Data finds the opportunity, fundamentals verify the value

QT Quantamental brings financial theory, data science, quantitative models and fundamental investment logic into one systematic research framework.

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What is QT Quantamental?

Built around systematic quantitative research, the QT Quantamental system brings together financial theory, data science, quantitative modelling and fundamental investment logic — analysing data across multiple dimensions to identify high-quality, reasonably priced companies with genuine investment potential.

Traditional Quantitative

Data and models find opportunities

Systematic screening across large universes, processing market and financial data at a scale no individual analyst could cover.

Breadth

Traditional Fundamental

Business analysis finds value

Deep examination of how a company operates, the quality of its financials and the durability of its long-term value.

Depth
Quantitative Fundamental Quantamental

The QT system screens and scores a broad universe of equities through systematic data analysis and quantitative models, building a pool of high-quality candidates. Fundamental investment logic is then applied to review the portfolio and control risk, forming a more complete investment decision framework.

Core process

  1. Market Data
  2. QT Quantitative Analysis
  3. Multi-Factor Scoring
  4. High-Quality Stock Pool
  5. Fundamental Review
  6. Risk Control
  7. Portfolio

How the QT system works

Five stages take raw market information through to a constructed portfolio — each one systematic, repeatable and reviewable.

See the core logic
  • Data
  • Signals

Data Analysis

The system continuously analyses large volumes of market, corporate and transaction data, translating complex information into investment signals that can be quantified and compared.

Scope
Market · Corporate · Transaction
Mode
Continuous
Output
Quantified signals
Aim
Comparability
  • Factors
  • Screening

Multi-Factor Screening

Equities are analysed systematically across multiple quantitative dimensions, searching a broad universe for companies with potential investment value.

  • Business Quality
  • Growth
  • Valuation
  • Profitability
  • Market Behaviour
  • Investor Sentiment
  • Risk Characteristics
Dimensions
Seven factor groups
Universe
Broad coverage
Method
Systematic screening
Aim
Potential value
  • Scoring
  • Ranking

Quantitative Scoring

The QT system integrates distinct investment signals into a composite score, ranking equities to establish a pool of high-quality candidates.

Input
Distinct signals
Method
Composite scoring
Output
Ranked candidate pool
Aim
High quality
  • Fundamental
  • Review

Fundamental Review

Where the models find opportunity, the investment research team reviews the portfolio through a fundamental and investment-logic lens — testing whether model output holds up against real business and investment reasoning.

Reviewer
Investment research team
Lens
Fundamental logic
Test
Against business reality
Aim
Logical consistency
  • Portfolio
  • Risk

Portfolio Construction

Candidate holdings are optimally allocated based on stock scores, risk characteristics and portfolio constraints, producing the final investment portfolio.

Inputs
Scores · Risk · Constraints
Method
Optimised allocation
Output
Investment portfolio
Aim
Balance

The seven factor dimensions

Multi-factor screening reads a company from seven angles at once. No single dimension decides anything on its own — a score only means something once all seven are seen together.

  1. Corporate Quality

    The question it asks
    Is the business soundly run and financially solid?
    What is generally looked at
    Earnings quality, balance-sheet structure, stability of cash flow.
    Why it matters
    Filters out companies that look good on paper but are operationally fragile.

  2. Growth Capability

    The question it asks
    Can revenue and profit keep expanding?
    What is generally looked at
    Revenue and earnings growth rates, and how durable that growth has been.
    Why it matters
    Separates real growth from a one-off swing.

  3. Valuation

    The question it asks
    Where does the price sit relative to fundamentals?
    What is generally looked at
    Valuation multiples on earnings, book value and cash-flow bases.
    Why it matters
    Guards against paying too much for growth that is already priced in.

  4. Profitability

    The question it asks
    How efficiently does capital turn into profit?
    What is generally looked at
    Return on capital, margin levels and the consistency of both.
    Why it matters
    Measures how well a company converts resources into earnings.

  5. Market Behaviour

    The question it asks
    What do price and volume reveal about behaviour?
    What is generally looked at
    Trend persistence, volatility structure, trading activity.
    Why it matters
    Captures the price drift that collective behaviour creates.

  6. Investor Sentiment

    The question it asks
    How are expectations changing?
    What is generally looked at
    Analyst estimate revisions and sentiment indicators.
    Why it matters
    A turn in expectations often precedes the turn in reported numbers.

  7. Risk Profile

    The question it asks
    Where does the risk in this position come from?
    What is generally looked at
    Volatility, correlation, style and sector exposure.
    Why it matters
    Keeps the source of return explainable and the exposure controllable.

The core logic of the QT system

Quality × Value × Growth × Behavior

QT is not a search for whatever is rising fastest. It is a search for:

Better-Quality Businesses

Companies with stronger operational and financial quality.

More Reasonable Prices

Equities trading at valuations that make sense.

Real Growth Capability

Companies whose growth is substantiated by data and business logic.

Behavioural Mispricing

Opportunities where market behaviour has pushed price away from value.

Use data to find mispricing. Use fundamentals to judge real value.

Why Quantamental?

Each approach is strong on its own axis and limited on the other. Quantamental is the attempt to keep both.

Breadth of Quantitative

Powerful data-processing efficiency — but historical data and the models themselves carry real limitations.

Depth of Fundamental

Deep understanding of a business — but hard to cover a large universe and vast market datasets at once.

Systematic Synthesis

Models raise research efficiency; fundamental logic validates the signals they produce.

Quantamental seeks the strengths of both. Systematic models raise research efficiency, while fundamental logic validates model signals — so investment decisions rest not only on statistical correlation, but on sounder economic and business reasoning.

How the framework disciplines itself

A systematic process is only as good as the constraints it accepts. Six principles hold the framework to account.

