13.1 Informational vs. Operational Efficiency and Market Friction

Dual Dimensions of Capital Market Efficiency

When evaluating how effectively a financial market operates, financial economists distinguish between two complementary concepts:

+---------------------------------------------------------------------------------------------------+
|                                CAPITAL MARKET EFFICIENCY DIMENSIONS                               |
+-----------------------------------+---------------------------------------------------------------+
| Informational Efficiency          | - Speed, completeness, and accuracy with which new information|
|                                   |   is reflected in security market prices                      |
|                                   | - Governed by information dissemination, analyst coverage,    |
|                                   |   and absence of insider advantages                           |
+-----------------------------------+---------------------------------------------------------------+
| Operational / Internal Efficiency | - Cost, speed, and reliability of executing transactions      |
|                                   | - Governed by tight bid-ask spreads, low brokerage, rapid     |
|                                   |   clearing, automated DvP, and minimal market impact costs    |
+-----------------------------------+---------------------------------------------------------------+

The Mechanism of Informational Efficiency

An informationally efficient market is one where market prices adjust instantaneously and without bias to the arrival of new information. Because information arrives randomly and unpredictably, successive price changes in an efficient market must be independent and identically distributed (i.i.d.), forming a Random Walk:

$$P_{t+1} = P_t + \epsilon_{t+1}$$ Where $\epsilon_{t+1}$ represents the unpredictable price impact of new information arriving at time $t+1$, with expected value $E(\epsilon_{t+1}) = 0$.

Impact of Real-World Market Frictions

In practice, markets operate along a spectrum of efficiency influenced by frictions:

• Transaction Costs & Slippage: Bid-ask spreads, STT, exchange turnover fees, and stamp duties impose friction that prevents arbitrageurs from eliminating minute pricing discrepancies.
• Short-Sale Constraints: Inability to borrow and short illiquid shares allows overvalued stocks to persist longer than undervalued stocks.
• Information Asymmetry: Disparities between institutional investors deploying sophisticated research algorithms and retail participants relying on delayed public news.

13.2 The Efficient Market Hypothesis (EMH): Weak, Semi-Strong, and Strong Forms

Eugene Fama's Efficient Market Hypothesis (EMH)

Formulated by Nobel Laureate Eugene Fama (1970), the Efficient Market Hypothesis asserts that financial asset prices fully reflect all available information. Fama categorized market efficiency into three progressively encompassing forms based on the definition of "available information":

+---------------------------------------------------------------------------------------------------+
|                                  THE THREE FORMS OF MARKET EFFICIENCY                             |
+-----------------------------------+---------------------------------------------------------------+
| Strong-Form Efficiency            | All Information (Public + Private / Insider Information)      |
|                                   | Prices reflect all information, including insider knowledge   |
+-----------------------------------+---------------------------------------------------------------+
| Semi-Strong-Form Efficiency       | All Publicly Available Information                            |
|                                   | (Historical prices + Financial statements + Macro releases)   |
+-----------------------------------+---------------------------------------------------------------+
| Weak-Form Efficiency              | Historical Market Data Only                                   |
|                                   | (Past prices, trading volumes, and technical chart patterns)  |
+-----------------------------------+---------------------------------------------------------------+

1. Weak-Form Efficiency

• Information Set: Historical trading data (past price history, trading volume, short interest).
• Core Implication: Past price patterns contain no predictive power regarding future price movements because all historical information is already impounded in the current market price.
• Impact on Investment Strategies: Technical analysis (chart patterns, moving averages, RSI, support/resistance) cannot generate consistent risk-adjusted abnormal returns (Alpha).
• Empirical Tests: Autocorrelation tests, run tests, and filter rules generally support weak-form efficiency in major liquid equity markets.

2. Semi-Strong-Form Efficiency

• Information Set: All publicly available information, encompassing past price data plus corporate financial statements, earnings reports, dividend announcements, management guidance, and macroeconomic releases.
• Core Implication: Security prices adjust rapidly and without bias to public news announcements.
• Impact on Investment Strategies: Fundamental analysis (analyzing P/E ratios, DCF valuations, balance sheets, and competitive moats) cannot generate persistent risk-adjusted excess returns, because by the time financial statements are published, the market has already factored the data into stock prices.
• Empirical Tests: Event studies examining price reactions to earnings surprises, stock splits, and acquisition announcements indicate that large-cap equities exhibit near semi-strong efficiency.

