Envision Future Prices of Select Power Sector Equities : An Application of an AI Model
Authors
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Sudarsana Murthy
Professor (Corresponding Author), Department of Management, School of Management and Commerce, Brainware University, Barasat, Kolkata - 700 125, West Bengal
https://orcid.org/0000-0002-3970-0878
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T. Deva Prasad
Research Scholar, Department of Management, School of Commerce and Management, Mohan Babu University, [Erstwhile Sree Vidyanikethan Institute of Management], Sree Sainath Nagar, Rangampet, Tirupati - 517 102, Andhra Pradesh
https://orcid.org/0009-0009-4850-1633
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J. Katyayani
Professor, Department of Business Management, Sri Padmavati Mahila Visvavidyalayam, Tirupati - 517 502, Andhra Pradesh
https://orcid.org/0000-0002-4236-4457
- B. Gangaiah Associate Professor, Department of Business Management, Yogi Vemana University, Kadapa - 516 005, Andhra Pradesh
DOI:
https://doi.org/10.17010/ijf/2025/v19i9/174429Keywords:
equity price prediction, artificial intelligence (AI), machine learning (ML), linear regression, KNN, LSTM.JEL Classification Codes : G1, G17
Publication Chronology: Paper Submission Date : September 25, 2024 ; Paper sent back for Revision : March 31, 2025 ; Paper Acceptance Date : May 15, 2025 ; Paper Published Online : September 15, 2025
Abstract
Purpose : This study aimed to predict impending power sector equity prices listed on the National Stock Exchange (NSE) by applying an AI Model. In a dynamic market, investors want quick and reliable information on trade trends and expected price movements. The demand for power in India has increased significantly in recent years. A research-based approach has been developed to assess and predict equity prices in order to address this paradigm, incorporating underlying market risks into the analytical framework.
Methodology : To predict future stock values for four power-sector stocks with significant price volatility, the study employed sophisticated machine learning (ML) methods, such as Linear Regression, k-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks. A five-year dataset, covering July 15, 2020, to July 14, 2024, was obtained for the empirical study from reliable and authoritative sources.
Original Value : The study evidently demonstrated the effectiveness of cutting-edge machine learning approaches in forecasting equity values, an undertaking that has historically presented significant analytical hurdles, with the aim of improving well-informed investing strategies and sensible risk management.
Findings : The consistently negative Sharpe ratios suggested that investors contemplating these stocks should take caution, since the projected returns fell short of the risk-free benchmark. Despite its ease of use and interpretability, the linear regression model found it difficult to account for the intricate, non-linear dynamics present in changes in stock prices. On the other hand, the LSTM model, a sophisticated version of recurrent neural networks, produced encouraging outcomes, giving investors more assurance to put their money into stocks.
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References
1) Adani Power Ltd. (2025). EquityPandit.com. https://equitypandit.com/historical-data/ADANIPOWER
2) Agarwal, S. (2022). Deep learning in financial analytics: Exchange rate modelling. Indian Journal of Finance, 16(9), 8–25. https://doi.org/10.17010/ijf/2022/v16i9/172157
3) Aggarwal, C. C. (2018). Neural networks and deep learning: A textbook (1st ed.). Springer. https://doi.org/10.1007/978-3-319-94463-0
4) Ayala, J., García-Torres, M., Noguera, J. L., Gómez-Vela, F., & Divina, F. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225, Article ID 107119. https://doi.org/10.1016/j.knosys.2021.107119
5) Bai, J., Philippon, T., & Savov, A. (2016). Have financial markets become more informative? Journal of Financial Economics, 122(3), 625–654. https://doi.org/10.1016/j.jfineco.2016.08.005
6) Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE, 12(7), Article ID e0180944. https://doi.org/10.1371/journal.pone.0180944
7) Barber, B. M., & Odean, T. (2013). Chapter 22 - The behavior of individual investors. Handbook of the Economics of Finance, 2(Part B), 1533–1570. https://doi.org/10.1016/B978-0-44-459406-8.00022-6
