# Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
## 5 Discussion and Conclusions  
Predicting the price of an equity is a complex task. There, have been multiple studies in this area, each achieving success in their own way. This study combines the 2 schools of thought to identify a set of fundamental and technical indicators that could successfully predict the price movement of equities on the S&P500 and consequently be used to generate excess returns on the stock market. Overall, our model showed high promise with strong numbers in all regards including accuracy, precision and recall. Specifically, our model's recall of 100% means that the model performed really well for sell signals which could be used for shorting strategy. A shorting strategy is a strategy in which trader borrow and sell in a hope that the equity of the equity we decrease in future when it could be bought to settle the trade. This strategy is considered riskier and is usually adopted by experienced traders, however, an appropriate stop loss with the confidence in machine learning along with the context of our study's results traders can be assured of a profit-making strategy. On the other hand, we have achieved a good precision of 85% on predicting the possibility of a stock's price going up by 1% on 10 th day. This means that though the model missed a few buying opportunities, the ones that were predicted were true buys. In our opinion, this property is important for investors as they wouldn't mind to miss out on a few opportunities as long as they are comfortable and know that the stock they've invested in is indeed going to give them at least 1% profit. This also means that for 250 US working days there is a possibility of generating a minimum of 25% annual return (1% minimum gain on every 10 th day) on investment which beats the S&P 500 annual return. The PCA analysis showed 15 components to be representative of \(\sim\) 99% variance in the data, suggesting that though most of the 28 features were contributing towards the predictions yet it was these 15 forming the foundation of the data. By aggregating based on eigenvalue, we further identified a subset of 6 features that contribute most in the PCA-components and could predict stock prices almost as well as the original 28 features. Through this, we also established that the indicators 'Total Hold', 'Total Buy' and 'Total Sell Recommendation' which come from the analyst recommendations were highly significant in predicting the stock prices. Finally, during the backtesting of the classifier, it generated promising returns on the initial investment. Though we acknowledge that it was carried out on a small set of equities due to the manual nature of the task, but with more sophisticated backtesting algorithms and combination of other common trading strategies, the positive returns should hold even for other kinds of equities in the S&P500, if not simply now. Another factor for future research is that our model did not consider the effect of buying of selling on the equity price movement, especially in the case of large funds whose actions of buying or selling immediately change the market dynamics. However, we still believe that our model is immediately useful for a small investor and the focus of the study was always on long-term fundamental investing and not based on the patterns of the trading chart. Finally, with rapid improvements in the tools offered by Machine learning and deep learning [2; 48], this is an ever-evolving field and our quest to understand how the markets work and identify trends is never-ending. Going forward, the results from this study could be tested in a different market like The Bombay Stock Exchange or the Shanghai Stock Exchange or the results could be further drilled down to identify the most optimal subset. In summary, we provide a complete workflow which is very easy to understand and implement for a real-trading system and our rigorous testing shows that the system has a potential to gain excess returns compared to the usual S&P 500 annual returns. Conceptualization, J.S. and M.K. ; methodology, J.S. and M.K.