  The Efficient Market Hypothesis has been a staple of economics research for
decades. In particular, weak-form market efficiency -- the notion that past
prices cannot predict future performance -- is strongly supported by
econometric evidence. In contrast, machine learning algorithms implemented to
predict stock price have been touted, to varying degrees, as successful.
Moreover, some data scientists boast the ability to garner above-market returns
using price data alone. This study endeavors to connect existing econometric
research on weak-form efficient markets with data science innovations in
algorithmic trading. First, a traditional exploration of stationarity in stock
index prices over the past decade is conducted with Augmented Dickey-Fuller and
Variance Ratio tests. Then, an algorithmic trading platform is implemented with
the use of five machine learning algorithms. Econometric findings identify
potential stationarity, hinting technical evaluation may be possible, though
algorithmic trading results find little predictive power in any machine
learning model, even when using trend-specific metrics. Accounting for
transaction costs and risk, no system achieved above-market returns
consistently. Our findings reinforce the validity of weak-form market
efficiency.

Statistical Finance;Computational Engineering, Finance, and Science;Machine Learning;Econometrics