# Machine Learning for Quantitative Finance Applications: A Survey
## 10 Discussion
Furthermore, the authors considered SB1 shares from January 2010 to December 2010 to test the performance of proposed approach. The error performance measures (MAE, MaxAPE, etc.) confirmed that the proposed method obtains better results among others model (ARIMA and GARCH single scenario etc.). Also, the proposed hybrid system minimizes error performance. Despite achieving considerable results, autoregressive models present several limitations for stock price prediction compared to ML-based techniques. In [22], the authors chose stock prices of National Stock Exchange (NSE) India data to evaluate the proposed pipeline. More specifically, they collected Infosys data for the period from July 2014 to October 2014 as training data and they used stock price for Infosys, Tata Consultancy Service (TCS) and Chemical Industrial and Pharmaceutical Laboratories (CIPLA) from October 2014 to November 2014. The reported results in Table 8 are related to Error Percentage. In particular, they compared results achieved by RNN, LSTM and CNN to ARIMA model. They observed that the ARIMA model \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Techniques** & **MAPE** & **MAXAPE** & **MAE** & **RMSE** & **Dataset** \\ \hline \multirow{4}{*}{[PERSON] et al. (2014) [7]} & \multirow{4}{*}{Hybrid ARIMA-GARCH} & 0.1976 * & 0.4285 * & 0.5533 & 0.7514 & NSE (TSD1) \\ \cline{3-8}  & & 3.41 & 7.04 & 55.5303 & 66.1981 & NSE (TSD2) \\ \cline{2-8}  & & 2.4 * & 5.3657 * & 6.3765 & 6.7195 & NSE (TSD1) \\ \cline{3-8}  & & 9.8 & 15.73 & 160.9963 & 169.7036 & NSE (TSD2) \\ \hline \multirow{4}{*}{RNN} & \multicolumn{6}{c}{**Error Percentage**} \\ \cline{3-8}  & & & 3.90 & & NSE (Infosys) \\ \cline{3-8}  & & & 7.65 & & NSE (TCS) \\ \cline{3-8}  & & & 3.83 & NSE (Cipla) \\ \cline{3-8}  & & & 4.18 & NSE (Infosys) \\ \cline{3-8}  & & & 7.82 & NSE (TCS) \\ \cline{3-8}  & & & 3.94 & NSE (Cipla) \\ \hline \multirow{4}{*}{Selvin al. (2017) [22]} & \multirow{4}{*}{CNN} & 2.36 & NSE (Infosys) \\ \cline{3-8}  & & & 8.96 & NSE (TCS) \\ \cline{3-8}  & & & 3.63 & NSE (Cipla) \\ \cline{3-8}  & & & 31.91 & NSE (Infosys) \\ \cline{3-8}  & & & 21.16 & NSE (TCS) \\ \cline{3-8}  & & & 36.53 & NSE (Cipla) \\ \hline \hline \end{tabular}
* The values shown in fractions instead of percentages.  
\end{table} 
Table 8: Summary of studies based on ML/DL models. MAPE: mean absolute percentage error. MAXAPE: maximum absolute percentage error MAE: mean absolute error. RMSE: root mean square error. NSE: Nigeria Stock Exchange. present a higher error percentage value than other models, confirming that neural models are more suitable for the prediction of stock markets affected by high volatility. In order to confirm the effectiveness of DL, we reported performance results of two surveyed works [17; 48] based on applying a recurrent model (LSTM) and the RL approach, respectively. The work of [PERSON] et al., proposed a LSTM-based model. The authors reported performance results in terms of risk characteristics considering S&P 500 data before and after transaction costs. In Table 9, we summarized the performance results of the LSTM model. As reported in [17], LSTM shows the lowest maximum drawdown compared to other models. In terms of annualized risk-return, LSTM achieves highest returns (0.8229) compared to random forest, DNN, etc. We reported results of [48] in which the authors applied a time-driven feature-aware jointly deep reinforcement-learning model (TFJ-DRL) to improve the financial signal representation learning. They considered stocks from S&P 500 to evaluate the proposed model which takes advantages of Reinforcement Learning paradigm. The evaluation metrics, total profits (TP), annualized rate of return (AR), annualized Sharpe ratio (SR) and transaction times (TT), highlighted that the proposed TFJ-DRL outperforms other competitors. Also, the reported results in [48] confirmed that the proposed framework could be applied on real data stock market to achieve reliable results.