Ultra high frequency financial data

Sewell, Martin; Yan, Wei

doi:10.1145/1388969.1388988

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Li & Kuo, 2008;Tenti, 1996) that have reported accuracies under 60% with ML models which have shown impressive performance in areas other than financial prediction. According to Sewell and Yan (Sewell & Yan, 2008), for certain markets such as futures and FOREX, it may be necessary to generate predictions with an accuracy marginally higher than the one obtained by a random classifier to obtain profits due to two main factors: low costs and leverage.…”

Section: Machine Learning In Financial Forecastingmentioning

confidence: 99%

Evaluating machine learning classification for financial trading: An empirical approach

Gerlein

McGinnity

Belatreche

et al. 2016

Expert Systems with Applications

134

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Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.

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Section: Machine Learning In Financial Forecastingmentioning

confidence: 99%

Evaluating machine learning classification for financial trading: An empirical approach

Gerlein

McGinnity

Belatreche

et al. 2016

Expert Systems with Applications

134

View full text Add to dashboard Cite

show abstract

“…When one analyzes thick-by-thick data, the problem of ultra-high frequency data arises (see: Engle & Russell, 2004;Scalas et al, 2004;Sewell et al, 2008). The main problems that are faced by analysts are the following: an overnight duration, transactions registered at the same moment in time and intraday cyclical patterns.…”

Section: Characteristics Of Time Seriesmentioning

confidence: 99%

Performance of American and Russian joint stock companies on financial market. A microstructure perspective

Osińska

Dobrzyński

Shachmurove

2016

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This paper compares the periods before and after the Ukrainian crisis of 2014 from the perspective of market microstructure. The hypothesis is that the crisis influenced the fragile Russian financial market equilibrium. As financial markets adapt to the new equilibrium, the paper studies the effects of the crisis and the imposition of economic sanctions on Russia in terms of volatility, duration, prices and volume for selected joint stock companies listed on the U.S. and the Russian stock markets. Results reveal that the Moscow Stock exchange lacks an appropriate transmission mechanism from informed investors to the rest of the market.

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“…For example, consider an algorithmic trading or high frequency trading application that enables fast investments decisions. A typical request for such an application could require latest information about stocks of multiple companies from different stock exchanges and related information from risk management agencies [7]. Based on the most recent data, the application continually provides suggestions for investments.…”

Section: A Context-aware Applicationmentioning

confidence: 99%

Management of SOA-Based Context-Aware Applications Hosted in a Distributed Cloud Subject to Percentile Constraints

Boloor

Chirkova

Salo

et al. 2011

2011 IEEE International Conference on Services Computing

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Ultra high frequency financial data

Cited by 7 publications

References 8 publications

Evaluating machine learning classification for financial trading: An empirical approach

Evaluating machine learning classification for financial trading: An empirical approach

Performance of American and Russian joint stock companies on financial market. A microstructure perspective

Management of SOA-Based Context-Aware Applications Hosted in a Distributed Cloud Subject to Percentile Constraints

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