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

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...

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Vydané v:	Computational economics Ročník 33; číslo 2; s. 131 - 154
Hlavní autori:	Shmilovici, Armin, Kahiri, Yoav, Ben-Gal, Irad, Hauser, Shmuel
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.03.2009 Society for Computational Economics Springer Nature B.V
Edícia:	Computational Economics
Predmet:	62M20 62P05 91B84 Algorithms Behavioral/Experimental Economics C22 C49 C53 C63 Capital market theory Compression Computer Appl. in Social and Behavioral Sciences Data Data compression Economic models Economic statistics Economic theory Economic Theory/Quantitative Economics/Mathematical Methods Economics Economics and Finance Efficient Market Hypothesis Efficient markets Exchange market Forecasting Forecasting techniques Foreign exchange Foreign exchange rates Forex Intra-day trading G14 Hypotheses Market efficiency Markets Markov analysis Math Applications in Computer Science Measurement Online securities trading Operations Research/Decision Theory Process controls Studies Universal prediction Variable Order Markov C53 C63 Variable Order Markov C22 Universal prediction Forex Intra-day trading C49 62P05 G14 Efficient Market Hypothesis 91B84 62M20
ISSN:	0927-7099, 1572-9974
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Abstract	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 1, 5, 10, 15, 20, 25 and 30 min. Statistically significant compression is detected in all the time-series and the high frequency series are also predictable above random. However, the predictability of the model is not sufficient to generate a profitable trading strategy, thus, Forex market turns out to be efficient, at least most of the time.
AbstractList	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 1, 5, 10, 15, 20, 25 and 30 min. Statistically significant compression is detected in all the time-series and the high frequency series are also predictable above random. However, the predictability of the model is not sufficient to generate a profitable trading strategy, thus, Forex market turns out to be efficient, at least most of the time. [PUBLICATION ABSTRACT] Universal compression algorithms can detect recurring patterns in any type of temporal dataDLincluding financial dataDLfor 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 1, 5, 10, 15, 20, 25 and 30_min. Statistically significant compression is detected in all the time-series and the high frequency series are also predictable above random. However, the predictability of the model is not sufficient to generate a profitable trading strategy, thus, Forex market turns out to be efficient, at least most of the time. Reprinted by permission of Springer 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 1, 5, 10, 15, 20, 25 and 30 min. Statistically significant compression is detected in all the time-series and the high frequency series are also predictable above random. However, the predictability of the model is not sufficient to generate a profitable trading strategy, thus, Forex market turns out to be efficient, at least most of the time.
Author	Shmilovici, Armin Ben-Gal, Irad Kahiri, Yoav Hauser, Shmuel
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Snippet	Universal compression algorithms can detect recurring patterns in any type of temporal data—including financial data—for the purpose of compression. The... Universal compression algorithms can detect recurring patterns in any type of temporal data - including financial data - for the purpose of compression. The... Universal compression algorithms can detect recurring patterns in any type of temporal dataDLincluding financial dataDLfor the purpose of compression. The...
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Title	Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm
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