Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm

  title={Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm},
  author={Armin Shmilovici and Yoav Kahiri and Irad Ben-Gal and Shmuel Hauser},
  journal={Computational Economics},
Universal compression algorithms can detect recurring patterns in any type of temporal data—including financial data—for the purpose of compression. The universal algorithms actually find a model of the data that can be used for either compression or prediction. We present a universal Variable Order Markov (VOM) model and use it to test the weak form of the Efficient Market Hypothesis (EMH). The EMH is tested for 12 pairs of international intra-day currency exchange rates for one year series of… 
Estimating the Algorithmic Complexity of Stock Markets
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A continuous time Bayesian network classifier for intraday FX prediction
Performance analysis and comparison evince a predictive power of these models for FX rates at high frequencies and show that the proposed CTBNC is more effective and more efficient than dynamic Bayesian network classifier.
Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates
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An Introduc-tion to High-Frequency Finance
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Results on 12 non-overlapping epochs of the DJIA strongly suggest that PFMs can outperform both traditional Markov models and (continuous-valued) GARCH models in the task of predicting volatility one time-step ahead.
Estimating the Complexity Function of Financial Time series: An Estimation Based on Predictive Stochastic Complexity
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Branch prediction based on universal data compression algorithms
  • E. FederovskyM. FederS. Weiss
  • Computer Science
    Proceedings. 25th Annual International Symposium on Computer Architecture (Cat. No.98CB36235)
  • 1998
This work considers two universal compression algorithms: prediction by partial matching (PPM), and a recently developed method, context tree weighting (CTW), and describes the prediction algorithms induced by these methods.
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On Prediction Using Variable Order Markov Models
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Computational learning techniques for intraday FX trading using popular technical indicators
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Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
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