Section One: Theory and History

Compare the terms “alpha” and “beta”

Alpha refers to the excess returns earned by relative-return investors compared to their benchmark index. When a portfolio outperforms its benchmark, the portfolio has positive alpha; underperformance results in negative alpha. Alpha measures a portfolio manager’s ability to generate returns independent of market influence.

Beta is a measure of risk that applies to individual stocks or portfolios. The market average beta equals 1.0. Beta indicates how much volatility a stock or portfolio is expected to contribute to a diversified portfolio’s overall volatility.

These measures work together. The relationship between them is expressed using the standard alpha formula: α=Rp−(Rf+β×(Rm−Rf)), where Rp​ is portfolio return, Rf​ is the risk-free rate, β is beta, and Rm​ is the benchmark return. For example, a utility stock portfolio with lower beta (0.53) can still generate significant alpha (13.32%), while a growth portfolio with higher beta (1.24) may achieve slightly higher alpha (14.02%).

Compare and contrast the EMH, AMH, and FMH

Efficient Market Hypothesis (EMH): According to EMH, studying price action is unlikely to help find predictable sources of new trends worth exploiting. However, even Eugene Fama acknowledged that “It’s a model, so it’s not completely true. No models are completely true. They are approximations to the world.”

Adaptive Market Hypothesis (AMH): Developed by Andrew Lo, the AMH suggests that markets do not efficiently adjust to information because investors’ attitudes and behaviors evolve over time. Markets may experience periods of both efficiency and inefficiency. During inefficient periods, when price behavior is better explained by cognitive biases than new information, investors must adapt to survive. Research supports this: trading in Pakistan’s KSE-100 Index showed returns are not random, and investors can generate above-market returns using technical analysis—findings consistent with the AMH.

Fractal Market Hypothesis (FMH): Edgar Peters’ FMH argues that differing decision horizons and risk tolerance among investors lead to varied reactions to both fundamental and technical market events. Unlike EMH, FMH suggests investors make decisions based on different information sets rather than aggregate historical prices. The combination of diverse investor behaviors and variable information sets creates periods of market uncertainty and potential price predictability.

Analyze possible anomalous supply-demand scenarios that may be opportunities

Known market anomalies that challenge the EMH and suggest supply-demand imbalances include:

• Correlations of returns in related securities: Prices are not adjusting efficiently; investors may predict trend continuation when supply or demand affects groups of similar securities.

• Mean reversion: Portfolios with highest returns underperform while those with worst returns outperform. The desire to capture returns creates supply; redeploy desires create demand for undervalued shares.

• Underreaction to news: Prices may predictably continue trends following good or bad news. Supply and demand sometimes have more influence than information, and investors may consider news unimportant.

• The size effect: Small stocks may outperform large stocks despite higher volatility. Relative volume increases may trigger recognition of improved volume.

• The P/E ratio effect (value investing): Lower P/E scores tend to predict greater outperformance. Low demand (or higher supply) for stocks over longer periods creates underpriced opportunities.

• The January effect: Small-cap stocks outperform early in the year due to high demand for these stocks at year-start.

• Patterns in volatility: Bear markets are more volatile than bull markets; opening and closing minutes have more volatility than the rest of the day. Excess supply creates volatility; excess-demand volatility dissipates quickly.

Contrast the roles of a fundamental analyst and a technical analyst

Fundamental analysts focus on the “what and why” (the cause): company earnings, management, growth prospects, and products. They conduct deep industry research, speak with executives and competitors, build financial models with revenue/cost/earnings assumptions, calculate enterprise value, and develop stock price targets. Because of this depth, many specialize in specific sectors. Fundamental analysts rely heavily on forward-looking assumptions and guidance that changes quarterly.

Technical analysts focus on the “when and where” (the effect): price behavior, buyer/seller interaction, volume, and investor psychology. They can be sector and asset class agnostic, analyzing soybeans in the morning, oil in the afternoon, and individual stocks in the evening. Technical analysts develop price targets more quickly with less detailed data than fundamental analysts. Rather than relying on manipulable assumptions, technicians work with historical price data, which cannot be restated, and focus on how all information and news is reflected in market price.

Using a medical metaphor: fundamental analysts are specialists (e.g., cardiologists) focused deeply on one domain, while technical analysts are general practitioners able to diagnose across multiple conditions. The key insight is that the company and its stock can move in opposite directions, and technical position going into catalyst events (like earnings) can determine reaction magnitude.

Outline ways technicians can work with fundamentalists

• Remember Dow Theory’s economic rationale: Dow originally constructed the railroad average to measure economic strength and later added the industrial average. Use confirmation and non-confirmation between market averages to identify economic strength or potential reversals, ensuring technicals have underlying economic reasoning.

• Weave technicals into stories: Demonstrate how price action can diverge from fundamental narratives. For example, renewable energy stocks outperformed from 2016-2020 (when fundamentals suggested selling) but underperformed after 2021 (when fundamentals became bullish due to government investment). Price and fundamentals sometimes move in opposite directions.

• Help identify investable themes: Use technical analysis to spot related sectors and stocks that benefit from broader trends. For example, 2023 homebuilder strength signaled opportunities in paint (Sherwin-Williams), transportation (Union Pacific, Old Dominion Freight), and flooring (Mohawk) companies—demonstrating how one theme can identify second-derivative opportunities.

• Use intermarket relationships: Leverage understanding of global markets and currency impacts to provide broader context. In 2023, while media focused on the “Magnificent 7,” global markets were breaking out, and a weakening U.S. dollar provided tailwinds to multinational companies and emerging markets.

• Provide top-down and bottom-up perspective: Add value to sector analysts by offering insights on price action across sectors, indexes, and asset classes, validating or challenging their investment theses.