Predicting financial market movements in today’s fast-paced and complex environment is challenging more than ever. For many investors, online resources are a major source of information. Researchers can use Google Trends to access the number of search queries of a particular topic by internet users. The search volume index provided by Google then can be used as a proxy for importance of that topic. To predict the collective response to a particular news, we can use the search index for relevant search terms in our forecasting model. The focus of our study is forecasting food stock movement. A unique feature of the food industry is that besides common fundamental information, stakeholders are responsive to food safety news. In this study, we test whether including relevant search terms would reduce the forecasting error and improve the predictive power of traditional models. We use the market data and Google Trends index for 46 listed food companies. The empirical results show that on average the use of search terms reduces forecasting error by 2 to 31 percent for predicting trading volume, and reduces forecasting error by 3.5 to 77 percent for predicting the closing price, depending on the company. We also applied a model confidence set (MCS) to create a set of specifications that have statistically least forecasting error. The average forecasting error of the models in the set is lower than all models with search terms which implies that the MCS approach is efficient in identifying models with best predictive power.