Reputation, Sentiment, Time Series and Prediction

Peter Mitic

2024

Abstract

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 goodness-of-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.

Paper Citation

in Harvard Style

Mitic P. (2024). Reputation, Sentiment, Time Series and Prediction. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 51-61. DOI: 10.5220/0012762600003756

in Bibtex Style

@conference{data24,
author={Peter Mitic},
title={Reputation, Sentiment, Time Series and Prediction},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={51-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012762600003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Reputation, Sentiment, Time Series and Prediction
SN - 978-989-758-707-8
AU - Mitic P.
PY - 2024
SP - 51
EP - 61
DO - 10.5220/0012762600003756
PB - SciTePress