On this page
Step 1
Define the research question
A useful research task begins with a company, asset, industry, event, position, or risk that needs to be understood. The question should make the time horizon and the decision context clear enough to test.
- Identify the object of research and the time horizon.
- Separate the current observation from the conclusion being tested.
- State what evidence could strengthen or weaken the judgment.
Step 2
Connect evidence and research perspectives
Depending on the question and available product workflow, Nashnova can organize market data, financial statements, earnings materials, news, macro and industry information, research materials, trading signals, and other relevant sources.
Agents provide distinct research lenses, such as fundamental analysis, cycle analysis, macro transmission, industry-chain analysis, or risk validation. They are tools for examining a question—not substitutes for the user’s judgment.
Step 3
Expose assumptions, counter-evidence, and failure conditions
A research answer is more useful when the reasoning can be checked. Material outputs should distinguish facts from interpretation and identify the assumptions, key variables, contrary evidence, and conditions that would invalidate the current view.
- Check sources and the time attached to important data.
- Compare more than one research frame when the conclusion is sensitive to assumptions.
- Treat confident language as a claim to verify, not a guarantee.
Step 4
Create a path for ongoing review
A question can be carried forward through briefs, saved context, scheduled tasks, and Agent updates where those features are available. New market data, filings, news, or events can then be compared with the original thesis and its failure conditions.
The goal is not to automate a buy or sell decision. It is to make the evidence trail and the reasons for changing a view easier to revisit.
Output standards
What a decision-useful output should show
- The information source or citation where available
- The relevant data or publication time
- The main assumptions and variables
- Important risks, uncertainties, and counterarguments
- A practical point for later review
Information sources
- Current Nashnova website, contact page, user agreement, and privacy policy
- Published Nashnova product documentation and feature descriptions
- Current publicly available product and service information
- Nashnova product documentation and published research workflows
Known limitations
- AI-generated output can be incomplete, inaccurate, delayed, or internally inconsistent and must be independently checked before use.
- Available tools, sources, and tracking features vary by market, data license, region, and product workflow.
- Agent features and available research workflows can differ by Agent, product version, and user access.
Corrections and questions
If a statement is incomplete, outdated, or inconsistent with the product, send the page URL and supporting detail to our general support team.
contact@nashnova.com ↗