# Machine Learning for Quantitative Finance Applications: A Survey
## 5 Machine-Learning Approaches
### Recurrent Neural Network (RNN) for Financial Forecasting
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Techniques** & **Main Goal** & **Dataset** \\ \hline [PERSON] et al. (2014) [14] & \begin{tabular}{c} Fusion approach involving ANN, \\ RF and SVR models \\ \end{tabular} & Stock prices prediction & Indian Stock market indices \\ \hline [PERSON] et al. (2015) [15] & LSTM model & Stock returns prediction & China Stock Market data \\ \hline [PERSON] et al. & bat-neural network & \begin{tabular}{c} Stock price prediction \\ \end{tabular} & DAX stock price \\ \hline (2015) [16] & \begin{tabular}{c} LSTM model \\ \end{tabular} & \begin{tabular}{c} Large-scale financial \\ market prediction \\ \end{tabular} & \begin{tabular}{c} S\&P 500 \\ (1992–2015) \\ \end{tabular} \\ \hline [PERSON] et al. (2005) [21] & \begin{tabular}{c} Global, recurrent and \\ smoothed-piecewise neural models \\ \end{tabular} & Financial time series prediction & \begin{tabular}{c} Istanbul Stock Exchange \\ (1990–2002) \\ \end{tabular} \\ \hline [PERSON] et al. (2017) & \begin{tabular}{c} LSTM-RNN-CNN sliding \\ window-ARIMA \\ \end{tabular} & Short term future prediction & \begin{tabular}{c} NSE listed companies’ data \\ (2014–2015) \\ \end{tabular} \\ \hline [PERSON] et al. (2017) [23] & RNN model & Daily stock prices prediction & Sri Lankan Stock Exchange \\ \hline [PERSON] et al. (2018) [24] & LSTM vs. ARIMA & Comparison & N225, IXC, HIS, GSPC, DJ \\ \hline [PERSON] et al. (2019) [25] & LSTM model & Forecasting & \
\begin{tabular}{c} ENEL.MI \\ ISP.MI \\ UCG.MI \\ CVAL.MI \\ MPS.MI \\ \end{tabular} \\ \hline [PERSON] et al. (2019) [26] &
\begin{tabular}{c} Grid algorithmic trading system \\ (Non-linear Regression Network) \\ \end{tabular} & Forecasting & FX market (EUR/USD) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of studies based on machine-learning (ML)/deep-learning (DL) models. ANN: artificial neural network. SVR: support vector regression. RF: Random Forest. models, they present some drawbacks in different contexts whereas feedforward present a lower error. To conclude, it has been noted that GRU networks do not perform stock price forecasting accurately. The work proposed by [PERSON] et al. [24] conducted a comparative study with the aim of investigating if ML based approaches can be superior to traditional methods in terms of accuracy. In this work, the authors compared a LSTM, a deep-learning architecture, with the ARIMA model which falls into the class of statistical models. In this approach, historical monthly financial time series from Jan 1985 to Aug 2018 from the Yahoo finance Web have been considered as input of the proposed models in order to predict the trend of the stock exchanges. The experiment results reported that LSTM, compared to the ARIMA model, provided the best overall performance, confirmed by RMSE values. [PERSON] et al. [25] proposed an innovative LSTM-based framework in order to perform careful stock price prediction. The proposed approach includes two pipelines: the first is defined to perform Trend prediction and the second is developed to predict stock price values. Regarding stock close price prediction, the proposed system is based on the usage of LSTM together with mathematical price correction approach performed by Markov statistical model. Results revealed that the proposed framework is able to perform stock price forecasting accurately. Furthermore, the proposed method outperforms statistical model such as SMA, in terms of accuracy. In [26], the authors presented an innovative framework composed by a non-linear regression network which performs close price forecasting. The output, computed by Regression Network, is fed to a trend classifier block which estimates the most probable short-term financial trend. After organizing the trading grid by analyzing the current state of trading, the block called the basket equity system manager evaluates the financial exposure of the trading grid.