AWS is the world number one AI-Powered cloud hosting solution for smart Trader
Empowering Hong Kong's finance sector with cutting-edge cloud technology and AI-powered trading solutions.
Our Achievements
Recognition and innovation in financial services technology
Best Cloud Hosting Solution
HFM Asia Services Awards
Tech for Trading Letter to Customers
Dear Valued Customers,
We're thrilled to share our latest wins and tech strides with you. AWS has clinched the Best Cloud Hosting Solution at the HFM Asia Services Awards. This nod, from top hedge fund execs in Asia, shows our pledge to arm Hong Kong's finance sector with top-notch cloud tech.
Our v-team snagged the AWS GCR Strategy Pioneer Award for aiding hedge funds in fast-tracking their strategy dev with AWS. Key feats include:
- Factor Modeling: We've upped the ante with GenAI sentiment analysis. This tech dives deep into market moods, giving you a leg up in your trades.
- Generic Backtesting: Our new framework lets you test trade ideas with ease, ensuring your strats are rock-solid before you go live.
A big shout-out to our stellar hedge fund clients and the AWS v-team for making this win possible. We're jazzed to keep pushing the tech envelope with you, ensuring you stay ahead in this cut-throat game.
Trust us, it's always day one here at AWS. We're all in on making your trading tech top-tier.
Warm regards,
AWS HK FSI Business Team
2025 AWS New Finance, New Economy
Taipei Presentation
Dear Valued Customers,
We're stoked to share our recent talk at the 2025 AWS New Finance, New Economy meet in Taipei. Our chat, "Smarter Trades, Faster Decisions: Unlocking Alpha with AI-Powered Market Insights on AWS", dove deep into how new tools are reshaping trading.
For Macro Traders, we showed how Amazon Bedrock, Amazon Q Developer, and Kiro are boosting data grab and choice-making. The gains? Huge.
On the quant side, we spilled the beans on top-notch factor find using AWS gear. If you're keen, dive into the factor mining GitHub repo.
It's all about trust, ownership, and pushing tech for trading.
We're all in on making your trading tech top-tier. It's always day one here at AWS.
Warm regards,
AWS HK FSI Solution Team
Appendix:
AWS Buy-Side Solutions
Comprehensive Investment Lifecycle Technology
What is AWS Buy-Side Solutions?
Buy-Side Solutions offer comprehensive, fully hosted technology across entire investment lifecycle - from idea generation through post-trade settlement, providing fresh data insights, cutting-edge portfolio management tools, and streamlined operations.
Research & Idea Generation
- Market insights
- Company & industry analysis
- Quantitative analytics
- Research Management
Portfolio & Risk Management
- Portfolio Analytics
- Portfolio construction
- Performance measurement & attribution
- Risk analysis
- Benchmark indexes
Order & Execution Management
- Liquidity & Price Discovery
- Order & Allocation Management
- Execution Management & electronic trading
- Trade analytics
Post Trade & Operations
- Reconciliation & Exception Management
- Allocation matching & trade settlement
- Collateral Management
- Corporate action processing
Compliance & Risk Oversight
- Investment & Trade Compliance
- Transaction cost analysis
- Surveillance & trade reconstruction
- Regulatory & risk Management
Technology & Data Management
- Content & Data
- Integration & distribution
- Financial data Management
- 3rd party system integration
Our comprehensive Buy-Side Solutions provide end-to-end technology infrastructure that empowers investment firms to operate efficiently across all phases of the investment lifecycle, from initial research and idea generation to final settlement and compliance oversight.
Warm regards,
AWS Buy-Side Solutions Team
Tech for Trading Workshop Series
Hands-on workshops driving innovation in financial technology
Factor Modeling
GenAI in Finance Deep Dive
Dear Valued Customers,
We just wrapped up our Tech for Trading series workshop 1 on Factor Modeling. We had ~80 fund bigwigs and tech gurus join us for a deep dive into GenAI in finance!
Morning Business Track Highlights:
- GenAI basics and real use cases
- Live demos with real fund scenarios
- Partner showcase of top buy-side solutions
Afternoon Tech Track - The Real Deal!
Tech folks from different funds dug deep into the factor mining workshop. Hope you found your own factors and alpha using this method.
And guess what? I bumped into my old broker from 15 years back! It's a small world. These moments remind me why I love bringing this community together.
Missed out? No worries! Dive into our GenAI for Hedge Fund and Factor Modeling workshop and our detailed blog on the topic.
Get hands-on with:
- Real-time factor calc using market data
- Unstructured data processing tricks
- Advanced ML model setup
Led by our top-notch Industry Specialists and Solutions Architects, this workshop arms you with:
- Practical implement strats
- Best practices for live deploy
- Perform optimization tricks
- Direct chat with AWS tech gurus
Warm regards,
AWS HK FSI Solution Team
Sensitivity and Data Privacy
Crypto Trade Security
Dear Valued Customers,
We just wrapped up our Tech for Trading series workshop 2 on sensitivity and data privacy for crypto trade. This was a deep dive into secure GenAI solutions for the capital markets sector.
Workshop Highlights:
- Learn how to roll out safe GenAI tools for your org's internal use.
- See real-world apps in action.
- Change your ops without risking security.
GenAI Use Cases (GenU):
- Chatbots
- Text summarization
- Content correction
- Image generation
Achievements:
- AI-driven help
- Multi-format trade processing
Results:
- Build with trust
We're all in on making your trading tech top-tier. It's always day one here at AWS.
Warm regards,
AWS HK FSI Solution Team
Multi Assets Delta Hedge
Long-Short Risk Parity Portfolio
Dear Valued Customers,
We just wrapped up our Tech for Trading series workshop 3 on Multi Assets Delta Hedge & Long-Short Risk Parity Portfolio. Here's the lowdown:
Workshop Highlights:
I, a Macro Trader, built a backtrader python for Delta Hedge Indexing using just two prompts at the AWS Hong Kong for Capital Markets on 10 June 2025.
Live AWS Q CLI practice on booth:
- Macro Hedge Portfolio with Multi Assets Delta Hedge
- Macro Hedge Portfolio with Long-Short Risk Parity Portfolio
Tech on AWS:
- Amazon Bedrock
- Amazon Q Developer
- Kiro
- AgenticAI
Achievements:
- Generative AI backtrader
- Investment Advisor
- Macro Hedge
- Multi Assets Delta
- Long-Short Risk Parity
Results:
- High absolute return
- Low risk
We're all in on making your trading tech top-tier. It's always day one here at AWS.
