Section1 - Chapter 4 - Efficient Market Hypothesis (EMH).pptx

Section1 - Chapter 4 - Efficient Market Hypothesis (EMH) - Presented by Rohan Sharma - The CMT Coach - Chartered Market Technician CMT Level 1 Study Material - CMT Level 1 Chapter Wise Short Notes - CMT Level 1 Course Content - CMT Level 1 2025 Exam Syllabus Visit Site : www.learn.ptaindia.com and www.ptaindia.com

1. Chapter 4 - Efficient Market Hypothesis (EMH) Section 1 – Theory and History of Technical Analysis Presented By : This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

2. Agenda The Opportunity of the Efficient Markets Hypothesis The Opportunity of the Efficient Markets Hypothesis Three Forms of the EMH Challenges to the EMH The Nature of Randomness and the Arcsine Law Additional Challenges and Alternatives to the EMH Using Technical Analysis Within a Randomized Market Fama’s Revision of the Three Forms Conclusion This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

3. Efficient Market Hypothesis (EMH) 1. Definition: The EMH states that asset prices fully reflect all available information, making it impossible to consistently achieve above-market returns. 2. Three Forms of EMH: Weak Form: Prices reflect all past market data (historical prices & volume). Technical analysis is ineffective, but fundamental analysis may work. Semi-Strong Form: Prices reflect all publicly available information (earnings, news, reports). Neither technical nor fundamental analysis can provide an edge, but insider information may. Strong Form: Prices reflect all public and private (insider) information. No one can consistently outperform the market. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

4. Efficient Market Hypothesis (EMH) 3. Implications for Traders & Investors: o If markets are weak-form efficient, technical analysis is unlikely to work. o If markets are semi-strong efficient, fundamental analysis is ineffective. o If markets are strong-form efficient, even insider trading wouldn’t give an advantage. 4. Challenges to EMH: o Behavioral finance shows that irrational investor behavior can create inefficiencies. o Market anomalies like the momentum effect and value investing success challenge EMH. o Technical and fundamental strategies sometimes outperform, suggesting inefficiencies exist. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

5. Efficient Market Hypothesis (EMH) This Content is Copyright Reserved Rights Copyright 2025@PTAIndia EMH Form Information Reflected Impact on Trading Strategies Weak Form Past prices & volume Technical analysis is useless; fundamental may work Semi-Strong Public info (news, earnings) Fundamental & technical analysis are ineffective Strong Form All public & private info No strategy can consistently beat the market

6. Interpreting EMH in Practice 1. Are Markets Fully Efficient? o Real-world markets show inefficiencies (e.g., bubbles, panic selling). o High-frequency trading exploits short-term inefficiencies. o Passive investing (index funds) follows EMH logic, assuming no consistent alpha exists. 2. Does Technical Analysis Work Under EMH? o In a weak-form efficient market, technical analysis should not work long-term. o In practice, traders still profit from market inefficiencies and behavioral biases. 3. Does Fundamental Analysis Work? o If the market is semi-strong efficient, then all public information is already priced in. o However, deep-value investing and event-driven strategies show mixed evidence. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

7. Market Anomalies That Challenge EMH 1. Momentum Anomaly Description: Stocks that have performed well in the past (3–12 months) tend to continue outperforming, while losers tend to keep underperforming. Why It Challenges EMH: If past price trends can predict future returns, weak-form EMH is violated. Example Strategy: Momentum traders buy strong-performing assets and short weak ones (e.g., the 52-week high effect). 2. Value Anomaly Description: Stocks with low valuation ratios (P/E, P/B) tend to outperform those with high valuations over time. Why It Challenges EMH: If all public information is priced in (semi-strong form), undervaluation should not persist. Example Strategy: Warren Buffett’s value investing—buying stocks that appear undervalued based on fundamentals. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

