# Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
## 3 Experiment Design
### Approach for Transformation and Analysis  
Firstly, to achieve the primary aim for this study i.e., to evaluate if a set of technical or fundamental features can be used to predict the price movement of an equity or not, we structured this problem as a classification machine learning problem, similar to the approach adopted by [13; 15; 19]. Here, we labelled the equity a 'buy' if the price went up in the following days by 1% and'sell' if the price went down by 1% or if it remained between those 2 thresholds then as a 'hold'. Post this, classification machine learning models could then be run on these to check for an association between the features of an equity and the labels based on price movement for every day. Secondly, to investigate the price movement for up to 10 days from any given day, each instance in the data (aka row), representing one day, was given 10 labels [day1, day2, day3, day4, day5, day6, day7, day8, day9, day10]. Similar to above, each one of these labels stated 'buy','sell' or 'hold' based on the price change on 'day n' in the future compared to the current day. For example, if the label 'day3' of an arbitrary equity on 26/11/11 had a 'buy' value, that would mean that3 days in the future i.e., 29/11/11 the closing price of the equity was greater than its closing price on 26/11/11 by 1% (Table 2). Thirdly, to prevent the machine learning models from reporting higher accuracies by incorporating biases and over-reliance on the trends it may pick up, like the Great Economic Recession of 2007-2008 or the Dot-com bubble of 1994 to 2000 or the following Dot-com crash of 2000-2001 where the market showed either meteoric rise or decline for an \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Type** & \multicolumn{1}{c}{**Indicator**} & \multicolumn{1}{c}{**Symbol on Bloomberg**} & **Studies** \\ \hline Fundamental & PE Ratio & PE\_RATIO & [5; 9; 11; 17; 21; 24] \\ Indicators & PB Ratio & PX\_TO\_BOOK\_RATIO & [11; 9; 21] \\ EPS\({}^{*}\) & BEST\_EPS & 10,17,18,23,24] \\  & EPS Low \({}^{*}\) & BEST\_EPS\_LO & [10; 17; 18; 23; 24] \\  & EPS High \({}^{*}\) & BEST\_EPS\_HI & [10; 17; 18; 23; 24] \\  & CAPEX \({}^{*}\) & BEST\_CAPEX & [5; 7; 23] \\  & CAPEX Low \({}^{*}\) & BEST\_CAPEX\_LO & [5; 7; 23] \\  & CAPEX High \({}^{*}\) & BEST\_CAPEX\_HI & [5; 7; 23] \\  & Return on Asset & RETURN\_ON\_ASSET & [12; 16; 17; 19; 21] \\ Technical & Volume & PX\_VOLUME & [9] \\ Indicators & Market Cap. & CUR\_MKT\_CAP & \\  & Historical Market Cap. & HISTORICAL\_MARKET\_CAP & \\  & Short Interest & SHORT\_INT\_RATIO & \\ Analyst & \# of Analyst Ratings & TOT\_ANALYT\_REC & [12; 27; 28; 30] \\ Recommendations & \# of Buy Recs & TOT\_BUY\_REC & [12; 27; 28; 30] \\  & \# of Sell Recs & TOT\_SEL\_REC & [12; 27; 28; 30] \\  & \# of Hold Recs & TOT\_HOLD\_REC & [12; 27; 28; 30] \\  & Equity Recommendation Consensus & EQY\_REC\_CONS & [12; 27; 28; 30] \\  & Analyst Rating \({}^{*}\) & BEST\_ANALYT\_RATING & [12; 27; 28; 30] \\ Others & Closing Price & PX\_OFFICAL\_CLOSE & [13] \\  & Long-Term Growth \({}^{*}\) & BEST\_EST\_IONG\_TERM\_GROWTH & \\  & Target Price for day \({}^{*}\) & BEST\_TARGET\_PRICE & \\ Derived & \%age of Hold Recs & hold\_percent & \\ Indicators & \%age of Buy Recs & buy\_percent & \\  & \%age of Sell Recs & sell\_percent & \\  & Standard Deviation in price over 5 days & std\_5 day & \\  & Standard Deviation in price over 10 days & std\_10 day & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features and indicators used in this study. \({}^{*}\) BEst ratings (BEst EPS, BEst CAPEX etc) refer to ratings and numbers calculated and issued by Bloomberg’s internal division, Bloomberg Estimate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Numerical Equivalent** & **Criterion** \\ \hline Buy & 2 & \(T_{n}\geq 1.01*T_{0}\) \\ Hold & 1 & \(0.99*T_{0}<T_{n}<1.01*T_{0}\) \\ Sell & 0 & \(T_{n}\leq\ 0.99*T_{0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Criteria for Labels.