  To reject the Efficient Market Hypothesis a set of 5 technical indicators and
23 fundamental indicators was identified to establish the possibility of
generating excess returns on the stock market. Leveraging these data points and
various classification machine learning models, trading data of the 505
equities on the US S&P500 over the past 20 years was analysed to develop a
classifier effective for our cause. From any given day, we were able to predict
the direction of change in price by 1% up to 10 days in the future. The
predictions had an overall accuracy of 83.62% with a precision of 85% for buy
signals and a recall of 100% for sell signals. Moreover, we grouped equities by
their sector and repeated the experiment to see if grouping similar assets
together positively effected the results but concluded that it showed no
significant improvements in the performance rejecting the idea of sector-based
analysis. Also, using feature ranking we could identify an even smaller set of
6 indicators while maintaining similar accuracies as that from the original 28
features and also uncovered the importance of buy, hold and sell analyst
ratings as they came out to be the top contributors in the model. Finally, to
evaluate the effectiveness of the classifier in real-life situations, it was
backtested on FAANG equities using a modest trading strategy where it generated
high returns of above 60% over the term of the testing dataset. In conclusion,
our proposed methodology with the combination of purposefully picked features
shows an improvement over the previous studies, and our model predicts the
direction of 1% price changes on the 10th day with high confidence and with
enough buffer to even build a robotic trading system.
