Not every chart needs a trade. Sometimes the job is simply to identify where the risk/reward starts becoming interesting. While screening for potential shorting opportunities, I came across this setup. The idea was to combine market structure + liquidity + imbalance + trend rather than short simply because price was falling. 🔍 What caught my attention? 1️⃣ Bearish structure Price had already experienced a strong decline and was trading below a downward-sloping moving average. That suggested that the broader momentum was still favouring sellers. 2️⃣ Equal High = liquidity above The Equal High marked on the chart is an area I would watch closely. Rather than assuming resistance will hold, I look at clustered highs as a potential area of buy-side liquidity. A move toward or through that area can become important when assessing a potential reversal. 3️⃣ IFVG — potential resistance The purple Inverse Fair Value Gap (IFVG) becomes the key area I'm watching. If price retraces into this z...
“The mathematics of a financial model can be precise. The assumptions behind it rarely are. The real danger is confusing precision in calculation with confidence in the conclusion.”
Financial modeling is often viewed primarily as a mathematical exercise. But mathematics is usually not the most difficult part.
A model can calculate perfectly and still produce a misleading conclusion if the inputs used to project the future are poorly supported, outdated, inconsistent, or insufficiently challenged.
Historical financial data can be measured. Ratios can be calculated. Cash flows can be modeled. But when we move from what is known to what is expected, uncertainty enters the model.
Future growth, margins, capital expenditure, working capital, terminal growth, financing costs and other forward-looking variables require evidence, analysis and judgment. They should not simply be accepted because they make the model work.
This is where financial modeling can become a mess.
Assumptions are often scattered across spreadsheets, buried inside formulas, based on historical figures that may no longer be relevant, or carried forward without asking whether the underlying conditions have changed.
A stronger model treats assumptions as a separate layer that can be identified, sourced, dated, challenged, updated and clearly distinguished from historical facts and calculated outputs.
The objective is not to eliminate uncertainty. That is impossible.
The objective is to make uncertainty visible and make the reasoning behind the model auditable.
A good financial model should therefore do more than produce an answer.
It should allow the analyst to ask:
- What is known?
- What is estimated?
- What is assumed?
- What is driving the result?
- What evidence supports the assumption?
- And what would need to change for the conclusion to change?
Because in financial modeling, precision in calculation is not the same thing as certainty in conclusion.
Disclaimer:
This post is shared solely for educational and informational purposes and reflects my understanding of equity research and market analysis concepts. It does not constitute investment advice, a research recommendation, or a recommendation to buy, sell or hold any security.
K
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