Validating Weak-form Market Efficiency in United States Stock Markets with Trend Deterministic Price Data and Machine Learning

TL;DR

This study tests weak-form market efficiency in US stocks by combining econometric analysis and machine learning, finding little predictive power and supporting the hypothesis that past prices do not predict future returns.

Contribution

It bridges econometric evidence with machine learning approaches, demonstrating that current algorithms do not outperform the market in weak-form efficient markets.

Findings

Stationarity in stock prices suggests technical analysis may be feasible.

Machine learning models do not consistently outperform the market after costs.

Results support the weak-form market efficiency hypothesis.

Abstract

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.