Generative AI Enhanced Financial Forecasting Using Multimodal Consumer and Market Behavior Data

Abstract

The paper presents a new generative model of financial forecasting, which is known as Generative AI-Enhanced Financial Forecasting Using Multimodal Consumer and Market Behavior Data. Through the Diffusion-Augmented Multimodal Transformer (DAMT) and using PyTorch Lightning, the research combines indicators of the market, indicators of consumer behavior, and sentiment-driven texts to form an all-in-one predictive model. In contrast to traditional methods that are price centric, DAMT uses generative diffusion that emulates scenarios in the future market, thereby improving strength under volatility and uncertainty. The cross-modal fusion of the model allows further alignment of the trend patterns of behavior and financial flows, which leads to a higher quality of direction and risk-sensitive forecasts. The superiority of DAMT to traditional LSTM, Transformer, and sentiment-only models can be proved by empirical assessments, which claim the greater stability of the model in shocks and uncertainty prediction. This contribution to finance intelligence is the shift between reactive prediction and proactive prediction based on scenarios in which it provides a scalable basis of decision-making in dynamic economic settings. The results emphasize the disruptive potential of the generative AI in the contemporary financial forecasting.