8. Market Anomalies That Challenge EMH 3. Size Effect (Small-Cap Anomaly) Description: Historically, small-cap stocks outperform large-cap stocks on a risk-adjusted basis. Why It Challenges EMH: If risk is the only factor determining returns (as per EMH), small caps shouldn’t systematically outperform large caps. Example Strategy: Investing in small-cap ETFs or micro-cap stocks. 4. January Effect Description: Stocks, especially small-caps, tend to rise more in January than other months. Why It Challenges EMH: If markets are efficient, this seasonal pattern should not persist. Possible Explanation: Tax-loss harvesting in December followed by reinvestment in January. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

9. Market Anomalies That Challenge EMH 5. Post-Earnings Announcement Drift (PEAD) Description: Stocks tend to continue trending in the direction of an earnings surprise for weeks or months after the announcement. Why It Challenges EMH: If semi-strong efficiency held, stock prices should instantly reflect earnings surprises. Example Strategy: Buying stocks after strong earnings beats and shorting stocks after weak earnings reports. 6. Dividend Yield Anomaly Description: Stocks with high dividend yields tend to outperform lower-yielding stocks on a risk-adjusted basis. Why It Challenges EMH: If investors rationally price assets, higher yields shouldn’t consistently provide excess returns. Example Strategy: Dividend growth investing (e.g., the "Dogs of the Dow" strategy). This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

10. Market Anomalies That Challenge EMH 7. Behavioral Biases & Psychological Factors Description: Investors often act irrationally due to fear, greed, overconfidence, or herd mentality. Why It Challenges EMH: If markets were efficient, irrational behavior wouldn’t systematically impact prices. Examples: • Overreaction: Stocks that drop sharply on bad news tend to rebound (contradicting semi-strong EMH). • Underreaction : Investors slowly adjust to new information, causing trends and momentum. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

11. Market Anomalies That Challenge EMH 8. Bubble & Crash Phenomena Description: Markets experience unsustainable price surges (bubbles) followed by sharp declines (crashes). Why It Challenges EMH: If all information is priced in, extreme mispricings like the Dot-Com Bubble or 2008 Crisis shouldn’t occur. Examples: • Dot-Com Bubble (1999-2000): Tech stocks soared far beyond intrinsic value before collapsing. • 2008 Financial Crisis: Market inefficiencies led to mispricing of mortgage-backed securities. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

12. Market Anomalies That Challenge EMH Conclusion: Does EMH Hold? • Critics argue: Persistent anomalies indicate that markets are not perfectly efficient. • Supporters argue: Once anomalies are widely known, they get arbitraged away, restoring efficiency. • Practical take: Markets are often efficient but not always—short-term inefficiencies create trading opportunities. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

13. Three Forms of the Efficient Market Hypothesis (EMH) This Content is Copyright Reserved Rights Copyright 2025@PTAIndia Form Information Priced In Implication for Traders Can Beat the Market? Weak Form Past prices & volume Technical analysis is ineffective Maybe (using fundamental or insider info) Semi-Strong Form All public information (news, earnings, reports) Fundamental & technical analysis are ineffective Maybe (using private/insider info) Strong Form All public & private (insider) information No strategy can consistently outperform No

14. Challenges to the EMH (Market Anomalies & Inefficiencies) 1. Momentum Effect • Stocks that have performed well tend to keep rising, while losers continue falling. • Challenge: Weak-form EMH states that past prices shouldn’t predict future returns, yet momentum trading often works. 2. Value Anomaly • Stocks with low Price-to-Earnings (P/E) or Price-to-Book (P/B) ratios tend to outperform high-valuation stocks. • Challenge: Semi-strong EMH implies all public info should be priced in, yet undervalued stocks often beat growth stocks. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

15. Challenges to the EMH (Market Anomalies & Inefficiencies) 3. Small-Cap Effect • Small-cap stocks tend to outperform large-cap stocks over time. • Challenge: EMH suggests risk-adjusted returns should be equal, but small-cap stocks have historically delivered excess returns. 4. Post-Earnings Announcement Drift (PEAD) • Stocks continue moving in the direction of an earnings surprise for weeks after the announcement. • Challenge: Semi-strong EMH implies earnings info should be instantly priced in, but traders can profit from delayed reactions. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

