Does high frequency algorithmic trading matter for non-AT investors?

The extant literature has typically measured the impact of high frequency algorithmic trading (HFT) on short term outcomes, in seconds or minutes. We focus on outcomes of concern for longer term non-algorithm investors. We find in some cases HFT increases volatility arising from news relating to fundamentals. Furthermore HFT is associated with the transmission of that volatility across industries, and that transmission is based on short term correlations. Finally, we find that the period since the introduction of algorithmic trading (AT) has seen increases in both the variances and covariances of return volatility in most industries. However increases in the variances has not been uniform in that it has fallen sharply in a few industries. The magnitudes are such that, overall, AT has coincided with reduced return volatility variance.     

Performance of Moving Average Trading Rules in a Volatile Stock Market: The Russian Evidence

This article examines the profitability of dual moving average crossover (DMAC) trading strategies in the Russian stock market over the 2003–12 period. It contributes to the existing technical analysis (TA) literature by testing, for the first time, the applicability of ordered weighted moving averages (OWMA) as an alternative calculation basis for determining DMACs. In addition, this article provides the first comprehensive performance comparison of DMAC trading rules in the stock market that is known as one of the most volatile markets in the world. The results show that the best trading strategies of the in-sample period can also outperform their benchmark portfolio during the subsequent out-of-sample period. Moreover, the outperformance of the best DMAC strategies is mostly attributable to their superior performance during bearish periods and, particularly, during stock market crashes.     

Informal Traders and the Battlegrounds of Revanchism in Cusco,Peru

Informal trading in the global South, particularly in Latin America, is the subject of revanchist urban policy and yet few studies have examined the longer‐term impacts of such intolerant policies on traders. This article explores the evolution and impacts of revanchist policies directed at informal traders in the Andean city of Cusco. It makes two key contributions. First, it documents a shift from early revanchist policies to a post‐revanchist era where policies have become more tolerant of informal traders. However, contemporary policies fall short of a supportive environment for informal trading, hence the authors recommend changes that will ensure informal traders can access the city's streets and become an accepted part of the urban fabric. Second, given the lack of theoretical attention given to the impacts of revanchism, a battlegrounds framework is developed, consisting of spatial, political, economic and socio‐cultural battlegrounds. This framework provides a comprehensive insight into the complex set of interactions that exist between informal traders and the state. It is hoped that the framework will provide a tool for further research into the highly damaging impacts of revanchism across the globe.     

Non-technical skills for managing risk and performance in financial trading

Recent large-scale failures in financial institutions have been found to be caused, in-part, by human factors-related issues in financial trading. In other environments where risk management and performance are intertwined, a human factors approach is often adopted to understand how the ‘non-technical skills (NTS)’ (leadership (LD), decision-making (DM), situation awareness (SA), teamwork) of organisational actors influence outcomes. Yet, to date, there has been minimal application of human factors research in financial trading. This study (i) identifies ‘real-world’ (i.e. non-laboratory) research studies investigating the NTS important for performance in financial trading, (ii) examines and synthesises data on the NTS found to underpin good or poor performance and (iii) considers the quality and coverage of research investigating NTS in financial trading, and identifies potential areas for future research. Nineteen studies were identified through a systematic literature search and then content-analysed for associations between NTS and performance in financial trading. The review found a range of decision-making (e.g. heuristics and biases, intuitive DM, emotional regulation) and LD skills (e.g. setting standards, monitoring behaviour, encouraging speaking-up) to have been identified as important for managing risk and performance in financial trading environments. Furthermore, SA (e.g. information search and assessment strategies, vigilance, identifying ‘noise’ data) and teamwork (e.g. avoiding ‘role’ conflict, communication between traders) were found to be important, yet remain less explored within the literature, and should be the focus of future research. NTS appear essential for effective risk management within the financial sector, yet further field research is required to examine the context-relevant behaviours that underpin safe activity. This will facilitate the development of evidence-based systems for assessing and training NTS competencies.     