3. Strong-Form Efficiency

• Information Set: All information from both public and private (insider) sources.
• Core Implication: Even corporate insiders (directors, executive officers) possessing non-public material information cannot generate abnormal profits because prices instantaneously reflect insider data.
• Empirical Reality: Strong-form efficiency does NOT hold in real-world markets. Insiders routinely earn abnormal profits when trading illegally ahead of corporate announcements, which is precisely why SEBI enforces strict Insider Trading regulations.

13.3 Market Anomalies (Calendar, Size, Momentum) and Implications for Active Management

Conceptual Definition of Market Anomalies

A market anomaly is an empirical return pattern that appears to contradict the Efficient Market Hypothesis by generating persistent risk-adjusted excess returns that cannot be explained by standard capital asset pricing models (CAPM).

+---------------------------------------------------------------------------------------------------+
|                                   MAJOR CAPITAL MARKET ANOMALIES                                  |
+-------------------+-----------------------------------+-------------------------------------------+
| Anomaly Category  | Empirical Phenomenon              | Underlying Economic / Behavioural Driver  |
+-------------------+-----------------------------------+-------------------------------------------+
| Calendar Anomaly  | January Effect / Turn-of-Year     | Tax-loss selling in December followed by  |
|                   | Weekend / Monday Effect           | aggressive repurchases in January         |
+-------------------+-----------------------------------+-------------------------------------------+
| Cross-Sectional   | Size Effect (Small-Cap Premium)   | Small firms generate higher risk-adjusted |
| Anomaly           |                                   | returns than large firms (Banz, 1981)     |
|                   | Value Anomaly (Low P/E, Low P/B)  | Cheap value stocks systematically beat    |
|                   |                                   | expensive glamor/growth stocks long term  |
+-------------------+-----------------------------------+-------------------------------------------+
| Time-Series       | Momentum Effect                   | Assets with strong trailing 3-12 month    |
| Anomaly           | (Jegadeesh & Titman, 1993)        | performance continue to outperform        |
|                   | Long-Term Reversal                | Extreme 3-5 year winners mean-revert to   |
|                   | (De Bondt & Thaler, 1985)         | underperform; extreme losers rebound      |
+-------------------+-----------------------------------+-------------------------------------------+

1. Cross-Sectional Anomalies

• Size Anomaly: Empirical evidence demonstrates that portfolios composed of small-market-capitalization equities earn higher risk-adjusted returns over extended multi-decade horizons than large-cap equities. Explanations cite higher liquidity risk, lower analyst coverage, and elevated business uncertainty.
• Value Anomaly: Low valuation multiples (low Price-to-Earnings $P/E$, low Price-to-Book $P/B$, high dividend yield) systematically outperform high-multiple growth stocks. Behavioural economists attribute this to investor overreaction—market participants extrapolate past growth rates too far into the future, overpricing glamor stocks and underpricing unloved value stocks.

2. Time-Series Anomalies: Momentum vs. Reversal

• Momentum (3 to 12 Months): Equities exhibiting top-quintile performance over the prior 3 to 12 months continue to deliver superior returns over the subsequent holding period. Driven by cognitive anchoring and under-reaction to positive corporate earnings surprises.
• Long-Term Mean Reversal (3 to 5 Years): Over extended 3-to-5-year horizons, historical winner portfolios underperform, while historical loser portfolios outperform, consistent with investor over-reaction correcting over time.

Strategic Implications for Portfolio Management: Active vs. Passive

The degree of market efficiency across segments dictates the optimal portfolio construction approach:

Large-Cap Liquid Markets (Highly Efficient)  ---> Tilts toward Passive Indexing / Smart Beta (Low Cost)
Mid & Small-Cap Markets (Information Gaps)   ---> Active Fundamental Management (High Alpha Potential)

In large-cap equities (e.g., Nifty 50), high analyst coverage and institutional trading compress information gaps, making consistent net-of-fee alpha generation challenging. Conversely, in mid-cap and small-cap universes, lower analyst coverage and information asymmetry create fertile ground for skilled PMS managers to generate substantial alpha.