TY - GEN
T1 - Using probabilistic approach to joint clustering and statistical inference
T2 - 3rd IEEE International Conference on Big Data, IEEE Big Data 2015
AU - Fang, Hua
AU - Wang, Honggang
AU - Wang, Chonggang
AU - Daneshmand, Mahmoud
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - This paper proposes a Contrarian Probabilistic Model (CPM) to evaluate the effectiveness of contrarians' investment in preferred stocks using big data from Tradeline. CPM accommodates the unique features of investment data which are often correlated, nested, heterogeneous, non-normal with missing values. The clustering and statistical inference are integrated in CPM, which enables joint investment behavior trajectory pattern recognition and risk analyses based on the entire variance-covariance structure between and within clusters. The empirical study using CPM provides a finer and comprehensive evaluation of contrarian investment in preferred stocks. Two distinctive investment behavior trajectory clusters were identified, showing a few high-risk-seeking contrarians achieved high returns over five year long-term investment, while the majority of contrarians did not outperform glamour stockholders in preferred stock investment. Although CPM was developed using historical data, it could be developed into an analytical tool for online near real time big investment data analyses.
AB - This paper proposes a Contrarian Probabilistic Model (CPM) to evaluate the effectiveness of contrarians' investment in preferred stocks using big data from Tradeline. CPM accommodates the unique features of investment data which are often correlated, nested, heterogeneous, non-normal with missing values. The clustering and statistical inference are integrated in CPM, which enables joint investment behavior trajectory pattern recognition and risk analyses based on the entire variance-covariance structure between and within clusters. The empirical study using CPM provides a finer and comprehensive evaluation of contrarian investment in preferred stocks. Two distinctive investment behavior trajectory clusters were identified, showing a few high-risk-seeking contrarians achieved high returns over five year long-term investment, while the majority of contrarians did not outperform glamour stockholders in preferred stock investment. Although CPM was developed using historical data, it could be developed into an analytical tool for online near real time big investment data analyses.
KW - Big data
KW - Clustering
KW - Contrarian Investment
KW - Contrarian Probabilistic Model
KW - Statistical Inference
KW - preferred stocks
UR - http://www.scopus.com/inward/record.url?scp=84963754591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963754591&partnerID=8YFLogxK
U2 - 10.1109/BigData.2015.7364121
DO - 10.1109/BigData.2015.7364121
M3 - Conference contribution
AN - SCOPUS:84963754591
T3 - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
SP - 2916
EP - 2918
BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
A2 - Luo, Feng
A2 - Ogan, Kemafor
A2 - Zaki, Mohammed J.
A2 - Haas, Laura
A2 - Ooi, Beng Chin
A2 - Kumar, Vipin
A2 - Rachuri, Sudarsan
A2 - Pyne, Saumyadipta
A2 - Ho, Howard
A2 - Hu, Xiaohua
A2 - Yu, Shipeng
A2 - Hsiao, Morris Hui-I
A2 - Li, Jian
Y2 - 29 October 2015 through 1 November 2015
ER -