# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 2 Literature Review  
In recent years, most of the existing studies have focused on different types of models and parameter optimizations that are beneficial to improve the accuracy of existing information for predicting future stock prices. Therefore, the literature related to this paper can include the following two aspects. One is to improve the accuracy of stock price prediction mainly through the analysis of models. The second is to study the optimization of parameters between different algorithms. In terms of models, most of the literature in this category focuses on financial forecasting of time series data such as stock prices, and their methods of predicting stock prices are more traditional. This includes ARCH models, GARCH models, etc. For example, [PERSON] [3] used ARCH models to study the response of Nikkei 225 closing prices to different types of data during the period from 5 January 1998 to 29 December 2017. [PERSON] [2] estimated a multivariate GARCH model for weekly stock index data to study the interaction of stock markets between Iran, the United States, Turkey, and the United Arab Emirates. While these methods have some advantages, such linear forecasting models have strict assumptions in the application of data distribution and are not suitable for forecasting financial data sets. Therefore, some scholars have started to focus on nonlinear forecasting models in recent years. They try to forecast financial asset yields and prices by machine learning methods and construct models. The empirical study proves that machine learning methods are better than traditional time series analysis models in terms of forecasting accuracy. [PERSON] [4] employs a nonparametric approach to forecast high-frequency exchange rates, which significantly improves the forecasting ability of both linear and nonlinear models. The nonlinear model outperforms the random walk and linear models based on some recursive out-of-sample forecasts. The artificial neural network model outperforms the random walk and any linear competing model for high frequency exchange rate forecasting. Given the growing interest in Chinese stock volatility, [PERSON] [5] extracts global stock information by combining forecasts from a time-varying parametric (TVP) volatility model to forecast Chinese stock volatility. [PERSON] [6] reports the results of a study that collects data on stock market participants' expectations of future levels of stock indices. [PERSON] and [PERSON] [7] also focus heavily on Chinese stock volatility, they conducted a comprehensive study on the prediction of Chinese stock market variance using 24 commonly used predictor variables, documenting the unstable relationship between scaled stock market prices and market variance. While [PERSON] and [PERSON] [8] apply machine learning concepts to predict stock prices, demonstrating the trade-off between sensitivity and specificity of forecasts. [PERSON] [9] proposes a novel fuzzy time series forecasting model based on technical analysis, affine propagation clustering, and support vector regression models. The performance of the method is proved using the Taiwan Capitalization Weighted Stock Index, Standard and Poor's 500 Index, and Dow Jones Industrial Average datasets. However, the prediction process of machine learning and artificial intelligence often requires the initial parameters under specific models to be set in advance. For the more sensitive model parameters, the appropriateness of their settings can greatly affect the effectiveness of model fitting and forecasting. Intelligent optimization algorithms have been used to initialize the parameters of forecasting models to improve the accuracy of financial asset price forecasting. Among them, the bat algorithm has been increasingly used in management and operations research. It benefits from the bionic principle of the population algorithm and its better results in optimizing parameters. The bat algorithm can optimize the parameters as a way to achieve improved prediction performance. For example, [PERSON] et al.