Warm regards,
AWS HK FSI Solution Team
Trading Lessons
Comprehensive learning modules for quantitative trading and factor analysis
Introduction of Trading Factors Mining
Leveraging generative AI and cloud services to rapidly mine quantitative trading factors
Factor Modeling: A Quantitative Approach to Guiding Investment Decisions
- Use a systematic approach to identify the drivers of asset returns
- Decomposing security returns into interpretable risk factors
- Achieve quantitative analysis of investment performance
- Factor modeling is the cornerstone of modern quantitative investment strategies, especially in hedging
- Fund and institutional asset management fields
Examples:
- Cumulative market performance (MSCI ACWI)
- Annualized up/down market excess returns
- Value
- Scale
- Momentum
- Quality
- Highest dividend
- Minimum Volatility
- Excess Returns (Up Markets)
- Excess Returns (Down Market)
- Cumulative Performance Over Market (MSCI ACWI)
- Annualized Up/Down Market Excess Return
- Value
- Size
- Momentum
- Quality
- High Dividend
- Minimum Volatility
- Excess Returns (Up Markets)
- Excess Returns (Down Markets)
Key Models:
- Capital Asset Pricing Model
- Fama–French three-factor model
- Arbitrage Pricing Theory (APT) Three-Factor Model
Reference: NEPC, "Guide to Factor Investing" (2024)
What is "Alpha" and "Beta"?
Alpha:
Represents a portfolio manager's ability to outperform a specific benchmark (like the S&P 500). A positive alpha indicates the investment did better than expected, given its risk, while a negative alpha suggests it underperformed.
Beta:
Measures the volatility of an investment relative to the overall market. A beta of 1 means the investment moves with the market, while a beta greater than 1 indicates higher volatility and potential for greater gains or losses.
Factor Expansion and Alpha Decay
Factor Surge:
More than 400 factors have been documented in top academic literature
Alpha Falloff:
- Performance after public release is usually weak
- Overuse of popular factors
- Alice in Factor Wonderland: Three Misconceptions Plaguing Factor Investing
- Alice's Adventures in Factor land: Three Blunders That Plague Factor Investing
Factor Mining Workflow
Factor mining is a combination of financial theory, a systematic process of statistical analysis and computational methods designed to discover and validate investment factors.
- Data collection and preprocessing (covering market data, fundamental data, and alternative data)
- Factor definition and calculation (mathematical formula design and regression analysis)
- Statistical analysis (including significance tests, t-statistics, R-squared)
- Robustness testing (validated on out-of-sample data, different markets, and different time periods)
- Factor trading and monitoring (integrating portfolios, tracking investment performance)
Example: Debt-to-Equity (D2E) Factor Mining
Definition and Calculation Method of Factors:
Calculation formula: D2E [Current Liabilities / Shareholders' Equity], calculates the stock factor value for all stocks within a specific date range.
Build an Investment Portfolio:
Sort by D2E value every day and select stocks with high D2E value (top third) and low D2E value (bottom third)
Calculate the Factor Value:
Factor value = High Portfolio D2E Value (High P) - Low Portfolio D2E Value (LowP)
Regression Analysis Process:
For each stock, perform regression analysis on the factor and obtain various statistical indicators:
- Beta coefficient is used to measure the sensitivity of stock returns to changes in factors
- The t-statistic is used to measure the statistical significance of a factor's ability to predict returns
- R-squared indicates the proportion of stock returns explained by the factor
- Calculate the average value of each indicator for overall evaluation factor
Factor Analysis with AWS Bedrock
Advanced data analysis using traditional and alternative data sources
What is Traditional Data?
Traditional data refers to structured financial information that has been used in quantitative trading for a long time and published by authoritative institutions in a fixed format. This data is easy to obtain, has a standard format, and is historically traceable, and is the basis for building and testing quantitative models.
- Data sources: market data, financial statements, SEC regulatory filings
- Data format: structured, standardized
- Accessibility: easy to obtain and regulated
- Insights: Historical, fundamental, and based on market consensus
What is Alternative Data?
Alternative data refers to non-traditional and unconventional data sources that can provide strategic data insights. Unlike traditional data sources, alternative data can reveal hidden trends and patterns that are often overlooked in standard data analysis.
- Social media, web search, news
Why is Alternative Data Important?
- Traditional data has a low barrier to entry, making it difficult to gain a unique competitive advantage. Alternative data can help uncover more potential investment opportunities
- Maintaining a competitive advantage in today's data-driven market environment. According to a survey, 98% of investment professionals agree or strongly agree that using alternative data to discover innovative investment ideas and thereby increase excess returns is becoming increasingly important
- Combining traditional data with alternative data can provide more comprehensive market insights and investment opportunity perspectives
Guidance for Generating Sentiment Analysis of Stock Market News
- The sentiment scoring results are stored in the Analytics OLAP database as input data for subsequent mining processes
- Web search data ingestion pipeline based on Tavily search API
- Request a summary of the content and suggested tips to guide Large Language Models (LLMs) to act as CFA and FRM charterholders
- 5-factor sentiment analysis
- S3 event notification triggers Lambda ETL processing
AWS Bedrock Implementation
- Sentiment scoring results stored in Analytics OLAP database
- Web search data ingestion pipeline based on Tavily search API
- LLMs acting as CFA and FRM charterholders
- 5-factor sentiment analysis
- S3 event notification triggers Lambda ETL processing
Prompt Examples
Prompt:
"As an experience CFA and FRM holder, please analyze the attached annual report and extract concise summaries (2-3 sentences each)"
Key factors:
- CEO statement
- ESG initiative
- Market trends and competitive landscape
- Risk factors
- Strategic priorities
Prompt:
"As an experienced Chartered Financial Analyst (CFA) and Financial Risk Manager (FRM) charterholder, please analyze the attached annual report and extract a brief summary (2-3 sentences each)"
Key factors:
- CEO Statement
- ESG Initiatives
- Market trends and competitive landscape
- Risk Factors
- Strategic Priorities
Prompt:
"Generate sentiment for the stock's market news: Analyze the sentiment of the following text about a company's stock and financial performance."
Subsequent Plans:
- Backtesting and Optimizing Factor-Based Trading Strategies
- Factor Mining
- Factor screening and evaluation
- Backtesting and Evaluation
- Combination
- Strategy Development
Popular 20 Factors
Essential trading factors for quantitative analysis
Valuation & Growth Factors
Full Term: Price/Earnings to Growth Ratio
Key Takeaway: Measures a stock's price relative to its earnings growth. A lower PEG ratio may indicate better value.
Full Term: Price to Book Ratio
Key Takeaway: Compares a company's market value to its book value. A lower PB ratio might indicate undervaluation.
