Abstract
In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into one, making it easier for single financial market risk problems to form systemic risk through a high degree of market linkage effects. Based on the characteristics of financial markets containing both linear and nonlinear components, this paper chooses to use Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the ARIMA model and the SVR model, taking into account their respective linear and nonlinear characteristics. The paper chooses to use the (Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the strengths of the ARIMA and SVR models in terms of linearity and nonlinearity to perform forecasting analysis of financial markets. One of the important functions of forecasting is to transform future uncertainty into measurable risk, so that we can base our plans and actions on it. In this paper, the combined ARIMA-SVR model is compared with the single ARIMA model and SVR model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), where MAE and RMSE measure the absolute error between the predicted and true values, and MAPE measures the relative error between the predicted and true values. and the relative error between the true value. The results show that the combined ARIMA-SVR model has a better forecasting effect and higher forecasting accuracy than the single ARIMA model and SVR model, and the SVR model has higher forecasting accuracy than the ARIMA model in forecasting financial markets.
Highlights
Financial markets are volatile and unpredictable, and all entities are often exposed to uncertainty about profit and loss. e factors affecting their risk are complex and volatile, making it difficult to grasp patterns
Literature [14] selected the daily exchange rates of four currencies, China, India, Switzerland, and Korea, and predicted the exchange rate series based on the nonlinear ARI model by using the support vector regression (SVR) model, and by comparing the prediction results with the maximum likelihood method (MLE) and artificial neural network (ANN), we found that the SVR model considers both fitting and prediction, while the maximum likelihood method and artificial neural network are more inclined to in-sample fitting, and SVR has better prediction results
It can be seen that the error curve of the SVR-ARIMA model is smoother than that of the SVR network during the fine-tuning phase, which indicates that the stability of the SVRARIMA model is better than that of the SVR model
Summary
Financial markets are volatile and unpredictable, and all entities are often exposed to uncertainty about profit and loss. e factors affecting their risk are complex and volatile, making it difficult to grasp patterns. E advent of the era of big data has created a revolution in information technology, especially the widespread use of cloud computing, artificial intelligence, and big data technologies in traditional industries [1]. Deep learning techniques essentially include a new generation of artificial neural networks that use statistical modeling arguments to overcome problems that have plagued the tradition, such as the fading gradient problem, and the overfitting problem. E development of deep learning techniques has led to their widespread use in the study of financial market sequences and other problems, where deep learning has excellent nonlinear mapping capabilities and excellent fitted generalization to better predict and deal with volatility, nonlinearity, correlation, and time series dependence in financial markets in dynamic forecasting. A combined linear and nonlinear model is used to forecast and empirically analyze the financial market, and a linear ARIMA model and a nonlinear SVR model are selected for combined forecasting and compared with a single model
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