PMID- 36832646
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230228
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 25
IP  - 2
DP  - 2023 Feb 2
TI  - An IPSO-FW-WSVM Method for Stock Trading Signal Forecasting.
LID - 10.3390/e25020279 [doi]
LID - 279
AB  - Trading signal detection is a very popular yet challenging research topic in the 
      financial investment area. This paper develops a novel method integrating 
      piecewise linear representation (PLR), improved particle swarm optimization 
      (IPSO) and a feature-weighted support vector machine (FW-WSVM) to analyze the 
      nonlinear relationships between trading signals and the stock data hidden in 
      historical data. First, PLR is applied to generate numerous trading points 
      (valleys or peaks) based on the historical data. These turning points' prediction 
      is formulated as a three-class classification problem. Then, IPSO is utilized to 
      find the optimal parameters of FW-WSVM. Lastly, we conduct a series of 
      comparative experiments between IPSO-FW-WSVM and PLR-ANN on 25 stocks with 2 
      different investment strategies. The experiment results show that our proposed 
      method achieves higher prediction accuracy and profitability, which indicates the 
      IPSO-FW-WSVM method is effective in the prediction of trading signals.
FAU - Chen, Yingjun
AU  - Chen Y
AD  - Department of Computer Science and Technology, Tongji University, Shanghai 
      201804, China.
FAU - Zhu, Zhigang
AU  - Zhu Z
AUID- ORCID: 0000-0002-0911-179X
AD  - School of Health Science and Engineering, University of Shanghai for Science and 
      Technology, Shanghai 200093, China.
LA  - eng
PT  - Journal Article
DEP - 20230202
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC9955070
OTO - NOTNLM
OT  - feature-weighted support vector machine (FW-WSVM)
OT  - improved particle swarm optimization (IPSO)
OT  - trading signals forecasting
COIS- All authors declare that they have no conflict of interest.
EDAT- 2023/02/26 06:00
MHDA- 2023/02/26 06:01
PMCR- 2023/02/02
CRDT- 2023/02/25 02:00
PHST- 2022/12/16 00:00 [received]
PHST- 2023/01/19 00:00 [revised]
PHST- 2023/01/30 00:00 [accepted]
PHST- 2023/02/25 02:00 [entrez]
PHST- 2023/02/26 06:00 [pubmed]
PHST- 2023/02/26 06:01 [medline]
PHST- 2023/02/02 00:00 [pmc-release]
AID - e25020279 [pii]
AID - entropy-25-00279 [pii]
AID - 10.3390/e25020279 [doi]
PST - epublish
SO  - Entropy (Basel). 2023 Feb 2;25(2):279. doi: 10.3390/e25020279.