Full Term: Revenue Growth Rate
Key Takeaway: Measures the percentage change in a company's revenue over time. Higher growth rates can indicate a strong business performance.
Technical & Market Factors
Full Term: Relative Strength Index
Key Takeaway: A momentum oscillator that measures the speed and change of price movements. RSI values above 70 suggest overbought conditions, and below 30 suggest oversold conditions.
Full Term: Small Minus Big
Key Takeaway: Represents the difference in returns between small-cap stocks and large-cap stocks. It is part of the Fama-French three-factor model.
Full Term: High Minus Low
Key Takeaway: Represents the difference in returns between value stocks (high book-to-market ratio) and growth stocks (low book-to-market ratio). Also part of the Fama-French model.
Full Term: Market Risk Premium
Key Takeaway: The excess return of a market portfolio over the risk-free rate. It represents the additional risk taken by investing in the market.
Full Term: Trading Volume
Key Takeaway: The number of shares traded during a given period. High trading volume can indicate strong interest in a stock, while low volume can suggest the opposite.
Full Term: Rate of Change
Key Takeaway: Measures the percentage change in price over a period. It helps identify the speed and direction of price movements.
Financial Health & Liquidity
Full Term: Current Ratio
Key Takeaway: A liquidity ratio that measures a company's ability to pay short-term obligations with current assets. A ratio above 1 is generally favorable.
Full Term: Cash Ratio
Key Takeaway: A stringent liquidity ratio that measures a company's ability to pay off its short-term liabilities with cash and cash equivalents. A higher ratio indicates better liquidity.
Full Term: Inventory Turnover Ratio
Key Takeaway: Measures how quickly a company sells its inventory. A higher ratio indicates efficient inventory management.
Full Term: Gross Profit Margin
Key Takeaway: Indicates the percentage of revenue that exceeds the cost of goods sold (COGS). A higher margin suggests better profitability.
Full Term: Debt to Equity Ratio
Key Takeaway: Measures a company's financial leverage. A lower ratio indicates less debt relative to equity, which may be seen as less risky.
Full Term: Interest Coverage Ratio
Key Takeaway: Measures a company's ability to pay interest on its outstanding debt. A higher ratio indicates better ability to cover interest expenses.
Governance & Sentiment Factors
Full Term: Average Age of Board Members
Key Takeaway: Reflects the average age of the board of directors. A mix of younger and older directors can bring diverse perspectives and experience.
Full Term: Executive Compensation
Key Takeaway: The total compensation paid to top executives. High compensation relative to performance can be a concern for investors.
Full Term: Environmental Rating
Key Takeaway: Assesses a company's environmental practices and impact. Higher ratings can indicate better sustainability practices.
Full Term: Average Sentiment Score
Key Takeaway: Reflects the overall sentiment of analysts, investors, and the public towards a stock. Positive sentiment can drive stock prices up.
Full Term: News Sentiment
Key Takeaway: Measures the sentiment of news articles and media coverage about a company. Positive news can boost stock performance.
Deep Dive in T-Statistics
Statistical analysis for factor validation and significance testing
3 Popular Analysis List
Full Term: T-Statistic
Key Takeaway: A statistical measure that determines the significance of an estimated parameter. It compares the estimated value to its standard error. A higher absolute t-value indicates that the parameter is statistically significant.
Full Term: R-Squared (Coefficient of Determination)
Key Takeaway: A statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. An R-squared value closer to 1 indicates a better fit of the model to the data.
Full Term: Beta Coefficient
Key Takeaway: A measure of a stock's volatility in relation to the overall market. A beta greater than 1 indicates higher volatility, while a beta less than 1 indicates lower volatility. Beta is used to assess the risk of a stock relative to the market.
What Is a T-Test?
- T-test compares the mean values of two samples to determine a statistically significant difference
- T-test are used when the data sets follow a normal distribution and have unknown variances
Example:
- Data set recorded from flipping a coin 100 times
- Statistically significant difference between the means of two data sets
- Used for hypothesis testing in statistics
- Calculating a t-test requires the difference between the mean values from each data set, the standard deviation of each group, and the number of data values
Example: Placebo-fed control group of a drug test and those taken from the drug-prescribed group should have a slightly different mean and standard deviation
Four Assumptions for T-Test
- The data collected must follow a continuous or ordinal scale, such as the scores for an IQ test.
- The data is collected from a randomly selected portion of the total population
- The data will result in a normal distribution of a bell-shaped curve.
- Equal or homogenous variance exists when the standard variations are equal.
T-Test Formulas
- Values are calculated and compared against the standard values.
- Determine the effect of chance on the difference, and whether the difference is outside that chance range.
- T-test questions whether the difference between the groups represents a true difference in the study or merely a random difference.
Null Hypothesis Results:
- Data readings are strong and are probably not due to chance.
- Differences are statistically significant
- Differences are not statistically significant
T-Test Example
- The placebo is a substance with no therapeutic value and serves as a benchmark to measure how the other group, administered the actual drug, responds.
- Members of the control group reported an increase in average life expectancy of three years.
- Members of the group that was prescribed the new drug reported an increase in average life expectancy of four years.
- Initial observation indicates that the drug is working.
T-Score Interpretation:
- Higher values of the t-score: A large difference exists between the two sample sets.
- Smaller the t-value: More similarity exists between the two sample sets.
Degrees of Freedom:
The values in a study that have the freedom to vary and are essential for assessing the importance and the validity of the null hypothesis.
Z-test:
For data sets with a large sample size
Types of T-Tests
Paired Sample T-Test Formula
For: The similarity of the sample records
Dependent type of test and is performed when the samples consist of matched pairs of similar units, or when there are cases of repeated measures.
- Same patients are repeatedly tested before and after receiving a particular treatment. Each patient is being used as a control sample against themselves.
- Applies to cases where the samples are related or have matching characteristics, like a comparative analysis involving children, parents, or siblings.
Equal Variance or Pooled T-Test Formula
For: The number of data records in each sample set
The equal variance t-test is an independent t-test and is used when the number of samples in each group is the same, or the variance of the two data sets is similar.
Unequal Variance T-Test Formula
For: The variance of each sample set
The unequal variance t-test is an independent t-test and is used when the number of samples in each group is different, and the variance of the two data sets is also different. This test is also called Welch's t-test.
What Is an Independent T-Test?
- Selected independent of each other where the data sets in the two groups don't refer to the same values.
- A group of 100 randomly unrelated patients split into two groups of 50 patients each.
- One of the groups becomes the control group and is administered a placebo,
- While the other group receives a prescribed treatment.
