Quantamental Investing: Blending Discretion and Models

Quantamental investing blends quantitative signals with fundamental or discretionary judgment — neither pure systematic nor pure stock-picking. Done well, it uses models for breadth and humans for sparse, high-value information. Done poorly, it is discretionary trading with a quant marketing label, plus override bias that destroys the backtest. This article defines workable quantamental processes and the governance that keeps them honest.

The spectrum

Style	Signal source	Portfolio construction
Pure quant	Models only	Rules / optimizer
Quantamental	Models + human views	Rules with veto / tilt
Fundamental + tools	Human first	Screens and risk models assist
Pure discretionary	Human only	Ad hoc

Quantamental sits in the middle: the default book is systematic; humans adjust a controlled subset.

Where humans can add value

Discretion helps when information is:

Sparse — one merger document, one plant visit, one regulatory filing nuance
Non-numeric — governance quality, fraud risk, management credibility
Regime-defining — "this accounting change breaks the historical SUE signal"
Operational — halt trading ahead of a known impossible borrow

(securities lending)

Discretion hurts when it:

Overrides the model every time a position hurts (stop-loss theater)
Concentrates the book into a few "stories" (crowding)
Uses information already in price and factors
Cannot be timestamped for research

Process architecture

systematic alpha → risk model → optimizer → proposed book
                         ↓
              discretionary tilts (bounded)
                         ↓
              compliance / risk veto
                         ↓
              execution

Hard rules that preserve scientific control:

Max % of risk from discretionary tilts (e.g. 10–20%)
Written thesis + exit criteria for every override
Timestamped catalog of overrides for later analysis
No increase in gross leverage via "conviction" without risk approval

def apply_tilts(w_model: dict, tilts: dict, max_tilt_l1: float) -> dict:
    """Apply bounded discretionary tilts to model weights."""
    w = dict(w_model)
    budget = 0.0
    for sym, dw in tilts.items():
        if budget + abs(dw) > max_tilt_l1:
            continue
        w[sym] = w.get(sym, 0.0) + dw
        budget += abs(dw)
    return w

Research on overrides

Treat overrides like a strategy sleeve:

override_alpha = performance of tilted book − performance of pure model book

If override alpha is negative over 50+ decisions, remove discretion — regardless of anecdotes. This is the same discipline as strategy significance testing.

Track:

Hit rate of vetoes (avoided disasters vs missed recoveries)
Factor exposure drift after tilts
Turnover added by human changes (cost-aware)

Quantamental data

Fundamental quants industrialize discretionary inputs:

Structured filings NLP (sentiment)
Expert surveys / alternative data
Earnings quality scores next to PEAD

The point is to encode judgment into features over time, shrinking the need for live overrides.

Failure modes

Marketing quantamental — model exists, PMs ignore it
Committee risk — slow overrides that miss the edge horizon

(signal decay)

Responsibility diffusion — nobody owns drawdowns
Backtest theater — research shows model-only results; live is tilted

Governance fix: report model-only and live books side by side, like shadow trading.

When to stay pure quant

Stay systematic if:

Universe is large and edge is statistical
Override latency is slower than signal half-life
Team cannot maintain override logging discipline
Clients expect a rules-based product

Quantamental is a organization design choice, not a magic alpha source.

Key takeaways

Quantamental = systematic default + bounded discretionary tilts
Humans help on sparse, non-numeric, regime-break information
Cap tilt budget; log every override with thesis and exit
Measure override alpha vs the pure model — cut if negative
Encode repeated judgments into features; shrink live discretion over time