PMID- 38435608
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240305
IS  - 2376-5992 (Electronic)
IS  - 2376-5992 (Linking)
VI  - 10
DP  - 2024
TI  - The application of the propensity score matching method in stock prediction among 
      stocks within the same industry.
PG  - e1819
LID - 10.7717/peerj-cs.1819 [doi]
LID - e1819
AB  - Stock price prediction is crucial in stock market research, yet existing models 
      often overlook interdependencies among stocks in the same industry, treating them 
      as independent entities. Recognizing and accounting for these interdependencies 
      is essential for precise predictions. Propensity score matching (PSM), a 
      statistical method for balancing individuals between groups and improving causal 
      inferences, has not been extensively applied in stock interdependence 
      investigations. Our study addresses this gap by introducing PSM to examine 
      interdependence among pharmaceutical industry stocks for stock price prediction. 
      Additionally, our research integrates Improved particle swarm optimization (IPSO) 
      with long short-term memory (LSTM) networks to enhance parameter selection, 
      improving overall predictive accuracy. The dataset includes price data for all 
      pharmaceutical industry stocks in 2022, categorized into chemical 
      pharmaceuticals, biopharmaceuticals, and traditional Chinese medicine. Using 
      Stata, we identify significantly correlated stocks within each sub-industry 
      through average treatment effect on the treated (ATT) values. Incorporating PSM, 
      we match five target stocks per sub-industry with all stocks in their respective 
      categories, merging target stock data with weighted data from non-target stocks 
      for validation in the IPSO-LSTM model. Our findings demonstrate that including 
      non-target stock data from the same sub-industry through PSM significantly 
      improves predictive accuracy, highlighting its positive impact on stock price 
      prediction. This study pioneers PSM's use in studying stock interdependence, 
      conducts an in-depth exploration of effects within the pharmaceutical industry, 
      and applies the IPSO optimization algorithm to enhance LSTM network performance, 
      providing a fresh perspective on stock price prediction research.
CI  - (c)2024 Li and Xue.
FAU - Li, Shengnan
AU  - Li S
AD  - School of Communication and Information Engineering, Shanghai University, 
      Shanghai, China.
FAU - Xue, Lei
AU  - Xue L
AD  - School of Communication and Information Engineering, Shanghai University, 
      Shanghai, China.
LA  - eng
SI  - figshare/10.6084/m9.figshare.24115149.v1
PT  - Journal Article
DEP - 20240130
PL  - United States
TA  - PeerJ Comput Sci
JT  - PeerJ. Computer science
JID - 101660598
PMC - PMC10909155
OTO - NOTNLM
OT  - Improved particle swarm optimization
OT  - Long short-term memory
OT  - Propensity score matching method
OT  - Stock interdependence
COIS- The authors declare there are no competing interests.
EDAT- 2024/03/04 06:51
MHDA- 2024/03/04 06:52
PMCR- 2024/01/30
CRDT- 2024/03/04 05:03
PHST- 2023/09/21 00:00 [received]
PHST- 2023/12/21 00:00 [accepted]
PHST- 2024/03/04 06:52 [medline]
PHST- 2024/03/04 06:51 [pubmed]
PHST- 2024/03/04 05:03 [entrez]
PHST- 2024/01/30 00:00 [pmc-release]
AID - cs-1819 [pii]
AID - 10.7717/peerj-cs.1819 [doi]
PST - epublish
SO  - PeerJ Comput Sci. 2024 Jan 30;10:e1819. doi: 10.7717/peerj-cs.1819. eCollection 
      2024.