TY - GEN
T1 - Clustering Stock Prices of Financial Sector Using K-Means Clustering with Dynamic Time Warping
AU - Aqsari, Hasri Wiji
AU - Prastyo, Dedy Dwi
AU - Puteri Rahayu, Santi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - An investor is a person who invests money in a business to make for a profit. Investment instruments in the capital market include stocks, bonds, warrants, rights, mutual funds, and various other derivative instruments. According to the IDX, the number of stock investors has increased to 3,988,341 SID as of June 24, 2022, an increase of more than 536 thousand SID or 15.6% from the previous year. Every investor wants to benefit from the shares they own. So it is necessary to consider which groups have desired price fluctuations. In this study, data on the share prices of financial sector companies are used for the period April 1, 2021, to March 31, 2022. The variables used are open, close, and HML (High Minus Low) stock prices. The method used is K-Means clustering with Dynamic Time Warping (DTW) distance. The K-Means was chosen because it is commonly used for large data scales; besides that, K-means with DTW was chosen because it is a non-linear sequence alignment distance, so it is considered suitable to be applied to stock price data in the form of time series data. The analysis was carried out by comparing the results of K-means with Euclidean and DTW distances. It was concluded that the DTW distance gave a higher silhouette score than the Euclidian distance.
AB - An investor is a person who invests money in a business to make for a profit. Investment instruments in the capital market include stocks, bonds, warrants, rights, mutual funds, and various other derivative instruments. According to the IDX, the number of stock investors has increased to 3,988,341 SID as of June 24, 2022, an increase of more than 536 thousand SID or 15.6% from the previous year. Every investor wants to benefit from the shares they own. So it is necessary to consider which groups have desired price fluctuations. In this study, data on the share prices of financial sector companies are used for the period April 1, 2021, to March 31, 2022. The variables used are open, close, and HML (High Minus Low) stock prices. The method used is K-Means clustering with Dynamic Time Warping (DTW) distance. The K-Means was chosen because it is commonly used for large data scales; besides that, K-means with DTW was chosen because it is a non-linear sequence alignment distance, so it is considered suitable to be applied to stock price data in the form of time series data. The analysis was carried out by comparing the results of K-means with Euclidean and DTW distances. It was concluded that the DTW distance gave a higher silhouette score than the Euclidian distance.
KW - dynamic time warping
KW - euclidean
KW - k-means
KW - silhouette score
KW - stock
UR - http://www.scopus.com/inward/record.url?scp=85150463407&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE57756.2022.10057714
DO - 10.1109/ICITISEE57756.2022.10057714
M3 - Conference contribution
AN - SCOPUS:85150463407
T3 - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
SP - 503
EP - 507
BT - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Y2 - 13 December 2022 through 14 December 2022
ER -
