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Mad Money and Smart Money: The Discrepancy of Rationality between Options and Equity Markets

Mad Money and Smart Money: The Discrepancy of Rationality between Options and Equity Markets. Presented By Carl R. Chen – University of Dayton. Introduction We examine the impact of analyst’s recommendations on stocks and options

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Mad Money and Smart Money: The Discrepancy of Rationality between Options and Equity Markets

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  1. Mad Money and Smart Money: The Discrepancy of Rationalitybetween Options and Equity Markets Presented By Carl R. Chen – University of Dayton

  2. Introduction • We examine the impact of analyst’s recommendations on stocks and options • Different reactions to the recommendations could distinguish trader types in these two markets • We focus on the recommendations made by the popular CNBC Mad Money show hosted by Jim Cramer

  3. Is this guy sexy? • The show is aired every weekday @ 6:00 p.m. • The show draws more than 398,000 viewers daily

  4. Price Pressure vs. New Information • Price Pressure Hypothesis Price could temporarily diverge from true value due to uninformed shift in excess demand • New Information Hypothesis Price adjusts to new information permanently • Test both the stock and options markets response to the Cramer’s stock recommendations • Market that shows the price pressure effect is driven by the uninformed

  5. II. Literature • Stock Market • Engelberg et al. (2007) and Balcarcel & Chen (2007) • Motivation of Balcarcel & Chen • Mad Money show produces a short-lived price pressure effect on the stock market  uninformed trading • Options Market? • No work related to the Mad Money effect • there are literature related to the price discovery function of the options market

  6. Options price does not predict stock (spot) price • Stock Options: Stephan & Whaley (1990), Chan et al (1993), O’Connor et al (1999), Chan et al (2002) • Currency Options: Brenner et al (1996), Pan et al (1996) • Informed trades occur in the options market • Using Hasbrouck’s (1995) information share analysis, Chakravarty, Gulen, Mahyhew (2004) find that 17% of the price discovery occurs in the options market • Easley, O’Hara, Srinivas (1998), Pan, Poteshman (2003) use signed options volume to show that options markets contain information of the underlying asset price changes • Cao et al (2000): abnormal trading before takeover announcements

  7. Data and Methodology • Data • Final sample contains 368 buy and 358 sell recommendations for the months of August and September 2005 • Daily stock returns are taken from CRSP • Stock dividend yields are from COMPUSTAT • Daily options data are obtained from the Market Data Express (MDX) by CBOE • To minimize the non-synchronous trading, we match MDX underlying stock price with intraday bid-ask from Trade and Quote (TAQ) • We drop the observation when stock price in options data differs from CRSP by more than 0.5%

  8. Methodology • Event Study Procedure • Rit=αi + βi Rmt + εit, t=-124,…, -5 • ARit = Rit – (α`i + β`i Rmt) = ε`it, t = 1, 2,…, 30

  9. We also compute the average abnormal share turnover (ATO) and average abnormal bid-ask spread (ASPREAD) for each day follow the same procedure • In the revised version of the paper (in progress) • We use three-factor model to compute the abnormal returns • We expand the sample to cover more than 1,000 recommendations

  10. B. Implied Price Changes in the Options Market 1.The Sequential Approach • Since Where Rt represents other observable variables such as risk-free rate, options maturity, and strike price, then • Implied volatility of the previous period serves as a proxy of t. Together with options premium Ot and Rt , we estimate the implied stock price backwardly using the Generalized Newton Method algorithm (see Chakravarty et al 2004). This is a direct measurement of the stock price embedded in options

  11. 2. The Option Boundary Approach • This approach gauges the degree of divergence between option-implied and actual stock price • American option boundary with market friction of Bodurtha and Courtadon (1986) is employed • This is a model-free estimate, but not a direct measure of stock price • With market frictions, the upper boundaries for American call and put are as follows: • (Pa + Sa – Xe-rt ) + (TX +TS +TP )≥ Cb - Tc • (Ca - Sbe-qt + X) + (TX +TS +TC)≥ Pb -Tp

  12. S, P, C, X, r, q and t are stock price, put premium, call premium, strike price, risk-free rate, dividend yield, and time to maturity. Superscript « a » and « b » denore ask and bid of the quotes. T represents transaction cost for trading respective instruments. • Sa ≥ Cb – Pa +Xe-rxt – (Tx+Ts+Tp+Tc) =L • Sb ≤ [Ca – Pb +X + (Tx+Ts+Tp+Tc)]/e-qt =H • (L – Sa)  distance between lower bound and the observed stock price • (Sb – H)e–qt  distance between higher bound and observed stock price

  13. Divergence= (Ca + Cb - Pa – Pb ) - (Sa + Sbe-qt ) + X(1+e-rt) • A positive (negative) “Divergency” indicates that the implied stock price is more likely to be larger (smaller) than the observed stock price. |-------------------------------------*---------*--------------------------------------------| Low Sb Sa High |_______Divergence=0_______| Low High |________Divergence<0_______| Low High |_________ Divergence>0________|

  14. 3. Options data • Three options maturities: • short: 10-30 days; • Mid: 31-60 days; • Long: 61-120 days • Three options moneyness • OTM:  =0.02~0.45; • ATM:  =0.45~0.55; • ITM:  =0.55~0.98

  15. IV. Empirical Results Table 1: Summary Statistics

  16. Table 2: Summary Statistics of the Options Data Panel A – Call Options for the Buy Recommendations

  17. Table 2: Panel B – Call Options for Sell Recommendations

  18. Table 2: Panel C – Put Options for Buy Recommendations

  19. Table 2: Panel D – Put Options for Sell Recommendations

  20. Table 3: The Price Pressure Effect (Stock Markets)

  21. Only Days 1 is reported Synthetic Short stock Table 4: The Price Pressure Effect (Options Markets) Panel A – Buy Recommendations

  22. Table 4: The Price Pressure Effect (Options Markets) Panel B – Sell Recommendations

  23. Table 5: Correlations between Abnormal Stock Returns and Abnormal Implied Price Changes (day -124 ~ -5)

  24. Table 6: Effect on Options Trading Panel A – Buy Recommendations

  25. Table 6: Effect on Options Trading Panel B – Sell Recommendations

  26. Buy a day after recommendation Table 7: Trading Profit (Long Short-term Puts) Rt = (Vt – Ot)/Ot Modified Johnson t-test Day -125 ~ -30 Get rich quick? • In the revised version where longer data period is employed, we find most of the profit opportunity disappeared in the latter period.

  27. V. Conclusions • Small-cap stocks show a short-lived price run-up followed by a price reversal for the stocks Cramer recommended to buy  evidence of uninformed trading • Options markets largely lack such price pressure effect  informed trading • Short-term ITM put options react counter the buy recommendations. Bid-ask spreads decreased and trading increased  Take advantage of naïve trading in the spot markets. • Buy short-term puts generate high profits during the event periods. ITM puts generate the highest returns. • Longer data period and different market model are employed in the revision. Results are not materially changed. There is evidence that market learns.

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