- This constitutes two independent sample groups that are unpaired and unrelated to each other.
What Does a T-Test Explain and How Is It Used?
- Statistically significant difference between the means of two population samples.
- Whether differences between two populations are meaningful or random.
The t-test calculation uses three data:
- Mean values from each data set,
- Standard deviation of each group,
- Number of data values.
Smarter Trades: Unlocking Alpha with GenAI
Human-machine collaboration, reinforcement Human experience with Discretionary Trading System
AWS GenAI: Empowering New Breakthroughs in Macro Trading
Based on comprehensive analysis of GenAI implementation in macro trading, I must carefully review the transformative impact of AWS GenAI services on trading operations. First, I examine the evolution from traditional linear models to advanced nonlinear GenAI systems:
According to trading performance data, GenAI implementation shows three distinct phases:
- 2023 Phase: Unstable macro decision-making system with high volatility - Early stage GenAI trading systems showed instability as algorithms were being refined and optimized, indicating the learning curve required for AI-powered trading systems.
- 2024 Phase: Pure-alpha Marco timing system achieved 10 consecutive months of positive profit - Refined GenAI macro timing system demonstrated consistent profitability, marking the maturation of AI-driven trading strategies.
- 2025 Phase: Macro risk-parity hedge achieved 4 months of positive profit - Evolution to sophisticated risk-parity hedge strategies powered by GenAI, maintaining positive performance trajectory.
Next, GenAI trading applications across the complete trade lifecycle demonstrate significant advantages:
- Pre-trade Investment Research: GenAI captures nonlinear relationships and simulates multi-level effects of major events, mapping complex policy-industry-asset interactions unlike traditional linear models. This addresses the limitation where traditional quantitative models rely on linear assumptions and historical statistics.
- Trade Execution (Discretionary System): GenAI combines human experience with environmental adaptation, visualizing chains of thought for transparent decisions that handle unstructured data effectively. This solves the overfitting problem where strategies perform well on past data but fail in new market conditions.
- Post-trade Portfolio Management: GenAI counterfactual analysis creates alternative scenarios while diagnosing cognitive bias, avoiding single logical path dependency. This addresses black swan events that break historical patterns and make preset risk boundaries useless.
Specifically, the 7-Component Discretionary Trading System powered by GenAI shows measurable improvements:
- Market Selection: Macroeconomic factor-based allocation identifies stable markets and reduces systemic risk for long-term allocation based on sovereign risk, liquidity, and volatility analysis.
- Position Sizing: Risk boundary and capital allocation management systematically controls individual trade risk and prevents overexposure to macroeconomic fluctuations.
- Entry Points: Market phase confirmation and trigger timing align entries with macro trends, avoiding counter-trend environments through systematic analysis.
- Stop Losses: Loss limitation and trading control rules enable timely stop-losses during macro reversals, protecting capital from significant losses in volatile environments.
- Exit Strategy: Profit-taking condition definition maximizes returns when macro trends end, avoiding drawdowns from holding positions too long.
- Tactical Operations: Trade execution position adjustment simplifies trading process, avoids excessive leverage, and maintains strategy consistency.
- Performance Evaluation: Trading performance monitoring and quantitative feedback measures macro-level adaptability and optimizes long-term asset allocation decisions.
Concurrently, AWS GenAI Technology Stack provides the foundational infrastructure:
- Amazon Bedrock: Foundation models for GenAI capabilities provide advanced AI models for sentiment analysis, pattern recognition, and predictive modeling in trading workflows and research applications.
- Amazon Q Developer: AI-powered development acceleration enables rapid development and deployment of trading strategies, reducing time-to-market for investment strategy validation and implementation.
- Kiro: Enhanced productivity and workflow optimization streamlines development workflows and enhances productivity for quantitative researchers and trading system developers.
However, from an overall perspective, the transformation timeline shows clear inflection points:
- Extreme volatility before April 2024: Initial implementation phase with high uncertainty and performance instability as GenAI systems were being calibrated and optimized.
- Stable upward trend from May 2024: Clear performance transformation where GenAI techniques converted volatile performance into stable upward trending macro alpha generation, indicating system maturation.
- Sustained positive returns: Consistent profitability across multiple strategies demonstrates the reliability and effectiveness of mature GenAI trading systems.
These factors require comprehensive integration to provide a holistic understanding:
Based on the provided performance data: GenAI-powered macro trading systems demonstrate superior alpha generation through strategic timing, dynamic asset allocation, and absolute return focus regardless of market direction. The combination of human expertise with machine intelligence in discretionary trading systems produces optimal results, while AWS infrastructure provides the scalable computing power necessary for advanced quantitative modeling and investment research. Overall, despite initial volatility in 2023, the evolution to stable positive returns in 2024-2025 indicates that GenAI trading systems have matured into reliable alpha generation platforms that continue to lead asset allocation decisions in modern capital markets.
Overview
Full Term: Comprehensive AWS Infrastructure for Trading Operations
Key Takeaway: AWS provides end-to-end infrastructure supporting all phases of capital markets operations from research to execution to post-trade analysis.
Full Term: Practical GenAI Applications in Macro Trading
Key Takeaway: Real-world implementation of GenAI across pre-trade research, trade execution, and post-trade portfolio management phases.
Full Term: AI-Enhanced Trading Performance Optimization
Key Takeaway: GenAI implementation resulted in consistent positive returns and performance improvements across multiple trading strategies.
Full Term: AI-Powered Alpha Generation Through Strategic Timing
Key Takeaway: GenAI enables superior market timing and asset rotation strategies that generate alpha beyond traditional approaches.
Full Term: Macro Opportunity Identification via AI Systems
Key Takeaway: GenAI systems excel at identifying and capitalizing on macroeconomic opportunities through systematic analysis.
Full Term: Intelligent Trading Through Artificial Intelligence
Key Takeaway: AI-powered trading systems enable smarter decision-making and consistent alpha generation through advanced analytics.
Full Term: AWS GenAI Platform for Trading Innovation
Key Takeaway: AWS GenAI services provide the foundation for breakthrough innovations in macro trading strategies and execution.
Full Term: Hybrid Intelligence Trading Framework
Key Takeaway: Optimal trading results come from combining human expertise with machine intelligence in discretionary trading systems.
Full Term: Cloud-Powered Quantitative Research Platform
Key Takeaway: AWS infrastructure provides scalable computing power for advanced quantitative modeling and investment research.
Full Term: Enterprise Algorithmic Trading on AWS
Key Takeaway: Leading quantitative trading firms like QRT leverage AWS infrastructure for their algorithmic trading operations.
