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This study looks into employees' communication, offering novel metrics whichcan help to predict a company's stock price. We studied the intranet forum of alarge Italian company, exploring the interactions and the use of language ofabout 8,000 employees. We built a network linking words included in the generaldiscourse. In this network, we focused on the position of the node representingthe company brand. We found that a lower sentiment, a higher betweennesscentrality of the company brand, a denser word co-occurrence network and moreequally distributed centrality scores of employees (lower group betweennesscentrality) are all significant predictors of higher stock prices. Our findingsoffers new metrics that can be helpful for scholars, company managers andprofessional investors and could be integrated into existing forecasting modelsto improve their accuracy. Lastly, we contribute to the research on wordco-occurrence networks by extending their field of application.

Look inside. Predicting stock prices by analysing an enterprise intranet social network and using word co-occurrence networks