TY - GEN
T1 - An algorithm for supporting decision making in stock investment through opinion mining and machine learning
AU - Jeong, Yujin
AU - Kim, Sunhye
AU - Yoon, Byungun
N1 - Publisher Copyright:
© 2018 Portland International Conference on Management of Engineering and Technology, Inc. (PICMET).
PY - 2018/10/4
Y1 - 2018/10/4
N2 - This paper suggests an algorithm for supporting decision making in stock investment through opinion mining and machine learning. Within the framework of supporting decision making, this research deals with (1) fake information filtering to accurate foresight, (2) credit risk assessment, and (3) prediction based on critical signal detection. At first, financial data including news, SNS, the financial statements is collected and then, among them, fake information such as rumors and fake news is refined by author analysis and the rule-based approach. Second, the credit risk is assessed by opinion mining and sentiment analysis for both social data and news in the form of sentimental score and trend of documents for each stock. Third, a risk signal in stock investment is detected in accordance with the credit risk derived from opinion mining and financial risk identified by the financial database. Consequently, the possibility of credit events such as delisting and bankruptcy will be forecast in the near future based on the risk signal. The proposed algorithm helps investors to monitor relevant information objectively through fake information filtering as well as to make correct judgments in stock investment.
AB - This paper suggests an algorithm for supporting decision making in stock investment through opinion mining and machine learning. Within the framework of supporting decision making, this research deals with (1) fake information filtering to accurate foresight, (2) credit risk assessment, and (3) prediction based on critical signal detection. At first, financial data including news, SNS, the financial statements is collected and then, among them, fake information such as rumors and fake news is refined by author analysis and the rule-based approach. Second, the credit risk is assessed by opinion mining and sentiment analysis for both social data and news in the form of sentimental score and trend of documents for each stock. Third, a risk signal in stock investment is detected in accordance with the credit risk derived from opinion mining and financial risk identified by the financial database. Consequently, the possibility of credit events such as delisting and bankruptcy will be forecast in the near future based on the risk signal. The proposed algorithm helps investors to monitor relevant information objectively through fake information filtering as well as to make correct judgments in stock investment.
UR - http://www.scopus.com/inward/record.url?scp=85056480357&partnerID=8YFLogxK
U2 - 10.23919/PICMET.2018.8481802
DO - 10.23919/PICMET.2018.8481802
M3 - Conference contribution
AN - SCOPUS:85056480357
T3 - PICMET 2018 - Portland International Conference on Management of Engineering and Technology: Managing Technological Entrepreneurship: The Engine for Economic Growth, Proceedings
BT - PICMET 2018 - Portland International Conference on Management of Engineering and Technology
A2 - Steenhuis, Harm-Jan
A2 - Niwa, Kiyoshi
A2 - Perman, Gary
A2 - Kocaoglu, Dundar F.
A2 - Anderson, Timothy R.
A2 - Kozanoglu, Dilek Cetindamar
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Portland International Conference on Management of Engineering and Technology, PICMET 2018
Y2 - 19 August 2018 through 23 August 2018
ER -