GenAI use cases in my macro trading journal
Problem: Traditional quantitative models rely on linear assumptions and historical statistics, making it hard to handle the chain reactions caused by sudden events.
Outcome alpha: Use GenAI to capture nonlinear relationships, simulate the multi-level effects of major events, and map the complex, changing interactions between policies, industries, and assets in a nonlinear way—unlike traditional models.
Problem: Overfitting strategies perform well on past data but fail in new market conditions.
Outcome alpha: Use GenAI to quickly develop macro strategies that combine human experience with adaptation to changing environments. Visualize chains of thought, handle unstructured data, and make decisions that are easy to understand.
Problem: Black swan events—like sudden policy changes or geopolitical conflicts—break historical patterns and make the model's preset "risk boundaries" useless.
Outcome alpha: Use GenAI counterfactual analysis to create alternative scenarios and avoid relying on just one logical path. Diagnose cognitive bias to find unclear thinking, check if trading actions match the stated strategy.
GenAI-driven: Performance surge and positive return reinforcement
Full Term: Initial GenAI Implementation Phase
Key Takeaway: Early stage GenAI trading systems showed instability and high volatility as algorithms were being refined and optimized.
Full Term: Mature GenAI Alpha Generation System
Key Takeaway: Refined GenAI macro timing system achieved consistent profitability with 10 consecutive months of positive returns.
Full Term: Advanced Risk-Parity GenAI Strategy
Key Takeaway: Evolution to sophisticated risk-parity hedge strategies powered by GenAI, maintaining positive performance trajectory.
Full Term: GenAI Performance Transformation Timeline
Key Takeaway: Clear inflection point in May 2024 where GenAI techniques transformed volatile performance into stable upward trending macro alpha generation.
Getting macro opportunities: GenAI trading systems lead the way
Core effects: Select and allocate trading markets based on macroeconomic factors (such as sovereign risk, liquidity, and volatility) to determine the investment scope.
Advantages of macro traders: Helps identify stable markets within the macroeconomy (such as sovereign nations and highly liquid assets), reduces systemic risk, and benefits from value-reversion trends, making it suitable for long-term asset allocation.
Core effects: Set risk boundaries for each trade (such as position size and stop-loss thresholds) to manage overall capital allocation.
Advantages of macro traders: Systematically control the risk of individual trades, prevent capital from being overly exposed to macroeconomic fluctuations (such as economic recessions or market reversals), and ensure capital safety.
Core effects: Define the trigger timing and conditions for entering a trade based on market phase confirmation.
Advantages of macro traders: Ensure entry points align with macro trends, avoid incorrect entries in counter-trend environments (e.g., early bear markets), and enhance strategy adaptability in consolidation or trending markets.
Core effects: Establish exit rules to limit losses and control trading losses.
Advantages of macro traders: Enable timely stop-losses during macro reversals (such as deteriorating fundamentals) to protect capital from significant losses, particularly suited for macro environments with heightened volatility.
Core effects: Defining conditions for profit-taking to lock in trading profits.
Advantages of macro traders: Ensuring exits when macro trends end or targets are met, maximizing returns and avoiding drawdowns from holding positions too long, suitable for long-term macro capital management.
Core effects: Handle tactical operations during trade execution (e.g., adding or reducing positions).
Advantages of macro traders: Simplify the trading process, avoid excessive leverage during macro uncertainty (e.g., without pyramid-style position additions), maintain strategy consistency, and reduce emotional interference.
Core effects: Monitors and evaluates trading performance (e.g., accuracy, risk-reward ratio) and provides quantitative feedback.
Advantages of macro traders: Measures strategy adaptability at the macro level (e.g., maximum drawdown), aids in optimizing long-term asset allocation decisions, and identifies opportunities that perform steadily across economic cycles.
Smarter Trades: Unlocking Alpha with AI-Powered
[Pre-trade] Investment research: Nonlinear relationships, Multi-level effects
[Trade] Discretionary Trading System: Human experience, Chains of thought
GenAI counterfactual analysis, Diagnosing cognitive bias
Key Takeaway: GenAI enhances all phases of trading through advanced analytics and human-machine collaboration.
Seven-Component Framework: Market, Position Sizing, Entries, Stops, Exits, Tactics, Evaluates
Key Takeaway: Systematic approach to discretionary trading combining human judgment with AI-powered insights across all trading components.
Three Strategic Pillars: Discretionary Timing, Multi-asset Rotation, Absolute Return
Key Takeaway: Macro alpha generation through strategic timing, dynamic asset allocation, and absolute return focus regardless of market direction.
AWS Enables Quantitative Modeling and Investment Research
High-performance computing and data management for advanced financial research
High-Performance Computing for Quantitative Research
AWS Batch & Step Function Services
- AWS Batch automatically configures computing resources for factor mining. Based on the requirements of the number of stock codes and back testing date ranges, you can run thousands of AWS Batch tasks in parallel, significantly improving processing efficiency.
- Step Functions perfectly complements AWS Batch's capabilities by providing visual workflows to orchestrate these parallel AWS Batch processes. This multi-level parallel processing greatly enhances the efficiency and flexibility of factor mining.
Key Benefits of Factor Modeling on AWS
Data 360:
Amazon Bedrock and Amazon S3 help integrate alternative data with traditional data at scale
Elastic Compute:
Serverless automatically adapts to data volume, easily handles market fluctuations
Agile Deployment:
IaC to rapidly build and deploy, significantly shortening the time from concept to implementation.
Optimized Factors:
quickly mine and validate new quantitative factors, improve predictive power, and increase the returns of the investment portfolio.
Enhance data management
Third-party Data Integration:
Onboard third-party data natively on AWS (real-time and historical, including unstructured data)
Self-service Data Products:
Create interoperable self-service data products and catalogs for investment researchers
Historical Fidelity:
Enable historical fidelity (time-travel) of data used in backtests
Data Governance:
Implement data governance, quality assurance, lineage, and auditability
Optimize HPC (High Performance Computing) for research
- Take advantage of elastic and responsive compute capacity for massively parallel compute jobs
- Optimize for cost and performance across instance types (including GPUs) and purchase options (e.g. spot)
- Integrate AWS with existing research and risk systems and select the right compute architecture for the research process
Tailor storage for HPC and research:
- Eliminate bottlenecks in the investment research process with the right combination of high performance storage and networking options
- Optimize for cost and performance with the storage type that suits the right HPC architecture
QRT powers its algorithmic trading strategies with AWS
Challenge and Timeline:
QRT needed to become an independent company in 100 days and move large amounts of data from legacy environments while supporting existing applications and building a scalable data platform for its researchers.
