# Machine Learning for Quantitative Finance Applications: A Survey
## 6 Comparisons  
In literature, there are a huge number of papers which provide comparison between the AR model with ARIMA methodology or BPANN aiming to define the suitable model for time-series forecasting. Other related works have also analyzed the performance of SVMs and the traditional back propagation (BP) model that seem to provide a better overall performance of these models compared to AR models. In [27], the authors investigated the stock market indices forecasting applying a GARCH model in order to compare it with standard BP. Defining a proper strategy should be provided to fit an adequate model for the time series prediction. In the past few decades, several works have applied ARMA approach for univariate time series forecasting. A time series represents a process where observations are sequences of equally spaced points in time. But in financial domain, time series present large volatility and non-linearity which are difficult to process by classical methods which their assumptions are based on linearity. Several ML techniques were proposed to overcome some drawbacks of classical methods by taking advantage of their ability to capture hidden patterns between input and output data. The authors analyzed the performance of ARMA, GARCH and BP models in predicting four stock market values. The results reported that GARCH and BP models offer a performance superior to the ARMA model by using deviation performance as a criterion. However, the ARMA model is extremely effective compared to other models in terms of direction and weighted direction performance criteria. Finally, the authors observed that GARCH fitting line is closer than BP one except for the Financial Times (FT) market. A similar comparison was approached in [28] in which the authors demonstrate that ANN can be engaged to improve financial time-series forecasting. The authors focused on applying ANN and ARIMA models to predict PT Aneka Tambang Tbk (ANTM) by using historical daily values. The authors also referred to previous works in which ANN is employed to predict stock movement with a satisfactory result. In this paper, the most interesting observation that emerges from the comparison of the two models is that ANN outperformed the auto regressive method. These experimental results provide further justification to apply ANN model for solving a time-series forecasting problem considering its ability to detect non-linear data pattern. In this paper, the authors reported as benchmark the each sum square (ESS) result achieved by ARIMA and by ANN, confirming the effectiveness of ML-based methods. In [29], the ISE Index has been considered to perform time-series forecasting. The authors provide a complete study for estimating the overall performance of an ANN and SVM model. To evaluate both models, ten technical indicators have been employed including SMA etc. After establishing the best combination of parameters for both models, the authors plugged the data into ANN and SVM model aiming to forecasting the future values of the stock market. The experiments revealed that ANN is more effective than the SVM model. However, the authors shed light that both models present lower accuracy in forecasting stock market related to year 2001 in which the economic and financial crises had an immense adverse impact on the Turkish financial market. In [30], the authors examine the results of ARIMA and ANN models in forecasting the NYSE stock data. By comparing all empirical results, ANN are more efficient than the statistical model, ARIMA. According to [31], a random forest model can be engaged to predict stock market trend achieving good results. In particular, the authors compared the performance of ANN, SVM, random forest and naive Bayes using Reliance Industries and Infosys Ltd., in addition to CNX Nifty and S&P Bombay Exchange (BSE) Sensex. The core idea is based on transforming data from continuous to discrete values of a fixed number (i.e., 10) of technical parameters.