# Machine Learning for Quantitative Finance Applications: A Survey
## 5 Machine-Learning Approaches  
### Support Vector Machine (SVM)  
Support vector machine (SVM) is a technique of Machine Learning for data classification. The objective of SVM is to find a separation hyperplane to correctly classify data with the maximum margin [9]. The SVM finds a decision function which maximizes the margin between classes. SVM is a supervised classification technique; this means that there are two main steps to be performed. First, a set of labelled data (training data) is provided to the SVM. During this step the algorithms performs a mathematical optimization based on the labelled data. The train examples that limit the maximum margin defined by the SVM during the training are called support vectors. A significant work based on the SVM model is [10], in which the authors aimed to develop an efficient system to gain high profits by analyzing stock markets. Specifically, the authors employed the SVM model to select the stock that exceed the percentage return of the market only selecting stocks that outperform it. The results confirmed the effectiveness of the proposed SVM, in fact the selected range of stocks reported a total return of 208% over 5 years. The work developed by [PERSON] et al. [11] was motivated by the fact that SVM achieved consistent results for forecasting financial time series. This document provides a comparison between SVM, a retro-propagation multilayer neural network (BP) and a regularized radial base function (RBF) neural network. These models have been evaluated by taking as input five datasets listed in the Chicago Mercantile Market. Results showed that SVM excels and achieves effective results considering both adaptive and free parameters. [PERSON] et al. [12] presented a framework which combines the SVM and K-approach (KNN) for predicting Indian stock indices. Specifically, the authors applied the proposed model by using different SVM kernel functions for estimating profits and losses. In order to improve the stock value prediction, the author used the output, computed by SVM, to calculate the best neighbor from a training set. Furthermore, they used two indices, the Bombay Stock Exchange (BSE Sensex) and CNX Nifty, to evaluate the proposed model, taking advantages of closing price forecast, volatility and stock market momentum related to data. To provide a complete study, the authors compared the hybrid model with Functional Link Interval Type 2 Fuzzy Neural System (FLIT2 NS) and Computationally Efficient Functional Link Artificial Neural Networks (CEFLANN) by analyzing mean squared error (MSE) results. Despite significantly complex procedure to update weight, the developed SVM-KNN hybrid model is relatively suitable for solving financial prediction problems. In [13], the authors explained a new approach called the state box method which consists of selecting a group of stock points and subdividing them into three different boxes. Each box refers to a category that indicates the stock status. In this approach, the authors implemented a new method incorporating the AdaBoost algorithm, the probabilistic vector support machine (PSVM) and the genetic algorithm (GA) with the aim to improve the classification of status boxes. Each box includes a limited number of points which indicates the share price trend over a specific time period. The authors investigated the effectiveness of the proposed model by selecting a group of shares listed in the Shenzhen Stock Exchange (SZSE) and the National Association of Securities Dealers Automated Quotations (NASDAQ) as inputs. By analyzing the results, the authors confirm that ensemble classifier achieves remarkable results and outperforms other approaches based on using only the PSVM or the back-propagation neural network (BPN). One of the major issues related to financial field is the limitation in learning the pattern because of high complexity and strong non-linear trend of stock data.