Section 1 – Chapter 1 – Alpha & Beta – Outperforming the benchmarks

Chapter 1 – Alpha & Beta – Outperforming the
Benchmarks
Section 1 – Theory and History of Technical Analysis
Presented By :
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Agenda
❖ Learning Objective Statements: Alpha and Beta -
Outperforming the Benchmarks
❖ Defining Alpha and Beta
❖ Where to Find Alpha
❖ The Adaptive Market Hypothesis (AMH)
❖ The Fractal Market Hypothesis (FMH)
❖ Supply and Demand
❖ Conclusion

What is a Alpha & Beta ?
Alpha and Beta are two key risk-return metrics used in portfolio
management to measure performance relative to a benchmark.
Alpha (α) – Measures Excess Returns
Positive Alpha (>0) → Investment outperforms the benchmark.
Negative Alpha (<0) → Investment underperforms the benchmark.
Often seen as the "skill" of a fund manager or strategy.

What is a Alpha & Beta ?
Beta (β) – Measures Market Sensitivity
Beta = 1 → Moves in line with the market.
Beta > 1 → More volatile than the market (e.g., tech stocks).
Beta < 1 → Less volatile, defensive (e.g., utilities, bonds).
Beta = 0 → No correlation with the market (e.g., cash, gold).

Alpha & Beta Cheat Sheet

Pro Tips for Maximizing Alpha & Managing Beta
📌 Pro Tips for Maximizing Alpha & Managing Beta
📌 Look for consistent Alpha – Short-term gains can be luck, long-term Alpha is skill.
📌 Control Beta based on risk appetite – Adjust Beta exposure based on market
conditions.
📌 Compare against a relevant benchmark – Use the right index (S&P 500,
Nasdaq, etc.).
📌 Combine Alpha & Beta strategies – Balance passive (Beta) & active (Alpha)
investments.
📌 Use Sharpe & Sortino Ratios – For better risk-adjusted performance analysis.

Standard formula for alpha:
📌 Alpha = Rp − (Rf + Beta x (Rm − Rf))
📌 Rp = return of the portfolio | Rm = return of the benchmark
Rf = risk-free rate (10-year note yield)
Example : Rp = return of the portfolio = 34% | Rm = return of the benchmark
(SPY) = 17% | Rf = risk-free rate (10-year note yield, TNX) = 4.6% as of the
beginning of the chart | Beta = portfolio beta (average beta score for the five
stocks) = 1.24
This allows us to calculate alpha as 𝛼 = (.34 − (.046 + 1.24 × (.17 − .046))) × 100,
which resolves to an alpha score of 14.02%.

1. Displacement (Innovation or Catalyst) 🚀
📌 A new innovation, technology, or economic shift attracts investors.
📌 Early smart money enters the market (institutions, insiders).
📌 Prices start rising slowly as confidence builds.
📌 Example: The internet (dot-com boom), block chain (crypto).
💡 Key Sign: Rational optimism, with real fundamentals backing early
investments.

Step-by-Step Alpha Strategy 🚀
Step 1 - Choose a Benchmark
If investing in large-cap U.S. stocks → S&P 500
If tech-focused → Nasdaq-100
If international → MSCI World Index
💡 Example: Suppose the S&P 500 has an annual return of 8%. To
generate Alpha, we need a portfolio that consistently outperforms
this return after adjusting for risk.

Step 2 - Identify Alpha-Generating Stocks
Look for high-growth companies with strong fundamentals and disruptive potential:
- High earnings growth (>20% YoY)
- Strong revenue growth & profit margins
- Competitive advantages (moat)
- Positive price momentum
💡 Example Portfolio:
Apple (AAPL) – Dominant ecosystem, services growth.
Nvidia (NVDA) – AI & GPU leader, strong pricing power.
Tesla (TSLA) – EV leader with expanding market share.
Amazon (AMZN) – E-commerce & cloud dominance.
Microsoft (MSFT) – Cloud & enterprise software leader.
📈 Goal: Pick stocks with strong potential for long-term outperformance compared
to the S&P 500.

Step 3 - Risk Management – Controlling Beta
High-growth stocks often have Beta > 1, meaning they are more volatile than
the market.
To manage Beta, mix in low-Beta defensive stocks (e.g., utilities,
healthcare) or hedge with options and bonds.
Use stop-loss orders to manage downside risk.
💡 Example:
If Nvidia (Beta = 1.7) and Apple (Beta = 1.2) are included, balancing with
Coca-Cola (Beta = 0.6) can help reduce risk.

Step 4 - Performance Measurement – Calculating Alpha
Suppose your portfolio delivers 15% return while the S&P 500 gains
8% in the same period.
If your Beta-adjusted return (risk exposure) is 10%, then your Alpha is:
α=Portfolio Return−(Benchmark Return×β)
α=15%−(8%×1.2)=15%−9.6%=5.4%
✅ Positive Alpha (5.4%) → Indicates portfolio outperformed the
benchmark on a risk-adjusted basis.

🚀 Pro Tips for Maintaining Alpha
📌Rebalance Portfolio Quarterly – Trim underperformers, add new high-
growth stocks.
📌 Use Technical & Fundamental Analysis – Track earnings reports,
industry trends.
📌 Leverage Sector Rotation – Shift investments based on economic
cycles.
📌 Monitor Institutional Flow – Track hedge fund & big money moves.
📌 Stay Ahead of Macro Trends – AI, EVs, cloud computing, biotech, etc.

