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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 article, the authors extend the model of insider trading to the case where noise trading volatility follows a general stochastic process and show that the volatility of price volatility appears excessive because insiders choose to trade more aggressively (and thus more information is revealed) when uninformed volume is higher and price impact is lower.
Abstract: We extend Kyle's (1985) model of insider trading to the case where noise trading volatility follows a general stochastic process. We determine conditions under which, in equilibrium, price impact and price volatility are both stochastic, driven by shocks to uninformed volume even though the fundamental value is constant. The volatility of price volatility appears 'excessive' because insiders choose to trade more aggressively (and thus more information is revealed) when uninformed volume is higher and price impact is lower. This generates a positive relation between price volatility and trading volume giving rise to an endogenous subordinate stochastic process for prices.

49 citations

Proceedings ArticleDOI
20 Sep 2004
TL;DR: The results show that the developed system performs comparably to its human counterparts across different market environments, despite these agents being rather primitive in nature.
Abstract: This work investigates the effectiveness of an agent based trading system. The system developed employs a simple genetic algorithm to evolve an optimized trading approach for every agent, with their trading decisions based on a range of technical indicators generating trading signals. Their trading pattern follows a simple fitness function of maximizing net assets for every evolutionary cycle. Their performance is analyzed compared to market movements as represented by its index, as well as investment funds run by human professionals to establish a relative measure of success. The results show that the developed system performs comparably to its human counterparts across different market environments, despite these agents being rather primitive in nature. Future forthcoming work refines and explores the potential of this approach further.

49 citations

Posted Content
01 Jan 2016
TL;DR: For the first time in 15 years, FX trading volumes contracted between two consecutive BIS Triennial Surveys as mentioned in this paper, and the decline in trading by leveraged institutions and "fast money" traders, and a reduction in risk appetite, have contributed to a significant drop in spot market activity.
Abstract: For the first time in 15 years, FX trading volumes contracted between two consecutive BIS Triennial Surveys. The decline in trading by leveraged institutions and "fast money" traders, and a reduction in risk appetite, have contributed to a significant drop in spot market activity. More active trading of FX derivatives, largely for hedging purposes, has provided a partial offset. Many FX dealer banks have become less willing to warehouse risk and have been re-evaluating their prime brokerage business. At the same time, new technologically driven non-bank players have gained firmer footing as market-makers and liquidity providers. Against this backdrop, FX trading is becoming increasingly relationship-driven, albeit in an electronic form. Such changes in the composition of market participants and their trading patterns may have significant implications for market functioning and FX market liquidity resilience going forward.

48 citations

Posted Content
TL;DR: This article examined whether the small firm/January effect is declining over time due to market efficiency and found that January returns are smaller after 1963-1979, but have simply reverted to levels that existed before that time.
Abstract: Using improved methodology and an expanded research design, we examine whether the small firm/January effect is declining over time due to market efficiency. First,we find that January returns are smaller after 1963–1979, but have simply reverted to levels that existed before that time. Second, we show that the January effect is not limited to mature markets but also appears in firms trading on the relatively new NASDAQ exchange in the 1970s. Third, trading volume for small firms in December and January is not different from other months, implying that traders are not actively arbitraging the anomaly. Together, our results suggest that this anomaly continues to defy rational explanation in an efficient market.

48 citations

Journal ArticleDOI
TL;DR: A multitree GP forest has been developed to extend the GP structure to extract multiple trading rules from historical data and significantly outperforms other traditional models of dynamic and static portfolio selection in terms of the portfolio return and risk adjusted return.
Abstract: Dynamic portfolio trading system is used to allocate one's capital to a number of securities through time in a way to maximize the portfolio return and to minimize the portfolio risk. Genetic programming (GP) as an artificial intelligence technique has been used successfully in the financial field, especially for the forecasting tasks in the financial markets. In this paper, GP is used to develop a dynamic portfolio trading system to capture dynamics of stock market prices through time. The proposed approach takes an integrated view on multiple stocks when the GP evolves and generates a rule base for dynamic portfolio trading based on the technical indices. In the present research, a multitree GP forest has been developed to extend the GP structure to extract multiple trading rules from historical data. Furthermore, the consequent part of each trading rule includes a function rather than a constant value. Besides, the transaction cost of trading which plays an important role in the profitability of a dynamic portfolio trading system is taken into account. This model was used to develop dynamic portfolio trading systems. The profitability of the model was examined for both the emerging and the mature markets. The numerical results show that the proposed model significantly outperforms other traditional models of dynamic and static portfolio selection in terms of the portfolio return and risk adjusted return.

48 citations


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