"基于数字数据的股市预测研究：MATLAB股票价格走势预测"

Stock price prediction is a crucial aspect of financial market analysis, as accurate forecasts can help investors make informed decisions and mitigate risks. In this study, we focus on using numerical data from the stock market to predict the future price movements of specific stocks or the overall market. By analyzing key indicators such as stock index prices and trading volumes, we aim to develop predictive models that can provide valuable insights to traders and investors. To achieve this goal, we employ a combination of traditional time series models such as ARIMA and advanced machine learning techniques like LSTM. By training our models on historical data and optimizing them using techniques such as mean squared error and the Adam algorithm, we aim to improve the accuracy of our predictions. In addition to using Stata12 for calculating ARIMA and GARCH models, we leverage the capabilities of Matlab for model training and testing. The methodology of this study involves splitting the data into training and testing sets, with 70% of the data used for training and the remaining 30% for testing. By evaluating the performance of our models on unseen data, we can assess their predictive power and reliability. Through this approach, we seek to enhance the understanding of stock price dynamics and improve the effectiveness of forecasting techniques in the financial markets. In summary, this study contributes to the field of stock price prediction by combining traditional statistical models with machine learning algorithms to enhance the accuracy and reliability of forecasts. By utilizing numerical data and advanced modeling techniques, we aim to provide valuable insights that can assist investors in making informed decisions and navigating the complexities of the stock market. The integration of Matlab in our analysis underscores the importance of leveraging technology to enhance predictive capabilities and drive innovation in financial market analysis.