  • Explainable

    A source of return should be attributable to an understandable factor, not to statistical correlation alone.

  • Tested

    A promising hypothesis is put through rigorous testing before it is allowed into the investment process.

  • Logically consistent

    Model output is checked against real business and investment logic, not accepted because the number looks good.

  • Breadth with depth

    Quantitative models supply the coverage; fundamental review supplies the depth neither could reach alone.

  • Risk-aware

    Portfolio construction weighs stock scores against risk characteristics and portfolio constraints together.

  • Repeatable

    Every stage is systematic, repeatable and reviewable — the same inputs lead to the same process.

Research & Development

A research-driven quantitative investment framework

The QT Quantamental investment framework references and builds upon the research methodologies of Rayliant Investment Research across quantitative finance, data science, behavioural finance, factor investing and portfolio construction.

Rayliant emphasises translating financial theory and data science into executable investment insight — rigorously testing effective hypotheses drawn from fundamental research before systematically integrating them into a quantitative investment process.

Its research team spans quantitative research, portfolio management, machine learning, global equities and asset allocation.

  • TheoryFinancial theory translated into executable investment insight
  • DataData science applied across market, corporate and behavioural signals
  • TestingFundamental hypotheses tested rigorously before adoption
  • IntegrationValidated signals integrated systematically into the process

Specialist research × systematic investing

The research framework behind QT Quantamental rests on cross-disciplinary research capability. Rayliant's publicly listed team includes:

QT Quantamental leadership

Rayliant Global Advisors

Jason Hsu, PhD 许仲雄

Founder & Chief Investment Officer

Dr. Jason Hsu is the founder and Chief Investment Officer of Rayliant, and one of the most recognised researchers in quantitative investing and smart beta.

He co-founded Research Affiliates and helped pioneer the RAFI™ Fundamental Index™ approach, publishing extensively across factor investing, smart beta, asset allocation and systematic investment research.

His research has been recognised with awards including the CFA Institute Graham and Dodd Award, the Bernstein Fabozzi/Jacobs Levy Award and the William F. Sharpe Award.

Dr. Hsu has long advocated combining rigorous academic research, data science and real market investment experience — the philosophy that shapes Rayliant's Quantamental research framework.

Selected awards

  • CFA Institute Graham and Dodd Award
  • Bernstein Fabozzi / Jacobs Levy Award
  • William F. Sharpe Award

  • Phillip Wool, PhD

    Chief Research Officer

  • Himanshu Surti

    Chief Operating Officer and Head of ETFs

  • Ben Ashby

    Head of Fixed Income & Foreign Exchange

  • Priscilla Liu, MFE

    Senior Vice President

Alongside a research team of quantitative researchers, portfolio managers and investment professionals.

Rayliant's official research team

Research domains

The framework rests on seven disciplines. Each contributes a distinct capability, and together they form the path from research through to risk control.

  • Quantitative Finance

    Turns asset-pricing theory, risk premia and market structure into computable, testable model assumptions — the theoretical floor the whole process stands on.

  • Data Science

    Cleans, aligns and engineers features from market, corporate and transaction data, turning raw information into comparable, auditable research inputs.

  • Factor Investing

    Distils explainable sources of return across quality, growth, valuation and profitability — the building blocks of multi-factor screening and scoring.

  • Machine Learning

    Finds non-linear relationships and signal combinations in high-dimensional data, held in check by strict out-of-sample testing to limit overfitting.

  • Portfolio Construction

    Solves the trade-off between stock scores, risk characteristics and portfolio constraints, turning research conclusions into executable allocations.

  • Behavioral Finance

    Explains how systematic biases among market participants push price away from value — the economic account of where mispricing comes from.

  • Fundamental Research

    Reviews model output against real business logic, testing whether a quantitative signal is backed by durable operations and financials.

  • Systematic research chain

    1. Research
    2. Models
    3. Signals
    4. Portfolio
    5. Risk Control

Common questions

Straight answers on what this framework is, what it is not, and where its limits lie.

  • How is Quantamental different from purely quantitative investing?

    A purely quantitative model processes data at scale but is bounded by history and by the model's own assumptions. Quantamental keeps that breadth and adds a fundamental review step, so a signal has to make business sense as well as statistical sense before it reaches the portfolio.

  • Does the system replace the investment research team?

    No. The division of labour is deliberate: the models find opportunity across a universe no individual could cover, and the investment research team reviews the result through a fundamental and investment-logic lens, judging whether the model's conclusion holds up against real business reasoning.

  • Which dimensions does multi-factor scoring actually look at?

    Seven: corporate quality, growth capability, valuation, profitability, market behaviour, investor sentiment and risk profile. Each is set out in the factor section above.

  • What is the relationship between QT and Rayliant?

    QT's framework references and builds upon Rayliant's publicly documented Quantamental research methodology. Rayliant Investment Research is an independent research organisation; the researchers named on this site are described in their public Rayliant capacity, and nothing here should be read as their endorsement of QT.

  • Can a quantitative model guarantee returns?

    No. Models, historical data, backtest results and fundamental research all describe the past and the present; none of them can guarantee future performance. Securities investment carries market volatility and the risk of principal loss.

  • Does a strong backtest mean the strategy will work?

    Not by itself. A backtest is fitted to history and can reward overfitting, which is why out-of-sample testing and a fundamental logic check matter more than an impressive historical curve.

Working with industry-leading brands

Standing alongside leading model providers to define the standard and the future.

  • Qwen
  • DeepSeek
  • Doubao
  • Claude
  • ChatGPT
  • Kimi

Find opportunity more systematically, build portfolios more rationally.

Moving investing from judgement by experience to dual verification by data and logic.

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