Modern-Era Trend Following in Global Futures
Time-series momentum across a diversified futures universe, examined through time, volatility regimes, and implementation assumptions.
Research question
Does systematic trend following continue to generate economically meaningful returns in modern futures markets, and how has its performance changed across time, market structure, volatility regimes, and implementation assumptions?
Why this matters
Trend following is one of the most widely documented systematic return sources, and also one of the most widely declared dead. Both claims are usually made without separating the three things that can change: whether prices still trend, whether diversification still reduces portfolio risk, and whether the cost of trading has grown relative to the signal. Distinguishing them matters for anyone deciding how much capital a trend program should receive.
Hypotheses
- 01H1: The sign of an asset's trailing 12-month excess return predicts the sign of its next-month excess return across a diversified futures universe.
- 02H2: The magnitude relationship is weaker than the sign relationship, implying most of the value is directional rather than proportional.
- 03H3: Predictability measured after 2008 is lower than predictability measured before 2009, and the difference is larger than sampling error alone would suggest.
- 04H4: Multi-horizon signal blending improves risk-adjusted results relative to a single 12-month horizon.
- 05H5: Correlation-aware allocation improves risk-adjusted results relative to equal weighting.
- 06H6: Realistic transaction costs reduce but do not eliminate the measured result at the turnover levels implied by monthly rebalancing.
Methodology
Signal definition
The canonical baseline is the sign of the trailing 12-month excess return, evaluated at month end and held for one month. Extended specifications blend fast, medium, and slow lookbacks so that horizon is treated as an explicit research variable rather than a fixed setting.
Portfolio construction
Two allocation schemes are compared: equal risk weighting across markets, and a correlation-aware allocation that reduces the weight of markets that are close substitutes. Both are constrained to a common target portfolio volatility so that comparisons are not driven by leverage.
Position sizing
Positions are scaled inversely to an estimate of each market's recent volatility, so that a fixed risk budget is expressed in each market regardless of its nominal price level.
Rebalancing frequency
Monthly rebalancing at month end is the primary specification. Weekly and quarterly alternatives are tested as robustness, and reported with their turnover so that cost is comparable.
Volatility estimation
Volatility is estimated from trailing daily returns with a stated window, lagged so that no information from the position-sizing day itself is used. Alternative estimators appear in the robustness section.
Return construction
Returns are built from continuous series constructed from individual contracts with a documented roll rule, converted to a single base currency where the contract is denominated otherwise.
Transaction-cost assumptions
Costs are applied per unit of traded exposure using a per-market estimate covering commission and spread, and results are reported gross and net. A cost-sensitivity table shows the assumption at which the conclusion would change.
Statistical tests
Sign and magnitude regressions use non-overlapping monthly returns; inference uses Newey–West standard errors and two-way clustering by date and market. Multiple-market tests carry a Holm correction, and headline statistics carry bootstrap confidence intervals.
Benchmark selection
The 12-month time-series momentum baseline serves as the internal benchmark for extended specifications, alongside a long-only diversified futures reference. [EXTERNAL BENCHMARK TO CONFIRM]
In-sample and out-of-sample decisions
The pre-2009 sample is used to fix specification choices; the post-2008 sample is reserved for evaluation. Any later change to the specification is documented as such rather than silently applied to the full history.
Data
Markets and instruments
[MARKET LIST TO CONFIRM] A diversified set of liquid futures spanning equity index, fixed income, currency, energy, metal, and agricultural sectors.
Sample period
[SAMPLE PERIOD TO CONFIRM]
Frequency
Daily settlement data aggregated to monthly evaluation points.
Data source
[DATA SOURCE TO CONFIRM] Licensed vendor data. Raw licensed files are not redistributed on this site or in any public repository.
Cleaning and validation
- Calendar alignment across exchanges and holidays
- Outlier screening against contract-level price limits
- Cross-checks between contract volume, open interest, and roll dates
- Duplicate and stale-quote detection
Missing-data treatment
Missing settlements are carried forward only within a stated tolerance; longer gaps remove the market from that period's universe rather than being interpolated.
