Forecasting the Performance of AI-Driven Stocks : A Time Series Analysis

Amirtha L. L.; Yazhini S.; Ramalakshmi R.; A. Duraisamy; K. Selvasundaram; R. Sridharan

doi:10.17010/ijf/2026/v20i5/175366

Forecasting the Performance of AI-Driven Stocks : A Time Series Analysis

Authors

Amirtha L. L. Research Scholar (Corresponding Author), Department of Economics, Faculty of Science and Humanities, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur - 603 203, Chengalpattu District, Tamil Nadu https://orcid.org/0009-0004-7455-8100
Yazhini S. Research Scholar, Department of Corporate Secretaryship, Accounting & Finance, Faculty of Science and Humanities, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur - 603 203, Chengalpattu District, Tamil Nadu https://orcid.org/0009-0007-5347-3433
Ramalakshmi R. Research Scholar, Department of Corporate Secretaryship and Accounting & Finance, Faculty of Science and Humanities, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur - 603 203, Chengalpattu District, Tamil Nadu https://orcid.org/0000-0003-0941-7462
A. Duraisamy Dean of Faculty of Science and Humanities, Department of Economics, Faculty of Science and Humanities, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur - 603 203, Chengalpattu District, Tamil Nadu
K. Selvasundaram Associate Co-Ordinator – Academics, Department of Corporate Secretaryship and Accounting and Finance, Faculty of Science and Humanities, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur - 603 203, Chengalpattu District, Tamil Nadu
R. Sridharan Head of the Department, Department of Corporate Secretaryship and Accounting and Finance, Faculty of Science and Humanities, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur - 603 203, Chengalpattu District, Tamil Nadu https://orcid.org/0000-0001-6397-0408

DOI:

https://doi.org/10.17010/ijf/2026/v20i5/175366

Keywords:

financial forecasting, stock market, time series models, AI-driven stocks, ARIMA, ARFIMA, EGARCH.
JEL Classification Codes :C32, G1, G170
Publication Chronology: Paper Submission Date : August 20, 2025 ; Paper sent back for Revision : April 13, 2026 ; Paper Acceptance Date : April 25, 2026 ; Paper Published Online : May 15, 2026

Abstract

Purpose : This study investigated the impact of artificial intelligence (AI) adoption on the price dynamics of AI-driven stocks. Within the Nifty India Digital Index, this study focused on stock prices of Tata Elxsi, Infosys, and Persistent Systems owing to their strategic commitment to AI through considerable investments in proprietary platforms, intellectual platforms, and other AI-driven revenue streams.

Methodology : Employing daily closing price data from April 1, 2022, to March 31, 2025, this study applied a suitable econometric time-series model to capture the heterogeneous statistical properties of stock prices. The best model was selected based on rigorous diagnostic testing and forecast performance evaluation.

Findings : The study found that AI-driven stocks exhibited different statistical properties, requiring customized forecasting models. The EGARCH (1,1) model best predicted Tata Elxsi stock prices, while ARFIMA models provided the best forecasts for Infosys and Persistent Systems stock prices.

Implications : These findings offered different perspectives to investors in framing stock-specific forecasting strategies while analyzing AI-driven stocks.

Originality : This study is the first of its kind, as it emphasizes the role of tailored econometric modeling in prediction accuracy while amalgamating AI-driven market context with time series forecasting in the Indian digital economy.

