Quote: Richard J. Dennis of C&D Commodities is accepting applications for the position of Commodity Futures Trader to expand his established group of traders. Mr. Dennis and his associates will train a small group of applicants in his proprietary trading concepts. Prior experience in trading will be considered but is not necessary.
Central Question: Can trading be taught?
What is Momentum?
Research Reference: Jegadeesh & Titman (1993)
- Title: "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency"
- Findings: U.S. stocks that perform the best (or worst) over a three- to twelve-month period tend to continue to perform well (or poorly) over the subsequent three to twelve months.
- Challenge: Momentum challenges the Efficient Market Hypothesis (EMH).
Jegadeesh & Titman (1993) Portfolio Strategy
Strategy Description: Portfolio J-month/K-month strategy
- Action Steps:
- Select stocks based on returns over the past J months.
- Hold these stocks for K months.
- Month t: At the beginning of each month, securities are ranked by returns of the past J months.
- Deciles: Top decile portfolio called the "losers" decile and bottom decile is called the "winners" decile.
- Weighting: Equally weight stocks within each decile.
- Implementation: In each month t, the strategy buys the winner portfolio and sells the loser portfolio, holding this position for K months. Furthermore, it closes out positions initiated in month t-K.
Returns Analysis
Performance Metrics:
- Sell: Average monthly returns of stocks that performed worst over past 3 months (losers).
- Buy: Average monthly returns of stocks that performed best over past 3 months (winners).
- Buy-sell Profit: The third row captures the momentum profit from buying winners and shorting losers.
Source of Profits
Findings: All zero-cost buy-sell portfolios exhibit statistically significant positive results.
Risk Compensation Question: Could profits arise from risk-compensation?
- Key Consideration: The Capital Asset Pricing Model (CAPM) links required rate of return to beta.
- Observation: Beta of buy-sell portfolios tends to be negative; for example, for the buy-sell 6-month/6-month, eta ext{ (beta)}
ightarrow -0.08
- Conclusion: Positive returns not adequately explained by risk-compensation.
Long-term Reversal
Observation: Stocks known for their momentum exhibit negative abnormal returns starting about 12 months post-formation and lasting until the 31st month.
- Related Work: Findings are associated with research from De Bondt and Thaler (1985).
- Earnings Announcement Dates: A similar pattern is observed in stock returns relative to earnings announcements, where past winners achieve higher returns in the first seven months, while past losers see higher returns than past winners in the following thirteen months.
Practice Question
Question: How can momentum and long-term reversal coexist?
- Options:
- A: Momentum captures risk compensation through CAPM beta while reversal reflects unwinding of that risk.
- B: Different subsets: momentum for high-beta stocks; reversal for low-beta stocks.
- C: Different time horizons: momentum at 3–12 months; reversal from 12 to 31 months.
- D: Momentum from under-reaction to earnings; reversal from over-reaction — different cognitive biases at play.
Importance of Momentum
Opposes Efficient Markets: Challenges the idea that returns will revert to a fundamental value.
- Under EMH, predictable patterns would be acted upon by investors quickly, eliminating any sources of predictability.
Validation of Trend Following: Supports simple trend-following strategies:
- Buy when prices rise, sell when they fall.
- Central to professional asset management.
Universality: Momentum appears consistent across different time periods and asset classes.
Focus: Foreign Exchange (FX) markets possess higher liquidity and volume compared to stock markets.
Cost Efficiency: Lower transaction costs for large institutional investors than in stock markets.
Study Objective: Examine momentum within FX markets.
Portfolio Construction Strategy
Method: Form six rank-ordered portfolios based on lagged returns (1, 3, 6, 9, and 12 month lags).
Identifying Portfolios: Determine best portfolio as "High" and worst as "Low."
Trading Portfolio: Long position in High and short position in Low.
Results of Menkhoff et al. (2012)
Cumulative Excess Returns Analysis (in %):
- Analysis spans from 1980 to 2008.
- Visual representation shows return statistics and performance variations over these years.
Business Cycle Risk Implications
Indicators Examined: Consumption, Employment, Consumer Price Index (CPI), Disposable Income, etc.
Findings: Momentum returns show little correlation with typical business-cycle indicators, consistent with studies of U.S. equity momentum.
Limits to Arbitrage
Returns Variation: Momentum does not result in constant positive returns and often requires a long horizon — a challenge for many financial professionals.
Country Risks: Additional factors contributing to market risks.
Summary of Menkhoff et al. (2012)
Excess Returns: Potential for unconditional excess returns up to 10 ext{%/year}.
Time Variation: Profits remain highly time-varying, presenting challenges for professional traders.
Conclusion: Momentum is a universal phenomenon across financial markets, independent of U.S. business-cycle risk.
Explanations for Momentum
Risk-based and Characteristics-based Explanations:
- Momentum is notably stronger with smaller firms.
- Stronger among firms with lower credit ratings; however, fundamental explanations remain elusive.
Cognitive Biases:
- Under-reaction to news leads to momentum; gradual changes in earnings forecasts can result in this underreaction.
- Weak analyst coverage correlates with stronger momentum effects.
Cognitive Biases Breakdown
Initial Under-Reaction: Investors under-react to news incrementally integrating information into prices (conservatism bias).
Subsequent Over-Reaction: Positive returns foster optimism leading to excessive price speculation and emphasize extrapolative expectations.
- Perception of trends can cultivate positive feedback trading, which sustains trends rather than nullifying them.
