Reputation, Sentiment, Time Series and Prediction
Peter Mitic · 2024
A formal formulation for reputation is presented as a time series of daily sentiment assessments. Projections of reputation time series are made using three methods that replicate the distributional and auto-correlation properties of the data: ARIMA, a Copula fit, and Cholesky decomposition. Each projection is tested for goodnessof-fit with respect to observed data using a bespoke auto-correlation test. Numerical results show that Cholesky decomposition provides optimal goodness-of-fit success, but overestimates the projection volatility. Expressing reputation as a time series and deriving predictions from them has significant advantages in corporate risk control and decision making.