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Published

2026-05-15

How to Cite

L. L., A., S., Y., R., R., Duraisamy, A., Selvasundaram, K., & Sridharan, R. (2026). Forecasting the Performance of AI-Driven Stocks : A Time Series Analysis. Indian Journal of Finance, 20(5), 8–36. https://doi.org/10.17010/ijf/2026/v20i5/175366

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Issue

Volume 20, Issue 5, May 2026

Section

Articles

References

1) Ahamed, G. T., Tallapalli, N. K., Akula, P., & Kumar, K. (2026). Global commodities and the Indian stock market: A post-pandemic volatility spillover analysis. Indian Journal of Finance, 20(2), 8–25. https://doi.org/10.17010/ijf/2026/v20i2/175359

2) Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705

3) Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In E. Parzen, K. Tanabe, & G. Kitagawa (eds.), Selected papers of Hirotugu Akaike (pp. 199–213). Springer. https://doi.org/10.1007/978-1-4612-1694-0_15

4) Ali Badhusha, M. A., & Kaliyaperumal, S. K. (2017). Forecasting national stock price using ARIMA model. Global and Stochastic Analysis, 4(1), 77–81. https://www.researchgate.net/publication/317223337_Forecasting_national_stock_price_using_arima_model

5) Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation (pp. 106–112). IEEE. https://doi.org/10.1109/UKSim.2014.67

6) Ausloos, M., Zhang, Y., & Dhesi, G. (2020). Stock index futures trading impact on spot price volatility: The CSI 300 studied with a TGARCH model. Expert Systems with Applications, 160, Article ID 113688. https://doi.org/10.1016/j.eswa.2020.113688

7) Awasthi, A., & Shaukat, M. (2025). Dynamics of long memory in the cryptocurrency market. Indian Journal of Finance, 19(8), 8–28. https://doi.org/10.17010/ijf/2025/v19i8/175231

8) Baillie, R. T., Chung, C.-F., & Tieslau, M. A. (1996). Analysing inflation by the fractionally integrated ARFIMA–GARCH model. Journal of Applied Econometrics, 11(1), 23–40. https://doi.org/10.1002/(SICI)1099-1255(199601)11:1<23::AID-JAE374>3.0.CO;2-M

9) Bayar, Y., Sezgin, H. F., Öztürk, Ö. F., & Şaşmaz, M. Ü. (2020). Financial literacy and financial risk tolerance of individual investors: Multinomial logistic regression approach. SAGE Open, 10(3). https://doi.org/10.1177/2158244020945717

10) Bhandari, H. N., Rimal, B., Pokhrel, N. R., Rimal, R., Dahal, K. R., & Khatri, R. K. (2022). Predicting stock market index using LSTM. Machine Learning with Applications, 9, Article ID 100320. https://doi.org/10.1016/j.mlwa.2022.100320

11) Bharadiya, J. P., Thomas, R. K., & Ahmed, F. (2023). Rise of artificial intelligence in business and industry. Journal of Engineering Research and Reports, 25(3), 85–103. https://doi.org/10.9734/JERR/2023/v25i3893

12) Black, F. (1976). Studies of stock price volatility changes. In Proceedings of the 1976 Meeting of the Business and Economic Statistics Section (pp. 177–181). American Statistical Association. https://cir.nii.ac.jp/crid/1370846643171164302

13) Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1

14) Box, G. E., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden-Day. https://openlibrary.org/works/OL2181533W/Time_Series_Analysis

15) Chopra, M., & Saldi, R. (2022). Investment in Bitcoin: A delusion or diligence? Indian Journal of Finance, 16(7), 8–22. https://doi.org/10.17010/ijf/2022/v16i7/170632

16) CompaniesMarketCap. (n.d.). Largest Indian companies by market capitalization. https://companiesmarketcap.com/inr/india/largest-companies-in-india-by-market-cap/

17) Dassanayake, W., Ardekani, I., Gamage, N., Jayawardena, C., & Sharifzadeh, H. (2021). Effectiveness of stock index forecasting using ARIMA model: Evidence from New Zealand. In 2021 3rd International Conference on Advancements in Computing (ICAC) (pp. 13–18). IEEE. https://doi.org/10.1109/ICAC54203.2021.9671132

18) Dhuri, V., & Patkar, S. (2024). Herding behaviour in the Indian stock market: An empirical study. Asian Economic and Financial Review, 14(4), 264–275. https://doi.org/10.55493/5002.v14i4.5018

