The Market Impact of Trends and Sequences in Performance:
Download
1 / 35

The Market Impact of Trends and Sequences in Performance: - PowerPoint PPT Presentation


  • 209 Views
  • Uploaded on

The Market Impact of Trends and Sequences in Performance: New Evidence by Greg Durham Mike Hertzel Spencer Martin College of Business, W.P. Carey School W.P. Carey School Montana State of Business, Arizona of Business, Arizona University State University State University.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'The Market Impact of Trends and Sequences in Performance:' - RoyLauris


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Slide1 l.jpg

The Market Impact of Trends and Sequences in Performance:

New Evidence

by

Greg Durham Mike Hertzel Spencer Martin

College of Business, W.P. Carey School W.P. Carey School

Montana State of Business, Arizona of Business, Arizona

University State University State University


Slide2 l.jpg

The Market Impact of Trends and Sequences in Performance:

New Evidence FROM THE COLLEGE FOOTBALL WAGERING MARKET

by

Greg Durham Mike Hertzel Spencer Martin

College of Business, W.P. Carey School W.P. Carey School

Montana State of Business, Arizona of Business, Arizona

University State University State University


Empirical pricing anomalies l.jpg

Empirical Pricing Anomalies

  • Momentum over Shorter Horizons

    • Indexes: Butler, Poterba, & Sum- mers (1991)

    • Stocks: Jegadeesh & Titman (1993), and others

  • Reversals over Longer Horizons

    • DeBondt & Thaler (1985),

      • and others


Behavioral models l.jpg

Behavioral Models

  • Daniel, Hirshleifer, & Subrahmanyam (1998)

  • — Self-Attribution Bias and Overconfidence

  • Hong & Stein (1999)

  • — Bounded Rationality

  • … of particular interest to this study is:

  • Barberis, Shleifer & Vishny (1998)

  • — Conservatism Bias

  • — Reliance on the Represen- tativeness Heuristic


Bsv s regime shifting model l.jpg

BSV’s Regime-Shifting Model

  • Conservatism Bias (Edwards, 1968)

    • Underestimation of the value of new information

    • Over-reliance on older information

  • Representativeness Bias (Tversky & Kahneman, 1974)

    • Over-reliance on similarities to the parent pop-

    • ulation and on the salient features

    • of an event

    • Insufficient regard to other

    • important factors


Bsv s regime shifting model6 l.jpg

BSV’s Regime-Shifting Model

  • In actuality, a firm’s earnings performance fol- lows a random walk ... yet, investors believe that performance switches between:

    • Continuation (or Trending) Regime

  • performance tends to be followed by like performance

    • Reversal Regime

  • performance tends to reverse;

  • i.e., returns are mean-reverting


Testable implications l.jpg

Testable Implications

  • The nature of the relation between prior performance and current prices turns out to be a key testable implication of the model developed by BSV

  • Performance follows a random walk

  • In formulating beliefs, investors examine past perfor- mance


Sports betting markets l.jpg

Sports Betting Markets

  • Bettors have real wealth at stake

  • Numerous parallels to securities markets:

    • Informed bettors “Experts”

    • Sentiment bettors Market makers

    • Point spreads are used to balance books

  • A sports bet has an obvious “settling up” point, at which terminal payoffs are unambig- uously realized


College football wagering dataset l.jpg

College Football Wagering Dataset

  • 8 seasons of games from Division I-A, 1991-98

  • For each game:

    • Opening Spread Change in Spread

    • Closing Spread Actual Outcome

  • Purchased from ComputerSportsWorld

  • Spreads posted by Las Vegas’ StardustCasino’s Sports Book


Point spread market mechanics l.jpg

Point-Spread Market Mechanics

  • Games almost always occur on Saturdays

  • Betting begins Sunday night prior

  • Odds and cash flows are fixed, so market makers quote point spreads

  • Investors pay $11 to win $21 or $0

  • For each pair of $11 bets on each team, $21 is paid  transax’n costs = 4.54%

  • Spreads fluctuate during week, but the expected change, in an efficient market, is zero


Mechanics demonstrated l.jpg

Sun.

