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The purpose of this paper is to examine the effectiveness of two regulatory initiatives in developing awareness of conduct risk associated with algorithmic and direct-electronic access (DEA) trading at broker-dealers: the UK Financial Conduct Authority’s algorithmic trading compliance in the wholesale markets and Commission Delegated Regulation 2017/589 (CDR 589) to the second Markets in Financial Instruments Directive.

A qualitative examination of 15 semi-structured interviews with representatives of London Metal Exchange member firms, their clients and regulators.

This paper finds that the key conduct related messages in algorithmic trading compliance in the wholesale markets may not yet be fully embedded at broker–dealers. This is because of a perceived simplicity of the algorithms deployed by broker dealers or, alternatively, a lack of reflection on their impact. Conversely, a concern exists that clients’ deployment of algorithms on DEA channels provided by broker–dealers increase conduct risk. However, the threat of harm posed by clients is not envisaged in current definitions of conduct risk. Accordingly, CDR 2017/589 does not currently require firms to evaluate clients’ awareness of it.

This study’s findings are limited to the insights provided by 15 participants.

This paper contributes to existing research by deepening understanding of conduct risk arising from algorithmic trading and DEA. To account for the potential harm arising from clients’ activities, this paper proposes a revision to Miles’s definition of conduct risk. This is complemented by a proposed amendment to CDR 2017/589 to require evaluation of clients’ understanding of conduct risk.

The purpose of this paper is to examine the effectiveness of UK investment firms’ implementation of the requirements in Commission Delegated Regulation 2017/589 (more commonly known as “Regulatory Technical Standard 6” or “RTS 6”) that govern the conduct of algorithmic trading activities.

A qualitative examination of 19 semi-structured interviews with practitioners working for, or with, UK investment firms engaged in algorithmic trading activities.

The paper finds that practitioners generally have a good understanding of the requirements in RTS 6. Some lack knowledge of algorithms, coding and algorithmic strategies but have used best efforts to implement RTS 6. However, regulatory fatigue, complacency, cost pressures, governance in international groups, overreliance on external knowledge and generous risk parameter calibration threaten to undermine these efforts.

The study’s findings are limited to the participants’ insights. Some areas of the RTS 6 regime attracted little comment from participants.

The paper proposes the introduction of mandatory algorithmic trading qualification requirements for key staff; the lessening of the requirements in RTS 6 for automated executors; and the introduction of a recognised software vendor regime to reduce duplication and improve coordination between market participants that deploy algorithmic trading systems.

To the best of the author’s knowledge, the study represents the first qualitative examination of firms’ implementation of the algorithmic trading regime in the second Markets in Financial Instruments Directive 2014/65/EU.

This paper aims to contribute to recent debates about financial knowledge by opening the black box of its algorithmization to understand how information systems can address the major challenges related to interactions between algorithmic trading and financial markets.

The paper analyses financial algorithms in three steps. First, the authors introduce the phenomenon of flash crash; second, the authors conduct an epistemological analysis of algorithmization and identify three epistemological regimes – epistemic, operational and authority – which differ in terms of how they deal with financial information. Third, the authors demonstrate that a flash crash emerges when there is a disconnection between these three regimes.

The authors open the black box of financial algorithms to understand why flash crashes occur and how information technology research can address the problem. A flash crash is a very rapid and deep fall in security prices in a very short time due to an algorithmic misunderstanding of the market. Thus, the authors investigate the problem and propose an interdisciplinary approach to clarify the scope of algorithmization of financial markets.

To manage the misalignment of information and potential disconnection between the three regimes, the authors suggest that information technology can embrace the complexity of the algorithmization of financial knowledge by diversifying its implementation through the development of a multi-sensorial platform. The authors propose sonification as a new mechanism for capturing and understanding financial information. This approach is then presented as a new research area that can contribute to the way financial innovations interact with information technology.

The purpose of this paper is to present an overview of the flash crash, and explain why and how it happened.

The author summarizes several studies suggesting various perspectives on the flash crash and its causes. Furthermore, the author highlights recently proposed and introduced improvements and regulations to reduce the risk of having similar market collapses in the future.

It is an overview paper that highlights the state of the art on the subject.