19) Dikshita, & Singh, H. (2019). Estimating and forecasting volatility using ARIMA model: A study on NSE, India. Indian Journal of Finance, 13(5), 37–51. https://doi.org/10.17010/ijf/2019/v13i5/144184

20) Ding, Z., Granger, C. W., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83–106. https://doi.org/10.1016/0927-5398(93)90006-D

21) Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773

22) Eyinade, W., Ezeilo, O. J., & Ogundeji, I. A. (2025). Artificial intelligence in financial forecasting: Accuracy and limitations. World Journal of Finance and Investment Research, 9(4), 61–79. https://www.researchgate.net/profile/Onyinye-Ezeilo/publication/393453389

23) Fama, E. F. (1965). Random walks in stock market prices. Financial Analysts Journal, 21(5), 55–59. https://www.jstor.org/stable/4469865

24) Francq, C., & Zakoïan, J.-M. (2010). GARCH models: Structure, statistical inference and financial applications. John Wiley & Sons Ltd. https://doi.org/10.1002/9780470670057

25) George, A. S. (2024). AI-enabled intelligent manufacturing: A path to increased productivity, quality and insights. Partners Universal Innovative Research Publication, 2(4), 50–63. https://doi.org/10.5281/zenodo.13338085

26) Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x

27) Gonzalez, C., & Wong, H.-Y. (2012). Understanding stocks and flows through analogy. System Dynamics Review, 28(1), 3–27. https://doi.org/10.1002/sdr.470

28) Graham, B. (1974). The future of common stocks. Financial Analysts Journal, 30(5), 20–30. https://doi.org/10.2469/faj.v30.n5.20

29) Granger, C. W., & Joyeux, R. (1980). An introduction to long memory time-series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15–29. https://doi.org/10.1111/j.1467-9892.1980.tb00297.x

30) Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Systems with Applications, 227, Article ID 120346. https://doi.org/10.1016/j.eswa.2023.120346

31) Gupta, P., & Kohli, B. (2021). Herding behavior in the Indian stock market: An empirical study. Indian Journal of Finance, 15(5–7), 86–99. https://doi.org/10.17010/ijf/2021/v15i5-7/164495

32) Hasan, T., Kadapakkam, P.-R., & Ma, Y. (2004). Tests of random walk for Latin American stock markets: Additional evidence. Latin American Business Review, 4(2), 37–53. https://doi.org/10.1300/J140v04n02_03

33) He, Y., & Rachev, S. (2025). Long-range dependence in financial markets: Empirical evidence and generative modeling challenges [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2509.19663

34) Hosking, J. R. (1981). Fractional differencing. Biometrika, 68(1), 165–176. https://doi.org/10.1093/biomet/68.1.165

35) Hurst, H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770–799. https://doi.org/10.1061/TACEAT.0006518

36) Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/jss.v027.i03

37) Jacobsen, B., & Dannenburg, D. (2003). Volatility clustering in monthly stock returns. Journal of Empirical Finance, 10(4), 479–503. https://doi.org/10.1016/S0927-5398(02)00071-3

38) Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184, Article ID 115537. https://doi.org/10.1016/j.eswa.2021.115537

39) Johnson, O. (2025). A qualitative study of IoT-enabled predictive maintenance and its influence on supply-chain sustainability. Preprints, Article ID 2025010882. https://doi.org/10.20944/preprints202501.0882.v1

40) Khanna, S., & Kumar, A. (2020). GARCH and TGARCH approach to information linkages. Indian Journal of Finance, 14(8–9), 35–51. https://doi.org/10.17010/ijf/2020/v14i8-9/154947

41) Kumar, A., & Khanna, S. (2018). GARCH-BEKK approach to volatility behavior and spillover: Evidence from India, China, Hong Kong, and Japan. Indian Journal of Finance, 12(4), 7–19. https://doi.org/10.17010/ijf/2018/v12i4/122791

