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Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors investigate the impact of tick size on price clustering and trading volume when the minimum price change varies with price level, and they find that a smaller trading tick tends to exacerbate price cluster.
Abstract: Proposals have been made for some stock exchanges to reduce the size of their trading tick in order to lower transactions costs and, as a result, attract more trading volume and firm listings. We investigate the impact of tick size on price clustering and trading volume when the minimum price change varies with price level. Controlling the firm specific variables, we find that a smaller trading tick tends to exacerbate price clustering. Furthermore, a reduction in tick size is more likely to increase trading volume if the shares are heavily traded. These results suggest that previous studies on other stock markets may have overstated the benefits of a smaller trading tick to traders.

55 citations

Journal ArticleDOI
TL;DR: In this article, the attractiveness of floor trading versus anonymous electronic trading systems for traders is analyzed and the authors hypothesize that in low information intensity, the insight into the order book of the electronic trading system provides more valuable information than floor trading, but in times of high information intensity this is not true.

55 citations

Posted Content
01 Jan 2011
TL;DR: The daily average foreign exchange market turnover reached $4 trillion in April 2010, 20% higher than in 2007 as discussed by the authors, attributed largely to the increased trading activity of "other financial institutions", which contributed 85% of the higher turnover.
Abstract: Daily average foreign exchange market turnover reached $4 trillion in April 2010, 20% higher than in 2007. Growth owed largely to the increased trading activity of "other financial institutions", which contributed 85% of the higher turnover. Within this customer category, the growth is driven by high-frequency traders, banks trading as clients of the biggest dealers, and online trading by retail investors. Electronic trading has been instrumental to this increase, particularly algorithmic trading.

55 citations

Journal ArticleDOI
TL;DR: This article investigates statistical properties of technical analysis in order to determine if there is any objective basis to the popularity of its methods.
Abstract: The attention that technical analysis receives from financial markets is somewhat of a puzzle. According to Wiener-Kolmogorov prediction theory, time-varying vector autoregressions (VARs) should yield best forecasts of a stochastic process in the mean square error (MSE) sense. Yet, quasitotality of traders use technical analysis in day to day forecasting although it bears no direct relationship to WienerKolmogorov prediction theory. In fact, technical analysis is a broad class of prediction rules with unknown statistical properties, developed by practitioners without reference to any formalism. This article investigates statistical properties of technical analysis in order to determine if there is any objective basis to the popularity of its methods. Broadly, there are two issues of interest. First, can one devise formal algorithms that can generate buy and sell signals identical to the ones given by technical analysis-that is, are any of these rules (mathematically) well defined? The second issue is to what extent well-defined rules of technical analysis are useful in prediction over and above the forecasts generated by Wiener-Kolmogorov prediction theory.

55 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a partial review of the potential for bubbles and crashes associated with high frequency trading (HFT) and suggest that the welfare gains derived from HFT are minimal and perhaps even largely negative on a long-term investment horizon.
Abstract: We present a partial review of the potential for bubbles and crashes associated with high frequency trading (HFT). Our analysis intends to complement still inconclusive academic literature on this topic by drawing upon both conceptual frameworks and indicative evidence observed in the markets. A generic classification in terms of Barenblatt’s theory of similarity is proposed that suggests, given the available empirical evidence, that HFT has profound consequences for the organization and time dynamics of market prices. Provided one accepts the evidence that financial stock returns exhibit multifractal properties, it is likely that HFT time scales and the associated structures and dynamics do significantly affect the overall organization of markets. A significant scenario of Barenblatt’s classification is called “non-renormalizable”, which corresponds to HFT functioning essentially as an accelerator to previous market dynamics such as bubbles and crashes. New features can also be expected to occur, truly innovative properties that were not present before. This scenario is particularly important to investigate for risk management purposes. This report thus suggests a largely positive answer to the question: “Can high frequency trading lead to crashes?” We believe it has in the past, and it can be expected to do so more and more in the future. Flash crashes are not fundamentally a new phenomenon, in that they do exhibit strong similarities with previous crashes, albeit with different specifics and of course time scales. As a consequence of the increasing inter-dependences between various financial instruments and asset classes, one can expect in the future more flash crashes involving additional markets and instruments. The technological race is not expected to provide a stabilization effect, overall. This is mainly due to the crowding of adaptive strategies that are pro-cyclical, and no level of technology can change this basic fact, which is widely documented for instance in numerical simulations of agent-based models of financial markets. New “crash algorithms” will likely be developed to trade during periods of market stresses in order to profit from these periods. Finally, we argue that flash crashes could be partly mitigated if the central question of the economic gains (and losses) provided by HFT was considered seriously. We question in particular the argument that HFT provides liquidity and suggest that the welfare gains derived from HFT are minimal and perhaps even largely negative on a long-term investment horizon. This question at least warrants serious considerations especially on an empirical basis. As a consequence, regulations and tax incentives constitute the standard tools of policy makers at their disposal within an economic context to maximize global welfare (in contrast with private welfare of certain players who promote HFT for their private gains). We believe that a complex systems approach to future research can provide important and necessary insights for both academics and policy makers.

55 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202397
2022190
2021144
2020167
2019126
2018160