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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 re-examine the profitability of relative strength, or momentum, trading strategies (buying past strong performers and selling past weak performers) and find that standard relative strength strategies require frequent trading in disproportionately high-cost securities so that trading costs prevent profitable strategy execution.
Abstract: In markets with trading friction, the incorporation of information into market prices can be substantially delayed through a weakening of the arbitrage process. We re-examine the profitability of relative-strength, or momentum, trading strategies (buying past strong performers and selling past weak performers). We find that standard relative-strength strategies require frequent trading in disproportionately high-cost securities so that trading costs prevent profitable strategy execution. In the cross section, we find that those stocks that generate large momentum returns are precisely those stocks with high trading costs. We conclude that the magnitude of the abnormal returns associated with these trading strategies creates an illusion of profit opportunity when, in fact, none exists.

185 citations

Journal ArticleDOI
TL;DR: In this paper, a genetic programming-based trading strategy was proposed to predict stock prices based on predictions of stock prices using genetic programming (or GP), and a metric quantifying the probability that a specific time series is GP-predictable is presented.
Abstract: Based on predictions of stock-prices using genetic programming (or GP), a possibly profitable trading strategy is proposed. A metric quantifying the probability that a specific time series is GP-predictable is presented first. It is used to show that stock prices are predictable. GP then evolves regression models that produce reasonable one-day-ahead forecasts only. This limited ability led to the development of a single day-trading strategy (SDTS) in which trading decisions are based on GP-forecasts of daily highest and lowest stock prices. SDTS executed for fifty consecutive trading days of six stocks yielded relatively high returns on investment.

185 citations

Journal ArticleDOI
TL;DR: In this paper, the authors show that the early years of the Turkish stock exchange were characterized by non-linear behaviour and inefficient pricing, and that regulatory changes encouraged participation, improved information quality and led to prices impounding information more rapidly.
Abstract: Emerging markets efficiency has been widely investigated, with mixed results. However such evidence is only reliable if the methodology adopted accounts for the institutional features of the market. Unlike previous studies this paper corrects for thin trading and incorporates possible non-linear behaviour and regulatory changes. Using Istanbul Stock Exchange data we show that in its early years the exchange was characterised by non-linear behaviour and inefficient pricing. However, regulatory changes encouraged participation, improved information quality and led to prices impounding information more rapidly, suggesting markets become efficient with high trading volume, reliable information and an appropriate institutional framework.

185 citations

Journal ArticleDOI
TL;DR: This paper found that short-term traders are the marginal investors in high-yield stocks, primarily since the introduction of negotiated commissions on the NYSE, and that this phenomenon is not evident in low-yielding stocks, nor does it appear prevalent before negotiated commissions.

184 citations

Journal ArticleDOI
TL;DR: In this paper, the authors compared the trade execution costs of similar stocks in an automated trading structure and a traditional trading structure, and found that execution costs are higher in Paris than in New York after controlling for differences in adverse selection, relative tick size, and economic attributes across samples.
Abstract: A global trend towards automated trading systems raises the important question of whether execution costs are, in fact, lower than on trading f loors. This paper compares the trade execution costs of similar stocks in an automated trading structure ~Paris Bourse! and a f loor-based trading structure ~NYSE!. Results indicate that execution costs are higher in Paris than in New York after controlling for differences in adverse selection, relative tick size, and economic attributes across samples. These results suggest that the present form of the automated trading system may not be able to fully replicate the benefits of human intermediation on a trading f loor. A TRADING MECHANISM IS DEF INED by the distinctive set of rules that govern the

183 citations


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