  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.
