Research on the Supervision Method of Big Data Technology in the Systemic Risk of Internet Finance in China

Abstract

Sub-new stock price prediction, forecasting the price trends of stocks listed less than one year, is crucial for effective quantitative trading. While deep learning methods have demonstrated effectiveness in predicting old stock prices, they require large training datasets unavailable for subnew stocks. In this paper, we propose Meta-Stock: a taskdifficulty-adaptive meta-learning approach for sub-new stock price prediction. Leveraging prediction tasks formulated by old stocks, our meta-learning method aims to acquire the fast generalization ability that can be further adapted to subnew stock price prediction tasks, thereby solving the data scarcity of sub-new stocks. Moreover, we enhance the metalearning process by incorporating an adaptive learning strategy sensitive to varying task difficulties. Through wavelet transform, we extract high-frequency coefficients to manifest stock price volatility. This allows the meta-learning model to assign gradient weights based on volatility-quantified task difficulty. Extensive experiments on datasets collected from three stock markets spanning twenty-two years prove that our Meta-Stock significantly outperforms previous methods and manifests strong applicability in real-world stock trading. Besides, we evaluate the reasonability of the task difficulty quantification and the effectiveness of the adaptive learning strategy.

Internet of things and Big Data Analytic: A State of the Art Review

“…provided a general overview of the security vulnerabilities in the Internet of things shell ranking optimization algorithm data processing of IoT technology and logistics unified information system [56] analyzes the heterogeneous multisource data collection paradigm heterogeneous multi-source data acquisition manufacturing chain for the use of extensive production data. [57] introduced a novel structural risk supervision approach based on Big Data technology for Internet finance in China.…”

“…Wang and Ma introduced a novel structural risk supervision approach based on Big Data technology for Internet finance in China [57]. The strategy is based on Big Data technologies, combines IT with Stuff Internet technology, can monitor Chinese Internet funding processes in real-time, and offers information to related staff with ease.…”