16. Challenges to the EMH (Market Anomalies & Inefficiencies) 5. Behavioral Biases (Investor Irrationality) • Investors often overreact or underreact due to fear, greed, or herd mentality. • Challenge: EMH assumes rational decision-making, but bubbles (e.g., Dot-Com, Crypto) and crashes contradict this. 6. Bubbles & Market Crashes • Irrational exuberance leads to asset bubbles, followed by sudden collapses. • Challenge: If markets are efficient, extreme mispricings (like 2008 or the Dot-Com Bubble) should not occur. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

17. Challenges and Implications of EMH Tests This Content is Copyright Reserved Rights Copyright 2025@PTAIndia EMH Form Challenges Implications Weak Form - Momentum effect contradicts the idea that past prices don’t predict future returns. - Technical indicators like moving averages sometimes show predictive power. - Technical analysis should not work, but anomalies like trend-following suggest inefficiencies. - Fundamental and insider information may still provide an edge. Semi-Strong Form - Post-Earnings Announcement Drift (PEAD) shows markets do not instantly adjust to news. - Value stocks outperform despite public knowledge of their fundamentals. - Fundamental analysis should not work, yet some investors (e.g., Warren Buffett) outperform long term. - Private/insider information may still give an advantage. Strong Form - Insider trading profits suggest that all private information is not fully priced in. - Regulatory actions against insider trading indicate that private info does impact prices. - No one should be able to beat the market, but real-world cases show insider advantages. - Insider trading laws exist precisely because private info creates profit opportunities.

18. How Traders Exploit Market Inefficiencies 1. Exploiting Weak Form EMH Inefficiencies Target: Past price trends & volume patterns 📌 Strategy: Momentum Trading • Buy stocks that have been rising (3-12 months) and short those that have been falling. • Uses moving averages, relative strength index (RSI), and MACD indicators. • Example: Trend-following strategies used by hedge funds and CTAs. 📌 Strategy: Mean Reversion • Stocks that experience sharp price movements (overbought/oversold) tend to revert to their average. • Uses Bollinger Bands, RSI, and support/resistance levels. • Example: Trading stocks that drop significantly due to short-term overreaction. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

19. How Traders Exploit Market Inefficiencies 2. Exploiting Semi-Strong Form EMH Inefficiencies Target: Delayed market reaction to public information 📌 Strategy: Post-Earnings Announcement Drift (PEAD) • Stocks with strong earnings surprises tend to continue trending up (or down for bad surprises). • Traders buy after positive earnings beats and short after negative ones. • Example: Buying Tesla (TSLA) after strong earnings before the market fully prices it in. 📌 Strategy: Value Investing • Stocks with low P/E, P/B, and high dividend yields outperform high-valuation stocks. • Example: Warren Buffett’s approach—buying undervalued companies with strong fundamentals. 📌 Strategy: Event-Driven Trading • Profiting from mergers, acquisitions, stock splits, and dividends. • Example: Buying a stock after an announced merger but before full price correction. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

20. How Traders Exploit Market Inefficiencies 3. Exploiting Strong Form EMH Inefficiencies Target: Private or insider information leaks 📌 Strategy: Insider Trading (Illegal but Profitable) • Insiders use non-public knowledge to trade stocks before major events. • Example: Executives buying shares before a big earnings beat (illegal but observed). 📌 Strategy: Smart Money Tracking • Following large institutional investors, hedge fund moves, or Form 13F filings (SEC reports). • Example: Mimicking positions of top hedge funds like Renaissance Technologies. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

21. How Traders Exploit Market Inefficiencies 4. Exploiting Behavioral Biases & Market Anomalies 📌 Strategy: Contrarian Investing • Buying when fear dominates (e.g., market crashes) and selling during euphoria. • Example: Buying during the 2008 financial crisis when markets were oversold. 📌 Strategy: Seasonal Trading (January Effect, Sell in May) • Stocks often rise in January due to tax-loss harvesting reversals. • Example: Buying small-cap stocks in December and selling in January. 📌 Strategy: Short Selling Overhyped Stocks • Stocks with excessive speculation (e.g., meme stocks) often crash later. • Example: Hedge funds shorting GameStop (GME) before the retail-driven short squeeze. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

