# Machine Learning for Quantitative Finance Applications: A Survey
## 10 Discussion
In Figure 1, we reported a timeline of strategies adopted for financial time-series modelling. The objective of this survey has been to discuss how machine-learning approaches outperform methods based on non-linear algorithm underlying their pros and cons. The main benefit on applying ML-based systems is to automatize trading operations and reduce computing time, ensuring to perform with high level in terms of accuracy. For this reason, we focused on ML algorithms such as SVM or random forest and DL models (LSTM or RNN). We compared some of these methods and evaluated the approaches, which obtained the best results related to time-series modeling. Our investigation suggested that a quite considerable number of investigated studies provide an evaluation by using the following stock market data:  
* **Bombay SE:** The Bombay Stock Exchange (BSE) is one of the largest exchanges in the world which includes around 6000 companies. A large part of these companies is also listed on the National Stock Exchange of India. * **S&P 500:** Standard & Poor's 500 Index is a market-capitalization-weighted index of the 500 largest U.S. publicly traded companies. * **DAX indices:** DAX is a stock index for German companies that trade on the Frankfurt Exchange. In order to calculate the DAX Index, an electronic trading system, called Xetra, is used. Unlike most indexes, the DAX is updated using futures prices for the next day. * In a different twist from most indexes, the DAX is updated with futures prices for the next day, even after the main stock exchange has closed. * **Shangai stock exchange:** The Shanghai Stock Exchange (SSE) represents the largest stock exchange in China. It is a non-profit organization run by the China Securities Regulatory Commission (CSRC). Stocks, funds, bonds, and derivatives are all traded on the exchange. * **NASDAQ index:** The NASDAQ is the world's fast electronic stock exchange which operates through computer and telephones, as opposite to traditional method. NASDAQ lists only technology-based companies. However, the proposed investigation highlighted the need to define a unique benchmark dataset. We observed that investigated methods conducted experiments by using different datasets. Benchmarks are important for researchers in order to measure the performance of an algorithm on a specific problem. Without the definition of a unique dataset and appropriate performance indicators, researchers cannot make a complete comparison between the proposed studies in order to select a  
Figure 1: Timeline of adopted approaches for time-series analysis. AR Auto-Regressive. ML: Machine Learning. DL: Deep Learning. RL: Reinforcement Learning. SVM: Support Vector Machine. RF: Random Forest. SVR: Support Vector Regression. ARIMA: Autoregressive Integrated Moving Average. GARCH: Generalized Autoregressive Conditional Heteroskedasticity. RNN: Recurrent Neural Network. LSTM: Long Short-Term Memory. SOM: Self-Organising Map. suitable solution for a specific problem. Also, the majority of investigated primary studies provide different evaluation metrics for time-series forecasting. In this work, we examined several proposed studies reporting the use of both traditional linear model and machine-learning approaches. Despite facing issues such as lack of a benchmark dataset and performance indicators, we attempted to provide a comparison between traditional and ML-based approaches, reported in this survey. Specifically, Table 8 summarizes experimental results regarding two of the investigated approaches [7; 22]. The work of [PERSON] et al. [7] proposed a hybrid system based on ARIMA and GARCH models. The authors collected data from January 2010 to January 2011 to define the first dataset (TD1) used for evaluating the performance of their model. As reported in [7], they compared results between the proposed approach and other autoregressive models such as ARIMA, GARCH etc. The corresponding errors measures (MAPE, MAE, MaxAPE and RMSE) show that the proposed approach outperforms other models.