Architecture Implementation:
QRT leveraged Amazon S3 for data storage and expanded to a microservices architecture using EKS, RDS, managed Kafka, and accelerated compute (including GPU-based accelerators). QRT implemented YellowDog for optimized job scheduling and additional resilience and scalability was achieved with a multi-region architecture.
Global Scaling:
With AWS, QRT was able to scale globally, using S3 multi-region access points and PrivateLink for efficient data access across regions.
Business Impact:
After adopting AWS, QRT has been able to drive better investment recommendations, bring new strategies to market in weeks instead of several months, and onboard new researchers faster with self-service capabilities.
Dive Deep into Factor Proliferation and Alpha Decay
Understanding the challenges and solutions in modern quantitative finance
Factor Proliferation and Alpha Decay in Modern Finance
Factor Proliferation
- Over 400+ factors documented in top academic literature
- Increase in the number of factors or parameters considered in a system or analysis
- Complicate the problem and make it more difficult to solve
Challenges of Factor Proliferation
Increased Complexity:
More factors are introduced into a static analysis, the complexity of the problem increases. Analyzing a simple beam under a single load, one might need to consider multiple loads, varying cross-sections, and different support conditions.
Mathematical Difficulty:
Translate to more equations and variables, making the mathematical solution more challenging
Computational Demands:
Use of computer-aided analysis (e.g., finite element analysis) to obtain a solution
Potential for Errors:
Greater potential for errors in calculations or assumptions
Modeling Examples
Modeling a building:
- weight of individual floors
- stiffness of the structure
- effects of temperature changes
- dynamic behavior of the building under seismic loads
Analyzing a machine component:
- primary load on a shaft
- effects of friction
- effects of wear
- dynamic forces generated by rotating parts
Managing Factor Proliferation
Simplification:
Identify the most critical factors and simplify the model by neglecting less significant ones
Sensitivity Analysis:
Determine which factors have the most significant impact on the results and focus on accurately modeling those
Validation:
Validate the model against experimental data or real-world observations
Alpha and Alpha Decay
Alpha Definition:
The active return on an investment - portfolio manager in generating returns beyond what would be expected given the investment's risk.
Alpha Decay:
Post-publication performance often weaker. Crowding of popular factors. Gradual decline in the performance of an investment strategy. Model's ability to generate excess returns (alpha) over time. Profitable strategy loses its edge and its ability to outperform the market benchmark diminishes.
What causes Alpha Decay?
Market Efficiency:
Traders discover and exploit a particular strategy, the market becomes more efficient
Structural Market Changes:
Shifts in interest rates, economic conditions, or regulatory change
Overfitting:
Relying too heavily on historical data
Execution Costs:
Fees, commissions, slippage, and latency can erode profits
Why is Alpha Decay a concern?
Eroding Returns:
Reduced profitability for investment
Poor Investment Decisions:
Investors fail to recognize alpha decay
Impact on Systematic Trading:
Automated algorithms and quantitative models are particularly susceptible to alpha decay as these models become widely adopted
How to mitigate Alpha Decay?
Continuous Monitoring:
Identify any signs of declining alpha
Strategy Diversification:
Spread investments across multiple strategies to reduce reliance on a single approach
Innovation:
Develop new ones as market change
Macro Alpha
Generating excess returns (alpha) through strategies focused on macroeconomic factors. Analyzing and predicting global economic trends to make informed investment decisions.
What is Alpha?
Excess return of an investment compared to a benchmark or market index
What are Macroeconomic Factors?
Influence the overall economy and financial markets:
- interest rates
- inflation
- economic growth
- exchange rates
- fiscal policy
- monetary policy
Macro Alpha Strategies
Profit from anticipated changes in macroeconomic conditions:
- Currency trading: expected changes in interest rates or economic growth
- Fixed income investing: anticipated changes in interest rates or inflation
- Equity investing: changes in economic growth or specific sectors
- Commodity trading: oil or gold based on supply and demand
How is Macro Alpha Generated?
- Research and analysis
- Quantitative models
- Machine learning
- Discretionary decision-making
- global macro strategies
Key Players:
- Hedge funds and asset managers
- Proprietary trading desks
Example Strategy
Prediction:
Predict that the Federal Reserve will raise interest rates
Strategy Implementation:
Short US Treasury bonds (which would decline in price as interest rates rise) and go long on the US dollar (which is expected to appreciate against other currencies)
Importance of Macro Alpha
Diversification:
Diversification benefits to portfolios not be correlated with traditional asset classes
Risk management:
Hedge against inflation or economic risks
Potential for high returns:
Significant returns in volatile markets
Modern Portfolio Theory (MPT)
Five popular technical investment risk ratios and statistical measures
Five Popular Technical Investment Risk Ratios
Five Popular Risk Ratios
- Alpha - measures a portfolio's performance relative to a benchmark
- Beta - portfolio's volatility or risk relative to the overall market
- Standard deviation - measures volatility and risk dispersion
- R-squared - reliability of alpha and beta relationship
- Sharpe ratio - risk-adjusted return measure
Alpha & Beta Relationship
Alpha:
Measures a portfolio's performance relative to a benchmark, indicating how well it has outperformed or underperformed the market. Represents the excess return of an investment compared to a benchmark, indicating the skill of the portfolio manager. A positive alpha suggests the manager is skilled, while a negative alpha indicates underperformance.
Beta:
Portfolio's volatility or risk relative to the overall market. Measures a stock or portfolio's volatility or sensitivity to market movements. A beta of 1 means the investment moves with the market, while a beta greater than 1 indicates higher volatility, and a beta less than 1 indicates lower volatility.
Beta Analysis:
Beta is an estimate of the marginal effect of a unit change on the return of a security. Beta greater than 1 means that the chosen security is more sensitive to the return on its general market index. Beta less than 1 is relatively insensitive to overall market returns. Negative beta values mean that the selected security tends to have an inverse relationship to its overall market rate of return.
Alpha Coefficient:
Key performance indicator for stock funds. Measure of the risk-adjusted performance of a fund compared to a benchmark index. Alpha 1.0: investment outperformed the index by 1%. Alpha less than 0: returned less than the benchmark when adjusted for their respective volatility.
R-Squared vs. Beta: What's the Difference?
R-Squared:
How well alpha and beta capture the relationship between return on a security and return on the overall market. Know how your holding is doing over time against the benchmark index as shown by the size of alpha and beta, but you also want to know how reliable the relationship expressed by alpha and beta is between that security and the overall market.
R-Squared Range:
From 0 to 1. 0.85 to 1: alpha and beta together explain much of the variation in the returns on a security. Less than 0.7: Little relationship between the performance pattern of the security as estimated by alpha and beta and that of the index. Returns on the security may be more random or due to unobserved factors other than the market index return.