Paper does not report any research findings of the author.

High-frequency trading (HFT) along with its pros and cons is the new normal for most of the current electronic trading activity in the markets. It is well recognized by the experts that HFT may have its important shortcomings whenever the rules and regulations are not up to date to match the technological progress offering faster computational and execution capabilities.

HFT has created a societal discussion about its benefits and potential deficiencies as the common practice for trading due to potentially unequal access to market data by various categories of participants. Such arguments help the regulators to develop improvements to reduce the market risk and nurture more robust and fair markets for all.

The paper has a tutorial value and summarizes the current state of HFT. The readers of more interest are guided to the most relevant literature for further reading.

The purpose of this paper is to contribute to a better understanding of governance choice in the area of algorithmic selection. Algorithms on the Internet shape our daily lives and realities. They select information, automatically assign relevance to them and keep people from drowning in an information flood. The benefits of algorithms are accompanied by risks and governance challenges.

Based on empirical case analyses and a review of the literature, the paper chooses a risk-based governance approach. It identifies and categorizes applications of algorithmic selection and attendant risks. Then, it explores the range of institutional governance options and discusses applied and proposed governance measures for algorithmic selection and the limitations of governance options.

Analyses reveal that there are no one-size-fits-all solutions for the governance of algorithms. Attention has to shift to multi-dimensional solutions and combinations of governance measures that mutually enable and complement each other. Limited knowledge about the developments of markets, risks and the effects of governance interventions hampers the choice of an adequate governance mix. Uncertainties call for risk and technology assessment to strengthen the foundations for evidence-based governance.

The paper furthers the understanding of governance choice in the area of algorithmic selection with a structured synopsis on rationales, options and limitations for the governance of algorithms. It provides a functional typology of applications of algorithmic selection, a comprehensive overview of the risks of algorithmic selection and a systematic discussion of governance options and its limitations.

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.

The purpose of this study is to define FinTech, differentiating it from financial technology and use the definition to develop an industry framework.

Using the existing literature on FinTech and incorporating these contributions into a traditional financial structure, characteristics are outlined and placed into a framework that describes the FinTech industry.

FinTech is a specific type of Financial Technology, defined as technology used to provide financial markets a financial product or financial service, characterized by sophisticated technology relative to existing technology in that market. Firms that primarily use FinTech are classified as FinTech firms. Using these definitions, the paper provides a structure for the FinTech industry, classifying each type of FinTech firm by FinTech characteristics.

Research that would inform the economic importance of FinTech would be served with an increased understanding of FinTech firms and the FinTech industry.

This paper contributes by defining FinTech and developing a comprehensive framework to describe the emerging FinTech industry.

This chapter explores Hong Kong's future as a major public securities market. It concludes that Hong Kong has the potential to become one of the world's major – if not the number one – public securities market in the coming decades. However, there are four major factors that will affect how much this potential is realized: (1) How Hong Kong's market is treated by the Central Government in Beijing vis-a-vis its competitors in Shanghai and Shenzhen. If Hong Kong is allowed full access to the Chinese saver/investor and Chinese firms are allowed the choice of listing in Hong Kong, then Hong Kong will outcompete its Shanghai and Shenzhen rivals regardless of whether Shanghai and Shenzhen are opened for listings by foreign companies and to foreign investors. Hong Kong will thrive in an environment of no capital constraints on the renminbi. Conversely, a retention of the renminbi capital controls combined with free access of foreign firms to list on Shanghai or Shenzhen and/or restrictions on Chinese firms listing in Hong Kong would be very harmful to Hong Kong. (2) How skillful and aggressive Hong Kong and the Hong Kong Exchanges and Clearing Ltd. are in making Hong Kong into a global competitor as a securities market. Hong Kong's principal competitors on a global basis are New York and London and the new electronic exchanges that have sprung up in Western countries. (3) The full force of new technologies is not inhibited in Hong Kong to protect a monopoly position of the Hong Kong Exchanges and Clearing Ltd. (4) Hong Kong maintains its stable relationship with the US dollar, no capital controls are introduced in Hong Kong, and that Beijing continues to respect Hong Kong's information freedom as specified in the Basic Law.