# Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
###### Abstract  
Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further the research rejecting this idea, a rigorous literature review was conducted and a set of five 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 (Facebook, Amazon, Apple, Netflix & Google) 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 10 th day with high confidence and with enough buffer to even build a robotic trading system. stock index prediction; feature engineering; machine learning in stocks +
Footnote †: journal: Journal of the Accepted: 9 February  
current business and its future prospects whereas technical analysis revolves around analysing statistical trends gathered from trading activity, such as price and volume movement and to identify windows of opportunities to invest. Both of these techniques approach the task from quite different angles and often are chosen one over the other depending upon the context of many factors including the market, equity in consideration, investment period such as long term or short term etc [9; 10]. Typically, studies were carried to check the feasibility of one of those techniques [7; 11; 12] but in the study by [PERSON] [13], it was argued that technical indicators are not sufficient for long-term prediction of stock while fundamental and technical data together can make accurate long-term stock prediction possible. Thus, implying that no strategy serves as a silver bullet, and most strategies are developed and optimized specifically to work only in certain kind of markets or with certain kinds of equities. Thus, leaving a wide gap open and leaving room for further research in this domain. Parallelly, with improvements in machine learning and the availability of increased computational resources, many machine learning algorithms like Neural Networks [14; 15; 16; 17; 18], Random Forests [13], Bayesian Networks [19], hybrid models [20] and even very specific, optimised ML models [21] have all been applied in various scenarios to better predict the nature of the markets with a fair degree of success. Often, Support Vector Machines (SVM) have been leveraged for technical analysis to perform regression operations [16]; for fundamental analysis, Neural Networks and its variations have been used to capture patterns and relationships by classifying the stocks based on the selected accounting indicators and macroeconomic factors.