  In finance, the weak form of the Efficient Market Hypothesis asserts that
historic stock price and volume data cannot inform predictions of future
prices. In this paper we show that, to the contrary, future intra-day stock
prices could be predicted effectively until 2009. We demonstrate this using two
different profitable machine learning-based trading strategies. However, the
effectiveness of both approaches diminish over time, and neither of them are
profitable after 2009. We present our implementation and results in detail for
the period 2003-2017 and propose a novel idea: the use of such flexible machine
learning methods as an objective measure of relative market efficiency. We
conclude with a candidate explanation, comparing our returns over time with
high-frequency trading volume, and suggest concrete steps for further
investigation.
