We　developed　a　predictive　model　using　Long　Short-Term　Memory　(LSTM)　neural　networks　aimed　at　forecasting　a　specific　benchmark　index.　The　focus　was　on　refining　constituent　data　based　on　predefined　criteria　and　normalizing　and　scaling　daily　return　factor　data.　Multiindicator　data　was　processed　through　Principal　Component　Analysis　(PCA)　and　TOPSIS　(Technique　for　Order　Preference　by　Similarity　to　Ideal　Solution)　methods　to　identify　critical　components　affecting　predictions.　The　suitability　of　the　data　for　analysis　was　confirmed　through　a　Kaiser-Meyer-Olkin　(KMO)　measure　of　0.714　and　Bartlett＇s　test　of　sphericity　with　a　p-value　less　than　0.05.　This　approach　offers　insights　into　improving　forecast　accuracy　and　handling　dynamic　changes　in　the　underlying　dataset.　©2024　IEEE.