Cross-sectional 12-1 momentum, agri equities

Buy the farming companies that have climbed most over the past year and sell the laggards. Over twenty-five years it went essentially nowhere.

Conclusion: Failed

Failed on the reward for the risk, the confidence range, profit concentration, consistency across the history, the comparison against buying and holding and standing out from the search. Indicative performance of 0.2% a year is 1.5 points below the 1.7% returned by holding the same universe continuously, return for risk taken sits at 0.02 against the benchmark's 0.13, and the worst fall of 55.1% is 41.9 points below the benchmark's 97.0%.

Figure 1 · Growth of $1

2001–2026 · net of 15bp round-trip costs
What one dollar became, after costs: $1.05 by 2026. Shown against Control, S&P 500, rebased to the same starting dollar.
Return for risk
0.02
range (0.40) to 0.42
Ann. return
0.2%
volatility 9.4%
Max drawdown
55.1%
over 298 months
Beats the search
0.1%
chance, after correction

Method

Hypothesis

Equity-convention cross-sectional momentum on the ag-linked equity universe, skipping the most recent month to avoid short-term reversal.

Rule

UNIVERSE = 35 agribusiness equities, measured against their home index
LOOKBACK = months bar-12 .. bar-2 # skips the most recent month
MIN_NAMES = 12
function positions(bar):
for symbol in UNIVERSE:
score[symbol] = sum(returns(symbol, LOOKBACK))
if count(score) < MIN_NAMES:
return {}
return long_short(score, count(score) / 3, bar)
# ── execution ────────────────────────────────────────────────────────
COST_PER_TRADE = 15 bps of the notional that changes hands
function on_bar(bar, book):
if not bar.is_month_end:
return # decisions are made monthly only
target = positions(bar) # the rule above
target = scale_to_gross(target, 1.0)
rebalance(book, target, bar)
function scale_to_gross(w, limit):
gross = sum(abs(w)) # total capital at work
if gross == 0:
return w # flat is a valid target
return w * limit / gross # leverage fixed, never implicit
function rebalance(book, target, bar):
for symbol in union(book.symbols, target.symbols):
delta = target[symbol] - book.weight(symbol)
if delta > 0:
buy(symbol, delta, fill = next_bar.close)
else if delta < 0:
sell(symbol, -delta, fill = next_bar.close)
book.charge(COST_PER_TRADE * abs(delta))
# Orders are filled at the next month's close, never the one the decision
# was made on. There is no stop_loss(), take_profit(), limit order or
# position cap: a holding changes only when positions() returns a
# different target at the next month end.

Evaluation

Table 1 · Performance

2001-09 – 2026-06 · 298 months, 298 invested
MeasureValue
Annualised return0.2%
Annualised volatility9.4%
Return for risk taken0.02
95% range(0.40) to 0.42
t-statistic0.10
Maximum drawdown55.1%
Hit rate49.3%
Annual turnover5.62×
Return skew(0.35)
Return kurtosis4.16
The range is measured by resampling the history in blocks, so runs of good and bad months stay intact.

Table 2 · Robustness

DiagnosticValueReads asBadOkayYayHmm
Design half (pre-2015)0.20return for risk≤ 00 – 0.40≥ 0.40
Holdout half (2015+)(0.26)return for risk≤ 00 – 0.40≥ 0.40
Top-5-month share of profit683.8%how much rode on a few months — lower is better≥ 40%25 – 40%< 25%
Top-2-year share of profit916.9%concentration
Distinct trading episodes1how many independent runs this really is< 5050 – 100≥ 100
Chance the edge is real54.1%non-normality corrected
Chance it beats the whole search0.1%chance of beating the whole search by luck
bad/ok boundary is the pre-registered criterion · ok/good is a margin above it, not a criterion · blank where none was registered
The last row asks whether the result would stand out from the hundred-odd ideas tried here, rather than being judged alone.

Table 3 · Pre-registered criteria

measured against C4_EQ_LONG
1FAILED2FAILED3FAILED4FAILED5FAILED6FAILED
CheckWhat it asksResult
1Reward large enough for the riskFAIL
2Confidence range clear of zeroFAIL
3Profit not concentrated in a few monthsFAIL
4Worked in both halves of the historyFAIL
5Beat buying and holdingFAIL
6Stood out from the whole searchFAIL
These six checks were written down before the strategy was run.

Table 4 · Net return by calendar year

after costs
20012002200320042005200620072008200920102011201220132014201520162017201820192020202120222023202420252026+33%-33%
YearNet return
2001(8.0%)
200211.8%
2003(3.4%)
20043.1%
200511.0%
20064.7%
200732.9%
2008(29.5%)
20094.3%
20105.6%
20117.4%
2012(7.2%)
2013(1.1%)
2014(3.7%)
20152.3%
2016(2.7%)
20174.7%
2018(4.3%)
20194.8%
2020(11.8%)
2021(1.2%)
2022(4.8%)
2023(12.2%)
20248.2%
2025(3.7%)
2026(2.3%)

Table 5 · Concentration detail

MeasureValue
Best years2007:+33%, 2002:+12%, 2005:+11%
Worst year2008:-30%

Reference

Instruments

last price and one-month change, live from Yahoo Finance
SymbolNotes
GNC.AXGrainCorp — listed on ASX, in AUD.
ELD.AXElders — listed on ASX, in AUD.
NUF.AXNufarm — listed on ASX, in AUD.
DNL.AXDyno Nobel — listed on ASX, in AUD.
RIC.AXRidley Corporation — listed on ASX, in AUD.
AAC.AXAustralian Agricultural Company — listed on ASX, in AUD.
BALRAMCHIN.NSNot in the instrument table.
TRIVENI.NSTriveni Engineering & Industries — listed on NSE, in INR.
DHAMPURSUG.NSNot in the instrument table.
COROMANDEL.NSNot in the instrument table.
CHAMBLFERT.NSNot in the instrument table.
UPL.NSUPL Limited — listed on NSE, in INR.
M&M.NSMahindra & Mahindra — listed on NSE, in INR.
ESCORTS.NSEscorts Kubota — listed on NSE, in INR.
list truncated in the source record

Sample

2001-09 – 2026-06 · 298 months

Sources

NOAA Physical Sciences Laboratory. Climate indices. ONI, Niño 3.4, MEI v2, DMI, PDO, AMO, TNA, QBO, solar flux. psl.noaa.gov/data/climateindices/list
Queensland Department of Agriculture and Fisheries. Southern Oscillation Index. The Long Paddock. Monthly, 1876–. www.longpaddock.qld.gov.au/soi/soi-data-files
Yahoo Finance. Historical market prices. Adjusted close, month-end. Retrieved with yfinance. finance.yahoo.com

Revision history

RevStageStatusChange
01DraftDirection and criteria fixed before the experiment ran
02TestedFailedFell short on the reward for the risk, the confidence range, profit concentration, consistency across the history, the comparison against buying and holding, standing out from the search; control C4_EQ_LONG
algorithmic-trading-anthology-july-2026-p3-xsmom-agrieq | d/rksci