42) Lakshminarayana, S., Kashyap, A., & Ahmed, N. (2026). Herd behaviour, volatility clustering and the Indian stock market: An empirical investigation. Emerging Markets Review, 73, Article ID 101467. https://doi.org/10.1016/j.ememar.2026.101467

43) Lee, U. (1992). Do stock prices follow random walk? Some international evidence. International Review of Economics & Finance, 1(4), 315–327. https://doi.org/10.1016/1059-0560(92)90020-D

44) Lin, C. Y., & Marques, J. A. (2024). Stock market prediction using artificial intelligence: A systematic review of systematic reviews. Social Sciences & Humanities Open, 9, Article ID 100864. https://doi.org/10.1016/j.ssaho.2024.100864

45) Liu, J., Cheng, Y., Li, X., & Sriboonchitta, S. (2022). The role of risk forecast and risk tolerance in portfolio management: A case study of the Chinese financial sector. Axioms, 11(3), Article no. 134. https://doi.org/10.3390/axioms11030134

46) Liu, K., Chen, Y., & Zhang, X. (2017). An evaluation of ARFIMA (autoregressive fractional integral moving average) programs. Axioms, 6(2), Article no. 16. https://doi.org/10.3390/axioms6020016

47) Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303. https://doi.org/10.1093/biomet/65.2.297

48) Lütkepohl, H. (1991). Introduction to multiple time series analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-662-02691-5

49) Lütkepohl, H., & Krätzig, M. (2004). Applied time series econometrics. Cambridge University Press. https://econpapers.repec.org/bookchap/cupcbooks/9780521839198.htm

50) Mandelbrot, B. B. (1963). The variation of certain speculative prices. The Journal of Business, 36(4), 394–419. https://doi.org/10.1086/294632

51) Meher, B. K., Hawaldar, I. T., Spulbar, C., & Birau, R. (2021). Forecasting stock market prices using mixed ARIMA model: A case study of Indian pharmaceutical companies. Investment Management and Financial Innovations, 18(1), 42–54. https://doi.org/10.21511/imfi.18(1).2021.04

52) MohammadzadehMaleki, D. N., Abbaskhani, S., Farhadi, A., & Zamanifar, A. (2026). Artificial intelligence methods for financial market prediction: A systematic review. Computers and Electrical Engineering, 134, Article ID 111110. https://doi.org/10.1016/j.compeleceng.2026.111110

53) Mohanasundaram, T., Karthikeyan, P., & Shanthi, D. (2018). Forecasting foreign institutional investors' (FII) investment flows in India: An autoregressive model. Arthshastra Indian Journal of Economics & Research, 7(5), 47–59. https://doi.org/10.17010/aijer/2018/v7i5/139927

54) Murthy, D. S., Prasad, T. D., Katyayani, J., & Gangaiah, B. (2025). Envision future prices of select power sector equities: An application of an AI model. Indian Journal of Finance, 19(9), 8–36. https://doi.org/10.17010/ijf/2025/v19i9/174429

55) Naqvi, S. M., Khan, K. I., Ghafoor, M. M., & Rizvi, S. K. (2019). Evidence of volatility clustering and asymmetric behavior of returns in Asian emerging stock markets. Pakistan Economic and Social Review, 57(2), 163–197. https://pu.edu.pk/images/journal/pesr/PDF-FILES/3-v57_2_19.pdf

56) Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. https://doi.org/10.2307/2938260

57) Neusser, K. (2016). Time series econometrics (1st ed.). Springer. https://doi.org/10.1007/978-3-319-32862-1

58) NSE Indices Limited. (n.d.). Nifty indices. https://www.niftyindices.com/

59) Odonkor, A. A., Ababio, E. N., Amoah-Darkwah, E., & Andoh, R. (2022). Stock returns and long-range dependence. Global Business Review, 23(1), 37–47. https://doi.org/10.1177/0972150919866966

