Module 13 · Exercises

These exercises work across cases. Do the cases they cite first; the solutions use each case's own figures.

Warm-up

E13.01 State the decision-date discipline in one sentence, and give two ways hindsight can leak into an analysis even when you are trying to avoid it.

E13.02 For each forensic test, name the case in which it was most decisive and one other case where it would have helped: (a) the interest-income test; (b) the borrowing-despite-cash test; (c) "where does the profit come from?"; (d) the auditor-resignation signal; (e) a related-party acquisition at an unexplained price.

E13.03 Define, with a case example each: variant perception; implied expectations; value by subtraction; liquidity crisis vs solvency crisis; re-rating; value trap.

E13.04 Name three gatekeepers that appear in the cases (auditor, rating agency, regulator, index provider, ESG rater, institutional director, strategic investor) and, for each, what it actually tests and what it does not.

E13.05 Sort the 25 cases into four groups: decision-date buyers who (a) made money for the right reasons, (b) made money for reasons the case did not anticipate, (c) lost money on a business that did well, (d) lost money because the business or its accounts failed. Put at least three cases in each group.

Core

E13.06 (The interest-income test, twice.) Satyam reported ₹257 crore of other income on average cash of about ₹4,080 crore in FY2008, when one-year deposits paid 8.5–9.5% (I1). Wirecard reported €20.6 million of interest on about €3.27 billion of cash, deposits and securities in 2018, while claiming escrow balances in Asian banks (G9). Compute each company's maximum implied yield and the "missing" interest at 8.5% (Satyam, on all cash) and 2% (Wirecard, on all cash). Which result is more damning, and why?

E13.07 (Return decomposition.) Using the cases' figures, split each return into EPS growth and P/E change, and compute the share of the (log) return that came from re-rating: (a) Asian Paints, 31-Mar-2011 → 31-Mar-2022, ₹252.7 → ₹3,079, EPS ≈ ₹8.8 → ₹31.59 (consolidated), P/E 31× → 97.5× (I2); (b) ITC, 30-Oct-2020 → 14-Aug-2023, ₹165.25 → ₹449.20, TTM EPS ₹11.64 → ₹15.67 (I12); (c) Bajaj Finance, May-2010 → Mar-2019, ₹4.0 → ₹302.5 (today's basis), P/B 1.3× → 8.9× (I3).

E13.08 (Justified P/B as a forecast.) With the justified P/B formula P/B = (RoE − g)/(r − g), compute the RoE that Bajaj Finance's price implied (a) in May 2010 at 1.3× book, r = 14%, g = 12%, and (b) in March 2019 at 8.9× book, r = 12%, g = 8%. What actually happened to its RoE?

E13.09 (Value by subtraction.) (a) Tata Motors, 9-Mar-2009: market cap ₹6,081 crore; Indian business FY2008 operating income ₹2,369 crore; its own debt ≈ ₹10,000 crore (I15). At 6× operating income, what did the market implicitly pay for JLR's equity? (b) ITC, 30-Oct-2020: market cap ₹2,03,129 crore; non-cigarette parts and cash valued at ₹1,04,607 crore; cigarette EBITDA ₹15,125 crore (I12). What EV/EBITDA was the market paying for cigarettes? What do the two answers have in common?

E13.10 (Liquidity runway.) (a) Jet Airways at 31-Mar-2018 had ₹12,847 crore of cash obligations due within twelve months and ₹1,360 crore of cash and bank balances (I11). (b) DHFL repaid ₹17,876 crore of debt between 24-Sep and 31-Dec-2018 and raised ₹16,290 crore, of which ₹11,873 crore came from selling loans and only ₹575 crore from commercial paper (I4). Compute Jet's cash cover, DHFL's funding gap, and the share of DHFL's new money that came from sell-downs. What does the DHFL mix tell you about its access to markets?

