A Daily Funding Demand Forecasting Model for Fintech Investment Decisions and Its Impact on Investment Return Stability

The accuracy of financing demand prediction has a direct impact on the return on investment and risk exposure in fintech investment and asset allocation. Nevertheless, the real world financial transaction data often displays significant nonstationary features — for example, cyclical fluctuations, event shocks, and short-term anomalies — which make the traditional forecasting approach unstable in the real investment scenarios. This study builds a data set that includes 34 reproducible variables — including daily financing requirements, transaction peaks, capital occupation duration, and risk exposure levels — on the basis of 180 consecutive days of investment and operating data from a leading financial services firm. It systematically compares ARIMA, Prophet, Random Forest, and XGBoost models for financing demand forecasting. Empirical results show that XGBoost maintains a low forecast error (MAPE of 8.2%) in the case of market fluctuations and unusual events, which reduces the average error by about 22% in comparison with the baseline model. Based on these results, a model is built to analyze the effect of forecast errors on the stability of investment returns and the efficiency of capital turnover. Results show that keeping the forecast error under 10% significantly reduces the risk of capital misallocation in times of high volatility, while at the same time improving the stability of overall investment returns. This study provides a reusable model workflow and engineering reference for the establishment of the investment allocation and risk management system of the financial institutions.