Evaluating the Effectiveness of Common Technical Trading Models

TL;DR

This study evaluates common technical trading models like moving averages and machine learning methods on major stocks and indexes, revealing their strengths, weaknesses, and optimal parameters for performance and volatility management.

Contribution

It introduces original implementations of popular trading models and systematically compares their effectiveness, including extensive parameter optimization for moving averages.

Findings

Moving averages outperform continuous investment but are more volatile.

Machine learning models reduce losses on downward trends but do not significantly increase profits.

Optimal moving average periods are identified as 5,10 for performance and 33,44 for reduced volatility.

Abstract

How effective are the most common trading models? The answer may help investors realize upsides to using each model, act as a segue for investors into more complex financial analysis and machine learning, and to increase financial literacy amongst students. Creating original versions of popular models, like linear regression, K-Nearest Neighbor, and moving average crossovers, we can test how each model performs on the most popular stocks and largest indexes. With the results for each, we can compare the models, and understand which model reliably increases performance. The trials showed that while all three models reduced losses on stocks with strong overall downward trends, the two machine learning models did not work as well to increase profits. Moving averages crossovers outperformed a continuous investment every time, although did result in a more volatile investment as well. Furthermore, once finished creating the program that implements moving average crossover, what are the optimal periods to use? A massive test consisting of 169,880 trials, showed the best periods to use to increase investment performance (5,10) and to decrease volatility (33,44). In addition, the data showed numerous trends such as a smaller short SMA period is accompanied by higher performance. Plotting volatility against performance shows that the high risk, high reward saying holds true and shows that for investments, as the volatility increases so does its performance.