Noise reduction in a reputation index

Artikel

Assuming that a time series incorporates 'signal' and 'noise' components, we propose a method to estimate the extent of the 'noise' component by considering the smoothing properties of the state-space of the time series. A mild degree of smoothing in the state-space, applied using a Kalman filter, allows for noise estimation arising from the measurement process. It is particularly suited in the context of a reputation index, because small amounts of noise can easily mask more significant effects. Adjusting the state-space noise measurement parameter leads to a limiting smoothing situation, from which the extent of noise can be estimated. The results indicate that noise constitutes approximately 10% of the raw signal: approximately 40 decibels. A comparison with low pass filter methods (Butterworth in particular) is made, although low pass filters are more suitable for assessing total signal noise.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: International Journal of Financial Studies ; ISSN: 2227-7072 ; Volume: 6 ; Year: 2018 ; Issue: 1 ; Pages: 1-18 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: reputation ; reputation index ; signal to noise ; S/N ; state-space ; Kalman ; time series ; low pass filters ; butterworth ; moving average

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Mitic, Peter
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2018

Further information

• DOI: doi:10.3390/ijfs6010019
• Handle: http://hdl.handle.net/10419/195685
• Last update: 10.03.2025, 11:42 AM CET