60) Ogutu, C., Canhanga, B., & Biganda, P. (2018). Modeling exchange rate volatility using APARCH model. Journal of Institute of Engineering, 14(1), 96–106. https://doi.org/10.3126/jie.v14i1.20072

61) Olorunnimbe, K., & Viktor, H. (2023). Deep learning in the stock market—A systematic survey of practice, backtesting, and applications. Artificial Intelligence Review, 56, 2057–2109. https://doi.org/10.1007/s10462-022-10226-0

62) Pahari, S., Polisetty, A., Sharma, S., Jha, R., & Chakraborty, D. (2023). Adoption of AI in the banking industry: A case study on Indian banks. Indian Journal of Marketing, 53(3), 26–41. https://doi.org/10.17010/ijom/2023/v53/i3/172654

63) Parthasarathy, S. (2013). Long range dependence and market efficiency: Evidence from the Indian stock market. Indian Journal of Finance, 7(1), 17–25. https://www.indianjournaloffinance.co.in/index.php/IJF/article/view/72148

64) Pernagallo, G. (2025). Random walks, hurst exponent, and market efficiency. Quality & Quantity, 59(Suppl 2), 1097–1119. https://doi.org/10.1007/s11135-025-02052-7

65) Persistent Systems. (2025, July 10). Re(AI)magining the world: AI & digital engineering with Persistent. https://www.persistent.com/

66) Quinn, B. G. (1989). Estimating the number of terms in a sinusoidal regression. Journal of Time Series Analysis, 10(1), 71–75. https://doi.org/10.1111/j.1467-9892.1989.tb00016.x

67) Rai, S. K., & Sharma, A. K. (2022). Do lagged terms affect exchange rate volatility in India? An analysis using ARIMA model. Indian Journal of Finance, 16(10), 8–23. https://doi.org/10.17010/ijf/2022/v16i10/172385

68) Reddy, C. V. (2019). Predicting the stock market index using stochastic time series ARIMA modelling: The sample of BSE and NSE. Indian Journal of Finance, 13(8), 7–25. https://doi.org/10.17010/ijf/2019/v13i8/146301

69) Saâdaoui, F., & Rabbouch, H. (2024). Financial forecasting improvement with LSTM–ARFIMA hybrid models and non-Gaussian distributions. Technological Forecasting and Social Change, 206, Article ID 123539. https://doi.org/10.1016/j.techfore.2024.123539

70) Saberironaghi, M., Ren, J., & Saberironaghi, A. (2025). Stock market prediction using machine learning and deep learning techniques: A review. AppliedMath, 5(3), Article no. 76. https://doi.org/10.3390/appliedmath5030076

71) Sahoo, S., & Kumar, S. (2023). Volatility spillover among the sectoral indices of the Indian capital market: Evidence from the COVID period. Indian Journal of Finance, 17(9), 41–57. https://doi.org/10.17010/ijf/2023/v17i9/173183

72) Sahu, S., Ramírez, A. F., & Kim, J.-M. (2024). Exploring calendar anomalies and volatility dynamics in cryptocurrencies: A comparative analysis of day of the week effects before and during the COVID-19 pandemic. Journal of Risk and Financial Management, 17(8), Article no. 351. https://doi.org/10.3390/jrfm17080351

73) Schaefer, C., Lemmer, K., Kret, S., Ylinen, M., Mikalef, P., & Niehaves, B. (2021). Truth or dare? - How can we influence the adoption of artificial intelligence in municipalities? In Proceedings of the 54th Hawaii International Conference on System Sciences (pp. 2347–2356). https://doi.org/10.24251/HICSS.2021.286

74) Schaffer, A. L., Dobbins, T. A., & Pearson, S.-A. (2021). Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: A guide for evaluating large-scale health interventions. BMC Medical Research Methodology, 21, Article no. 58. https://doi.org/10.1186/s12874-021-01235-8