The Adaptive Market Hypothesis
The Adaptive Market Hypothesis (AMH), proposed by Andrew
Lo in 2004, combines principles of Efficient Market Hypothesis
(EMH) and behavioral finance, suggesting that markets evolve
based on competition, adaptation, and investor behavior.
During periods where markets show informational
inefficiency, or price behavior better explained by cognitive
biases than by new information, adaptations might become
necessary for many investors.

Key Takeaways on AMH
📌 Markets Are Not Fully Efficient
Unlike EMH, which assumes markets always reflect all information,
AMH suggests efficiency varies based on market conditions.
Efficiency fluctuates due to learning, innovation, and changing
investor behavior.
📌 Investors Are Adaptive, Not Rational
Instead of always being rational, investors adapt strategies based on
experience, market conditions, and competition.
Survival and evolution dictate which trading strategies work over
time.

Key Takeaways on AMH
📌 Market Conditions Change Over Time
Profitable strategies can become ineffective as more traders exploit them.
Example: High-frequency trading (HFT) strategies may lose their edge as more firms
adopt them.
📌 Risk & Reward Are Dynamic
Market cycles shift, affecting how risk is perceived and priced.
Example: In bull markets, investors underestimate risk, while in bear markets, they
become overly risk-averse.
📌 No Single Strategy Works Forever
Technical analysis, fundamental analysis, and passive investing can all work at
different times depending on market conditions.
Example: Value investing worked well for decades but underperformed in the 2010s as
growth stocks dominated.

Adaptive Market Hypothesis Cheat Sheet

Fractal Market Hypothesis (FMH)
📌 The Fractal Market Hypothesis (FMH), introduced by Edgar
E. Peters, challenges the Efficient Market Hypothesis (EMH)
by suggesting that markets are nonlinear, chaotic, and fractal
in nature.
📌 It explains why markets exhibit boom-bust cycles,
persistent trends, and periods of extreme volatility.

📌 Markets Are Fractal, Not Random
Price movements follow self-similar patterns across different time
frames.
Short-term, medium-term, and long-term traders coexist, affecting
price dynamics.
📌 Liquidity & Stability Depend on Diverse Investment Horizons
Markets remain stable when multiple time-frame traders (e.g., day
traders, swing traders, long-term investors) participate.
When short-term traders dominate, markets become unstable,
leading to crashes.

📌 Market Trends Are Nonlinear & Self-Similar Patterns repeat
across time scales – a trend on a 5-minute chart may resemble a
weekly trend.
This explains why technical analysis and trend-following strategies
can work.
📌 Fractals Explain Market Crashes & Volatility
During crises, different time-frame traders stop participating, reducing
liquidity.
This leads to high volatility and price swings (e.g., 2008 Financial
Crisis, 2020 COVID Crash).

Fractal Market Hypothesis (FMH) Cheat Sheet

🚀 Trading & Investing Applications of FMH
📌 Use Multi-Timeframe Analysis
•Check short-term (1H), medium-term (1D), and long-term (1W, 1M) charts
before making a decision.
•Example: A breakout on a 5-minute chart may be a pullback on a daily
chart.
📌 Follow Liquidity & Volatility
•If long-term investors exit, expect higher volatility and potential crashes.
•Example: 2008 crisis happened as long-term investors left, creating a
liquidity vacuum.

🚀 Trading & Investing Applications of FMH
📌 Apply Trend-Following & Mean Reversion Strategies
•Trend-following works in sustained trends.
•Mean reversion works in fractal ranges.
📌 Use Fractal Indicators
•Fractal indicators (e.g., Bill Williams’ Fractals) help spot
turning points.
•Example: Fractal signals on multiple timeframes can confirm
entry/exit points.

🚀 Market Anomalies & Their Relationship to Supply & Demand
📌 Anomalies Exist Due to Market Inefficiencies
Prices don’t always reflect true value due to investor psychology,
liquidity, and institutional constraints.
📌 Supply & Demand Drive Market Anomalies
Excess demand for certain stocks can lead to overpricing.
Lack of supply (e.g., low-float stocks) can cause excessive
volatility.
📌 Anomalies Can Be Exploited for Alpha
Traders & investors who recognize these patterns can outperform
the market.
Many anomalies fade over time as they become widely known.

🚀 Prominent Market Anomalies & Supply-Demand Relationship

🚀 Trading & Investing Implications
📌 Use Supply & Demand Signals
Monitor liquidity & volume to confirm anomalies.
High demand + low supply = price surge (e.g., short squeezes).
📌 Recognize Fading Anomalies
Some anomalies disappear as traders arbitrage them away.
Example: The January Effect has weakened due to institutional awareness.
📌 Adapt Strategies Based on Anomalies
Momentum traders capitalize on trends (momentum effect).
Value investors benefit from mean reversion anomalies.
Event-driven traders exploit earnings drift and IPO effects.

Chapter 2 - Fusion Analysis - Technical Analysis as Part of a
Team Approach
Next Section 1 – Theory and History of Technical Analysis
Presented By :

Section 1 – Chapter 1 – Alpha & Beta – Outperforming the benchmarks

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Section 1 – Chapter 1 – Alpha & Beta – Outperforming the benchmarks