22. Fama’s Acknowledgment of EMH Limitations 1. Market Anomalies Exist o Fama admitted that anomalies like the momentum effect and the value effect contradict pure EMH. o He co-authored studies recognizing that value stocks (low P/E, low P/B) tend to outperform growth stocks. 2. Behavioral Finance Challenges o Fama acknowledged that investor irrationality can create mispricings , but he argued that such inefficiencies are unpredictable. o He believes anomalies come and go, meaning traders cannot reliably exploit them over time. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

23. Fama’s Acknowledgment of EMH Limitations 3. Short-Term Inefficiencies o Fama agrees that markets may not be perfectly efficient at all times (e.g., bubbles, crashes), but he maintains that they self-correct quickly. o Example: Dot-Com Bubble (1999-2000)—irrational pricing existed, but eventually, markets corrected. 4. Limits to Arbitrage o He acknowledges that some inefficiencies persist because arbitrage is not always risk-free. o Example: During the 2008 crisis, liquidity constraints prevented rational traders from correcting mispricings . This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

24. Fama’s Response to Critics 📌 Markets Are "Efficient Enough" – While inefficiencies exist, they are often small, random, and difficult to profit from consistently. 📌 No Free Lunch – If a trading strategy beats the market, it usually comes with higher risk, not true inefficiency. 📌 Passive Investing Still Wins – Because anomalies are inconsistent, index investing remains the best strategy for most investors. Final Thought Fama acknowledges that markets are not perfectly efficient but argues they are efficient enough that consistently beating them is extremely difficult. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

25. How Fama’s Findings Influenced Modern Investing Strategies 1. The Rise of Passive Investing (Index Funds & ETFs) 📌 Fama’s Influence: • EMH suggests that stock prices already reflect all available information, making it nearly impossible to consistently outperform the market. • This led to the rise of index funds (e.g., S&P 500 ETFs) as a low-cost, long-term investment approach. 📌 Practical Impact: • Investors shifted from actively managed funds to passive ETFs, saving on fees and outperforming most active managers over time. • Vanguard’s Jack Bogle pioneered low-cost index funds, inspired by Fama’s work. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

26. How Fama’s Findings Influenced Modern Investing Strategies 2. Factor Investing & the Fama -French Model 📌 Fama’s Influence: • Fama (with Kenneth French) expanded the traditional Capital Asset Pricing Model (CAPM) by identifying key factors that explain stock returns. 📌 Fama -French Three-Factor Model: • Market Risk (Beta): Stocks outperform bonds in the long run. • Size Effect: Small-cap stocks outperform large-cap stocks. • Value Effect: Low P/E and low P/B stocks (value stocks) outperform growth stocks. 📌 Practical Impact: • Investors use factor-based funds like DFA (Dimensional Fund Advisors) that tilt portfolios toward small-cap and value stocks. • Smart Beta ETFs now apply these factors systematically. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

27. How Fama’s Findings Influenced Modern Investing Strategies 3. Behavioral Finance vs. EMH (Challenges & Adaptations) 📌 Fama’s Influence: • While Fama’s EMH suggests rational pricing, behavioral finance (e.g., Daniel Kahneman , Robert Shiller) argues that investors act irrationally. • Fama acknowledged that while behavioral biases exist, they are unpredictable, making it hard to profit from them systematically. 📌 Practical Impact: • Some hedge funds and quant traders attempt to exploit behavioral inefficiencies using algorithms that detect overreactions or momentum. • Institutional investors blend factor investing with behavioral insights (e.g., momentum trading). This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

