoring key indicators and setting the early warning mechanism is also a guarantee on sound operation of securities market. China has paid great attention to market monitoring at the same time of making effort to develop various financial markets. The existing monitoring indicators are relatively a few, data acquisition frequency is not high [1, 2], the amount of data that needs to be processed is not large, so the relatively simple data management is applied for monitoring data analysis and census. But as we know, many factors could be influential to securities market volatility, including price, interest rate, exchange rate, investment rate, saving amount, economic situation, listed securities quantity and their price movements, etc. [3–5], also with the scale of securities market growing geometrically, monitoring indicator data management information system is needed by building up database or information file system to accumulate a large amount of information and long-term data, and efficiently use the monitoring data to support the normative institutional operation of the securities system. Many information management systems on the securities company level have been established at home and abroad, The American COSO issued Enterprise Risk Management-Overall Framework in 2004, which expanded the connotation of internal control, and made a more detailed statement on the broader subject of enterprise risk management. In June 2006, the state-owned Assets Supervision and Administration Commission of the State Council drafted and published the Guidance of Central Enterprise Wide Risk Management in China. The commission requested the central enterprises to gradually establish a comprehensive risk management system, and clarified the requirement of risk management information systems in the Guidance of Central Enterprise Wide Risk Management. It is conducive to the risk management of securities market. But we haven’t found a management information system on the securities market level in China on the retrieval of existing literature. Drawing on the experience of existing management information system, based on B/S (browser/server) mode, this paper develops a monitoring data management information system to monitor the key indexes of securities market, complete the real-time massive data analysis, statistics, and realize the prediction and early warning function, which would provide a powerful guarantee for the financial system’s safe operation. The remainder of this paper is structured as follows. Section 2 gives a brief description about system framework. Section 3 discusses data acquisition, transmission, backup and processing. Section 4 describes the system structure and the method of data query. Section 5 develops the data report and alarm function of the management information system. The last section concludes.

1. Introduction

2. Literature

3. Methodology

4. Findings

5. Conclusion

References

Social media is one of the fastest and the most effective ways of information sharing. Thousands or even millions of people can be reached through social media. It might be stated that increased use of social media led many companies to be more interested in social media with varying reasons ranging from communication to advanced networking or providing information. Therefore, businesses have started to give importance not only websites but also social media interactions. In today’s business context several companies are promoting their goods on social media similar to the service companies including tourism industry. Social media is used as an information providing platform providing financial and non-financial information for the evaluation of investors and other stakeholders interested in investment activities. Major aim of this study is to analyze and evaluate the relationship between financial performance (market value, net sales, net profits, price/earnings ratio etc.) and efficient use of social media. Mann-Whitney U test of SPSS software is used in order to evaluate the data collected within the scope of this research.

Introduction

In consequence of rapid development of technology, there have emerged many changes in both social areas and business life as a result of increase in internet use and expansion in internet availability. These changes have brought new concepts into our lives. One of these concepts is “social media”. Social media is the whole of internet-based services where individuals communicate with other individuals in an open or half-open system which is surrounded by rules, they can create their own contact groups and watch videos or monitor posts shared by their contacts (Ellison, Steinfield and Lampe, 2007). Kim, Jeong and Lee (2010) expresses social media as online communities whose content is created and shared by users. In another definition, social media is described as high-level measurable and accessible communication technologies or techniques which allow an individual to easily interact with other individuals (Blossom, 2009). In order to meet the components of this definition an application or a website must have users independent from the publisher, have a user-generated content, there must be interaction among users and there must not be limitations of time and space (Erkul, 2009). Web 1.0 where users could only see internet sites was the period in which there was no interference such as making comments on the content, creating an attachment to the content or contributing to the content (Ozmen and Villi, 2014). Social media built on the second generation Web 2.0 internet infrastructure, which was developed after this period, is a platform operating on an interface where internet users may publish anything that they want to share and other users may comment on these contents according to their demands and ideas or make changes when they want without any space and time limitations (Evans, 2010; Wilson, 2010).

