# Machine Learning for Quantitative Finance Applications: A Survey
## 7 Hybrid Systems
Since ESM, ARIMA and BPNN models have been widely used with remarkable results to forecast time-series, this document proposed a hybrid approach involving the aforementioned models. In particular, the authors shed light on hybrid methods performs better than all three models in a single scenario in which they have been considered in the original form. Also, the proposed model achieves better results than the equal weight hybrid model (EWH), and the random walk model (RWM). In [40] the authors have devised a hybrid system by using a genetic fuzzy system with an ANN model. The main contribution of this work is the stepwise regression analysis which has been developed to identify the factors that affect stock prices. To evaluate the overall performance of the proposed model, the experiments were carried out considering the data related to the stock of IBM and DELL corporations as well as Ryanair data, already used in previous studies. In general, it has been noted that the artificial neural networks as well as the fuzzy algorithms have been applied successfully to solve forecasting problems since they are able to obtain better results than using a single model. In [41], the authors assembled a new hybrid approach combining the ARIMA with ANN model in order to predict financial time-series. Before applying the proposed hybrid model, the nature of financial time series has been explored in order to analyze its volatility. Numerical results showed that the hybrid model excels over the individual ARIMA and ANN models, and existing hybrid ARIMA-ANN models. [PERSON] et al. [42] have elaborated a high-efficiency methodology combining ETS and ANN model. The proposed hybrid model takes advantages of linear as well as non-linear modeling capability. In this approach, the ETS model performs prediction of a given time series in order to capture data linear patterns. To evaluate the accuracy of predictions, the authors calculated the residual error by considering the difference between ETS predictions series and original time-series. The second stage used ETS-predictions and ANN-predictions in order to merge them. To verify the applicability and superiority of the proposed model, the authors compared this approach with some existing methods such as ARIMA, ETS, MLP and other ARIMA-ANN models. The experiments confirmed the promising results achieved by the ETS-ANN model. In [43], the authors presented an innovative hybrid system which consists of a long short-term memory (LSTM) to forecast stock price volatility with GARCH-type models. In order to conduct the experiments, the authors used KOSPI 200 index data. The prediction performance confirmed that the proposed model yield to consistent results. In Table 5, we reported the surveyed studies based on applying hybrid systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Techniques** & **Main Goal** & **Dataset** \\ \hline [PERSON] et al. (1996) [34] & ARIMA-based Recurrent Neural Network & \multirow{2}{*}{Market trend prediction} & Taiwan stock exchange \\ \cline{1-1} [PERSON] et al. (2003) [35] & & & \\ \cline{1-1} [PERSON] et al. (2009) & Hybrid ARIMA-ANN model & & & \\  & & & & \\ \cline{1-1} [PERSON] et al. (2009) [36] & Hybrid asymmetric & & & \\ \cline{1-1} [PERSON] et al. (2009) [36] & volatility approach & & & \\ \cline{1-1}  & & & & \\ \cline{1-1} [PERSON] (2009) [37] & SVM with Hybrid feature selection method (F\_SSPS) & \multirow{2}{*}{Stock trend prediction} & \multirow{2}{*}{NASDAQ Index} \\ \cline{1-1} [PERSON] et al. (2009) [38] & Hybrid ARIMA-ANN-Fuzzy Logic model & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of studies based on hybrid models. IBM: International Business Machines NASDAQ: National Association of Securities Dealers Automated Quotation ESM: Exponential Smoothing Model.