Using conditional probability to identify trends in intra-day high-frequency equity pricing

Rechenthin, Michael; Street, W. Nick

doi:10.1016/j.physa.2013.08.003

Cited by 14 publications

(16 citation statements)

References 17 publications

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“…Schulmeister (2009) points towards possible market inefficiencies and profitability of technical trading rules at higher frequencies, this being driven by faster algorithmic trading. Recently, Rechenthin and Street (2013) claimed that when price shocks break the bid-ask spread, which was identified to happen anywhere in between 5 to 10 seconds, price movements can be predicted for up to one minute. Beyond this point prediction probabilities remained significant for A C C E P T E D M A N U S C R I P T about the next 5 minutes, dying out completely beyond 30 minutes.…”

Section: A C C E P T E D Mmentioning

confidence: 99%

“…The latter is attributed to market microstructure noise (McAleer and Medeiros, 2008), mainly resulting from non-synchronous trading effect and bid-ask bounce. In view of this, a core consideration in designing HFT models is to manage the tension between moving to higher price frequencies, hoping to benefit from possible price correlations, but at the same time be able to manage the increasing noise levels which give rise to perceived price movements and volatility (see Andersen and Bollerslev, 1997;Rechenthin and Street, 2013).…”

Section: High Frequency Data and Technical Indicatorsmentioning

confidence: 99%

“…The set of signals that we select for this study is based on findings by Zhang (2010); Brogaard et al (2014) who identified that the main determinants of current HFT activity are past returns, liquidity, and HFT activity. The time-window selection is based on claims from Rechenthin and Street (2013), who state that the stock price typically broke the price reversal pattern due to bid-ask bounce after 5 to 10 seconds, and that traces of predictability existed up to 30 minutes, beyond which markets became efficient. Recent studies (Gradojevic and Gençay, 2013;Vella and Ng, 2014a;Naranjo et al, 2015) show the effectiveness of combining moving average signals with fuzzy logic to capture trend information.…”

Section: High Frequency Data and Technical Indicatorsmentioning

confidence: 99%

“…This noise is attributed as one of the key modelling challenges and sources of uncertainty in HFT (Anderson, 2011;Anderson et al, 2013;Rechenthin and Street, 2013). T1 fuzzy sets cannot fully represent the uncertainty associated with the inputs since as a contradiction, the membership function of a T1 fuzzy set has no uncertainty associated with it.…”

Section: Generalizing Anfis Model To T2 Flsmentioning

confidence: 99%

“…A number of authors (e.g. Schulmeister, 2009;Zhang, 2010;Rechenthin and Street, 2013;Holmberg et al, 2013;Brogaard et al, 2014) insist that the presence of efficient pricing becomes more questionable when investigating short-lived (milli-seconds to few minutes) trades. However, Kearns et al (2010) validate the EMH in their study and argue that generating profits from aggressive high-frequency trading (HFT) is next to impossible.…”

Section: Introductionmentioning

confidence: 99%

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Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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Highlights• We investigate the viability of Type-2 fuzzy systems in high frequency trading.• We propose Type-2 models based on a generalisation of the popular ANFIS model.• Type-2 models score significant risk adjusted performance improvements over Type-1.• Benefits of Type-2 models increase with higher trading frequencies. AbstractIn this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT.

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Section: A C C E P T E D Mmentioning

confidence: 99%

Section: High Frequency Data and Technical Indicatorsmentioning

confidence: 99%

Section: High Frequency Data and Technical Indicatorsmentioning

confidence: 99%

Section: Generalizing Anfis Model To T2 Flsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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Early prediction of Ibex 35 movements

Miranda‐García

Vargas

Mori

et al. 2022

Journal of Forecasting

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In this paper, we examine the early predictability of the market's directional movement using intraday high‐frequency data (695,764 observations) from an stock index (Ibex 35 Index) to provide, either private or institutional investors, an early warning system based on an “early indicator” of the financial market fluctuations with an optimal combination of the two more relevant variables for this strategy, accuracy, and earliness. A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high‐frequency time series of both price and certain technical indicators. The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24 min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.

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Constructing a multilayer network for stock market

Chen

Jiang

2019

Soft Comput

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Using conditional probability to identify trends in intra-day high-frequency equity pricing

Cited by 14 publications

References 17 publications

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Early prediction of Ibex 35 movements

Constructing a multilayer network for stock market

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