8) Bhardwaj, A., Narayan, Y., Vanaraj, Pawan, & Dutta, M. (2015). Sentiment analysis for Indian stock market prediction using Sensex and Nifty. Procedia Computer Science, 70, 85–91. https://doi.org/10.1016/j.procs.2015.10.043
9) Cakra, Y. E., & Trisedya, B. D. (2015). Stock price prediction using linear regression based on sentiment analysis. In 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 147–154). IEEE. https://doi.org/10.1109/ICACSIS.2015.7415179
10) Chauhan, P. (2023). Stock prediction and forecasting using LSTM (long-short-term-memory). Medium. https://medium.com/@prajjwalchauhan94017/stock-prediction-and-forecasting-using-lstm-long-short-term-memory-9ff56625de73
11) Cowles, A. (1933). Can stock market forecasters forecast? Econometrica, 1(3), 309–324. https://doi.org/10.2307/1907042
12) Dhingra, B., Batra, S., Aggarwal, V., Yadav, M., & Kumar, P. (2024). Stock market volatility: A systematic review. Journal of Modelling in Management, 19(3), 925–952. https://doi.org/10.1108/JM2-04-2023-0080
13) Dikshita, & Singh, H. (2019). Estimating and forecasting volatility using an ARIMA model: A study on NSE, India. Indian Journal of Finance, 13(5), 37–51. https://doi.org/10.17010/ijf/2019/v13i5/144184
14) Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105. http://www.jstor.org/stable/2350752
15) Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, Article ID 100190. https://doi.org/10.1016/j.cosrev.2019.08.001
16) Gao, Q. (2016). Stock market forecasting using a recurrent neural network (Doctoral dissertation, University of Missouri). https://hdl.handle.net/10355/56058
17) Gautam, B., Kandel, S., Shrestha, M., & Thakur, S. (2024). Comparative analysis of machine learning models for stock price prediction: Leveraging LSTM for real-time forecasting. Journal of Computer and Communications, 12(8), 52–80. https://doi.org/10.4236/jcc.2024.128004
18) Hasin, K. R., Hasan, S., & Rahman, R. M. (2022). Prediction of stock market price movements based on sentiment analysis on various news headlines. International Journal of Knowledge Engineering and Soft Data Paradigms, 7(2), 57–76. https://doi.org/10.1504/IJKESDP.2022.127624
19) Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia Computer Science, 132, 1351–1362. https://doi.org/10.1016/j.procs.2018.05.050
20) Ionescu, S.-A., & Diaconita, V. (2023). Transforming financial decision-making: The interplay of AI, cloud computing, and advanced data management technologies. International Journal of Computers Communications & Control, 18(6), 1–19. https://doi.org/10.15837/ijccc.2023.6.5735
21) Kapoor, R., & Ghosal, I. (2022). Will artificial intelligence compliment or supplement the human workforce in organizations? A shift to a collaborative human–machine environment. International Journal on Recent Trends in Business and Tourism, 6(4), 19–28. https://doi.org/10.31674/ijrtbt.2022.v06i04.002
22) Kaur, R., & Sharma, A. (2023). Prediction of stock prices of blue-chip companies using machine learning algorithms. International Journal of Business Intelligence and Data Mining, 23(4), 375–395. https://doi.org/10.1504/IJBIDM.2023.134316
23) Kavitha, K., Sivapuram, S., Tirumalasetty, S. V., Kummari, V., Varada, S. N., & Ravuri, V. (2025). Stock prices using Arima and Sarima with macroeconomic indicators predicting. In 2025 6th International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 1314–1319). IEEE. https://doi.org/10.1109/ICIRCA65293.2025.11089539
24) Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 13(7), 3433–3456. https://doi.org/10.1007/s12652-020-01839-w
25) Lee, E. (2024, April 8). Advanced stock pattern prediction using LSTM with the attention mechanism in tensorflow: A step by step guide with Apple Inc. (AAPL) data. Medium. https://drlee.io/advanced-stock-pattern-prediction-using-lstm-with-the-attention-mechanism-in-tensorflow-a-step-by-143a2e8b0e95
26) Lee, J., Kim, S. B., Kim, Y., & Kim, S. (2023). Predictive analytics for efficient decision making in personnel selection. International Journal of Management and Decision Making, 22(1), 106–122. https://doi.org/10.1504/IJMDM.2023.127688
27) Madhuri, C. R., Chinta, M., & Kumar, V. (2020). Stock market prediction for time-series forecasting using Prophet upon ARIMA. In 7th International Conference on Smart Structures and Systems (ICSSS) (pp. 1–5). IEEE. https://doi.org/10.1109/ICSSS49621.2020.9202042
28) Mahgoub, A. A. (2021). Stock prediction using regression algorithm in Python. Medium. https://medium.com/codex/stock-predication-using-regression-algorithm-in-python-fb8b426453b9
