Shuangyi Han
ABSTRACT
Quantitative investment refers to securities trading strictly guided by computer algorithms. With the advancements in big data technology, quantitative investment has gained increasing significance in the global financial trading market. However, the current market information is complex, and product prices are influenced by various factors, making it challenging to extract effective indicators from the vast amount of market data and formulate trading strategies. To address this issue, this study proposes a forecasting model for quantitative investment based on BP neural networks and time series analysis, resulting in improved forecasting accuracy. The paper adopts the median to substitute indicators during the relevant time, homogenizing the time interval. The chosen BP neural network prediction model exhibits robust nonlinear mapping capabilities and fault tolerance. The model undergoes testing, yielding MAE and MAPE test results primarily at 0.08 and 0.03, respectively. The predicted values demonstrate a high degree of alignment with real values, showcasing an effective prediction outcome.
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