TY - GEN
T1 - Mining Illegal Insider Trading of Stocks
T2 - 2018 IEEE International Conference on Big Data, Big Data 2018
AU - Islam, Sheikh Rabiul
AU - Khaled Ghafoor, Sheikh
AU - Eberle, William
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns.
AB - Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns.
KW - Illegal Insider Trading;
KW - Natural Language Processing
KW - Neural Network
KW - Stock Market
KW - Time Series Prediction
UR - https://www.scopus.com/pages/publications/85062622552
U2 - 10.1109/BigData.2018.8622303
DO - 10.1109/BigData.2018.8622303
M3 - Conference contribution
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 1397
EP - 1406
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Abe, Naoki
A2 - Liu, Huan
A2 - Pu, Calton
A2 - Hu, Xiaohua
A2 - Ahmed, Nesreen
A2 - Qiao, Mu
A2 - Song, Yang
A2 - Kossmann, Donald
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Saltz, Jeffrey
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 December 2018 through 13 December 2018
ER -