29) Mali, P., Karchalkar, H., Jain, A., Singh, A., & Kumar, V. (2017). Open price prediction of stock market using regression analysis. International Journal of Advanced Research in Computer and Communication Engineering, 6(5), 418–421. https://doi.org/10.17148/IJARCCE.2017.6578
30) Mehta, Y., Malhar, A., & Shankarmani, R. (2021). Stock price prediction using machine learning and sentiment analysis. In 2nd International Conference for Emerging Technology (pp. 1–4). IEEE. https://doi.org/10.1109/INCET51464.2021.9456376
31) Mehtab, S., Sen, J., & Dutta, A. (2021). Stock price prediction using machine learning and LSTM-Based deep learning models. In S. M. Thampi, S. Piramuthu, K.-C. Li, S. Berretti, M. Wozniak & D. Singh (eds.), Machine Learning and Metaheuristics Algorithms, and Applications: Second Symposium (pp. 88–106). https://doi.org/10.1007/978-981-16-0419-5_8
32) Milosevic, N. (2018). Equity forecast: Predicting long-term stock price movement using machine learning. arXiv. https://doi.org/10.48550/arXiv.1603.00751
33) Minami, S. (2018). Predicting equity price with corporate action events using LSTM-RNN. Journal of Mathematical Finance, 8(1), 58–63. https://doi.org/10.4236/jmf.2018.81005
34) NHPC Ltd. (2025). EquityPandit.com. https://www.equitypandit.com/historical-data/NHPC
35) NTPC Ltd. (2025). EquityPandit.com. https://www.equitypandit.com/historical-data/NTPC
36) Olubusola, O., Mhlongo, N. Z., Daraojimba, D. O., Ajayi-Nifise, A. O., & Falaiye, T. (2024). Machine learning in financial forecasting: A U.S. review: Exploring the advancements, challenges, and implications of AI-driven predictions in financial markets. World Journal of Advanced Research and Reviews, 21(2), 1969–1984. https://doi.org/10.30574/wjarr.2024.21.2.0444
37) Oyewole, A. T., Adeoye, O. B., Addy, W. A., Okoye, C. C., Ofodile, O. C., & Ugochukwu, C. E. (2024). Predicting stock market movements using neural networks: A review and application study. Computer Science & IT Research Journal, 5(3), 651–670. https://doi.org/10.51594/csitrj.v5i3.912
38) Pandya, J. B. (2024). Deep learning approach for stock market trend prediction and pattern finding (Doctoral dissertation, Gujarat Technological University). https://gtusitecirculars.s3.amazonaws.com/uploads/Final%20Thesis%20(16)_581862.pdf
39) Pathak, R., Ranajee, & Kumar, S. (2014). Price discovery in the equity derivatives market: A literature survey. Indian Journal of Finance, 8(6), 47–57. https://doi.org/10.17010/ijf/2014/v8i6/71913
40) Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend prediction, Expert Systems with Applications, 135, 60–70. https://doi.org/10.1016/j.eswa.2019.06.014
41) Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M.-L., Chen, S.-C., & Iyengar, S. S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5), 1–36. https://doi.org/10.1145/3234150
42) Prasad, B., & Ghosal, I. (2021). Forecasting buying intention through artificial neural network: An algorithmic solution on direct-to-consumer brands. FIIB Business Review, 11(4), 405–421. https://doi.org/10.1177/23197145211046126
43) Pujitha, J., & Mahesh, G. (2021). Stock market price predictions using Python. In 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS) (pp. 1–5). IEEE. https://doi.org/10.1109/FABS52071.2021.9702683
44) Rajkar, A., Kumaria, A., Raut, A., & Kulkarni, N. (2021). Stock market price prediction and analysis. International Journal of Engineering Research & Technology, 10(6), 115–119. https://doi.org/10.17577/IJERTV10IS060047
45) Ranjan, S., Singh, I., Dua, S., & Sood, S. (2018). Sentiment analysis of stock blog network communities for prediction of stock price trends. Indian Journal of Finance, 12(12), 7–21. https://doi.org/10.17010/ijf/2018/v12i12/139888
46) Raza, M. W., & Ye, J. (2025). Beyond sharpe ratio: Comparison of risk-adjusted performance of Shariah-compliant and conventional indices. International Journal of Islamic and Middle Eastern Finance and Management, 18(1), 184–200. https://doi.org/10.1108/IMEFM-01-2024-0013
47) Reddy, V. K. (2018). Stock market prediction using machine learning. International Research Journal of Engineering and Technology, 5(10), 1032–1035. https://doi.org/10.13140/RG.2.2.12300.77448
48) Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H.-C. (2021). Stock market prediction using machine learning techniques: A decade survey on methodologies, recent developments, and future directions. Electronics, 10(21), 2171. https://doi.org/10.3390/electronics10212717
