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CHAPTER 5
EFFICIENT CAPITAL MARKETS, BEHAVIORAL FINANCE, AND TECHNICAL ANALYSIS
5.1 Efficient Capital Markets
5.1.1 Why Should Capital Markets Be Efficient?
▪ Assumptions:
– A large number of independent profit maximizing participants who analyze and value
securities
– New information comes in random fashion.
– The buy and sell decisions of profit-maximizing investors cause security prices to adjust
5.1.2 Alternative Efficient Market Hypotheses
▪ Random walk hypothesis – changes in security prices occur randomly
▪ Fair Game Model – current market price reflects all available information about a security
5.1.3 Tests and Results of Efficient Market Hypotheses
1. Weak-Form Hypothesis: Tests and Results
2. Tests of Trading Rules
▪ Potential pitfalls
3. Results of Simulations of Specific Trading Rules
4. Semistrong-Form Hypothesis: Tests and Results
▪ Studies to predict future rates of return using public information beyond pure market
information, such as prices and trading volume
– These studies involve time-series analysis of returns or the cross-section distribution
of returns for individual stocks.
– Quarterly earnings reports
– Quarterly earnings reports
– The January anomaly
– Other calendar effects
– Monthly effect
– Initial public offerings (IPOs) (Exhibit 5.1)
– Exchange listing
– Unexpected world events and economic news
– Announcement of accounting changes
– Corporate events
5. Strong-Form Hypothesis: Tests and Results
▪ Tests whether any group can consistently enjoy abnormal returns
– Corporate insider trading
– Security analysts
5.2 Behavioral Finance
5.2.1 Explaining biases
5.2.2 Fusion investing
5.3 Implications of Efficient Capital Markets
5.3.1 Efficient Markets and Fundamental Analysis
1. Aggregate Market Analysis with Efficient Capital Markets
2. Industry and Company Analysis with Efficient Capital Markets
3. How to Evaluate Analysts or Investors
▪ Examine the performance of numerous securities that this analyst or investor
4. Conclusions about Fundamental Analysis
5.3.2 Efficient Markets and Portfolio Management
1. Portfolio Managers with Superior Analysts
2. Portfolio Managers without Superior Analysts
▪ Determine and quantify your risk preferences
3. The Rationale and Use of Index Funds and Exchange-Traded Funds
4. Insights from Behavioral Finance
▪ Growth companies will usually not be growth stocks due to the overconfidence of
5.4 Technical Analysis
5.4.1 Underlying Assumption of Technical Analysis (Exhibit 5.2)
▪ The market value of any good or service is determined solely by the interaction of supply and
5.5 Advantages of Technical Analysis
▪ Not heavily dependent on financial statements that can have problems or not contain
5.6 Challenges to Technical Analysis
5.6.1 Challenges to the Assumptions of Technical Analysis
5.6.2 Challenges to Specific Trading Rules
5.7 Technical Trading Rules and Indicators
▪ Typical Stock Market Cycle (Exhibit 5.3)
5.7.1 Contrary-Opinion Rules
1. Mutual Fund Cash Positions
2. Credit Balances in Brokerage Accounts
3. Investment Advisory Opinions
4. Chicago Board Options Exchange (CBOE) Put-Call Ratio
5. Futures Traders Bullish on Stock Index Futures
5.7.2 Follow the Smart Money
1. Confidence Index
▪ The Confidence Index (CI) is the ratio of the Barron’s average yield on the “Best Grade
2. T-Bill-Eurodollar Yield Spread
3. Debit Balances in Brokerage Accounts (Margin Debt)
5.7.3 Momentum Indicators
1. Breadth of Market (Exhibit 5.4)
2. Stocks above Their 200-Day Moving Average
▪ The market is considered to be overbought and subject to a negative correction when
5.7.4 Stock Price and Volume Techniques
1. Dow Theory (Exhibit 5.5)
▪ Oldest technical trading rule
– When the 50-day MA line is below the 200-day MA line, it would be a bearish
environment.
▪ Relative Strength (Exhibit 5.8)
– The RS ratio is equal to the price of a stock or an industry index divided by the value
for some stock-market index, such as the S&P 500.
5.7.5 Efficient Markets and Technical Analysis
▪ The assumptions of technical analysis directly oppose the notion of efficient markets.