28. How Fama’s Findings Influenced Modern Investing Strategies 4. The Shift Toward Risk-Adjusted Returns 📌 Fama’s Influence: • EMH suggests that higher returns come with higher risk, making risk-adjusted performance the key metric. • Instead of chasing raw returns, investors now focus on Sharpe ratios, alpha, and risk exposure. 📌 Practical Impact: • Modern Portfolio Theory (MPT) and Factor-Based Investing dominate institutional strategies. • Multi-factor ETFs now adjust for risk-return trade-offs (e.g., low-volatility factor funds). This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

29. How Fama’s Findings Influenced Modern Investing Strategies 5. Limits of Active Management (Decline of Stock Picking) 📌 Fama’s Influence: • EMH implies that professional fund managers rarely beat the market after fees and costs. • Studies show that over 85% of active managers underperform their benchmarks over long periods. 📌 Practical Impact: • Investors shifted from mutual funds to ETFs, avoiding high-fee active managers. • Hedge funds focus more on quant strategies rather than traditional stock picking. Final Takeaways ✅ Fama’s EMH reshaped investing, leading to the dominance of passive investing and factor-based strategies. ✅ Most investors now use index funds, ETFs, and factor tilts, rather than trying to time the market. ✅ Behavioral and quant investing challenge EMH, but inefficiencies are hard to exploit consistently. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

30. The Nature of Randomness and the Arcsine Law Understanding Randomness in Financial Markets In finance, randomness refers to the unpredictable nature of price movements. If markets are efficient, stock prices should follow a random walk, meaning: • Future price movements are independent of past movements. • Prices follow a probabilistic distribution rather than a deterministic pattern. • Short-term trends exist, but they don’t imply predictable future movements. However, randomness in financial markets doesn’t always behave intuitively, which is where the Arcsine Law comes in. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

31. The Nature of Randomness and the Arcsine Law What is the Arcsine Law? The Arcsine Law is a surprising result from probability theory that describes how long a random process spends in a particular state (e.g., above or below a starting point). In the context of finance, it tells us that: 1. Stock prices spend most of their time near extreme values rather than at the average. 2. Winning or losing streaks tend to be longer than expected, challenging the idea of "normal" reversals. 3. The most extreme event often happens early or late in the process, rather than in the middle. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

32. The Nature of Randomness and the Arcsine Law This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

33. Implications of the Arcsine Law for Markets 1. Stock Prices Spend More Time at Extremes • If you flip a fair coin 1,000 times, you might expect the number of heads and tails to be roughly equal throughout. • Arcsine Law suggests this is not true—the process spends more time at extreme imbalances (e.g., far more heads than tails for long periods). • Market Implication: Stock prices tend to hover near their highs or lows, rather than near their average price. 2. Winning and Losing Streaks are Longer Than Expected • Traders often expect that a stock will “revert” quickly after going up or down for a while. • Arcsine Law suggests streaks last longer than intuition suggests. • Market Implication: Momentum strategies (buying winners, selling losers) can work because trends persist longer than expected. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

34. Challenges and Alternatives to the Efficient Market Hypothesis (EMH) 🔑 Key Takeaways 📌 1. Market Anomalies Challenge EMH • Momentum Effect → Stocks that have performed well continue to do so. • Value Anomaly → Low P/E and low P/B stocks tend to outperform. • Post-Earnings Announcement Drift (PEAD) → Stocks react gradually to earnings news instead of instantly. • Small-Cap Effect → Small stocks tend to outperform large caps over time. 📌 2. Behavioral Finance Challenges EMH • Investors are irrational due to emotions (fear, greed, overconfidence). • Herding behavior leads to bubbles and crashes (e.g., Dot-Com, Crypto). • Loss aversion → Investors hold losing stocks too long but sell winners too quickly. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

35. Challenges and Alternatives to the Efficient Market Hypothesis (EMH) 🔑 Key Takeaways 📌 3. Limits to Arbitrage (Real-World Constraints) • Capital constraints prevent rational investors from correcting mispricings . • Short-selling restrictions limit the ability to profit from overvalued stocks. • Market crashes and bubbles show that markets are not always rational. 📌 4. Alternative Theories to EMH • Adaptive Market Hypothesis (AMH) → Markets evolve, and efficiency depends on investor behavior. • Fractal Market Hypothesis (FMH) → Markets are structured across different time scales, challenging the random walk assumption. • Reflexivity (Soros) → Prices influence fundamentals, creating self-reinforcing trends. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