49) Sarangi, P., Sinha, D., Sinha, S., & Dubey, M. (2019). Financial modeling using ANN technologies: Result analysis with different network architectures and parameters. Indian Journal of Research in Capital Markets, 6(1), 21–33. https://doi.org/10.17010/ijrcm/2019/v6/i1/144039
50) Sharma, A., Bhuriya, D., & Singh, U. (2017). Survey of stock market prediction using machine learning approach. International Conference of Electronics, Communication and Aerospace Technology (ICECA) (pp. 506–509). IEEE. https://doi.org/10.1109/ICECA.2017.8212715
51) Sheth, D., & Shah, M. (2023). Predicting stock market using machine learning: Best and accurate way to know future stock prices. International Journal of System Assurance Engineering and Management, 14(1), 1–18. https://doi.org/10.1007/s13198-022-01811-1
52) Shetty, J., Cottur, K., Shobha, G., & Prajwal, Y. R. (2023). A weighted ensemble of VAR and LSTM for multivariate forecasting of cloud resource usage. Journal of Advances in Information Technology, 14(2), 264–270. https://doi.org/10.12720/jait.14.2.264-270
53) Singh, V., Chen, S.-S., Singhania, M., Nanavati, B., Kar, A. K., & Gupta, A. (2022). How are reinforcement learning and deep learning algorithms used for big data-based decision making in financial industries–A review and research agenda. International Journal of Information Management Data Insights, 2(2), Article ID 100094. https://doi.org/10.1016/j.jjimei.2022.100094
54) Stock price prediction using machine learning in Python. (2025). GeeksforGeeks. https://www.geeksforgeeks.org/machine-learning/stock-price-prediction-using-machine-learning-in-python/
55) Sun, T., & Vasarhelyi, M. A. (2018). Predicting credit card delinquencies: An application of deep neural networks. Intelligent Systems in Accounting, Finance and Management, 25(4), 174–189. https://doi.org/10.1002/isaf.1437
56) Tata Power Company Ltd. (2025). EquityPandit.com. https://www.equitypandit.com/historical-data/TATAPOWER
57) Thakar, C., & Singh, V. (2023). K-nearest neighbors algorithm: Steps to implement in Python. Quantinsti.com. https://blog.quantinsti.com/machine-learning-k-nearest-neighbors-knn-algorithm-python/
58) Vadlamudi, S. (2017). Stock market prediction using machine learning: A systematic literature review. American Journal of Trade and Policy, 4(3), 123–128. https://doi.org/10.18034/ajtp.v4i3.521
59) Vardhan, B. V., Swain, A., Khedkar, M., Srivastava, I., & Bokde, N. D. (2024). An overview of the Indian power sector and its energy management. Renewable Energy Focus, 50, Article ID 100597. https://doi.org/10.1016/j.ref.2024.100597
60) Varshney, R. P., & Sharma, D. K. (2024). Optimizing time-series forecasting using a stacked deep learning framework with enhanced adaptive moment estimation and error correction. Expert Systems with Applications, 249(Part A), Article ID 123487. https://doi.org/10.1016/j.eswa.2024.123487
61) Vazirani, S., Sharma A., & Sharma, P. (2020). Analysis of various machine learning algorithms and hybrid models for stock market prediction using Python. In 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) (pp. 203–207). IEEE. https://doi.org/10.1109/ICSTCEE49637.2020.9276859
62) Vodwal, S., & Negi, V. (2023). Applications of machine learning and determinants of dividend decision: Evidence from Indian firms. Indian Journal of Finance, 17(5), 8–24. https://doi.org/10.17010/ijf/2023/v17i5/171154
63) Wadhawan, A., & Kumar, P. (2020). Deep learning-based sign language recognition system for static signs. Neural Computing & Application, 32, 7957–7968. https://doi.org/10.1007/s00521-019-04691-y
64) Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153–163. https://doi.org/10.1016/j.eswa.2017.02.041
65) Winkler, S. M., Castaño, B., Luengo, S., Schaller, S., Kronberger, G., & Affenzeller, M. (2016). Heterogeneous model ensembles for short-term prediction of stock market trends. International Journal of Simulation and Process Modelling, 11(6), 504–513. https://doi.org/10.1504/IJSPM.2016.10001558
66) Yang, J., Wang, Y., & Li, X. (2022). Prediction of stock price direction using the LASSO-LSTM model combines technical indicators and financial sentiment analysis, PeerJ Computer Science, 8, Article ID e1148. https://doi.org/10.7717/peerj-cs.1148
67) Yue, X., Zhou, Y., & Yuan, C. (2021). Stock closing price prediction based on a combined model of PCA-IMKNN. International Journal of Modelling, Identification and Control, 39(3), 221–228. https://doi.org/10.1504/IJMIC.2021.123491