Competition in the stock market with asymmetric information

We build a game theoretical model to examine how the level of information advantage of insiders and the competition between insiders and sophisticated investors affect stock price movements and traders’ trading strategies and profits. We show that the competition between insiders and sophisticated investors can reduce the losses of less sophisticated investors, and thus alleviates the disadvantaged position of the less sophisticated investors. Further, traders’ profits are affected by the accuracy of insiders’ private information, and the number of days that insiders have obtained the information in advance. These findings show the importance of information transparency and the role of sophisticated investors in limiting insiders’ trading advantages and mitigating the expropriation of investors by insiders.     

Liquidity,information, strategic trading in an electronic order book: New insights from the European carbon markets

The electronic limit order book (LOB hereafter) has rapidly become the primary way of trading European carbon assets over the 4 years of the EU ETS programme (2008–2012). In this first attempt of examining the informational content of an electronic order book, we evidence that order flow imbalances have a moderate capacity to predict short term price changes. However, we find that both LOB slope and immediacy costs help to forecast quote improvements and volatility in the next 30 min. Further, we explain why informed trading is highly influential and show that it consists in mixing order splitting strategies and posting fleeting orders once the asymmetric information is reduced (Rosu, 2009). Overall, the consolidated status of the order book mirrors a high level of market uncertainty and a low degree of informational efficiency. In this way, strategic trading can in itself explain some of order book properties, independently of the degree of traders’ sophistication and market competition.     

Multi-party computation mechanism for anonymous equity block trading: A secure implementation of turquoise plato uncross

Dark pools are financial trading venues where orders are entered and matched in secret so that no order information is leaked. By preventing information leakage, dark pools offer the opportunity for large volume block traders to avoid the costly effects of market impact. However, dark pool operators have been known to abuse their privileged access to order information. To address this issue, we introduce a provably secure multi-party computation mechanism that prevents an operator from accessing and misusing order information. Specifically, we implement a secure emulation of Turquoise Plato Uncross, Europe's largest dark pool trading mechanism, and demonstrate that it can handle real world trading throughput, with guaranteed information integrity.     

Informed trading around earnings and mutual fund alphas

We examine whether informed trading around earnings announcements drives mutual fund performance. The measure is motivated by prior studies arguing that a mutual fund is skilled if it buys stocks with subsequent high earnings announcement returns. We find that this measure predicts future mutual fund returns. On average, after adjusting for Carhart’s four risk factors, the top decile of mutual funds outperforms the bottom decile by 44 basis points per quarter. By decomposing fund alphas into two components in their relations to earnings, we find that this measure is only associated with earnings-related fund alphas. This measure can also be used to predict stock returns at future earnings announcements.     

Corporate Governance and Corporate Bond Liquidity

Using a unique dataset of corporate bond trading information and corporate governance evaluation scores, this study examines the determinants of corporate bond market liquidity in Korea. In particular, this study explores whether corporate governance performance of a company influences liquidity of bonds issued by the company. The paper reports three important findings. First, the issue size and age of bond are important determinants of bond liquidity. Second, liquidity of corporate bonds is influenced by changes in macroeconomic conditions. Third, and most importantly, better corporate governance increases liquidity of corporate bonds. This result suggests that corporate governance is an important determinant of bond liquidity, as it lowers transaction costs by improving transparency and reducing asymmetry of information. This paper contributes to the literature by providing new evidence that corporate governance performance is an important determinant of liquidity in corporate bond markets.     

Optimal order display in limit order markets with liquidity competition

Order display is associated with benefits and costs. Benefits arise from increased execution-priority, while costs are due to adverse market impact. We analyze a structural model of optimal order placement that captures trade-off between the costs and benefits of order display. For a benchmark model of pure liquidity competition, we give a closed-form solution for optimal display sizes. We show that competition in liquidity supply incentivizes the use of hidden orders to prevent losses due to over-bidding. Thus, because aggressive liquidity competition is more prevalent in liquid stocks, our model predicts that the proportion of hidden liquidity is higher in liquid markets. Our theoretical considerations ares supported by an empirical analysis using high-frequency order-message data from NASDAQ. We find that there are no benefits in hiding orders in il-liquid stocks, whereas the performance gains can be significant in liquid stocks.