Quantitative investment management

Clear vision. Precise execution.

Sightline Capital's systematic strategies are built on peer-reviewed research and the proprietary models that support it: a process designed to be tested, explained and repeated.

Our method Start a conversation

FIG. 1Simulated paths under stochastic volatility, zero drift. Illustrative only; not a model of any Sightline strategy or its results.

01Thesis

Markets are mostly noise. The work is separating what persists from what is merely random, then acting on it with discipline.

Capital is allocated across three areas

A.

Systematic trading

Rules-based strategies driven by quantitative models.

B.

Strategic partnerships

Capital committed alongside aligned partners.

C.

Alternative investments

Opportunities beyond traditional public markets.

02Method

From hypothesis to position.

Four stages, each with its own burden of proof. The figure runs each one live, on synthetic data, as you read.

  1. 01

    Hypothesis

    Research begins with a question, not a backtest. Candidate signals are drawn from the academic literature and our own work, and framed so the data can reject them.

    Illustrated with · Ordinary least squares

  2. 02

    Estimation

    Markets never show their true state directly. Filtering methods estimate what is changing beneath day-to-day noise, and revise that estimate as each observation arrives.

    Illustrated with · Kalman filter

  3. 03

    Construction

    Assets that look distinct often share hidden drivers. Clustering the correlation structure exposes those groups, so diversification is measured across genuine sources of risk rather than ticker counts.

    Illustrated with · Hierarchical clustering

  4. 04

    Risk

    Returns are not normally distributed, and models that assume they are understate the tails. Risk is evaluated where losses concentrate, before capital is committed.

    Illustrated with · Expected shortfall

Fig. 2a Testing a candidate signal
Computed live from the points shown

400 synthetic observations with an ordinary least squares fit and its 95% confidence band.

Synthetic data. Not a representation of any Sightline model, portfolio or result.

03Execution

Precision at the point of trade.

A signal is only worth what survives the trade. Spread, market impact and timing all carry a cost, so execution deserves the same rigor as the model.

  1. Price, with the algorithm's fills marked
  2. Order book: liquidity resting at each price
  3. Time and sales: every trade as it prints
  4. Execution: filled quantity against schedule

FIG. 3 A simulated algorithm works a large order against a synthetic order book, slicing it into child orders to limit market impact. Synthetic data throughout: no real instruments, prices or orders, and not a representation of Sightline trading, strategies or results.

04Research

Grounded in academic research.

Sightline's process pairs models from peer-reviewed research with proprietary models that support them under the hood. Both are held to the same discipline: explicit assumptions, out-of-sample testing, and methods we can explain.

Much of that research is Dr. Sherwood's own: peer-reviewed work in The Journal of Investing on building portfolios around a target return or a future capital need, and the skew-risk portfolio theory that underpins it.

Selected papers · Matthew W. Sherwood, Ph.D.

  1. [1] 2019 · The Journal of Investing · Vol. 28, No. 5 Targeted Return Portfolio Construction Peer-reviewed Read (opens in a new tab)
  2. [2] 2021 · The Journal of Investing · Vol. 30, No. 2 Risk Capacity Portfolio Construction Peer-reviewed Read (opens in a new tab)
  3. [3] 2021 · SSRN Skew-Risk Portfolio Theory Working paper Read (opens in a new tab)
Fig. 4 Skew relative to a target, through time
Computed live from the highlighted curve

A visual concept inspired by Dr. Sherwood's research on skew-risk and targeted return portfolio construction: outcome distributions at successive horizons, each measured against a target (red). Downside, upside and time all count as risk.

Illustrative synthetic distributions. Not output of any Sightline model, and not a projection of results.

Published research adopted at

MIT Harvard Oxford UC Berkeley

Responsible Investing (Routledge), authored by Dr. Matthew Sherwood, is used in coursework at these institutions. Course adoption of a publication is not an endorsement of Sightline Capital LLC or its services by any institution listed.

05Firm

A different kind of firm.

Sightline Capital's leadership brings together diverse expertise across quantitative finance, risk management, and operational excellence. Our collective experience spans multiple market cycles and investment disciplines.

100%
Disabled & veteran-owned Including 25% disabled veteran ownership
Chief Executive
Dr. Matthew Sherwood, Ph.D.
Credentials
Ph.D. in Finance
Disciplines
Quantitative finance · Risk management · Operations
Headquarters
McKinney, Texas

06Contact

Let's talk.

Learn more about our investment strategies or discuss potential opportunities with our team.

info@sightline.capital
5900 S Lake Forest Dr, Suite 300 McKinney, TX 75070