Research objective
The objective is to improve decision quality by using a repeatable sequence. Research begins with the market environment, moves to instrument-specific evidence, defines what would invalidate the thesis, and ends with a review of whether the process was followed.
The methodology does not attempt to predict every market move. It creates a disciplined way to decide when evidence is sufficient, when exposure should be limited, and when waiting is the more defensible action.
Question definition
A useful research question identifies the instrument or market, the relevant period, the decision being evaluated, the evidence required, and the conditions that would change the conclusion. Broad questions are narrowed before evidence is collected so that the process does not simply search for support after a preferred answer has been chosen.
Evidence hierarchy
- Primary and official information: filings, issuer reports, exchange notices, government releases, official legal or regulatory materials, and direct provider documentation.
- Observable market information: price, volume, breadth, volatility, liquidity, leadership, yield, spread, and other data appropriate to the question.
- High-quality secondary sources: established reporting, research, and analysis that add context or identify conflicting evidence.
- Interpretation and scenario judgment: conclusions drawn from the evidence, with assumptions and uncertainty stated.
Lower-quality or promotional sources can identify a question but require stronger corroboration before they influence a conclusion.
Market context
Instrument analysis is reviewed within the broader environment. Relevant context can include trend, breadth, volatility, liquidity, rates, credit conditions, sector leadership, correlations, scheduled events, and market structure. A setup that appears attractive in isolation can have a different risk profile when the broader environment is unstable or moving against it.
Evidence categories
Price and market structure
Trend, support and resistance, volume, range, volatility, relative strength, liquidity, and market participation may be reviewed when relevant.
Fundamental and macroeconomic evidence
Financial statements, business conditions, valuation inputs, policy decisions, economic releases, rates, credit, currencies, and commodity conditions may provide context.
Catalysts and events
Earnings, guidance, filings, regulatory actions, corporate events, economic releases, and scheduled market events can change assumptions or timing.
Position and portfolio risk
Exposure, concentration, correlation, liquidity, leverage, gap risk, event risk, and maximum acceptable loss are considered before potential reward.
Verification and conflicting evidence
Material facts are checked against the strongest reasonably available source. A timestamp, instrument mapping, unit, adjustment, or later revision can materially change a data point. When reliable sources conflict, the difference should be identified rather than hidden.
Evidence is not counted merely by the number of indicators. Several indicators derived from the same underlying price series do not necessarily provide independent confirmation.
Quantitative methods
Quantitative information may include returns, volatility, breadth, ratios, rates of change, drawdowns, correlations, distributions, scenario ranges, and other calculations. Methods should identify inputs, units, period, sampling frequency, and material assumptions where necessary to understand the result.
Calculations can be affected by missing observations, survivorship, look-ahead bias, selection bias, corporate-action adjustments, benchmark choice, and rounding. A statistically precise output can still be economically misleading if the question or data is poorly defined.
Qualitative analysis
Qualitative analysis considers business conditions, incentives, policy, market narrative, management statements, competitive position, legal developments, and the quality or consistency of sources. Qualitative evidence is documented as interpretation rather than presented as a measured fact.
Narrative is evaluated against observable conditions. A persuasive story does not replace price, liquidity, financial, regulatory, or other evidence relevant to the decision.
Scenario analysis
Scenario analysis describes more than one plausible path. Each scenario should identify the conditions that support it, evidence that would weaken it, and the risk if it is wrong. Scenarios are not forecasts or probabilities unless the method and basis for a probability are clearly explained.
The purpose is to prepare for changing evidence rather than to attach certainty to a single path. A scenario can be revised or retired when the underlying conditions no longer apply.
Risk-first decision framework
Before exposure, the process defines why the idea is being considered, which evidence is required, what would invalidate it, how much loss is acceptable, and what conditions require an exit or review. Position size should reflect uncertainty, liquidity, correlation, and the ability to absorb loss.
An invalidation point is a decision boundary, not a guarantee of execution at a particular price. Gaps, illiquidity, outages, and market events can produce losses beyond the planned amount.
Concrete research outputs
The methodology should produce a reviewable output that matches the decision question. Common outputs include:
- Market-state classification: supportive, mixed, or defensive conditions, with the evidence and conflict that support the label.
- Evidence register: confirmed facts, source quality, interpretation, conflicting evidence, and missing verification.
- Scenario comparison: base case, credible alternative, conditions for each case, and the loss path if assumptions fail.
- Risk boundary: invalidation condition, exposure constraint, liquidity and correlation considerations, and exit review trigger.
- Process review: comparison of the original thesis, evidence, rule compliance, execution, and result.
These outputs are decision records, not performance claims. Their purpose is to make reasoning visible enough to challenge and revise.
Challenge and review controls
Before publication or action, the process asks which source is strongest, which evidence is independent, which assumption is most fragile, what fact would falsify the view, and whether an alternative explanation fits the same data. A conclusion that cannot answer those questions remains provisional.
Review should compare the decision with the information available at the time. Later information can improve future rules, but it should not be used to pretend that an earlier result was obvious.
Review and learning
After a decision, the process compares the original thesis, evidence, risk boundary, execution, and outcome. A profitable outcome does not prove the process was sound, and a losing outcome does not prove the reasoning was careless. Review separates decision quality from a single result.
Useful rules can be updated when repeated evidence supports a change. Rules should not be rewritten after each outcome merely to explain what already happened.
Method selection and result interpretation
The method must match the question. A short-term liquidity question, a long-term business question, a macroeconomic scenario, and an event-driven setup require different evidence, periods, and failure conditions. A method is not selected merely because it produces a preferred conclusion.
Results are interpreted within their data quality, sample, market regime, transaction constraints, and uncertainty. Precision in a calculation does not create certainty in the decision.
Limitations
The methodology cannot eliminate uncertainty, data errors, behavioral pressure, execution risk, liquidity risk, or unexpected events. It does not determine suitability for an individual and does not replace account-specific, tax, legal, or professional advice.
Detailed market, model, data, and technology limitations appear in the Market and Research Disclaimer.