A study of high-frequency data analysis and risk prediction techniques in corporate finance

Facing the rapid fluctuation of the financial market, a real-time risk prediction system based on high-frequency trading data is constructed, which contains a monitoring index system of three dimensions, namely, market risk, liquidity risk and trading risk. Sliding window and EWMA methods are adopted to realize real-time risk monitoring, and a dynamic threshold adjustment mechanism is introduced to improve the accuracy of early warning. Empirical analysis shows that the system can predict abnormal market fluctuations 4.6 minutes in advance, with a prediction accuracy of 84.2%. The multi-scale LSTM model reduces the VaR prediction error by 23.5%, and the annualized Sharpe ratio of the portfolio is increased to 2.12, which has significant practical value.