Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency
This provides a novel measure of market efficiency for finance, though it is incremental in applying existing methods to new data.
The paper tackled the problem of predicting intra-day stock prices to test the weak form of the Efficient Market Hypothesis, showing that machine learning-based trading strategies were profitable until 2009 but not after, with effectiveness diminishing over time.
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.