Corporate Cash Flow Forecasting Based on Counterfactual Time Series and Strategy Simulation

Abstract

This study addresses the instability and limited decision usability of corporate cash flow forecasting under conditions of multi-source heterogeneous data coupling, pronounced inter-period lag transmission, and frequent strategic interventions. A unified modeling approach based on counterfactual time series is proposed. The method organizes corporate operating state features, controllable action variables, and exogenous environmental factors into structured historical sequences. An encoder is used to learn low-dimensional latent state representations. An explicit state transition mechanism then models the dynamic evolution of latent states driven by actions and environments. A decoder generates multi-step cash flow forecasts. To support counterfactual analysis, alternative action sequences are substituted under the same historical starting point to construct counterfactual paths. Comparable cash flow trajectories are produced to characterize fund response differences under different strategies. In addition, inflow and outflow components are modeled separately to ensure consistency with financial definitions. Representation alignment and counterfactual consistency constraints are introduced to suppress the influence of selection bias and noise-driven correlations on inference reliability. Sensitivity analyses are further conducted with respect to the number of attention heads, input noise injection intensity, proportion of extreme fluctuation samples, and training data ratio. These analyses systematically characterize the impact boundaries of key configurations and data conditions on error metrics. The framework, therefore, provides an interpretable and scenario-driven modeling and evaluation basis for corporate cash flow forecasting and cash management.