Analysis of Enhanced Hidden Markov Models for Improved Stock Market Price Forecasting and Prediction
Yazarlar (7)
Rishabh Saxena
Sharda University, Hindistan
Sharda University, Hindistan
Adarsh Upadhayay
Sharda University, Hindistan
Sharda University, Hindistan
Gaurav Raj
Sharda University, Hindistan
Sharda University, Hindistan
Tanupriya Choudhury
Graphic Era Deemed To Be University, Hindistan
Graphic Era Deemed To Be University, Hindistan
Ketan Kotecha
Symbiosis Institute Of Technology, Hindistan
Symbiosis Institute Of Technology, Hindistan
Ayan Sar
University Of Petroleum And Energy Studies, Hindistan
University Of Petroleum And Energy Studies, Hindistan
Prof. Dr. Turgut ÖZSEVEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1145/3660853.3660922 | ||
| Kongre Adı | Cognitive Models and Artificial Intelligence Conference | ||
| Kongre Tarihi | 25-05-2024 / 26-05-2024 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | İstanbul |
| Bildiri Linki | http://dx.doi.org/10.1145/3660853.3660922 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
|
||
| Özet |
| In the ever-evolving landscape of financial markets, the pursuit of accurate stock price predictions remains a formidable challenge. This study addresses the challenge of profitable stock market predictions by exploring modified HMM approaches involving fixed parameterisation and hyper-heuristic methods while also considering sequence lengths and adaptability to heuristic applications. This study extends its focus to the intricacies of determining optimal buy and sell times. Recognising the nonstationary nature of financial time series, the research explores threshold autoregressive models and mixture models for time series analysis. The results of this research indicate that the K-Fold method consistently exhibits strong performance in terms of fitting and robustness, with accuracy percentages exceeding 90% for certain stocks. The Sliding Window method proves effective for short-term forecasting but falls short in … |
| Anahtar Kelimeler |
| Closing Price | CT-HMM | Hidden Markov Model | Hybridization | K-fold Validation | Sliding Window | Stock Market |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |