Portfolio Scenario Analysis: Turn Stock Outlooks into Account Gains and Losses
Weighting asset returns in bullish, base, and bearish scenarios reveals total profit or loss. Understand the limits of assumed probabilities and the risk of assets falling together in a crisis.
Scenario-based profit and loss assumes how each asset would react under a particular economic or business situation and calculates the result for the whole account. Multiplying invested amounts by assumed returns and adding them shows how the same outlook creates different risks at different weights. The calculated result is conditional; it does not automatically tell you the probability of the scenario actually occurring.
Write out the causal links before entering numbers
Do not stop at applying the same decline to every asset in a bearish scenario. For example, describe the sequence linking lower demand, rising inventories, shrinking margins, and lower valuation multiples. Interest rate cuts can also coincide with a weakening economy, so do not choose favorable and unfavorable variables independently and combine them arbitrarily. Check that each asset's response is consistent with the same economic situation.
Weighting returns reveals large exposures rather than just large forecasts
Multiplying each asset's assumed return by its starting weight gives its contribution to account returns. Adding these contributions yields the account return, assuming no deposits, withdrawals, or interim trades. Include exchange rate effects for foreign-currency assets and liabilities and interest for investments financed with borrowing. Nonlinear products such as options cannot readily be modeled using simple proportional changes as stocks can, so check their payoff structures separately for the relevant expiration and price conditions.
A probability-weighted average can hide extreme losses
You can assign subjective probabilities to calculate expected profit or loss, but do not present them as precise predictive probabilities. Check how much the conclusion changes with small probability adjustments, and separately flag losses you cannot bear even if their probability is low. Include cases in which assets with low historical correlations fall together in a liquidity crisis. Place observable indicators that would support or contradict each scenario beside the results table so you can update your judgment later.
If sales or additional contributions occur along the way, distinguish a single result calculated from starting weights from the actual path. Adding money after a sharp decline produces a different final amount from adding the same money before it. If earmarked withdrawals are planned, calculate the required sales using asset prices at those dates. Also check whether an interim cash shortfall could derail the plan even when the scenario's final return is positive.
Worked example
Hypothetical example: Assume a KRW 20 million account with 40% in stock A, 30% in B, and 30% in cash. In a bearish scenario, A returns −30%, B −20%, and cash 0%, giving an account return of −12%−6%=−18% and a loss of KRW 3.6 million. In a bullish scenario, A returns +25% and B +10%, giving the account +13% and a gain of KRW 2.6 million. Arbitrarily assuming equal probabilities for the two scenarios gives an average of −2.5%, but those probabilities are not verified facts.
What to check
- Are all asset assumptions consistent with the same economic situation?
- Have you included starting weights, exchange rates, and interest?
- Have you distinguished probability assumptions from calculated results?
- Have you separately examined large losses with small probabilities?
Sources and primary materials
This is educational material about research methods. For an actual company decision, verify the filings and primary sources for the relevant period.