Business Analytics and Making Decision Based on Kalman Filter in Stock Prediction Case
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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5th Congress on Intelligent Systems, CIS 2024: Volume 1. ed. / Sandeep Kumar; E.A. Mary Anita; Joong Hoon Kim; Atulya Nagar. Springer Science and Business Media Deutschland, 2025. p. 309-320 (Lecture Notes in Networks and Systems; Vol. 1275).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Business Analytics and Making Decision Based on Kalman Filter in Stock Prediction Case
AU - Kurniawan, Stephen
AU - Mercorelli, Paolo
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/5/27
Y1 - 2025/5/27
N2 - This paper explores the application of the Kalman filter algorithm in estimating the true value of stocks in finance. By filtering out noise from stock price data, the Kalman filter provides insights into the intrinsic worth of stocks, aiding investment decision-making. Combining the Kalman filter (KF) with the Rolling Windows technique enhances trading strategies based on market conditions. The study highlights the simplicity and effectiveness of the Kalman filter and suggests future enhancements, such as incorporating financial models and adapting to market volatility. These directions aim to refine the algorithm’s capabilities in capturing complex financial dynamics and improving prediction accuracy in uncertain market environments.
AB - This paper explores the application of the Kalman filter algorithm in estimating the true value of stocks in finance. By filtering out noise from stock price data, the Kalman filter provides insights into the intrinsic worth of stocks, aiding investment decision-making. Combining the Kalman filter (KF) with the Rolling Windows technique enhances trading strategies based on market conditions. The study highlights the simplicity and effectiveness of the Kalman filter and suggests future enhancements, such as incorporating financial models and adapting to market volatility. These directions aim to refine the algorithm’s capabilities in capturing complex financial dynamics and improving prediction accuracy in uncertain market environments.
KW - Financial modeling
KW - Investment decision-making
KW - Kalman filter
KW - Market volatility
KW - Rolling windows
KW - Stock valuation
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=105007225985&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-2694-6_21
DO - 10.1007/978-981-96-2694-6_21
M3 - Article in conference proceedings
AN - SCOPUS:105007225985
SN - 9789819626939
T3 - Lecture Notes in Networks and Systems
SP - 309
EP - 320
BT - 5th Congress on Intelligent Systems, CIS 2024
A2 - Kumar, Sandeep
A2 - Mary Anita, E.A.
A2 - Kim, Joong Hoon
A2 - Nagar, Atulya
PB - Springer Science and Business Media Deutschland
T2 - 5th Congress on Intelligent Systems, CIS 2024
Y2 - 4 September 2024 through 5 September 2024
ER -