Samuel Showalter, Jeffrey Gropp
arXiv 11 Sep 2019 · Finance — Statistical Finance
arXiv:1909.05151 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
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| 6 | Fama, E. F., and French, K. R (2004) The capital asset pricing model: Theory and evidence | 0.843 | 3 | 3 | 100% |
| 7 | Charles, A., and Darné, O (2009) Variance-ratio tests of random walk: an overview | 0.811 | 4 | 2 | 100% |
| 8 | Cheung, Y.-W., and Lai, K. S (1995) Lag order and critical values of the augmented dickey–fuller test | 0.811 | 4 | 2 | 100% |
| 9 | Nalkiel, B. G., and Malkiel, B. G (1991) Random walk down wall street: Including a life-cycle guide to personal investing, 1991 | 0.811 | 4 | 2 | 100% |
| 10 | Ng, A. Y., and Jordan, M. I (2002) On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 52 scored citations.