Our Methodology

Discover how we combine proprietary alternative data signals with enhanced quantitative factors to deliver superior risk-adjusted returns for financial advisors and their clients.

Our Approach

Beyond Traditional Factor Investing

Traditional factor models rely on historical financial data that often reflects information already known to the market. Our methodology enhances these traditional factors with proprietary alternative data sources and advanced analytics to generate superior alpha.

Alternative Data Integration

We leverage unique datasets including earnings call sentiment, patent filings, and H1B visa data to gain insights unavailable to traditional approaches.

Enhanced Quantitative Factors

Our enhanced versions of traditional factors incorporate forward-looking data and sophisticated analytics for improved predictive power.

Rigorous Risk Management

Advanced portfolio optimization techniques ensure consistent risk-adjusted returns while managing downside exposure across market cycles.

Alternative Data

Proprietary Data Sources

Our alternative data components provide unique insights that traditional financial analysis cannot capture.

Earnings Expectations

Our proprietary earnings expectations model goes beyond simple consensus estimates by analyzing analyst quality, peer trends, and management guidance patterns. We leverage machine learning to identify which analysts have historically provided the most accurate forecasts and weight their estimates accordingly. This approach generates more accurate forward-looking EPS and revenue forecasts, providing a significant informational advantage over traditional valuation metrics.

Key Benefits:

  • Superior forward-looking earnings accuracy
  • Analyst quality weighting system
  • Real-time guidance incorporation
  • Peer trend analysis integration

Earnings Calls Sentiment

We employ advanced natural language processing and machine learning techniques to analyze earnings call transcripts, extracting sentiment signals that traditional financial metrics miss. Our models identify subtle language patterns, tone changes, and executive confidence levels that correlate with future stock performance. This alternative data source provides insights into management outlook and company fundamentals that are not captured in traditional financial statements.

Key Benefits:

  • Advanced NLP sentiment analysis
  • Management tone detection
  • Forward-looking insight extraction
  • Real-time transcript processing

Innovation Signal

Our innovation signal combines patent filing data with H1B visa application patterns to predict industry innovation intensity and future growth potential. Companies with strong patent portfolios and high-skilled worker acquisition often outperform their peers over the medium term. This unique dataset allows us to identify companies positioned for technological advancement and competitive advantage before it becomes apparent in traditional financial metrics.

Key Benefits:

  • Patent portfolio analysis
  • H1B visa trend tracking
  • Innovation intensity scoring
  • Competitive advantage prediction

Enhanced Factors

Beyond Traditional Quantitative Factors

Our enhanced quantitative factors improve upon traditional approaches by incorporating alternative data and advanced analytics.

Enhanced Value

Traditional value metrics rely heavily on historical data and can miss important forward-looking information. Our Enhanced Value factor incorporates our proprietary earnings expectations to create a more dynamic valuation framework. By using forward-looking earnings estimates rather than trailing metrics, we can identify truly undervalued companies before the market recognizes their potential.

Key Improvements:

  • Forward-looking valuation metrics
  • Dynamic price-to-earnings ratios
  • Enhanced book value analysis
  • Improved value identification accuracy

Enhanced Quality

Our Enhanced Quality factor goes beyond traditional quality metrics by differentially weighting accruals based on their sustainability and predictive power. We analyze the composition of earnings to identify companies with high-quality, sustainable business models. This approach helps us avoid value traps and identify companies with durable competitive advantages.

Key Improvements:

  • Sustainable earnings analysis
  • Accruals quality assessment
  • Business model durability scoring
  • Value trap avoidance

Enhanced Momentum

Traditional momentum strategies often suffer from overcrowding and mean reversion. Our Enhanced Momentum factor uses proprietary peer-benchmarking techniques with economically linked firms to capture cross-stock momentum effects. This approach identifies momentum signals that are more persistent and less susceptible to reversal.

Key Improvements:

  • Peer-benchmarked momentum signals
  • Cross-stock effect capture
  • Reduced mean reversion risk
  • Enhanced signal persistence

Enhanced Volatility

Our Enhanced Volatility factor adjusts traditional volatility measures to isolate the portion that is truly predictive of future returns. By controlling for other risk factors and market conditions, we present a purer measure of volatility that better predicts future performance and provides more effective risk management.

Key Improvements:

  • Risk-adjusted volatility measures
  • Predictive volatility isolation
  • Enhanced risk management
  • Market condition adjustments

Our Process

From Data to Portfolios

Our systematic approach transforms raw alternative data into actionable investment strategies.

1

Data Collection & Processing

We aggregate data from multiple alternative sources including earnings call transcripts, patent filings, H1B visa applications, and traditional financial data. Our proprietary data processing pipeline cleanses, normalizes, and structures this information for analysis.

2

Signal Generation

Advanced machine learning models transform raw data into predictive signals. Each signal is rigorously backtested and validated across multiple time periods and market conditions to ensure robustness and consistency.

3

Factor Construction

Individual signals are combined into comprehensive factors using sophisticated weighting schemes. Our enhanced quantitative factors integrate alternative data insights with traditional financial metrics for superior predictive power.

4

Portfolio Optimization

We employ advanced portfolio optimization techniques that balance expected returns with risk management. Our models consider transaction costs, liquidity constraints, and sector exposure to create implementable portfolios.

5

Continuous Monitoring

All models are continuously monitored for performance degradation and updated as new data becomes available. We employ rigorous risk management and position sizing to ensure consistent performance across market cycles.

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