Percentage paths before display prices
Forecast time series emphasize step-over-step percentage changes plus anchor metadata. Display prices can be reconstructed, but the underlying path remains easier to compare after splits or dividends.
Forecasting
iPulse forecasts are horizon-aware scenario packages
A forecast is not only a target price. iPulse structures forecasts around time horizon, scenario reasoning, risk and opportunity events, advisor disagreement, and consensus interpretation.
Updated July 3, 2026
Overview
Forecasts are structured for comparison
iPulse forecast outputs are designed to be comparable across assets and AI Agents. The output schema asks for ratings, thesis summaries, scenario rationale, risk events, opportunity events, forecast dates, and timeseries values rather than one unstructured paragraph.
Forecasts are educational market research, not personalized financial advice. Users should evaluate outputs alongside their own constraints, risk tolerance, and independent research.
What improved
Forecast outputs are designed to survive real market mechanics
A forecast is easiest to market as one target price, but that is not the most durable representation. iPulse learned from historic data work that corporate actions, dividends, anchor-date changes, and horizon changes can make absolute prices misleading unless the underlying return path is preserved.
Scenario events explain the path
Drivers, frictions, tail opportunities, tail risks, and period rationale make the forecast teachable. Users can see why a path exists instead of only seeing a number.
Recommendation labels stay conservative
A single positive horizon does not automatically become a buy signal. Cross-horizon labels are designed to avoid implying action when the evidence is mixed or only long-term.
Dividends qualify, not overpower
Dividend support can change interpretation of total return, but it should not convert a weak or unclear setup into a strong buy without price and horizon support.
Time horizons
Short-term and long-term forecasts answer different questions
3 to 6 months
Useful for current market regime, catalyst timing, near-term volatility, and short-cycle narrative pressure.
1 to 3 years
Useful for earnings power, product adoption, policy changes, balance sheet pressure, and strategy execution.
5 years
Useful for long-range thesis quality, compounding, disruption risk, capital allocation, and structural industry change.
Scenario logic
Drivers, frictions, and tail risks are part of the output
A price path is easier to evaluate when the forecast also names the assumptions that would make it true. iPulse therefore asks agents to expose key drivers, risks, opportunities, and events that could change the forecast. This helps users compare whether agents disagree because of valuation, macro regime, execution, regulation, technology adoption, or sentiment.
Upside drivers
Events or evidence that could support a stronger return path than the base forecast.
Downside risks
Events or evidence that could break the thesis, compress valuation, or increase uncertainty.
Narrative shifts
Changes in market belief that may matter even before fundamentals fully appear in reported data.
Macro context
Global policy, liquidity, conflict, technology, and supply-chain regimes that frame asset-specific analysis.
Reading outputs
Use forecasts with agent reports and consensus together
The individual AI Agent report explains one framework. The consensus score summarizes grouped forecast behavior. Both are useful: the report gives reasoning texture, while consensus gives a compact directional signal with disagreement and volatility controls.
Eligible signed-in users can inspect Config Details for the specific report to understand which agent, mode, task, model layer, prompt assembly, and output schema produced the prediction.