36. Cheat Sheet: Challenges & Alternatives to EMH This Content is Copyright Reserved Rights Copyright 2025@PTAIndia Challenge/Alternative Key Idea Market Implications Momentum Effect Trends persist longer than expected. Momentum trading can be profitable. Value Effect Undervalued stocks outperform over time. Value investing works long-term. Behavioral Biases Investors are irrational (overreaction, herding). Markets are not always rational. Limits to Arbitrage Mispricings may persist due to real-world constraints. Markets may not correct inefficiencies quickly. Adaptive Market Hypothesis (AMH) Market efficiency changes over time based on competition. Investors must adapt strategies as conditions change. Fractal Market Hypothesis (FMH) Markets behave differently across time scales. Short-term and long-term price movements follow different rules. Reflexivity (Soros) Prices impact fundamentals, creating feedback loops. Speculative bubbles and crashes are part of the market cycle.

37. Using Technical Analysis Within a Randomized Market 🔑 Key Takeaways ✅ Markets Exhibit Short-Term Inefficiencies – While long-term trends may be unpredictable, short-term momentum and support/resistance levels create trading opportunities. ✅ Traders Use Probabilities, Not Certainties – Technical indicators provide probable trade setups rather than guaranteed predictions. ✅ Some Patterns Persist Due to Investor Behavior – Herding, fear, and greed lead to self-fulfilling prophecies in price movements. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

38. Strategies to Apply Technical Analysis in a Random Market This Content is Copyright Reserved Rights Copyright 2025@PTAIndia Approach Key Idea Why It Works Despite Randomness? Trend Following Identifies stocks moving in one direction and trades in that direction. Momentum persists due to investor psychology and institutional trading. Support & Resistance Identifies price levels where assets repeatedly reverse. Market participants react to historical price points. Moving Averages Filters out noise and identifies directional bias. Helps smooth randomness and capture trends. Mean Reversion Trades based on extreme deviations from an average price. Prices tend to revert to fair value over time. Breakout Trading Enters trades when price moves beyond key levels (e.g., previous highs/lows). Large volume often follows breakouts, fueling trends. Volatility-Based Strategies Uses Bollinger Bands, ATR, or VIX to anticipate big price moves. Periods of low volatility often lead to high volatility.

39. 🚀 Practical Trading Approach in a Random Market 1️. Avoid Overfitting to Past Data → Many indicators work in backtests but fail in real markets. 2. Combine Indicators & Price Action → Relying on a single signal is unreliable in a random market. 3. Focus on Risk Management → Since randomness increases uncertainty, stop-losses and position sizing are crucial. 4. Use Probabilities, Not Predictions → No method is foolproof—focus on risk-reward ratios and win probabilities. 🎯 Final Thought While randomness challenges technical analysis, short-term inefficiencies and trader psychology create tradable patterns. The key is adapting strategies, managing risk, and focusing on probabilities rather than certainty. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

40. Random Walk & Martingale Process Random Walk Process ✅ Definition: A stochastic process where future price movements are independent of past movements and follow a probability distribution. ✅ Key Assumption: No Predictability → Price changes are purely random, making technical analysis ineffective. ✅ Implication: Markets are weak-form efficient, meaning past price data provides no edge. Martingale Process ✅ Definition: A stochastic process where the expected future value of a variable is equal to its current value. ✅ Key Assumption: Fair Game → No trader has an advantage over the long run. ✅ Implication: No Risk-Free Profits → Future prices incorporate all available information, reinforcing market efficiency. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

41. Random Walk & Martingale Process This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

42. Random Walk & Martingale Process

43. Cheat Sheet : Random Walk & Martingale Process This Content is Copyright Reserved Rights Copyright 2025@PTAIndia Concept Definition Key Assumption Implication for Markets Random Walk Future movements are independent of past prices. Prices follow a probabilistic path. Past data does not predict future prices (weak-form efficiency). Martingale Process Expected future price = Current price. No risk-free arbitrage opportunities exist. Markets are efficient, making it hard to generate excess returns. Difference? Random walk can have a drift term (trend). Martingale strictly implies no drift. If stock returns follow a martingale, excess returns are unpredictable.