It has repeatedly been stated that the outcomes of a firm’s alliance portfolio are influenced by characteristics of the partners (Saxton, 1997; Dasí-Rodríguez and Pardo-del-Val, 2015; Jiang et al., 2010). For alliance managers and top management teams, it is necessary to know how different portfolio configurations influence the organization’s outcomes (Leeuw et al., 2014). We study a rarely analyzed type of partner characteristics, portfolio internationalization, which has been shown to substantially impact firm performances (Zaheer and Hernandez, 2011; Lavie and Miller, 2008; Goerzen and Beamish, 2005). In our globalized world, alliances are increasingly international. Consequently, it is a key issue for firms to determine the optimal degree of alliance portfolio internationalization (API). However, studies pursuing this question concentrated on innovation driven exploration alliances and found an S-shaped relationship between portfolio internationalization and firm performances (Lavie and Miller, 2008). Less attention has been devoted to the internationalization of exploitation alliance portfolios. Exploitation alliances refer to agreements where the focus is put on the usage of existing resources and information (March, 1991). In many industries like air transport (Casanueva et al., 2014) or biotechnology (Rothaermel, 2001) they are used frequently and have a strong impact on firm performances. Compared to exploration alliance, they influence firm performances differently. Learning and innovation play only a minor role in exploitation alliances (Vassolo et al., 2004; Rothaermel, 2001). They affect operations more directly compared to the lagged impact of product development through exploration alliances (Yamakawa et al., 2011). Hence, exploitation alliances are less uncertain and more predictable (Levinthal and March, 1993; Nielsen and Gudergan, 2012). They offer a more “beaten track,” because the inputs and outputs are often well-known (March, 1991). The necessary tasks in exploitation alliances are therefore more standardized. Consequently, it is reasonable to assume that the internationalization of exploitation portfolios also affects firm performances in a unique way. We predict a U-shaped impact of API on firm performance in the context of exploitation alliances. Hence, we do not expect a decline of performances at high levels of portfolio internationalization as overserved in the study of Lavie and Miller (2008), which we use as a role model for our study.

Scholars in behavioral finance have expended considerable effort in trying to understand investment choices. Much of this research has focused on the decision-making behavior of the actors involved in the financial markets, particularly the institutional investors who account for the bulk of the activity (Gabaix et al., 2006; Gompers and Metrick, 2001). One strand within this research, at the finance and marketing interface, has investigated the effect of brands and brand variables in investment choices, both for individual and institutional investors (Frieder and Subrahmanyan, 2005; Huberman, 2001; Keloharju et al., 2012). The basic rationale underlying this research is that perceptions and sentiments towards corporate brands in the wider market spill over into investments in the stock market. For instance, prestigious brands (Billett et al., 2014; Frieder and Subrahmanyan, 2005), brands with high familiarity (Huberman, 2001); with high advertising spend (Grullon et al., 2004); brands with loyal customers (Keloharju et al., 2012), with high customer satisfaction, and a strong corporate reputation (Himme and Fischer, 2014), have been found to be more likely to be chosen as equity investments. While these studies have made an important contribution in identifying the link between consumer behavior and investor behavior, and the significant role of brands in both arenas, they ignore the influence of the intermediaries, the financial markets — and particularly the stock exchanges, through which the investment decisions are transacted. This research addresses a gap in the literature by approaching stock exchanges as brands that need to develop compelling value propositions and to promote themselves to potential customers/investors (Jansson and Power, 2006; Kokosalakis et al., 2006). In this sense, it brings the marketing and finance disciplines together, by exchanging research approaches between the two. It begins by considering the evidence on investor behavior from the finance and marketing literatures. It then develops a model of investor-based brand equity adapted from the well-known customer-based brand equity model developed by Aaker (1992). This research model is tested via an online survey of actual and potential private investors in two stock markets – Turkey and Ireland. The nature of the suggested interaction is discussed in the final section, together with the managerial implications of these findings.

Stock Exchange Markets provide a service to trade stocks and bonds. They have multiple roles in the economy [?] such as raising capital for companies, government capital raising for development projects and mobilizing savings for investment. Additionally, it is an indication of the general trend in the economy for a particular country. For many years, stock exchanges were physical locations where buyers and sellers met and negotiated. With the improvement in communication technology in the late 20th century, traders started to transact from remote locations in what became known as electronic trading (etrading). There are clear indications attest that the electronic crime threat in stock markets is an increasing threat, with the possibility of significant costs [3,4]. Therefore, implementing an effective information security measures and controls has become a critical success factor for all parties (regulators, brokerage companies and individual investors) to ensure a safe environment for electronic trading. Hereby, a threat modeling has to be developed and be followed for optimizing environment security. Threat modeling is a method for improving security by identifying assets that needs protection and weaknesses. Then defining actions reduce the effects of threats to the system. In stock exchange environment, the web servers are companies assents that need a protection. Digital Certificate is one of the technologies required to maintain the protection. It is based on Secure Socket Layer (SSL) [6] and Transport Layer Security (TLS) [8] protocols. SSL/TLS protocols are considered the de facto standard for providing secure communication over the Internet. The protocols have evolved over years to fix the weaknesses and drawbacks detected, add protection against discovered attacks and support new cryptographic algorithms that were defined. Several security assessments should be conducted to make sure that stock exchange markets are aligned with recent cyber threats. Moreover, companies should evaluate their systems and implemented technologies to identify any weakness. Then take actions toward enhancing the protection of their clients’ information. The rest of this paper is organized as following. Section 2 demonstrate the previous studies related to cyber threats in stock exchange. Followed by Sect. 3 which define the threat modeling for this environment. Section 4 introduce SSL/TLS protocols background, the known attacks and deployment best practices. The case study is presented in Sect. 5. The security assessment, its results and discussion are presented in Sect. 6. The conclusion is presented in Sect. 7 along with future work.