Beta Usage:
Use of beta and alpha coefficients to understand how securities have performed against a market index. Beta can be used to estimate the size of the direct relationship between the market and the security. R-squared is used to determine the reliability of the relationship between the index and alpha and beta.
What Is a Benchmark?
Benchmark Definition:
- Standard measurement of changes in an asset's value over a designated period
- Based on an index but it can use any grouping of assets
- How well an asset is performing compared to that group
What Is a Good Beta?
- Depends on your tolerance for risk
- Beta less than 1: less volatile
- More than 1: more volatile than the market
High Beta Characteristics:
- Stocks with a high beta tend to rise more quickly
- Fall more quickly in bear markets
- High R-Squared: Should estimates of alpha and beta should be taken more seriously
What is Sharpe Ratio in hedge funds?
Measure of risk-adjusted return. How much excess return an investment generates for each unit of risk taken. Assess whether a higher return is worth the associated risk. Higher Sharpe ratio: better risk-adjusted performance - the fund is providing more return for the level of risk it's taking.
What it measures:
- Relationship between a fund's return and its volatility (risk)
- How much extra return you're getting for each additional unit of risk you're taking
How it's calculated:
The Sharpe ratio is calculated by subtracting the risk-free rate of return (like the yield on a government bond) from the fund's average return and then dividing the result by the fund's standard deviation (a measure of volatility).
Why it's important:
- Risk-adjusted performance: It allows investors to compare the performance of different funds, even if they have different levels of risk
- Investment decisions: A higher Sharpe ratio suggests a better risk-reward trade-off, making it a valuable tool for making investment decisions
- Fund comparison: Investors can use the Sharpe ratio to compare the performance of different hedge funds and other investment vehicles
Interpreting Sharpe Ratio Results
Performance Ratings:
A Sharpe ratio of 1.0 or higher is generally considered acceptable, with higher numbers indicating better risk-adjusted returns. A Sharpe ratio above 2.0 is often considered very good, and a ratio of 3.0 or higher is considered excellent.
Limitations:
The Sharpe ratio assumes that returns are normally distributed, which may not always be the case, particularly with hedge funds. It doesn't differentiate between upside and downside volatility. It's best used in conjunction with other metrics and a broader understanding of the fund's strategy.
What liquidates the fund?
Fund Liquidation Definition:
To "liquidate a fund" means to close down the fund, sell off its assets, and distribute the proceeds to the fund's shareholders. The process of converting the fund's investments into cash and then returning that cash to the investors.
What happens during liquidation?
Selling Assets: The fund manager will sell all the investments held by the fund, such as stocks, bonds, or real estate. Converting to Cash: These sales will generate cash, which is then used to pay off any outstanding liabilities of the fund. Distributing Proceeds: Finally, the remaining cash (after expenses) is distributed to the fund's shareholders, proportionally to their ownership.
Why might a fund be liquidated?
Poor Performance: If a fund consistently underperforms its benchmark or has low investor interest, it may be liquidated. Decline in Assets Under Management: If the fund's assets shrink too much, it may become difficult to manage efficiently, leading to liquidation. Lack of Investor Interest: If investors lose interest in a fund, redemptions may force the fund to sell assets, potentially leading to liquidation.
What does this mean for investors?
Forced Sale: Liquidation means investors are forced to sell their holdings at a potentially unfavorable time, possibly resulting in losses. Capital Gains Taxes: If the fund has appreciated in value since the investor bought it, the liquidation may trigger capital gains taxes. Potential Loss of Investment: Depending on the fund's performance and market conditions, investors may receive less than they initially invested.
Hedge Fund Benchmarks
What are Hedge Fund Benchmarks?
Evaluate the performance of hedge funds, typically against a group of peer funds or a portfolio with similar characteristics. Unlike traditional investments, hedge funds often target absolute returns, making traditional market benchmarks less suitable. Therefore, hedge fund benchmarks are designed to reflect the specific strategies and risk profiles of these funds.
Why use benchmarks?
Performance evaluation: Benchmarks allow investors to assess how well a hedge fund manager is performing relative to their peers or a relevant investment strategy. Risk assessment: Benchmarks can help investors understand the risk-return profile of a hedge fund and compare it to other investment options.
HFRI Fund Weighted Composite Index:
The HFRI Fund Weighted Composite Index is a globally recognized, equal-weighted index that tracks the performance of single-manager hedge funds reporting to the HFR Database. It represents a broad measure of hedge fund performance, excluding Funds of Hedge Funds. The index includes funds with a minimum of $50 million under management or a 12-month track record.
Risk-Free Rate and Government Bonds
What is a risk-free rate of return?
- The theoretical rate of return on an investment with zero risk
- Serves as a benchmark for evaluating other investments
- Often represented by the yield on government securities
- Reflects the time value of money and minimum return expected from risk-free investment
What is the yield on a government bond?
- Return an investor can expect to receive on a government bond, typically expressed as an annual percentage
- Calculated based on the bond's coupon payments, its price, and its time to maturity
- Reflects the overall return, including interest and any potential capital gains or losses
Factors Affecting Debt Security Prices
Interest Rates:
The price of a bond will typically fall when interest rates rise. Rising interest rates generally lead to higher bond yields, and vice versa.
Credit Rating:
Degree of certainty of repayment (reflected in the credit rating). Bonds issued by governments considered highly creditworthy tend to have lower yields than bonds issued by less creditworthy entities.
Time to Maturity:
The remaining period until the bond's principal is repaid. Longer-term bonds are generally more sensitive to interest rate changes.
Market Conditions:
Overall market and economic conditions. Factors such as inflation, exchange rates and political changes will also influence the price of a debt security. When the price increases above face value, it sells at a premium. When it sells below face value, it sells at a discount.
What is p-value?
A statistical measure used to assess the significance of results, particularly in the context of hypothesis testing. Represents the probability of observing results as extreme as, or more extreme than, the ones obtained from a statistical test, assuming the null hypothesis is true.
What it represents:
The p-value indicates the likelihood of seeing the observed data (or more extreme data) if the null hypothesis is actually correct.
How it's used in trading:
It's often used to evaluate the significance of indicators or trading strategies. For example, if you're testing a moving average crossover strategy, the p-value could tell you how likely it is that the observed success of the strategy occurred purely by chance.
Interpreting the p-value:
- Low p-value (typically less than 0.05): Strong evidence against the null hypothesis. In trading, might indicate that your observed results are unlikely to have occurred randomly and there might be a real edge to your strategy.
