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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 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.

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.

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.

This paper aims to test the extent to which downward bias due to a floating-point exception in probability of informed trading (PIN) estimates obtained using the Easley, Hvidkjaer and O’Hara (EHO; 2002) method is remedied using the Yan and Zhang (YZ; 2012) method. The paper also aims to test the sample-size sensitivity of EHO PIN and identify PIN determinants for acquirers and targets in the biotech sector.

EHO and YZ PIN performances are compared for US biotech acquirers and targets around their mergers and acquisition (M&A) announcements. The sampling method of Kryzanowski and Lazrak (2007) is used to assess sample-size sensitivity of announcement window EHO PIN estimates. Cross-sectional regressions are estimated to identify PIN determinants.

EHO and YZ PIN are not significantly different. EHO PIN exhibits significant sample-size sensitivity. Information leakage prior to M&A announcements is strongly affected by some firm characteristics. Significant determinants of PIN behavior around M&A announcements include insider and institutional holdings and research and development (R&D) expense.

Findings imply that PIN partially reflects the activities of insiders and other informed investors about takeover intentions. Subsequent research can examine PIN behavior around pre-announcement rumors for M&As in the same or other industries and for potential targets that are peers of the M&A targets.

This paper contributes to the ongoing debate in the empirical finance literature on whether PIN measures informed trading by examining its behavior and the importance of some methodological issues associated with its use in examining market behavior around M&A announcements.

It is difficult to argue against the fact that research has focussed on artificial intelligence (AI) and robotisation over the past few decades. Additionally, during the past several years, it has taken off and is now extensively used in numerous businesses across various industries. Most of the time, AI has been associated with some industrial sector process automation. Still, recently, the authors have noticed more positive technology uses, especially in the financial services industry. Due to several factors, the financial sector needs to adopt AI and recognise its potential. The industry has historically been concerned about unpredictability, legislation, stronger cybersecurity, technological limitations and disruption of established lucrative operations.

Never before has there been more discussion about AI due to the advantages it provides to businesses that are providing financial services. That may explain why this change is referred to as the fourth industrial revolution. Both positively and negatively, it is quite disruptive. The effectiveness, accuracy and cost-effectiveness of solutions greatly increase. However, immense power also entails great responsibility.

Precautions and security are more crucial than ever for businesses since the financial sector is changing significantly and quickly. The various benefits and drawbacks of this technology are yet unknown to humans. Although AI was first shown to us in the 1950s, it has recently gained new prominence as processing power, and the available quantity of data has increased dramatically.

The KOSPI200 mini option introduced in July 2015 is the derivative of which trading multiplier is reduced to one-fifth of the regular options. This study explored the pairs trading opportunities arising from the price spread between the KOSPI200 regular options and the mini options during the sample period from August 2015 to March 2016 and measured the profits of pairs trading. The main results are summarized as follows. First, the most frequency of pairs trading with high profit was observed for in-the-money options. On the other hands, the frequency of pairs trading opportunities is low and the profit is relatively small for out-of (at)-the money options. Second, for in-the-money options, arbitrage opportunities were captured every three minutes on an average, but the elapsed time between arbitrage opportunity opportunities on out-of-the money options exceeded 10 minutes on average. Third, pairs trading opportunities occur uniformly throughout the day, but profit tends to increase in the afternoon than in the morning. This indicates that price efficiency in options market deteriorates and profit of arbitrage trading with price disparity is higher in the afternoon than that of the morning trading. In addition, the profitability of pairs trading with low liquidity was cross-sectionally higher than those with high liquidity.

The purpose of this paper is to empirically study the relationship between informed trading and overall corporate governance mechanisms.

A broad range of governance characteristics are used to measure the governance structure of firms in the Toronto Stock Exchange. The risk of informed trading is estimated using a PIN measure that avoids biases induced by trade classification errors. Our proxies for informed trading are regressed on measures of corporate governance.

Our most important result is that the observed trade‐off between CEO compensation and informed trading holds only for large firms. There is no correlation between CEO cash compensation and the risk of informed trading in small and medium sized firms. We find evidence that cross‐sectional differences in the risk of informed trading are explained by a firm's governance structure.

Research finding a trade‐off between CEO compensation and informed trading merits closer examination.

Limitations on insider trading, and more broadly on informed trading, may involve different costs and benefits for large firms than for medium and small firms.

This paper expands the set of governance characteristics shown to interact with informed trading activity. The Toronto market is well suited to focusing on relations between informed trading and firm‐level governance characteristics.

Using intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).

The authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.

The empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.

The study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.