Patterns in high-frequency FX data: discovery of 12 empirical scaling laws

Glattfelder, James B.; Dupuis, Alexandre; Olsen, Richard B.

doi:10.1080/14697688.2010.481632

Cited by 87 publications

(146 citation statements)

References 23 publications

Supporting

Mentioning

144

Contrasting

Order By: Relevance

“…A directional-change (DC) event-a downturn event or an upturn event-is usually followed by a price overshoot event rather than an opposite directional-change event direction Glattfelder et al (2011). The overshoot (OS) event represents the time interval of price movement beyond the directional-change event.…”

Section: Overshoot (Os) Eventmentioning

confidence: 99%

“…Guillaume et al (1997) describe the price evolution by the frequency of directionalchange events over a sampling period which provides an alternative measure of the risk. Later in 2011, using statistical analysis based on the directional-change event approach, Glattfelder et al (2011) discover 12 new empirical scaling laws related to foreign exchange data series across 13 currency exchange rates. These 12 scaling laws enhance our understanding of the behaviour of prices in financial markets, giving us new insights.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Directional-Change Event Approach for Studying Financial Time Series

et al. 2012

View full text Add to dashboard Cite

Financial markets witness high levels of activity at certain times but remain calm at others. This makes the flow of physical time discontinuous. Therefore, to use physical time scales for studying financial time series runs the risk of missing important activities. An alternative approach is to use an event-based time scale that captures periodic activities in the market. In this paper, the authors use a special type of event, called a directional-change event, and show its usefulness in capturing periodic market activities. The study confirms that the length of the price-curve coastline, as defined by directional-change events, turns out to be a long one. Special Issue New Approaches in Quantitative Modeling of Financial MarketsJEL G10

show abstract

Section: Overshoot (Os) Eventmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Directional-Change Event Approach for Studying Financial Time Series

et al. 2012

View full text Add to dashboard Cite

show abstract

“…P. Kaltwasser [10] describes an agent that uses multiple behavioral techniques to make bidding decisions in the face of market uncertainty. J. Glattfelder, A. Dupuis and R. Olsen [11] develop a system that supports multiple strategies, but they use only movingaverage crossover strategies in the current stage of development. The paper by R. Barbosa and O. Belo [12] describes a multi-agent system that consists of a set of trading models such as an ensemble of classifiers, regression models, case-based reasoning, and an expert system.…”

Section: Introductionmentioning

confidence: 99%

Fundamental analysis in the multi-agent trading system

Korczak¹,

Hernes²,

Bac³

2016

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The paper presents issues related to developing methods for fundamental analysis used to expand capabilities of multi-agent trading system, to better predict the financial market. The fundamental analysis indicators can be used as confirmation of decisions generated by other strategies of the system. The first part of the article discusses briefly the fundamental analysis issues in relation to the online trading on FOREX market. The statistical analysis of correlations of the different time series indicators and algorithms of fundamental analysis agents are examined. The final part discusses the results of the performance evaluation of selected investment strategies, including fundamental-based agents. I. INTRODUCTIONn trading support systems, the advices might be computed by one or many algorithms, or by one or many software agents using one or many information sources. An overview of of the agents operating on financial markets has already been given by B. LeBaron [1]. Currently, most trading systems are based on one or only a few algorithms. For example, the solutions described by L. Mendes, P. Godinho and J. Dias [2] use genetic algorithms to perform analysis on the basis of historical quotations. The system described by J.R. Thompson, J.R. Wilson and E. P. Fitts [3] is based on the multifractal time series. In the system described in [4,5] technical analysis indicators are used. There are many solutions based on multi-agent approach. H. C. Aladag, U. Yolco and E. Egrioglu [6] present an evaluation of the portfolio optimisation strategies by three agents: rational agent, interference agent, and technical analysis agent. P. Singh and B. Borah [7] apply a multi-agent system where the agents' intelligence is based on fuzzy expert system. O. Badawy and A. Almotwaly [8] developed the neural networks and neuro-fuzzy computing for taking into account the geometrical patterns of the financial data. M. Aloud, E.P.K. Tsang and R. Olsen [9] introduce an agent-based model for simple prediction of financial markets, where each agent predicts the development of selected subsets of the assets pairs in real time by separately examining the similarities between ask and bid assets histories. P. Kaltwasser [10] describes an agent that uses multiple behavioral techniques to make bidding decisions in the face of market uncertainty. J. Glattfelder, A. Dupuis and R. Olsen [11] develop a system that supports multiple strategies, but they use only movingaverage crossover strategies in the current stage of development. The paper by R. Barbosa and O. Belo [12] describes a multi-agent system that consists of a set of trading models such as an ensemble of classifiers, regression models, case-based reasoning, and an expert system. F. H. Westerhoff [13] presents a system where two groups of agents applying the methods of fundamental and technical analysis try to shape market dynamics.Summing up, more often the trading advice is provide by multiple software agents that use mainly technical analysis. However, many papers relate...

show abstract

“…The proposed prediction models use, for example, genetic algorithms [1], fundamental and technical analysis [2,3,4,5], neural networks and neuro-fuzzy computing [6], behavioral techniques [7]. There are also many multi-agent approach based solutions [8,9,10,11,12]. The trend is that in most cases multiple software agents that use different methods and techniques help provide trading advice.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Financial Time Series Forecasting in A-Trader System

Korczak¹,

Hemes²

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The paper presents aspects related to developing methods for financial time series forecasting using deep learning in relation to multi-agent stock trading system, called A-Trader. On the basis of this model, an investment strategies in A-Trader system can be build. The first part of the paper briefly discusses a problem of financial time series on FOREX market. Classical neural networks and deep learning models are outlined, their performances are analyzed. The final part presents deployment and evaluation of a deep learning model implemented using H20 library as an agent of A-Trader system.

show abstract

Patterns in high-frequency FX data: discovery of 12 empirical scaling laws

Cited by 87 publications

References 23 publications

A Directional-Change Event Approach for Studying Financial Time Series

A Directional-Change Event Approach for Studying Financial Time Series

Fundamental analysis in the multi-agent trading system

Deep Learning for Financial Time Series Forecasting in A-Trader System

Contact Info

Product

Resources

About