44. Random Walk & Martingale Process 🚀 Trading & Investing Takeaways 📌 No Free Lunch – If markets follow a martingale, trading strategies cannot systematically outperform. 📌 Trend vs. Randomness – If markets follow a pure random walk, technical analysis is unreliable; if trends exist, strategies like momentum trading can work. 📌 Risk Matters – Even if price changes are random, risk management (stop losses, position sizing) remains crucial. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

45. 📌 Fama’s Revision of the Three Forms of Market Efficiency Eugene Fama originally proposed the Efficient Market Hypothesis (EMH) in 1970, categorizing market efficiency into three forms: Weak Form Efficiency → Prices reflect all past market data (e.g., price & volume). Semi-Strong Form Efficiency → Prices reflect all publicly available information (e.g., earnings reports, news). Strong Form Efficiency → Prices reflect all public and private (insider) information. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

46. 🔄 Fama’s Revisions and Updates Weak Form Efficiency – Still Largely Holds 🔹 Original View: Past prices provide no useful predictive power (i.e., technical analysis doesn’t work). 🔹 Revision: • Acknowledged the momentum anomaly (short-term trends persist). • Suggested that while markets are mostly efficient, patterns like momentum exist due to behavioral biases. • Accepted that some anomalies may not be arbitraged away due to trading costs. 📌 Implication: While technical analysis is mostly ineffective, short-term trend-following strategies may work. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

47. 🔄 Fama’s Revisions and Updates Semi-Strong Form Efficiency – Partially Revised 🔹 Original View: Public information is instantly incorporated into prices (fundamental analysis has no edge). 🔹 Revision: • Acknowledged Post-Earnings Announcement Drift (PEAD) → stock prices continue to move after earnings surprises. • Recognized that some factor-based investing strategies (e.g., value, size, momentum) consistently generate excess returns. • Stated that anomalies like the value effect may be due to risk rather than inefficiency. 📌 Implication: Fundamental analysis is still challenging, but some anomalies (value, momentum) may persist due to risk factors or investor behavior. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

48. 🔄 Fama’s Revisions and Updates Strong Form Efficiency – Largely Rejected 🔹 Original View: Even insider information is reflected in stock prices. 🔹 Revision: • Acknowledged that insider trading exists and can generate excess returns. • Recognized that corporate executives and hedge funds can profit from private information. 📌 Implication: Markets are not perfectly strong-form efficient because insider trading provides an unfair advantage. This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

49. 🔄 Fama’s Revisions and Updates This Content is Copyright Reserved Rights Copyright 2025@PTAIndia Efficiency Form Original Belief Updated View Market Implication Weak Form Past prices cannot predict future prices. Momentum effects exist in the short run. Trend-following strategies may work short-term. Semi-Strong Form All public information is instantly priced in. Some anomalies (PEAD, value, size) persist. Factor-based investing (e.g., value, momentum) can generate excess returns. Strong Form Prices reflect all public and private information. Insider trading can lead to market inefficiencies. Insider trading bans are necessary for fairness.

50. 🔄 Fama’s Revisions and Updates 🚀 Final Thought ✅ Fama still believes markets are mostly efficient, but recognizes anomalies and limits to arbitrage. ✅ Factor investing (momentum, value) challenges pure EMH but aligns with risk-based explanations. ✅ Insider information creates inefficiencies, making strong-form EMH unrealistic . This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

51. Next Chapter 5 - The Fibonacci Sequence & Golden Ratio Section 1 – Theory and History of Technical Analysis Presented By : This Content is Copyright Reserved Rights Copyright 2025@PTAIndia