Smart Capital, Limiteless Horizons

Huajun Consulting Inc. is a cutting-edge financial consulting firm specializing in quantitative investment strategies. We leverage advanced algorithms, statistical models, and machine learning techniques to optimize firm selection, market exposure, and hedging strategies. Our data-driven approach enables us to identify high-potential investment opportunities, manage risk efficiently, and enhance portfolio performance. At Huajun Consulting Inc., we combine deep financial expertise with state-of-the-art technology to provide clients with innovative, systematic solutions for navigating complex markets.

Our mission is to achieve consistent, long-term returns exceeding 10% annually while building a centennial enterprise that stands the test of time. We believe that sustainable success comes from unwavering dedication to our principles, continuous innovation, and a commitment to creating lasting value for our clients across generations.

Who We Are

  • CEO

    Geoffrey Li is a current candidate of Master of Arts in Mathematical Finance at (MAFN) Columbia University. He holds a Bachelor's degree in Mathematics and furthered his studies with a Master of Mathematics from the University of Cambridge. His expertise spans technical trading, asset pricing models, and entrepreneurship.

  • Head Economist

    Junru Lyu is a PhD candidate in Economics at UC Berkeley, building upon his undergraduate degree in Applied Mathematics and Economics. He specializes in economic and financial analysis across firms, geographic regions, and investment strategies.

  • Investment Analyst

    Yaru Tao is a current Master of Financial Engineering (MFE) candidate at UC Berkeley's Haas School of Business. She brings a strong quantitative foundation from her undergraduate studies at Berkeley, where she earned a double major in Applied Mathematics and Economics. Yaru excels in equity research, financial modeling, and investor communications.

Dual-Lens Market Analysis Framework

Peer Analysis and Market Behavior Framework

Our analytical approach begins with comprehensive data collection across comparable securities. We aggregate key financial metrics (including income statement components, market performance indicators, and derived valuation multiples) for peer groups defined both by traditional industry classifications and behavioral market segments. This dual grouping methodology allows us to capture both fundamental business similarities and market sentiment patterns. By analyzing companies through both conventional peer comparisons and behavioral clustering, we gain deeper insights into relative valuations and market dynamics.

Predictive Economic Modeling Framework

Our proprietary forecasting model synthesizes granular data across diverse goods, services, and commodity markets to anticipate macroeconomic indicators. Through carefully calibrated regression analysis aligned with economic cycles, we identify leading signals that precede market movements. This methodical approach has achieved remarkable precision, with forecasts consistently falling within 5 basis points of official releases. The model's predictive power enables proactive positioning ahead of market responses to economic announcements.

Historical Performance

Our track record speaks for itself. Below, you'll find our comprehensive performance data since inception, demonstrating our commitment to delivering consistent results for our clients.

Cumulative Return

Our consolidated portfolio has achieved a 79.33% return since inception, outperforming all major benchmarks (QQQ: 51.24%, SPX: 35.41%, STI: 19.32%), while maintaining strong recent performance with YTD returns of 8.82% versus QQQ (5.27%), SPX (3.96%), and STI (2.37%), and showing consistent growth across all time periods.

Annual Return

Investing 101

  • Modern Portfolio Theory (MPT):

    • Framework for optimizing portfolio returns for a given level of risk

    • Key concepts: diversification, efficient frontier, risk-return tradeoff

    Capital Asset Pricing Model (CAPM):

    • Offshoot of MPT describing relationship between systematic risk and expected return

    • Equation: E(R) = α + Rf + β (E(Rm) - Rf)

      where: E(R) = expected asset return, Rf = risk-free rate, E(Rm) = expected market return

    Alpha (α):

    • Measures excess return compared to benchmark

    Beta (β):

    • Measures asset volatility relative to market

  • An option is a contract giving the right (but not obligation) to buy (call) or sell (put) an asset at a predetermined price (strike price) within a specific time period.

    Derivatives include options and other instruments whose value depends on underlying assets:

    • Futures: Contracts to buy/sell at future date at set price

    • Swaps: Agreements to exchange cash flows

    • Forwards: Custom contracts for future transactions

    • Structured Products: Complex combinations of derivatives

  • Delta (Δ)

    • Measures how much option price changes relative to a $1 change in underlying asset

    Gamma (Γ)

    • Measures rate of change in Delta with underlying price movement

    Theta (Θ)

    • Measures how much option value decreases each day (typically negative, which means options lose value over time)

    Vega (ν)

    • Measures price change for 1% change in implied volatility

    Rho (ρ)

    • Measures sensitivity in price change for 1% change in interest rates

  • FinTech and Quant Trading have brought significant advancements to the financial industry:

    1. Algorithmic trading: Complex mathematical models execute trades at high speeds and volumes.

    2. High-frequency trading (HFT): Extremely fast trading systems capitalize on small price movements.

    3. Big data analysis: Advanced analytics process vast amounts of financial data to identify trends and opportunities.

    4. Machine learning in trading: AI systems that can adapt and improve trading strategies based on new data.

    5. Automated risk management: Sophisticated models to assess and mitigate investment risks in real-time.

    6. Democratization of quantitative strategies: Tools that were once exclusive to large institutions are now available to smaller firms and individual traders.

    7. Enhanced backtesting: More robust methods to test trading strategies against historical data.

    8. Improved market efficiency: Quant trading has generally led to tighter spreads and more liquid markets.

    9. New data sources: Incorporation of alternative data like satellite imagery or social media sentiment in trading decisions.

    10. Blockchain in trading: Exploring uses of distributed ledger technology for faster, more transparent transactions.

    11. Cloud computing in finance: Enabling more powerful computations and data storage at lower costs.

    12. Automated portfolio management: Robo-advisors using quantitative methods to manage investments.

    13. Sentiment analysis: Using natural language processing to gauge market sentiment from news and social media.

    14. Regulatory technology (RegTech): Advanced systems to ensure compliance with complex financial regulations.

    These innovations have transformed how financial markets operate, offering new opportunities but also introducing new challenges and potential risks.