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.