E13.11 (Pricing a fraud tail.) Compute the expected return and the Kelly fraction for: (a) Wirecard at €107.50 with +40% if the cash is real and −100% if not, at a 30% fraud probability (G9); (b) a small cap with +30%/−90% at a 20% probability (I14); (c) Satyam at ₹178.95 with ₹250 if the cash is real and ₹80 if not, at the ≈42% fraud probability the market was implying (I1).

E13.12 (Operating leverage.) Tata Motors' Indian business had a 6.7% operating margin with costs 70% variable (I15); Kingfisher lost ₹1,827 per passenger at a 71% seat factor (I11). Compute Tata Motors' break-even revenue decline and Kingfisher's break-even fare rise. Why do airlines and truck makers both need a strong balance sheet?

E13.13 (Strategic and lender prices.) Compute the discount of the market price to the "strategic" price in each case: Kingfisher at ₹45.70 vs the banks' ₹64.48 conversion; Jet at ₹295.45 vs Etihad's ₹754.74; ITC Hotels' first-day close of ₹171.85 vs the ₹260 implied in the special pre-open session. What does each price actually measure?

E13.14 (Drawdowns for the patient.) Compute the worst drawdown suffered by: a Tata Motors buyer at ₹152.65 (low ₹65.30); an ITC holder from the ₹522.75 peak to ₹271.51 (ITC plus 0.1 ITC Hotels, Aug-2026); an Asian Paints holder from ₹3,590 to ₹2,116; a Jet buyer at ₹295.45 to the last trade of ₹34.16. Which of these holders was ultimately rewarded?

E13.15 (Auditor resignations as signals.) Vakrangee was ₹505 on 25-Jan-2018, ₹99.95 on 30-Apr-2018 (after PwC resigned) and ₹32.60 on 31-May-2018. Manpasand was ₹432.95 on 17-May-2018, ₹223.15 on 31-May-2018 (after Deloitte resigned) and ₹148.65 on 29-Jun-2018 (I14). Compute the falls. Would a rule "sell on the first auditor resignation" have saved most of the loss in each case?

E13.16 (Implied growth.) Rank these prices by the ten-year growth they implied, using each case's reverse DCF: Cisco at $80 (G3), Asian Paints at ₹3,079 (I2), Vakrangee at ₹505 (I14), ITC at ₹165.25 (I12, Gordon implied growth at r = 11%). Which were impossible, which demanding, and which undemanding?

E13.17 (Red, Amber, Green.) For Satyam (Jan-2009), Wirecard (Dec-2019), Manpasand (Jan-2018) and Vakrangee (Jan-2018), list the three strongest red flags that were public at the decision date and classify each as verifiable (a number you could compute) or inferential (a judgement about behaviour).

Stretch

E13.18 Build a "base-rate table": for every case, record whether a decision-date buyer beat the local index to September 2026 (use each case's section 4). What fraction won? Does the answer differ between the forensic cases, the lender cases and the quality cases? What does that tell you about where fundamental analysis adds the most value?

E13.19 Write the rule that distinguishes, ex ante, "expensive quality that still worked" (Bajaj Finance at 8.9× book in 2019; Apple in 2016) from "expensive quality that did not" (Asian Paints at 97× in 2022; DMart in 2021; Cisco in 2000). Test the rule on two companies trading at high multiples today.

E13.20 Pick one case whose outcome you think was luck (good or bad) rather than analysis. Argue it using only information that was public at the decision date, then argue the opposite.

Real-world task

E13.21 Run the interest-income test and the borrowing-despite-cash test on ten Indian listed companies with cash and investments above 20% of total assets. Report any whose treasury yield is below the one-year deposit rate, and find the disclosed reason.

E13.22 Choose one live situation from the cases — Vodafone Idea's AGR dues, ITC's cigarette volumes under the new excise, JLR's recovery after the cyberattack, Manpasand's insolvency, IL&FS's remaining resolutions — and write a one-page monitoring note: the three numbers to watch, where they are published, and what would change your view.

E13.23 Draft a case study of your own in this module's format for a company you know (it becomes the first entry in your casebook; see Module 15). Freeze a decision date at least three years ago.