Early influences & business curiosity
Growing up amid the development of Taiwan’s manufacturing, electronics and export sectors prompted an interest in company growth, industry competition and capital flows.
Kerui Capital Ltd. · PERSONAL PROFILE
Yao-Heng
Chuang
莊曜珩
Market experience.
A deeper research perspective.
From Taiwan equities and industry analysis to AI high-frequency trading and financial data technology. A research journey grounded in market observation, model validation and risk awareness.
Co-founder & Chief Investment Research OfficerAsia-Pacific Chairman
A research perspective, rooted in markets.
CAPITAL MARKETS · AI · FINANCIAL DATATAIPEI — GLOBAL PERSPECTIVE01 / THE PROFILE
From observing markets
to a systematic approach.
Research helps us understand markets.
It also reveals the limits of our judgment.
Yao-Heng ChuangKerui Capital Ltd.
Born in Taiwan in 1971, Yao-Heng Chuang has a longstanding focus on capital markets and financial technology research. He is a co-founder of Kerui Capital Ltd.
Beginning with equity and industry analysis, his work expanded into international futures, foreign exchange, global macro, algorithmic models and financial data technology. Research evidence, historical validation and risk management remain closely connected.
His current focus is AI high-frequency trading research: market microstructure, execution and real-time risk monitoring over shorter time horizons, alongside the application of data and modeling capabilities to business settings.
Co-founder & Chief Investment Research OfficerAsia-Pacific Chairman
02 / RESEARCH FOCUS
A closer look at trading.
A clearer basis for research.
Consider data, models and trading conditions together to understand the assumptions behind every research conclusion.
01Orders, quotes & transactionsMarket microstructure & liquidityStudy spreads, market depth and executable volume to understand how orders, quotes and transactions shape price formation.
02Data quality, features & modelsAI & financial data technologyConnect data collection, cleaning and storage with market classification, feature identification and anomaly detection.
03Historical evidence, cost & fillsStrategy validation & executionEvaluate models across market environments while considering research assumptions, transaction costs and execution conditions.
04Stress tests, assessment & revisionRisk monitoring & model reviewBring liquidity changes, extreme scenarios and model failure into research, with risk assessment throughout the process.
MARKET STRUCTURE+DATA INTELLIGENCE+RISK AWARENESS
THE RESEARCH PHILOSOPHY
Experience asks questions.
Evidence tests assumptions.
From equity analysis to high-frequency trading, one principle endures: every judgment should have a clear research foundation.
01
Traceable
Record data sources, research conditions and processing methods.
02
Testable
Retain evidence that challenges an assumption as well as evidence that supports it.
03
Contextual
Understand the environments in which a method applies—and where it may fail.
04
Evolving
Reassess conclusions as new data and risk observations become available.
THE JOURNEY1971 — 2026
First steps in capital markets
At 18, he began following business and financial information, observing Taiwan share prices and trading activity to explore how information, expectations and participant behavior interact.
Building a foundation in equity research
Taiwan equity studies combined financial statements, industry and operating information with prices and volumes, while considering how experience, emotion and information processing affect judgment.
Companies within their industry context
Analysis expanded to revenue, raw-material prices, interest rates, exchange rates and international capital flows, connecting company value with industry cycles and market conditions.
A stronger focus on risk
The Asian financial crisis sharpened his attention to currency, financing and liquidity risk, including the cost of mistaken judgments, potential losses and the conditions in which assumptions can fail.
Futures & derivatives
Research extended into futures and derivatives, considering volatility, leverage, cross-asset pricing and trading behavior, together with applications of mathematical and statistical methods.
Developing a global perspective
The research scope broadened to U.S. equities, international futures, FX and global macro, examining how rates, the dollar, energy and monetary policy affect capital flows, business costs and valuations.
Building a market data framework
Digital tools helped organize historical prices, volumes, volatility and market cycles, making it possible to revisit, compare and test research views across different periods.
Statistical analysis & historical validation
Experience-based investment reasoning was translated into explicit, comparable research conditions, with complete records and counterexamples helping avoid conclusions based only on selected successes.
Extreme scenarios & liquidity
The financial crisis deepened the focus on changing correlations, liquidity and model failure, including risks that emerge when assumptions about normal markets cease to hold.
Toward systematic research
Trends, volatility, unusual trading activity and cross-asset relationships were described through data, with attention to whether the same hypothesis could be repeatedly tested across environments.
Connecting the research process
The process connected data acquisition and preparation, feature analysis, hypotheses, historical validation and risk assessment, while reviewing market conditions and the applicability of methods.
