[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123573-en":3,"doc-seo-123573-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123573,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance","Research focuses on predicting Nifty 50 index stock market trends by combining hybrid machine learning with a quantum-finance-inspired modeling perspective. The work evaluates four forecasting approaches—artificial neural networks, support vector machines, naive Bayes, and random forest—using eight technical indicators converted into trend signals through a deterministic trend layer. Principal component analysis is then applied to the signal representation to reduce dimensionality and improve predictive quality. Experiments using the first three principal components (about 80% explained variance) show strong accuracy, with SVC (RBF) and random forest achieving near-identical top performance and perfect AUC scores.","Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance  \nChinthakunta Manjunath1, Balamurugan Marimuthu1, Bikramaditya Ghosh2  \n1Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University),  \nBengaluru, India  \n2Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Bengaluru, India  \nArticle history:  \nReceived Jul 14, 2022 Revised Sep 20, 2022 Accepted Oct 1, 2022  \nKeywords:  \nNational stock exchange fifty Principle component analysis Stock market  \nTechnical indicators Time series forecast  \nCorresponding Author:  \nPredicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF) . In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifier (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968) . In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1.  \nThis is an open access article under the CC BY-SA license.  \nChinthakunta Manjunath  \nDepartment of Computer Science and Engineering, School of Engineering, CHRIST (Deemed to be University)  \nMysore Road, Kengeri Campus, Kumbalgodu, Bengaluru  \n[Email: manju.chintell@gmail.com](Email: manju.chintell@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nPredicting financial market trends has attracted a lot of researchers for several decades. It can be divided into the fundamental analysis method, which uses the company’s critical information like dividend value, P/E, P/B, market position, expenditures, yearly growth rates, and the technical analysis method, which essences on prior equity prices [1] . The traditional statistical approaches, including logistic regression, exponential average, autoregressive integrated moving average (ARIMA), and generalized autoregressive conditionally heteroscedastic (GARCH), were employed to forecast equity market price [2], [3] . Traditional time series models, for example, generally handle linear forecasting models, and variables must follow a statistically normal distribution. On the other hand, statistical approaches assume that a linear process forms  \nthe sequence data and performs poorly in non-linear stock price movement predictions. Hence, machine learning (ML) and deep learning (DL) approaches are gradually being explored in price changes in stock market predictions due to their success in non-linear financial data [4] . Technical analysis is one of the most extensively utilized feature extraction analyses for predicting the financial market, leading to improved projections [5] .  \nIn financial applications, ML techniques have been effectively used on financial data due to their capacity to fit and forecast perfo","cbCaimAw2qdeSSwn","https://ap.wps.com/l/cbCaimAw2qdeSSwn","pdf",665503,1,12,"English","en",105,"# Abstract\n# Introduction\n## Background: fundamental vs technical analysis\n## Limitations of traditional time-series models\n## Role of machine learning and hybrid approaches","[{\"question\":\"What is the main goal of the study on the Nifty 50 index?\",\"answer\":\"To improve accuracy of Nifty 50 index trend predictions using machine learning models and a hybrid pipeline.\"},{\"question\":\"How are technical indicators used in the proposed model?\",\"answer\":\"Eight technical indicators are transformed into trend signals via a deterministic trend layer before further processing.\"},{\"question\":\"What role does PCA play in the forecasting framework?\",\"answer\":\"PCA is used to derive essential components from multiple indicators, reduce data dimensionality, and enhance model performance.\"}]","Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance | 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is the main goal of the study on the Nifty 50 index?","Question",{"text":75,"@type":76},"To improve accuracy of Nifty 50 index trend predictions using machine learning models and a hybrid pipeline.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are technical indicators used in the proposed model?",{"text":80,"@type":76},"Eight technical indicators are transformed into trend signals via a deterministic trend layer before further processing.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does PCA play in the forecasting framework?",{"text":84,"@type":76},"PCA is used to derive essential components from multiple indicators, reduce data dimensionality, and enhance model 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