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The emphasis on inter-organizational systems gave rise to concerns about inter-organizational relationships as trading partners became aware of the socio-political factors and trust that affect their relationships. This paper examines the importance of inter-organizational-trust in business-to-business E-commerce organizations. It examines how inter-organizational relationships impact trading partner trust, perceived benefits, perceived risks, and technology trust mechanisms in E-commerce that can in turn influence outcomes of business-to-business E-commerce. This paper develops a conceptual model and tests the model using a case study research methodology. The aim is to solicit qualitative in depth understanding of inter-organizational-trust in business-to-business E-commerce. Eight organizations from a cross section of industries that formed four bi-directional dyads participated in the third stage of this study. The first two stages include exploratory case studies in three organizations in the automotive industry that applied EDI via Value-Added-Networks in 1997, and a nationwide survey of organizations that examined the extent of E-commerce adoption in Australia and New Zealand in 1998. The findings identify the need for trustworthy business relationships in an E-commerce environment.

While China is currently moving toward the full development of its own financial derivatives markets, to date, China's experience with these has been a negative one. This paper examines the importance to China of developing a fully integrated financial derivatives market from both the economic and financial market perspectives. It examines the best way forward for derivative trading, both market based and over-the-counter, and the types of products best suited to both, given the current state of the Chinese financial markets. Consideration is given to market structure, regulation, trading and settlement systems and international cooperation.

The rapid change of technology has significantly affected the financial markets in Thailand. In order to enhance the market efficiency and liquidity, the Stock Exchange of Thailand (SET) has granted Thai stock brokers permission to develop and offer their customers algorithm and automatic stock trading. However, algorithm trading on SET was not widely adopted. This chapter intends to design and empirically estimate a model in explaining Thai investors’ acceptance of algorithm trading. The theoretical framework is based on the theory of reasoned action and technology acceptance model (TAM). A sample of 400 investors who have used online stock trading and 300 investors who have used algorithm stock trading were observed and analyzed using structural equations model (SEM) and generalized linear regression model (GLM) with a Logit specification. The results confirm that attitudes, subjective norm, perceived risks, and trust toward algorithm stock trading are factors determining investors’ behavior and acceptance of using algorithm stock trading. Investor’s perception and trust on algorithm stock trading as a trading strategy is a major factor in determining their perceived behavior and control, which affect their decision on whether to invest using algorithm trading. Accordingly, it can be concluded that Thai investors is willing to accept algorithm trading as a new financial technology, but still has concern about the reliability and profitable of this new stock trading strategy. Therefore, algorithm trading can be promoted by building investors’ trust on algorithm trading as a reliable and profitable trading strategy.

This chapter considers the application of climate mitigation policies to the aviation sector with reference to the inclusion of aviation in the EU Emissions Trading System (EU ETS). Assessments of the possible economic impacts of including aviation in the EU ETS are reviewed and an impact analysis using the macroeconometric E3ME model is conducted.

The aviation sector is a significant and rapidly increasing source of GHG emissions. Because international policy measures have not been agreed, the EU has incorporated aviation in the EU ETS. It is therefore important to consider the possible economic effects of the ETS on the aviation industry and the wider economy.

The paper describes the approach used by the EU to include aviation in the EU ETS. Assessments of economic impacts have been made, but have often been limited in their approach. The paper complements the existing literature by including an economic analysis using the E3ME macroeconometric model of the EU that covers 41 industrial sectors including aviation.

Microeconomic and macroeconomic assessments show the economic impacts of including the aviation sector in the EU ETS are small. The negative impacts, if any, on EU GDP and the air transport sector’s economic output are less than 0.1% and 1% respectively. Distortions in competition, both between countries and industrial sectors, are therefore likely to be small.

In the long term (beyond 2020), including aviation in the EU can be seen as a positive move. If and when aviation is fully included in the EU ETS, and when the cost impacts of GHG emissions through permit prices are made evident, it is anticipated that airlines will start monitoring and reducing their GHG emissions by investing in new, less carbon intensive technologies.

Several studies have observed that stocks tend to drop by an amount that is less than the dividend on the ex-dividend day, the so-called ex-dividend day anomaly. However, there still remains a lack of consensus for a single explanation of this anomaly. Different from other studies, this dissertation attempts to answer the primary research question: how can investors make trading profits from the ex-dividend day anomaly and how much can they earn? With this goal, I examine the economic motivations of equity investors through four main hypotheses identified in the anomaly's literature: the tax differential hypothesis, the short-term trading hypothesis, the tick size hypothesis, and the leverage hypothesis.

While the U.S. ex-dividend anomaly is well studied, I examine a long data window (1975–2010) of Thailand data. The unique structure of the Thai stock market allows me to assess all four main hypotheses proposed in the literature simultaneously. Although I extract the sample data from two data sources, I demonstrate that the combined data are consistently sampled. I further construct three trading strategies – “daily return,” “lag one daily return,” and “weekly return” – to alleviate the potential effect of irregular data observation.

I find that the ex-dividend day anomaly exists in Thailand, is governed by the tax differential, and is driven by short-term trading activities. That is, investors trade heavily around the ex-dividend day to reap the benefits of the tax differential. I find mixed results for the predictions of the tick size hypothesis and results that are inconsistent with the predictions of the leverage hypothesis.

I conclude that, on the Stock Exchange of Thailand, juristic and foreign investors can profitably buy stocks cum-dividend and sell them ex-dividend while local investors should engage in short sale transactions. On average, investors who employ the daily return strategy have earned significant abnormal return up to 0.15% (45.66% annualized rate) and up to 0.17% (50.99% annualized rate) for the lag one daily return strategy. Investors can also make a trading profit by conducting the weekly return strategy and earn up to 0.59% (35.67% annualized rate), on average.

We examine the informational roles of trades and time between trades in the domestic and overseas US Treasury markets. A vector autoregressive model is employed to assess the information content of trades and time duration between trades. We find significant impacts of trades and time duration between trades on price changes. Larger trade size induces greater price revision and return volatility, and higher trading intensity is associated with a greater price impact of trades, a faster price adjustment to new information and higher volatility. Higher informed trading and lower liquidity contribute to larger bid–ask spreads off the regular daytime trading period.

The evolution of the Fair Trade movement offers an apposite case through which to examine the idea of regulating risk through a “social sphere.” An analysis of Fair Trade through the lens of “defiance” reveals discrete models and actors of risk regulation that evolve in an iterative fashion. These findings not only add complexity and heterogeneity to the social actors and mechanisms of regulation in the social sphere, but also highlight the challenges this diversity poses for the project of alleviating market risk. In turn, the framework of defiance offers a fertile analytical framework for the study of transnational risk regulation by capturing the dynamic actor and institutional complexities that underpin, and embody challenges for, the regulation of risk through the social sphere. The article begins with an overview of the Fair Trade movement and consideration of Fair Trade’s approach to regulating market risk. It then introduces the notion of defiance, focusing on two of its subtypes: game playing and resistance. Following a short overview of the methodological framework employed to analyze these dynamics, the third section applies these analytical categories of defiance to explore primary data gathered on Fair Trade’s evolution. The article shows that the motivational posture of game playing, through its continued experimentation and entrepreneurship in transnational risk regulation, is pregnant with potential to mitigate the risks generated by economic activity.

The current security trade settlement life cycle presents several inefficiencies derived from intermediaries involved in the transaction between buyers and sellers. This chapter examines distributed ledger technology (DLT), the underlying technology of all blockchain applications, including trade settlements. It also reviews the implications of using blockchain in trade settlements for cryptoassets. Emerging blockchain technology provides investors, exchanges, regulators, and countless potential intermediaries with the most up-to-date technology with the highest efficiency, transparency, credibility, and automation enabled by smart contracts. Smart contracts allow an ecosystem to manage the process of trade settlements starting from execution to clearing and then settlement. These contracts reduce reconciliation and recordkeeping costs and streamline repetitive processes present in today’s trade settlement system. The chapter highlights the benefits of implementing DLT in financial markets globally in all trading aspects, including cryptoassets.