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

@article{Shmilovici2009MeasuringTE,
  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},
  year={2009},
  volume={33},
  pages={131-154}
}
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… 
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References

SHOWING 1-10 OF 109 REFERENCES
Using a Stochastic Complexity Measure to Check the Efficient Market Hypothesis
TLDR
In this research, Rissanen's context tree algorithm is used to identify recurring patterns in the data, and use them for compression, which indicates potential market inefficiency.
An Introduc-tion to High-Frequency Finance
A symbolic dynamics approach to volatility prediction
TLDR
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
TLDR
The catching-up effect indicates a short-lived property of financial signals, which may lend support to the hypothesis that financial time series are not random but are composed of a sequence of structures whose birth and death can be characterized by a jump process with an embedded Markov chain.
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
TLDR
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.
Universal prediction of individual sequences
TLDR
The authors define the finite state predictability of the (infinite) sequence x/sub 1/ . . . z/sub n/ .
On Prediction Using Variable Order Markov Models
TLDR
The results indicate that a "decomposed" CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks and a different algorithm, which is a modification of the Lempel-Ziv compression algorithm, significantly outperforms all algorithms on the protein classification problems.
Non-Linear Predictive Models for Intra-Day Foreign Exchange Trading
TLDR
Artificial neural networks were used to search for non- linear relations in high- frequency foreign exchange time series and showed statistically significant trading profit under moderate transaction costs, providing evidence for the non-linear nature of the foreign exchangeTime series under study.
Computational learning techniques for intraday FX trading using popular technical indicators
TLDR
It is found that although all methods are able to generate significant in-sample and out-of-sample profits when transaction costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction costs.
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
TLDR
This book shows how neural networks can learn complex patterns from vast quantities of data and generalize with amazing speed from learned experiences; how genetic algorithms can evolve solutions to problems in the way nature does; how fuzzy systems provide concrete solutions to Problems based on vague parameters; and how nonlinear dynamics, fractal analysis, and chaos theory define order in what once were considered random changes in financial markets.
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