A considerable body of literature supports the notion that the corporate governance environment(i.e., effective internal control systems and board oversight, reduction in risk taking behaviors, increase in firm value) in the US has improved since the passage of the Sarbanes—Oxley Act (SOX) in 2002 and the promulgation of US governance standards set by the Securities and Exchange Commission (SEC) in 2003 (Beasley et al., 2009; Chang and Sun, 2009; Chhaochharia and Grinstein, 2007; Cohen et al., 2008, 2010; Jain et al., 2008; Wang, 2010). Also, given audit committees’ strengthened role in monitoring the financial reporting process and heightened responsibility for reporting accuracy, firms have become more conservative during the SOX era (DeZoort et al., 2008). This is substantiated by research showing a reduction in the acquisition of risky investments as new governance rules are implemented (Bargeron et al., 2010; Cohen et al., 2007; Shadab, 2008). Few studies have examined the relationship between governance and risk behavior of firms operating in the emerging markets (Braga-Alves and Morey, 2012; Chang et al., 2015). It follows then that scant research has been dedicated to the governance — risk relation among crosslisted EM firms that are exposed to SEC regulations through the issuance of an American Depository Receipt (ADR) or non-cross-listed EM firms (Jayaraman et al., 1993; Litvak, 2007a, 2007b, 2008). Currently, the literature lacks an overarching theoretical model that depicts international corporate convergence in which firms commit to more rigorous regulatory and disclosure standards as a form of ‘‘bonding’’ in an effort to strengthen their governance while reducing their exposure to agency costs so that they may remain competitive both in local and global markets. To build our theoretical model, we employ four individual rules to determine if the riskiness of both cross-listed (ADRs) and non-cross-listed EM firms is affected by US best practices governance standards. Each of the rules are compulsory for firms listed on the NYSE or NASDAQ and identified in Section 303A of the New York Stock Exchange’s (NYSE) Listed Company Manual. Specifically, the four mandated rules employed are: the establishment of three independent committees (i.e., nomination, compensation and audit committees), independent directors, duality of the Chief Executive Officer (CEO) and the existence of a corporate ethics policy. The rules are employed to determine (1) if EM firms that adopt standards of corporate governance set in the US have the same impact on risk taking as US firms; and (2) if the effect of corporate governance on risk taking measures differs between cross-listed ADRs that are subject to US governance standards and non-cross listed EM firms that are not held to those standards.

Over the past two decades, there has been a substantial increase in corporate governance-related reforms in some parts of the Middle East and North Africa region (MENA). For example, new stock exchanges, such as the Iraq Stock Exchange, Damascus Stock Exchange, and Saudi Stock Market have been established. Securities legislation has also been amended in some countries to require all listed companies to prepare their accounting reports in accordance with International Financial Accounting Standards (IFRS) in order to improve transparency (e.g. Egypt, Jordan, Iraq, Oman and United Arab Emirates (UAE)). Investment law has also been reformed to encourage foreign direct investments and to grow domestic investments in Iraq, Jordan, Oman, Saudi Arabia, and UAE. These new investment laws provide equal treatment to both foreign and domestic investors, allowing foreign investors to engage in any economic activity. Further, while there are significant similarities, there is also substantial variation in economic growth and stock market features across countries in the region and across time within each country. Despite the fact that a recent economic and financial integration through mechanisms such as the Arab Federation of Exchanges has boosted development of stock exchanges in many countries across the region; the variation in economic growth and stock market features is expected to significantly influence market efficiency (e.g. El Mehdi, 2007; Jefferis & Smith, 2005). Prior empirical research has predominantly explored Asian economies as emerging markets, with emerging markets in the MENA region being largely overlooked. Moreover, a number of studies document an association between earnings quality and external governance mechanisms, including takeovers (e.g. Scharfstein, 1988); analysts following (e.g. Mei & Subramanyam, 2008); banks (e.g. Ahn & Choi, 2009); and institutional ownership (e.g. Ajinkya, Bhojraj & Sengupta, 2005). However, the efficiency with which the stock exchange 3 undertakes this role in ensuring the quality of reported earnings is not well understood. For this reason, this paper aims to examine whether the quality of reported earnings is related to the MENA stock exchanges efficiency.