Change one thing.
Add a signal or change the forecasting method while keeping the other rules fixed within that study.
Research contribution · backtesting and evaluation
Testing macro signals,
from forecasts to portfolios.
We built a framework to test whether new macro information and forecasting methods improve investment decisions. Within each experiment, we use shared dates and portfolio rules so we can see what each change adds.
Learn from earlier data.
Train on completed outcomes, fit preprocessing inside each training window, and trade after the decision.
Follow the whole result.
Measure prediction errors, then examine portfolio returns, losses, trading costs and consistency across periods.
Economic histories contain later revisions and some availability dates are assumed. These development experiments are exploratory, not a complete real-time reconstruction or a final test.
The research question
What improves the decision—and what only changes it?
A stronger research result requires more than a higher backtest return. We compare forecasts with a simple past-average prediction and portfolios with common reference strategies.
Completed · exploratory
Does adding a signal help?
Test unemployment claims, credit spreads and the yield curve separately against the original wider-input model.
Explore added signals → Completed · matched comparisonDoes the forecasting method matter?
Compare the historical mean, OLS, Ridge and Elastic Net, then test lags and interactions as separate Ridge extensions.
Compare forecasting methods → Completed · development extensionDoes the forecast horizon matter?
Compare one-, three- and six-month targets under the corrected model, with separate rules for overlapping forecasts and non-overlapping portfolio holdings.
Three- and six-month targets · development through Dec 2022Inspect horizon results →What we learned so far
The framework reveals tradeoffs, not just winners.
The studies use different information sets and samples. Read each result within its own experiment.
Extra information needs to earn its place.
The original signal study remains preliminary. A separate corrected-model rerun is now available on the added-signals page.
Judge predictions and portfolios separately.
Each forecasting method is scored on prediction errors and on the portfolio it produces. A high return alone does not establish forecast skill, and a better forecast does not automatically improve risk or trading costs.
Inspect both levels of evidence →Reliability checks can change the conclusion.
Read the reliability case study →What remains
A clear boundary between findings and next steps.
The framework is the contribution; the current findings show where the evidence is strong, weak or unfinished.
Keep the initial runs visible as exploratory evidence. A new signal comparison needs its own recorded rules and matched dates.
Use the new three- and six-month results as development evidence, while treating the short non-overlapping portfolio samples cautiously.
Collect historical vintages and verified release dates where possible. Timing checks do not remove revised-data bias.
The chosen reference model’s rules are locked. Confirm whether 2023 onward remains unexamined before a separately authorized evaluation.