Neural Network-based Market Fluctuation Prediction Model Construction and Corporate Strategy Research in Business Administration

Based on the complexity and nonlinear characteristics of market volatility, this paper proposes a market volatility prediction model that combines MA filtering method, autoregressive moving average (ARMA), and long-short-term memory (LSTM) neural network. And the back-propagation (BP) neural network is utilized to quantitatively solve the problem of corporate strategy formulation, and a corporate strategy formation model is established to determine the corporate strategic choice through the corporate strategic environment and strategic capabilities. In the ablation experiment, the combined model MA-ARMA-LSTM reduces its MSE, RMSE, MAE and MAPE by 0.0007, 0.0131, 0.0074 and 1.57%, respectively, compared to the ARMA model. Compared with common market volatility prediction models, the combined model has the smallest error in each assessment index. The output of BP neural network for corporate strategy selection is consistent with the expert ranking, which is verified to be in line with the actual business situation, indicating that the method in this paper can provide a reasonable corporate strategy.