# Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
## 2 Related Work
### Machine Learning Strategies for Good Performing Stock  
As touched upon earlier, with the advent of Machine learning and increased computational resources this field took a whole different course and since then many different kinds of ML models have been tried and tested in this domain. Though, more importantly, [PERSON] et al. [18] reported that a Boltzmann machine (a kind of Artificial Neural network) successfully classified stock returns into Positive-Negative up to 72% based on the selected fundamental indicators and if the classification was changed to Positive-Neutral-Negative then the results weren't as phenomenal but still better than a random guess. Overall, the ability of Neural Networks to perform better than random guess was confirmed when [PERSON] et al. [24] utilized ANNs to select stocks or [17] used it to predict stock returns for Dow Price Index or [PERSON] [8] leveraged a Multilayer Perceptron to test their custom fundamental analysis strategy. In another interesting study by [PERSON] [25], she investigated the ability of neural networks to integrate fundamental and technical analysis to predict the performance of a stock and developed a model averaging a return of 0.25398 which outperformed the minimum benchmark but did not meet the maximum benchmark set at 0.278. Referring to the application of ANNs across financial markets [26; 27; 28; 29; 30] and for both fundamental and technical analysis, [PERSON] et al. [21] acknowledged the wide application of ANNs but then proceeded to provide evidence that ANNs often suffer from over-fitting and local optimum problems. They also proposed that SVM's based on the principle of structural risk minimization could avoid such problems and employed them to model stock markets. Moreover, they developed a novel Sigmoid function to combine discrete and continuous inputs and later used differential evolution algorithms to optimise the stock selection model to find out that this strategy was significantly more powerful and efficient than other designed benchmarks. Similarly, many other custom and very-tailored approaches were adopted included [PERSON] et al. [20] proposing a Fuzzy-GA hybrid which beat the benchmarks and [PERSON] et al. [3] developed a custom \"distributed knowledge acquisition system\" which had dedicated components to perform specific analysis functions and when the output of it all was combined to select stocks, it outperformed NYSE and S&P500 on the parameters of monthly return, the Treynor Index and the Jensen Index. While [PERSON] et al. [31] used LSTM with statistical analysis of published messages to predict the direction of stock price and [PERSON] [32] combined technical and fractal indicators for the next day close price of the Polish stock market. Furthermore, insightful studies with the other kinds of ML models include [PERSON] [13] utilizing the Random Forest algorithm on selected indicators based on their short term and long-term impact and found that the accuracy of the model was 66.8% for buy signal for short term and 75.8% for long term. Whereas the experiments using Bayesian Networks by [PERSON] et al. [19] aiming to model financial ratios of Malaysian plantation stocks around the period of the Great Recession (2007-2009) received limited success as the algorithm was completely unable to predict the crash but its accuracy to classify stocks into 'buy' and 'don't buy' improved post the period of decline to 52.94% and 60.71% in 2009 and 2010 respectively. For the authors, this was a success to an extent as despite such a massive market disruption the model was still able to predict stock returns up to a certain accuracy. Lastly, [PERSON] [14] attempted to predict international equity risk premium using linear regression models and neural networks and benchmarked the results of each against the statistical Random walk model.