Research Article An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction Dushmanta Kumar Padhi , 1 Neelamadhab Padhy , 1 Akash Kumar Bhoi , 2,3,4 Jana Shafi , 5 and Seid Hassen Yesuf 6 1 School of Engineering and Technology, Department of Computer Science and Engineering, GIET University, Gunupur, India 2 KIET Group of Institutions, Delhi NCR, Ghaziabad 201206, India 3 Directorate of Research, Sikkim Manipal University, Gangtok 737102, Sikkim, India 4 AB-Tech eResearch (ABTeR), Sambalpur, Burla 768018, India 5 Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Wadi Ad-Dawasir 11991, Saudi Arabia 6 Department of Computer Science, College of Informatics, University of Gondar, Maraki 196, Gondar, Ethiopia Correspondence should be addressed to Seid Hassen Yesuf; seid.yesuf@uog.edu.et Received 12 April 2022; Revised 17 May 2022; Accepted 26 May 2022; Published 23 June 2022 Academic Editor: Yang Gu Copyright © 2022 Dushmanta Kumar Padhi et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. e study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed framework in the real-world datasets of 20 health sector indices for four different geo- graphical reasons for the performance evaluation. Lastly, we conduct a numerical comparison of our method’s outcomes to those generated via conventional solutions by previous studies. Our aftermath reveals that learning-based ensemble strategies with portfolio selection are effective in comparison. 1. Introduction Before the end of the twentieth century, low-frequency fi- nancial data were available for analysing and forecasting the stock market. Fewer professionals and academicians use these low-frequency data for their empirical studies, but as there are no sufficient related data available, the empirical research will not succeed [1]. Due to the rapid development of science and technology, the cost of data capture and storage has been reduced dramatically, which makes it easy to record each day’s trading data related to the financial market. As a result, significant financial analysis of data has become a prominent area of research in economics and a variety of other disciplines [2]. With the recent rapid economic expansion, the quantity of financial activities has expanded, and their fluctuating trend has also become more complex. Asset prices trend forecast is a classic and fascinating issue that has piqued the interest of numerous academics from several fields. Aca- demic and financial research subjects to understand stock market patterns and anticipate their growth and changes. Portfolio construction through competent stock selection has long been a critical endeavour for investors and fund managers. Portfolio enhancement and optimization have Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 7588303, 18 pages https://doi.org/10.1155/2022/7588303