TY - GEN
T1 - Clustering high-frequency stock data for trading volatility analysis
AU - Ai, Xiao Wei
AU - Hu, Tianming
AU - Li, Xi
AU - Xiong, Hui
PY - 2010
Y1 - 2010
N2 - This paper proposes a Realized Trading Volatility (RTV) model for dynamically monitoring anomalous volatility in stock trading. Specifically, the RTV model first extracts the sequences for price volatility, volume volatility, and realized trading volatility. Then, the K-means algorithm is exploited for clustering the summary data of different stocks. The RTV model investigates the joint-volatility between share price and trading volume, and has the advantage of capturing anomalous trading volatility in a dynamic fashion. As a case study, we apply the RTV model for the analysis of real-world high-frequency stock data. For the resultant clusters, we focus on the categories with large volatility and study their statistical properties. Finally, we provide some empirical insights for the use of the RTV model.
AB - This paper proposes a Realized Trading Volatility (RTV) model for dynamically monitoring anomalous volatility in stock trading. Specifically, the RTV model first extracts the sequences for price volatility, volume volatility, and realized trading volatility. Then, the K-means algorithm is exploited for clustering the summary data of different stocks. The RTV model investigates the joint-volatility between share price and trading volume, and has the advantage of capturing anomalous trading volatility in a dynamic fashion. As a case study, we apply the RTV model for the analysis of real-world high-frequency stock data. For the resultant clusters, we focus on the categories with large volatility and study their statistical properties. Finally, we provide some empirical insights for the use of the RTV model.
KW - Clustering analysis
KW - Price volatility
KW - Realized trading volatility
KW - Volume volatility
UR - http://www.scopus.com/inward/record.url?scp=79952379071&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952379071&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2010.56
DO - 10.1109/ICMLA.2010.56
M3 - Conference contribution
AN - SCOPUS:79952379071
SN - 9780769543000
T3 - Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
SP - 333
EP - 338
BT - Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
T2 - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Y2 - 12 December 2010 through 14 December 2010
ER -