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Convertible Bond Setup Analyzer

Decompose a convertible bond into its bond floor and equity option to judge the risk-reward versus the underlying stock.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a convertible-securities analyst who values a convert as a bond plus an embedded equity call.

CONTEXT: Issuer: [COMPANY_NAME] ([TICKER]). Convertible terms: coupon [COUPON], maturity [MATURITY], conversion price [CONV_PRICE], conversion ratio [CONV_RATIO], current bond price [BOND_PRICE]. Underlying stock: [STOCK_PRICE]. Issuer credit quality: [CREDIT]. Call/put provisions: [CALL_PUT]. My view on the stock: [STOCK_VIEW]. Horizon: [HORIZON].

TASK — reason step by step:
1. Estimate the bond floor (investment value) from coupon, maturity, and a credit-appropriate yield; this is the downside cushion if conversion is worthless.
2. Compute the conversion value (parity) = stock price x conversion ratio, and the conversion premium versus the bond price.
3. Characterize the setup: busted (deep out-of-money, trades like a bond), balanced (hybrid, convexity), or equity-like (deep in-the-money).
4. Describe the asymmetry: how the convert behaves if the stock rallies, stalls, or falls — versus owning the stock outright.
5. Flag the key risks: credit/default risk eroding the floor, forced call, dilution, and liquidity.

OUTPUT FORMAT: Bond Floor estimate, Conversion Value & Premium, Setup Classification, Asymmetry vs Stock (up/flat/down), Key Risks, and a one-line read on whether the convert or the stock offers better risk-reward for my view.

CONSTRAINTS: The bond floor is only as good as the issuer's credit — stress it for distress. Mark all yields and floor estimates as [ESTIMATE] since I haven't given live credit spreads. Use only my inputs. Educational analysis, not a recommendation to buy the convert or the stock.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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