Financial Performance, Working Capital and Distress Risk of Five Listed Coimbatore Spinning Mills, FY2019–24

Coimbatore and the surrounding western Tamil Nadu belt are India's best-known spinning cluster — the "Manchester of South India". Its listed spinning companies went through a rough six years: the pandemic shutdown of 2020, a cotton-price spike in 2021–22, a collapse in yarn demand afterwards and rising power costs. Some mills came out stronger, others ended up with thin margins, stretched working capital and heavy borrowing.

This M.Com project analyses the financial performance of five listed spinning companies headquartered in the Coimbatore region over FY2019–FY2024, using only their published annual reports and stock-exchange filings. The companies are chosen by clear purposive criteria and referred to as Company A–E in the analysis. The study covers liquidity, solvency, profitability and efficiency ratios, common-size and trend statements, a DuPont decomposition of ROE, the cash conversion cycle and the Altman Z″-score as a distress indicator.

Statistical tools go beyond tabulation: one-way ANOVA tests whether profitability differs across companies, paired comparisons test pre- vs post-pandemic performance, and a pooled regression on 30 firm-years examines how working-capital efficiency and leverage relate to return on assets. The project fits the University of Madras PG common regulations — Semester 4 Project (2 credits, 100 marks: 20 internal from the best two of three presentations, 60 report, 20 viva), and is a practical, data-rich individual project.

Abstract

This study examines the financial performance of five listed spinning companies from the Coimbatore region for FY2019–FY2024 using secondary data from annual reports. Ratio analysis, common-size and trend statements, DuPont analysis, cash-conversion-cycle computation and the Altman Z″-score are used to assess liquidity, solvency, profitability, efficiency and distress risk. One-way ANOVA, paired t-tests, correlation and pooled OLS regression test differences across firms and periods and the relationship of working capital and leverage with profitability. The study concludes with firm-wise observations and sector-level suggestions.

Introduction

The Indian textile industry is a major employer and exporter, and spinning is its most capital- and working-capital-intensive stage: mills must buy cotton in bulk during the season, carry large inventories and extend credit to weavers and exporters. This makes working-capital management and leverage decisive for profitability. The six-year window includes both a demand shock (2020) and a raw-material cost shock (2021–22), offering a natural setting to compare how companies with different financial structures performed.

Review of literature (summary)

Indian studies on textile companies commonly apply ratio analysis to one or two firms, often without statistical testing. Research on working capital and profitability (following Deloof's work on Belgian firms and subsequent Indian replications) generally finds a negative relation between the cash conversion cycle and profitability. Altman's Z″ model for non-manufacturing and emerging-market firms has been used in Indian distress studies. Gap: few recent studies combine multi-firm ratio analysis, distress measurement and statistical testing for the Coimbatore spinning cluster across the pandemic and cotton-price cycle.

Existing vs proposed

• Existing: single-firm, descriptive ratio projects that stop at "the current ratio is satisfactory".
• Proposed: a comparative, six-year, five-firm panel with statistical tests and a distress indicator.

Feasibility

• Data: annual reports are freely downloadable from company websites and BSE/NSE; no primary data or permission is needed.
• Tools: Excel for the ratio model; SPSS or Jamovi for tests.
• Time: about 16 weeks within Semester 4.

Listed spinning mills in the Coimbatore region operate in a cyclical, low-margin business where cotton prices, yarn demand and power costs can swing profits sharply within a year. Between FY2019 and FY2024 these companies faced a pandemic shutdown, an unprecedented cotton-price spike and a subsequent demand slump. Investors, lenders and the companies' own managements need to know which firms managed liquidity, leverage and working capital well enough to protect profitability, and which moved towards financial distress.

Published annual reports contain all the necessary information, but individual reports are read in isolation and rarely compared systematically across firms and years. The problem addressed by this study is to measure and compare the financial performance, working-capital efficiency and distress risk of five listed Coimbatore spinning companies over six years, test whether observed differences are statistically significant, and examine whether working-capital efficiency and leverage explain differences in return on assets.

In scope

• Five listed spinning companies with registered offices in the Coimbatore region, chosen by the purposive criteria stated in the methodology.
• Standalone audited financial statements for six financial years, FY2019 to FY2024 (FY2019 under Ind AS for all selected companies).
• Ratio, common-size, trend, DuPont, cash-conversion-cycle and Altman Z″ analysis; ANOVA, paired t-test, correlation and pooled regression.

Out of scope

• Primary data from company officials, and any non-public information.
• Share-price and valuation analysis (suggested as future scope).
• Consolidated statements, subsidiaries and segment reporting.
• Causal claims — the regression shows associations in a small panel.

Research design

Analytical research using secondary data — a longitudinal comparison of five firms over six years (30 firm-years).

Selection of companies (purposive sampling)

There is no sampling frame in the survey sense; companies are selected by stated criteria from the list of listed textile companies: (1) main activity cotton yarn spinning; (2) registered office in Coimbatore, Tiruppur or nearby districts; (3) listed on BSE/NSE throughout FY2019–FY2024; (4) complete standalone annual reports for all six years; (5) not under an insolvency process during the period. From the companies meeting all criteria, the five with the largest revenue in FY2019 are selected. The report names them in Chapter 3; the analysis labels them A–E. Sample-size logic: with 30 firm-years, the pooled regression is limited to at most three predictors (roughly ten observations per predictor), and results are treated as exploratory.

Data collection instrument

No questionnaire is used. A data-extraction template in Excel records 35 line items per firm-year (revenue, EBITDA, finance cost, PAT, inventory, trade receivables, trade payables, current assets and liabilities, total assets, borrowings, equity, retained earnings, working capital), with a column for the page number in the annual report so every figure can be traced. Ten percent of entries are re-checked independently.

Tools of analysis

• Ratios: current, quick, debt-equity, interest coverage, EBITDA margin, net margin, ROA, ROE, inventory, receivables and asset turnover.
• Common-size and trend (FY2019 = 100) statements.
• DuPont: ROE = net margin × asset turnover × equity multiplier.
• Cash conversion cycle = inventory days + receivable days − payable days.
• Altman Z″ = 6.56 X1 + 3.26 X2 + 6.72 X3 + 1.05 X4 (with zones: safe > 2.60, grey 1.10–2.60, distress < 1.10).

Hypotheses (α = 0.05)

• H1₀: mean ROA does not differ across the five companies — one-way ANOVA (Kruskal–Wallis as a non-parametric check).
• H2₀: mean ROA of FY2019–FY2021 does not differ from FY2022–FY2024 — paired t-test on firm-level period means (Wilcoxon signed-rank as a check given n = 5).
• H3₀: there is no correlation between the cash conversion cycle and ROA — Pearson correlation on 30 firm-years.
• H4₀: CCC, debt-equity and firm size (log total assets) do not jointly explain ROA — pooled OLS regression with year dummies as a robustness check.

Timeline (16 weeks)

Weeks 1–2 topic and review · Weeks 3–4 selection and data extraction · Weeks 5–7 ratio model · Weeks 8–9 statistical tests · Presentations 1–3 at weeks 4, 9 and 13 · Weeks 10–14 writing · Weeks 15–16 corrections, binding, viva.