A financial model was created to study how people's attitudes affect stock prices. The model used a small-world network where agents interacted like particles in a contact process. By simulating this model, researchers found that the structure of the network influenced price behaviors. The model accurately reproduced real market data, showing that nonnormality, volatility clustering, and multifractality are more prominent in small-world networks compared to ordered or random ones. This suggests that how people exchange attitudes can impact how stock prices fluctuate.