# Machine Learning for Quantitative Finance Applications: A Survey
## 7 Hybrid Systems  
A new hybrid approach [34] have been devised in order to forecast trend by using Taiwan stock exchange weighted stock index (abbreviated TSEWSI). The hybrid system was developed by combining a RNN model which uses features extracted by ARIMA model as input data. To improve the overall performance, neural networks have been fed with the difference of predictions sequence in order to adjust the connection weights during backpropagation training. Experiments revealed that the prediction performed by the proposed model was significant and reliable. In fact, it has the capability to predict 6 weeks ahead the market trend. Another work that takes advantages from both linear and non-linear models is [35]. In this work, the authors developed a hybrid method involving ARIMA and ANN models. The results confirmed that the proposed model is well suited to improve forecasting accuracy. The aim of the proposed models was to predict 35 and 67-period ahead. Results reported that neither the neural network nor the ARIMA model have the capability to understand the patterns within data. But it has been noted that the hybrid model combining two models is able to reduce the overall forecasting errors for 67-period forecasting. With regard to 35-period forecasting, the ANN achieved better results. The authors of [36] presented a new hybrid asymmetric volatility approach based on artificial neural networks. In particular, they focused on improving the forecasting ability of derivative securities prices. The benefit of using the proposed method is due to not only its ability to capture asymmetric volatility but also the ability to reduce the stochastic and non-linearity of the error term sequence. [PERSON] et al. [37] provides a mixture of the SVM model and hybrid feature selection method aiming to predicting the trend of stock markets. To improve the trend prediction, a hybrid selection approach has been defined as a method to select the optimal features. The experiments were carried out by performing a comparison between SVM model with F-score and supported sequential forward search (F_SSFS) feature selection methods and BPNN along with information gain, symmetrical uncertainty, and correlation-based feature selection. The final results indicated not only that SVM achieved better results than BPN in terms of stock trend prediction but also the proposed SVM incorporating F_SSFS shows a reliable level of accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Method** & **Main Goal** & **Dataset** \\ \hline [PERSON] et al. (2008) [27] & GARCH vs. & & Nikkei 225, Hang Seng, FTSE 100 and DAX \\ \hline [PERSON] et al. (2010) [28] & ARIMA vs. ANN & Comparison & \begin{tabular}{c} Indonesia Stock data \\ (ANTM) \\ \end{tabular} \\ \hline [PERSON] et al. (2011) [29] & ANN and SVM model & \begin{tabular}{c} Stock price index \\ movement prediction \\ \end{tabular} & Istanbul Stock Exchange (ISE) \\ \hline [PERSON] et al. (2014) [30] & ARIMA vs. ANN & Comparison & New York Stock Exchange \\ \hline [PERSON] et al. (2015) [31] & ANN, SVM, RF, Naïve Bayes & \
\begin{tabular}{c} Comparison \\ (direction of movement for stock) \\ \end{tabular} & Indian Stock Exchange \\ \hline [PERSON] et al. (2015) [32] & ANN & Forecasting & São Paulo Stock Exchange BM\&FBOvespa. \\ \hline [PERSON] et al. (2017) [33] & RNN, SVM & Stock prediction &
\begin{tabular}{c} Shanghai Stock Exchange \\ 50 (SSE 50) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of comparative studies. GARCH: Generalized Autoregressive Conditional Heteroskedasticity. In [38], the authors present a hybrid system to forecast financial time-series. The proposed method combines an ARIMA model and fuzzy logic in order to demonstrate that proposed model could overcome data limitations of individual ARIMA model. A reliable work based on applying hybrid method to solve stock index forecasting is [39].