AN ONLINE ADAPTIVE CLASSIFICATIONOF GOOGLE TRENDS DATA ANOMALIES FOR INVESTOR SENTIMENT ANALYSIS
Google Trends data has gained increasing popularity in the applications of behavioral finance,decision science and risk management.Because of Googles wide range of use,the Trends statistics provide significant information about the investor sentiment and intention,which can be used as decisive factors for corporate and risk management fields.However,an anomaly,a significant increase or decrease,in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends dataset.Since through time,the baseline noise power shows a gradual change an adaptive threshold method is required to track and learn the baseline noise for a correct classification.To this end,we introduce an online method to classify meaningful deviations in Google Trends data.Through extensive experiments,we demonstrate that our method can successfully classify various anomalies for plenty of different data.
Adaptive Data Processing Behavioral Finance Convex Optimization Online Learning
Duygu Dere Mert Ergeneci Kaan Gokcesu
Project Group International,Ankara,Turkey Bilkent University Nanotechnology Research Center(Nanotam),Ankara,Turkey Electrical Engineering and Computer Science Department,Massachusetts Institute of Technology,Cambrid
国际会议
香港
英文
79-81
2018-04-14（万方平台首次上网日期，不代表论文的发表时间）