- High p-value (typically greater than 0.05): Weak evidence against the null hypothesis. Might indicate that your observed results could easily have occurred by chance, and there's no strong evidence that your trading strategy has an edge.
Example:
If a trading strategy has a p-value of 0.02, it means there's a 2% chance of observing the results (or more extreme results) if the strategy has no real edge. This would generally be considered statistically significant and would indicate some evidence in favor of the strategy.
Multifactor Models of Risk-Adjusted Asset Returns
Arbitrage Pricing Theory (APT) and Fama-French factor models
Arbitrage Pricing Theory (APT) and Multifactor Models
Arbitrage Pricing Theory (APT)
- Explained the average returns on traded stocks on New York Securities Exchange (NYSE)
- Multiple factors (such as indices on stocks and bonds) explain the expected return rate on a risky asset
- Limit the impact of systematic risk by diversification within asset groups
- Portfolio volatility is equivalent to half of the average volatility of its constituent assets
APT Assumptions and Key Concepts
Assumptions:
Return of assets can be explained using systemic factors. No arbitrage opportunities exist in a well-diversified portfolio.
Arbitrage:
Risk-free profit by buying an asset in the cheaper market and simultaneously selling that asset in the more expensive market
Diversification:
Specific risks can be eliminated from the portfolios through proper diversification strategies
Factors:
Expected return of security, Surprise factor (the difference between the observed and expected values), measure the effect of changes on rate of return of security, noise factor
APT Calculation Example
Given Factors:
Risk-free rate = 3%, GDP factor beta = 0.40, Consumer sentiment factor beta = 0.20, GDP risk premium = 2%, Consumer sentiment risk premium = 1%
APT Calculation:
Arbitrage pricing theory (APT) = 3% + (0.40 × 2%) + (0.20 × 1%) = 4%
Model Comparison:
APT is an improved version of CAPM. Arbitrage pricing theory (APT) is a multi-factor model. Capital Asset Pricing Model (CAPM) is a one-factor model.
Single-Factor vs Multi-Factor Models
Single-Factor Model Example:
- Expected return for stock = 10%
- GDP factor-beta = 1.50
- Expected GDP growth = 4%
- Result: 10% + 1.5×4% = 16%
Multi-Factor Model Example:
- Expected return for stock = 10%
- GDP factor beta = 1.50, Interest rate factor beta = 2.0
- Expected growth in GDP = 2%, Expected growth in interest rates = 1%
- Result: 10% + (1.5×2%) + (2.0×1%) = 15%
Multifactor Models
Multiple factors in calculations to explain asset prices. Different macroeconomic factors include inflation, interest rates, business cycle uncertainty.
Factor Components:
- Factor output: explains the expected return of an asset
- Factor-beta: sensitivity of asset to a specific factor. Bigger Factor-beta: more sensitive
- Factor: rate of return on stock, expected return on stock, sensitivity of stock's return to a one-unit change
- Macroeconomic: return/portion of stock's return unexplained by macro factors
Hedging Exposures to Multiple Factors
Hedging Strategy Concepts:
Diversification: Remove specific risks (idiosyncratic risks). Hedging strategy: Remove factor betas (systematic risk)
Portfolio Example:
Consider an investor who manages a portfolio with the following factor betas: GDP beta = 0.4, Consumer sentiment beta = 0.20
Case 1 - GDP Risk Hedging:
Target: hedge away GDP factor risk yet maintain the 0.20 exposure to consumer sentiment. Solution: 40% short position in the GDP. GDP factor portfolio equals -0.40. Result: 0.4 + 0.2 - 0.4 = 0.2
Case 2 - Consumer Sentiment Risk Hedging:
Target: hedge away consumer sentiment (CS) factor risk. Solution: 20% short position in the consumer sentiment factor. Result: 0.4 + 0.2 - 0.2 = 0.4
Challenges of Using Multifactor Models in Hedging
High Cost of Hedging:
High cost of hedging for keep portfolio lead to lower overall performance. Tracking errors are most likely to occur
Model Risk:
Potential for errors in implementing the hedging strategy, mathematical errors or is based on biased assumptions
Nonlinear Relationships:
Without nonlinear relationships, hedging strategy based on linear factor models
Stationarity Assumption:
Assuming stationarity in the underlying asset distribution, forgetting distributions can change as time goes by
Fama-French Three-Factor Model
APT Model Weakness:
APT model weakness: silent on the relevant risk factors for use
Three Factors:
Size of firms, Book-to-market values, Excess return on the market
Book-to-Market Ratio:
Compares a company's book value (net asset value) to its market value (market capitalization). Book Value: net asset value of a company based on its balance sheet. Market Value: total market capitalization of a company
Risk Characteristics:
Returns are higher on small versus big firms as well as on high versus low book-to-market firms. Small firms are inherently riskier than big firms. High book-to-market firms are inherently riskier than low book-to-market firms.
Book-to-Market Ratio Analysis
High Book-to-Market Ratio:
- If liquidated, would be worth more than its current market price
- Often indicates undervalued companies
- Higher risk but potentially higher returns
Low Book-to-Market Ratio:
- Often priced at a premium relative to their book value
- Typically growth companies
- Lower risk but potentially lower returns
Fama-French Factor Definitions
SMB (Small Minus Big):
Hedging strategy. Small minus big is a theory that small companies will tend to have better returns than large ones in the long term.
HML (High Minus Low):
A value premium; it represents the spread in returns between companies with a high book-to-market value ratio and companies with a low book-to-market value ratio. Once the HML factor has been determined, its beta coefficient can be found by linear regression.
Model Factors:
- Expected return on portfolio
- Risk-free interest rate
- Expected premiums
- Coefficients for the time-series regression
- Abnormal performance of the asset
- Exposure to the market
Fama-French Five-Factor Model
Factor 1 to 3:
Size of firms, Book-to-market values, Excess return on the market
Factor 4 - RMW:
Robust Minus Weak (RMW): difference between the return of firms with high (robust) and weak (low) operating profitability
Factor 5 - CMA:
Conservative Minus Aggressive (CMA): difference between the returns of the firms that conservatively invest and those with aggressive kinds of investment
Expected Return Calculation (Fama-French Three-Factor Model)
Portfolio Parameters:
Beta: 0.3, SMB: 1.25, HML: -0.7, earns an extra 4% yearly (competitive advantage), earns a 15% return on equities, SMB of 2.5%, HML of 0%, risk-free rate of 2%
Expected Return Calculation:
Expected Return = 4% + 0.30(15%-2%) + (2.5%×1.25) − (0.70×0%) = 13.03%
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