Survivorship considerations
Delisted and discontinued contracts are retained for the periods in which they traded, so that the universe reflects what was investable at the time.
Contract-roll methodology
[ROLL RULE TO CONFIRM] A single documented roll rule is applied uniformly, with an alternative construction tested in robustness.
Currency conversion
Non-base-currency contracts are converted at the corresponding daily rate, with the conversion applied consistently to both the return and the risk estimate.
Licensing restrictions
Licensed data cannot be republished. The repository documents the exact schema and transformation steps so another researcher can reproduce the workflow with an equivalent authorized dataset.
Key results
No results published yet
Annualized return
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Awaiting final output
Annualized volatility
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Awaiting final output
Sharpe ratio (net)
—
Awaiting final output
Maximum drawdown
—
Awaiting final output
Annual turnover
—
Awaiting final output
Cost drag (bps/yr)
—
Awaiting final output
Planned exhibits
- Canonical 12-month time-series momentum benchmark
- Multi-horizon trend portfolio
- Equal-weight allocation
- Correlation-aware allocation
- Pre-2009 versus post-2008 evidence
- Turnover and transaction costs
- Market and asset-class attribution
- Robustness and limitations
Every published chart will carry a descriptive title, axis labels, units, legend where needed, its sample period, a gross or net label, a short written interpretation, and accessible colors with tooltips.
Interpretation
What the tests can show
Whether directional predictability is present in the sample, whether it is weaker in the later subperiod, and how much of the gross result survives a stated cost assumption.
What I would infer
[INTERPRETATION TO COMPLETE AFTER RESULTS] Inference will be written only once the estimates and their intervals exist, and will be kept separate from the empirical statement above.
What remains uncertain
Whether any measured change reflects a durable structural shift or an unusually adverse stretch of a stable process. The sample cannot cleanly separate these.
Robustness checks
- Alternative lookback windows
- Alternative holding periods
- Weekly and quarterly rebalancing
- Alternative volatility estimators
- Rolling and fixed subperiod tests
- Pre-2009 versus post-2008 comparison
- Asset-class exclusion tests
- Cost sensitivity across a range of per-market assumptions
- Parameter sensitivity surfaces
- Bootstrap inference on headline statistics
- Holm multiple-testing correction
- Alternative continuous-contract construction
Limitations
- Licensed data cannot be redistributed, which limits direct third-party replication.
- Continuous-contract construction is a modelling choice that affects measured returns.
- Cost estimates are assumptions, not observed fills; realized slippage may differ by market and period.
- Multiple specifications are examined, so nominal significance overstates evidence without correction.
- Capacity and market impact are not modelled at institutional size.
- Financing and margin treatment are simplified.
- Subperiod tests have low statistical power by construction.
Conclusion
[CONCLUSION TO COMPLETE] A concise answer will be written once results and robustness checks are final, stated in terms of evidence strength rather than certainty, and without causal claims the design cannot support.
Reproducibility
Repository structure
[REPOSITORY LAYOUT TO CONFIRM] Separate directories for data ingestion, signal construction, portfolio construction, statistics, and figure generation.
Environment
A pinned dependency file records exact package versions used to produce every published output.
Running the analysis
A single entry point regenerates every table and figure from cached intermediate data, given an authorized dataset in the documented schema.
Random seeds
Bootstrap procedures use fixed seeds recorded alongside each published interval.
Known replication differences
Differences in vendor data, roll conventions, and volatility windows will produce small deviations. Expected tolerances are documented per exhibit.
Downloads and links
Citation
This is working research, not a peer-reviewed publication. If you refer to it, please cite it as work in progress and note the status shown above.
BibTeX
@misc{bang_trend_following_modern_era,
author = {Bang, Pratik},
title = {Modern-Era Trend Following in Global Futures},
year = {2026},
note = {Status: In Progress. Working research, subject to revision.},
url = {[PROJECT URL TO ADD]}
}Plain text
Bang, P. (2026). Modern-Era Trend Following in Global Futures. Working research (In Progress). [PROJECT URL TO ADD]