# Machine Learning for Quantitative Finance Applications: A Survey
## 5 Machine-Learning Approaches
### Deep Learning  
Deep learning (DL) is a part of the machine-learning methods based on using data to train a model to make predictions from new data. In general, DL methods allow to perform difficult task without human involvement. In particular, DL methods represent an effective solution in solving financial market problems which are different from typical deep learning applications. For example, selecting a stock that is likely to perform well in future could represent a challenging task for a human. For this reason, the tools of deep learning may be useful in such selection problems, because deep learning techniques represent the best solution to compute any function mapping data (which include returns, economic data, accounting date, etc.) into the value of the return. This document [14] deals with stock market index forecasting. The experiments were carried out by considering two indices, namely CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex of the Indian stock markets. The main contribution of this work is the development of a two-phase fusion approach which involves the use of support vector regression (SVR) in the first phase. The second stage defined three different combination of SVR with other models including an artificial neural network (ANN), random forest (RF) and SVR. Thus, the authors provide a comparison between the resulting prediction models and single models ANN, RF and SVR for estimating the accuracy in forecasting time series 1-10, 15 and 30 days in advance. The authors of [15] presented a long short-term memory (LSTM) neural networks for modeling and predicting the return of Chinese shares. Furthermore, they compared their LSTM model with random forecasting method in order to verify the applicability and superiority of the proposed model. The input data of the model are data related to Chinese stock market. In order to evaluate the proposed model, the authors considered 30-day sequences of historical data with 10 learning features and labeling functions of the earnings rate in 3 days. Results confirmed the superior performance of LSTM over random forecasting method and its ability to improve the accuracy related to stock returns forecasting. In this document [16], the authors provided an innovative model called the bat-neural network multi-agent system (BNNMAS) aimed to perform stock prices prediction. In particular, the goal of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Techniques** & **Main Goal** & **Dataset** \\ \hline [PERSON] et al. & & & \\ (2001) [10] & Support Vector Machine & Stock selection & \begin{tabular}{c} Australian Stock Exchange \\ (1992–2000) \\ \end{tabular} \\ \hline [PERSON] et al. & & & \\ (2003) [11] & Support Vector Machine & Forecasting & Chicago Mercantile Market \\ \hline [PERSON] et al. & & & \\ (2015) [12] & Hybrid SVM-KNN model & Stock market prediction & Indian stock market indices \\ \hline [PERSON] et al. & & & \\ (2016) [13] & & & \\ \end{tabular} \begin{tabular}{c} Status box method (Hybrid AdaBoost \\ algorithm, genetic algorithm and \\ probabilistic SVM) \\ \end{tabular} \
\begin{tabular}{c} Stock trend prediction \\ \end{tabular}
\begin{tabular}{c} 20 shares from SZSE/ \\ 16 shares from NASDAQ \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of studies based on the support vector machine (SVM) model. KNN: K-nearest neighbors. proposed method is to predict eight years of Deutscher Aktienindex (DAX) stock indices. Furthermore, the results of BNNMAS have been compared with the results of the genetic algorithm-neural network (GANN) and teh general regression neural network (GRNN). Efforts have demonstrated the power of BNNMAS in predicting stock prices in the long term accurately. In [17] the authors investigated the problem of applying LSTM for financial time series forecasting. They proposed a cutting-edge technique in order to distribute LSTM for predicting off-directional movements. Stocks of the S&P 500 from 1992 to 2015 have been used as input data to validate the proposed model.