Authors : Jarman Nandi, Ankita Nag

Volume/Issue : Volume 11 - 2026, Issue 10 - October

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Google Scholar : https://tinyurl.com/4d839vj5

DOI : https://doi.org/10.38124/ijisrt/26Oct690

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This study developed a segment-sensitive accounting decision-support framework to examine whether retail vehicle-demand shocks anticipated next-quarter working-capital stress in the Indian automobile industry. The empirical design covered 2020Q1-2026Q2 and focused on Passenger Vehicles, Commercial Vehicles, Two-Wheelers and ThreeWheelers. The research question asked whether segment-level demand information improved early identification of inventory, cash-conversion and operating-cash-flow pressure beyond conventional accounting ratios.

Keywords : Working-Capital Stress; Segment Demand Shocks; Automobile Industry; Vehicle Registrations; Predictive Accounting Analytics; Explainable Machine Learning; Accounting Decision Support System; GDELT Sentiment; Market Liquidity.

References :

1. Aktas, N., Croci, E., & Petmezas, D. (2015). Is working capital management value-enhancing? Evidence from firm performance and investments. Journal of Corporate Finance, 30, 98-113. https://doi.org/10.1016/j.jcorpfin.2014.12.008
2. Boisjoly, R. P., Conine, T. E., Jr., & McDonald, M. B., IV. (2020). Working capital management: Financial and valuation impacts. Journal of Business Research, 108, 1-8. https://doi.org/10.1016/j.jbusres.2019.09.025
3. Carmona, P., Dwekat, A., & Mardawi, Z. (2022). No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure. Research in International Business and Finance, 61, 101649. https://doi.org/10.1016/j.ribaf.2022.101649
4. Chauhan, G. S. (2019). Are working capital decisions truly short-term in nature? Journal of Business Research, 99, 238-253. https://doi.org/10.1016/j.jbusres.2019.02.032
5. Chen, C., & Kieschnick, R. (2018). Bank credit and corporate working capital management. Journal of Corporate Finance, 48, 579-596. https://doi.org/10.1016/j.jcorpfin.2017.12.013
6. Federation of Automobile Dealers Associations. (2026). FADA releases February 2026 vehicle retail data. https://fada.in/
7. Johan, S., Kayani, U. N., Naeem, M. A., & Karim, S. (2024). How effective is the cash conversion cycle in improving firm performance? Evidence from BRICS. Emerging Markets Review, 59, 101114. https://doi.org/10.1016/j.ememar.2024.101114
8. Kim, S. (2023). Innovating knowledge and information for a firm-level automobile demand forecast system: A machine learning perspective. Journal of Innovation & Knowledge, 8(2), 100355. https://doi.org/10.1016/j.jik.2023.100355
9. Kiymaz, H., Haque, S., & Choudhury, A. A. (2024). Working capital management and firm performance: A comparative analysis of developed and emerging economies. Borsa Istanbul Review, 24(3), 634-642. https://doi.org/10.1016/j.bir.2024.03.004
10. Ministry of Road Transport & Highways, Government of India. (2026). VAHAN Public Dashboard. https://analytics.parivahan.gov.in/analytics/publicdashboard/vahan
11. National Stock Exchange of India. (2026). Historical reports: Capital market daily/monthly archives. https://www.nseindia.com/static/resources/historical-reports-capital-market-daily-monthly-archives
12. Shen, S., Xia, L., Shuai, Y., & Gao, D. (2022). Measuring news media sentiment using big data for Chinese stock markets. Pacific-Basin Finance Journal, 74, 101810. https://doi.org/10.1016/j.pacfin.2022.101810
13. Tsuruta, D. (2019). Working capital management during the global financial crisis: Evidence from Japan. Japan and the World Economy, 49, 206-219. https://doi.org/10.1016/j.japwor.2019.01.002
14. Zhang, Z., Wu, C., Qu, S., & Chen, X. (2022). An explainable artificial intelligence approach for financial distress prediction. Information Processing & Management, 59(4), 102988. https://doi.org/10.1016/j.ipm.2022.102988
15. Upstox. (2026). Historical Candle Data V3 API. https://upstox.com/developer/api-documentation/v3/get-historical-candle-data/
16. Reserve Bank of India. (2026). Database on Indian Economy (DBIE). https://dbie.rbi.org.in/
17. BSE Ltd. (2026). Corporate financial results. https://www.bseindia.com/corporates/comp_results.aspx
18. GDELT Project. (2026). Data: Querying, analyzing and downloading; GDELT 2.0 APIs. https://gdeltproject.org/data.html
19. Federation of Automobile Dealers Associations. (2026). Press releases: Vehicle retail data. https://fada.in/press-release-list.php
20. Chuang, C.-H., & Zhao, Y. (2019). Demand stimulation in finished-goods inventory management: Empirical evidence from General Motors dealerships. International Journal of Production Economics, 208, 208-220. https://doi.org/10.1016/j.ijpe.2018.11.013
21. Al Hajj Hassan, L., Mahmassani, H. S., & Chen, Y. (2020). Reinforcement learning framework for freight demand forecasting to support operational planning decisions. Transportation Research Part E: Logistics and Transportation Review, 137, 101926. https://doi.org/10.1016/j.tre.2020.101926