Momentum in the Stock Market
Limits to Arbitrage Effects: Issues arise from transaction costs when implementing momentum strategies, contributing to market noise.
Question: The cognition-based explanation of momentum involves:
- A: Initial under-reaction to news followed by over-reaction through extrapolation.
- B: Initial over-reaction through extrapolation, followed by correction to fundamentals.
- C: Persistent under-reaction integrating information without overshooting.
- D: Weak analyst coverage perpetuating mispricing that momentum strategies capitalize on.
Exponential Moving Average (EMA)
Definition: EMA is a moving average that underscores more recent data points significantly.
Formula:
- pˉ<em>n,t=p(t)1−e−aun+e−aunpˉ</em>n,t−1
- Where:
- pˉn,t = Exponential moving average at time t.
- p(t) = Price at time t.
- λn = Decay rate of the EMA.
Momentum: Trend-Following Over Time
Signal Definition: Lempérière et al. (2014) describes the signal as:
- s_n(t) = p(t-1) - ar{p}_{n,t-1} /
ew{ ext{volatility}} ext{(measured by }σ_n(t-1))
Trading Decisions:
- If sn(t) is positive, buy; if negative, sell.
- Framework for fictitious P/L (profit and losses):
- Qextαn(t)=extSumofsign[sn(t′)]imessn(t′−1)imes(p(t′+1)−p(t′)),extfort′<t.
Asset Pool from Lempérière et al. (2014)
Included Assets: 7 commodity contracts (e.g., Crude oil, Natural Gas) alongside sovereign bonds and stock-index contracts across multiple countries (including Australia, US, UK).
Futures Contracts: Highlighting the nature of futures — a standardized legal agreement for future buying or selling at a determined price.
Visuals of Profit and Loss
Fictitious P/L Spot Pools: Representation displays performance over time.
Fictitious P/L Futures Pools: Trends of performance metrics demonstrated over defined periods in plots (financial visualizations).
Differences from Jegadeesh & Titman (1993)
Cross-Sectional vs. Time-Series:
- Jegadeesh & Titman (1993): A cross-sectional momentum strategy focusing on ranking assets based on fixed look-back windows.
- Lempérière et al. (2014): Involves a time-series momentum strategy, enabling trading in the direction of a single asset’s price divergence from its moving average.
Moskowitz, Ooi, and Pedersen (2012)
Context: Time Series Momentum study across 58 liquid futures contracts across varied asset classes from 1965 to 2009.
Core Inquiry: Investigating if an asset’s past return can reliably predict future returns.
Key Concept: Unlike prior studies, it focuses solely on time-series predictability without asset comparison.
Trading Strategy from Moskowitz et al. (2012)
Simple Rule: Go long if the past 12-month excess return is positive; go short if negative.
Position Sizing: Inverse scaling with volatility to ensure constant risk levels across assets.
Results: Each of the 58 contracts yields a positive Sharpe ratio and a diversified TSMOM portfolio delivers an annualized Sharpe ratio over 1, showing roughly 2.5× that of equities.
Key Takeaways from Moskowitz et al. (2012)
Findings: Momentum returns appear minimal exposure to standard risk factors (like MSCI World).
Persistent Returns: Returns maintain consistency for 1-12 months before partial reversal — consistent with under-reaction followed by over-reaction.
Relation between Strategies: TSMOM distinguishes itself from cross-sectional momentum (Jegadeesh & Titman) by addressing additional returns unaccounted for by conventional comparative strategies.
Practice Question on Lempérière, et al. (2014) vs. Jegadeesh & Titman (1993)
Question: What distinguishes Lempérière et al. (2014) from Jegadeesh & Titman (1993)?
- A: Ranking by past returns with a long/short strategy across differences.
- B: Usage of exponential moving averages in signals versus past returns.
- C: Cross-sectional vs time-series strategies.
- D: Comparative returns across assets against single asset movements relative to averages.
Daniel and Moskowitz (2016): Momentum Crashes
Study Reference: "Momentum Crashes," Journal of Financial Economics.
Key Observations: While momentum strategies exhibit strong performance, they can face substantial crashes identified as forecastable events.
- Examples: August 1932 (-74% in one month); April 2009 (-46% in one month).
Conditions for Momentum Crashes
Identifying Crashes: Crashes typically occur during market panic states characterized by:
1. Significant preceding market declines (bear market).
2. High market volatility.
3. A sudden market rebound.
Beta Analysis: Past losers possess high betas during recoveries, while winners are less sensitive; this creates conditions for significant rebounds leading to momentum crashes.
Dynamic Momentum Strategy
Risk Management Strategy: Implement two criteria to mitigate risks from crashes:
1. Monitoring market declines over the past two years (bear market indicator).
2. Assessing current market volatility.
Outcome of Strategy: This dynamic strategy effectively doubles the Sharpe ratio compared to static momentum.
Fundamental Insight: Emphasizes the importance of risk management alongside understanding profitable strategies in investing.
Conclusion and Reflection
Efficiency vs. Momentum Strategies: Traditional EMH posits that predictable return patterns will vanish once acted upon by investors. However, momentum strategies have been substantial and sustained, representing a paradox to the EMH, revealing a definitive counterpoint in behavioral finance.
What's Next
Upcoming Lecture: Investor Psychology Overview.
- Focus Areas: Anatomy of investor anomalies, psychological perspectives on financial behaviors, inclusive of preferences and belief systems.
Pause for Questions
Utilize this opportunity to clarify any outstanding queries from the lecture materials presented today.