Automation & strategy backtesting
Work in automated trading, backtesting and risk models used software for ongoing information processing, with greater attention to defining research questions, designing models and evaluating their reasoning.
Classification & anomaly detection
Research into market classification, feature identification, anomaly detection and risk analysis incorporated market states, contextual differences and model applicability into more detailed assessment.
Multiple data sources & model evaluation
Prices, trading activity, macro and industry data were combined with exploration of structured and unstructured information, while reviewing historical comparability, anomalies and model reliability.
Defining a connected research framework
Market data, algorithmic models and risk management became core directions, linking collection, cleaning, storage, feature analysis, modeling, strategy validation and monitoring.
From individual research to an enterprise framework
As the volume of market information grew, he considered how research validation and technical infrastructure could work together, turning accumulated methods into sustainable organizational capabilities.
Co-founding Kerui Capital Ltd.
He co-founded Kerui Capital Ltd., focusing on capital market research, financial data and algorithmic technology as prior research experience developed into team-based, systematic workflows.
Establishing data & research foundations
Early work connected data acquisition, cleaning, storage and analysis with modeling, strategy validation and risk control, emphasizing data quality and clearly reasoned research.
Research processes & stress testing
The process linked feature analysis, modeling, backtesting, risk analysis, real-time monitoring and updates, with stress tests, unusual scenarios and comparisons across market cycles.
Broadening data & model research
Research explored information beyond prices, volumes and macro data, connecting features, validation and risk identification while considering applications in business settings.
Extending into enterprise applications
Data technology projects and collaboration with broadcasting-related and other businesses extended analysis to content distribution, viewing trends, user behavior and decision support.
Clarifying research & technology roles
Capital markets provided questions and context, algorithms provided analytical methods, and data infrastructure supported the process, while the limitations of data and models remained part of evaluation.
Integrating the long-term research framework
The long-term framework connected capital market research, algorithmic technology and data infrastructure, emphasizing accumulated data, repeated model validation and reassessment as conditions change.
A focus on AI high-frequency trading research
Market observation, algorithmic methods and risk management extend to shorter time horizons, focusing on orders, transactions, liquidity, trading costs, execution quality and real-time risk monitoring.
04 / ASIA-PACIFIC & GLOBAL PERSPECTIVES
A Taiwan foundation.
A regional perspective.
Through an Asia-Pacific research perspective, connect U.S. and Taiwan equities, regional economies, international futures, FX and global macro while preserving an understanding of companies and industries.
TAIPEI, TAIWAN25°N · 121°E
01
Taiwan equities
Company financials, industry cycles, prices and trading activity.
02
U.S. equities
Listed companies, valuations and the market environment.
03
Futures & foreign exchange
Volatility, leverage and cross-asset price relationships.
04
Global macro
Interest rates, the dollar, energy and monetary policy.
05
Asia-Pacific economies
Regional policy, technology supply chains and cross-market information.
FINANCIAL DATA / ENTERPRISE APPLICATIONS
Beyond financial market research
Data capabilities also extend to broadcasting and enterprise applications, including content distribution, viewing trends, user behavior and decision support. Each setting begins with understanding what its data means and how it will be used.
05 / COMMON QUESTIONS
A closer understanding
of the work and its methods.
What roles does Yao-Heng Chuang hold?
He is Co-founder and Chief Investment Research Officer of Kerui Capital Ltd., and Asia-Pacific Chairman, with a focus on capital market research, AI high-frequency trading and financial data technology.
Which markets does his research cover?
Starting with Taiwan equities and industry analysis, his research expanded to U.S. equities, international futures, foreign exchange and global macro, with attention to trading conditions, capital flows and risk relationships.
What does high-frequency trading research examine?
The work focuses on short-horizon market microstructure, orders and transactions, liquidity, transaction costs, execution quality and real-time risk monitoring.
How do AI, models and professional judgment fit together?
Data and models support comparison, analysis and validation. Defining research questions, understanding data, interpreting results and assessing risk still require clear reasoning and ongoing review of model limitations.
How is market risk considered?
Historical validation, stress testing, extreme-scenario analysis and ongoing monitoring examine liquidity, volatility and conditions under which models may fail. New observations feed back into the research process.
Does the work extend beyond finance?
Related capabilities extend into enterprise data applications, including content distribution, viewing trends and user behavior in broadcasting, as well as information processing and decision support.
06 / PROFESSIONAL EXCHANGE
Let research begin
the next conversation.
Explore professional dialogue around AI high-frequency trading, market data, algorithmic research and enterprise data applications.