75) Schmidt, R., Zimmermann, A., Möhring, M., & Keller, B. (2020). Value creation in connectionist artificial intelligence: A research agenda. In Proceedings: Advancing Information Systems Research (pp. 1–10). https://aisel.aisnet.org/amcis2020/ai_semantic_for_intelligent_info_systems/ai_semantic_for_intelligent_info_systems/14/

76) Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://www.jstor.org/stable/2958889

77) Setoudehtazangi, F., Manouchehri, T., Nematollahi, A. R., & Caporin, M. (2024). Time series clustering based on latent volatility mixture modeling with applications in finance. Mathematics and Computers in Simulation, 223, 543–564. https://doi.org/10.1016/j.matcom.2024.04.031

78) Singh, H., Aggarwal, R., Garg, P., & Aggarwal, D. (2025). AI and ESG performance: An empirical study of the high-tech sector. Prabandhan: Indian Journal of Management, 18(6), 8–25. https://doi.org/10.17010/pijom/2025/v18i6/174487

79) Singla, A., Sukharevsky, A., Yee, L., Chui, M., & Hall, B. (2025, March 12). The state of AI: How organizations are rewiring to capture value. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

80) Soares, M. (2025). Symbiotic relationships in environments of change: The potential of biological interactions in artificial intelligence [Doctoral dissertation, School of Design]. Royal College of Art. https://researchonline.rca.ac.uk/6438/

81) Tata Elxsi. (n.d.). Tata Elxsi: Integrating digital technology and design. https://www.tataelxsi.com/

82) Teare, G. (2024, January 4). Global startup funding in 2023 clocks in at lowest level in 5 years. Crunchbase News. https://news.crunchbase.com/venture/global-funding-data-analysis-ai-eoy-2023/

83) Vasudevan, R. D., & Vetrivel, S. C. (2016). Forecasting stock market volatility using GARCH models: Evidence from the Indian stock market. Asian Journal of Research in Social Science and Humanities, 6(8), 1565–1574. https://doi.org/10.5958/2249-7315.2016.00694.8

84) Vuković, D. B., Dekpo-Adza, S., & Matović, S. (2025). AI integration in financial services: A systematic review of trends and regulatory challenges. Humanities and Social Sciences Communications, 12, Article no. 562. https://doi.org/10.1057/s41599-025-04850-8

85) Wang, Y., Xiang, Y., Lei, X., & Zhou, Y. (2022). Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market. Economic Research-Ekonomska Istraživanja, 35(1), 2530–2554. https://doi.org/10.1080/1331677X.2021.1967771

86) Yadava, A. (2025). The use of artificial intelligence in financial forecasting: A business analyst's perspective. IRE Journals, 8(9), 1112–1130. https://www.irejournals.com/formatedpaper/1707626.pdf

87) Yang, Y.-L., & Chang, C.-L. (2008). A double-threshold GARCH model of stock market and currency shocks on stock returns. Mathematics and Computers in Simulation, 79(3), 458–474. https://doi.org/10.1016/j.matcom.2008.01.048

88) Yongchareon, S. (2025). AI-driven intelligent financial forecasting: A comparative study of advanced deep learning models for long-term stock market prediction. Machine Learning & Knowledge Extraction, 7(3), Article no. 61. https://doi.org/10.3390/make7030061

89) Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. https://doi.org/10.1016/0165-1889(94)90039-6

90) Zhang, J., Liu, H., Bai, W., & Li, X. (2024). A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting. The North American Journal of Economics and Finance, 69 (Part B), Article ID 102022. https://doi.org/10.1016/j.najef.2023.102022

91) Zhang, T., & Zou, S. (2022). Carbon market evaluation based on random walk hypothesis in China. Mathematical Problems in Engineering. https://doi.org/10.1155/2022/5726108