Mechanics Demonstrated

  • Spreads fluctuate during week in response to an imbalance of orders (wagers) on one team

MSU v. UofM

MSU 5

kickoff


Mechanics demonstrated12 l.jpg

Mechanics Demonstrated

  • Spreads fluctuate during week in response to an imbalance of orders (wagers) on one team

MSU v. UofM

MSU 5

MSU 5

Sun.

kickoff


Mechanics demonstrated13 l.jpg

Mechanics Demonstrated

  • Spreads fluctuate during week in response to an imbalance of orders (wagers) on one team

MSU v. UofM

MSU wins by >5!!

MSU 5

MSU 5

Sun.

kickoff


Mechanics demonstrated14 l.jpg

Mechanics Demonstrated

  • Spreads fluctuate during week in response to an imbalance of orders (wagers) on one team

MSU v. UofM

MSU 5

MSU 5

MSU wins by <5 or loses outright!!

Sun.

kickoff


Mechanics demonstrated15 l.jpg

Mechanics Demonstrated

  • Spreads fluctuate during week in response to an imbalance of orders (wagers) on one team

MSU v. UofM

MSU 5

MSU 5

MSU wins by 5!!

Sun.

kickoff


Mechanics demonstrated16 l.jpg

Mechanics Demonstrated

  • Spreads fluctuate during week in response to an imbalance of orders (wagers) on one team

MSU v. UofM

MSU 5

Good NEWS for MSU!!

Sun.

kickoff


Mechanics demonstrated17 l.jpg

Mechanics Demonstrated

  • Spreads fluctuate during week in response to an imbalance of orders (wagers) on one team

MSU v. UofM

MSU 6.5

MSU 5

Good NEWS for MSU!!

Sun.

kickoff


Now the tests l.jpg

Now, the Tests …

  • Testable implications of the BSV model:

  • Performance follows a random walk

  • In formulating beliefs, investors examine past perfor- mance


Does performance follow random walk l.jpg

Does Performance Follow Random Walk?

  • Sorting Observations by Streak Length Suggests “Yes” (Table I)

    • Observations per bin fall by ≈50% with each successive increment in streak length

  • Team-by-Team Runs Tests Suggest “Yes” (Table II)

    • For 105 of 113 teams, num- ber of runs is normal



  • Does performance follow random walk21 l.jpg

    Does Performance Follow Random Walk?

    • Sorting Observations by Streak Length Suggests “Yes” (Table I)

      • Observations per bin fall by ≈50% with each successive increment in streak length

  • Team-by-Team Runs Tests Suggest “Yes” (Table II)

    • For 105 of 113 teams, num- ber of runs is normal


  • Random walk22 l.jpg

    RandomWalk?


    Do investors use recent freq s of reversals l.jpg

    Do Investors Use Recent Freq’s of Reversals?

    • Identified teams with same 16 patterns as used by Bloomfield and Hales (JFE, 2002) (Table III)

      • Spread is insignificant for all groups

      • Mean changes are not different across low-, medium-, & high-reversal groups

      • Findings are inconsistent with the experimental subject results

      • Findings are inconsistent with predictions of the BSV Model



    Do investors use recent freq s of reversals25 l.jpg

    Do Investors Use Recent Freq’s of Reversals?

    • Identified teams with same 16 patterns as used by Bloomfield and Hales (JFE, 2002) (Table III)

      • Spread is insignificant for all groups

      • Mean changes are not different across low-, medium-, & high-reversal groups

      • Findings are inconsistent with the experimental subject results

      • Findings are inconsistent with predictions of the BSV Model


    Do investors use recent freq s of reversals26 l.jpg

    Do Investors Use Recent Freq’s of Reversals?

    • Sorted observations according to the 256 possible 8-game historical patterns (Table IV)

      • Spread is insignificant for all groups

      • Mean changes are not different across low-, medium-, and high-reversal groups

    • Football market participants appear completely insensitive to the number of re-

    • cent reversals in performance

    • Findings are inconsistent

    • with BSV’s predictions


    Do investors use longer histories l.jpg

    Do Investors Use Longer Histories?

    • Expanded histories to include 16- and 30-game historical patterns (Table V)

      • 8- and 16-game: Mean changes are not dif- ferent across low- and high-reversal groups

      • 30-game: Mean changes ARE stat.-signif. different across low- and high-reversal groups

    • Findings for 30-game histories

    • are weakly consistent with

    • BSV’s predictions


    Do investors use streak lengths l.jpg

    Do Investors Use Streak Lengths?

    • Streak-Based Tests (Table VI)

      • Piece-wise regression analysis:

        • Spread = α + βHW1HWStrk1 + βHW2HWStrk2

        • + βAW1AWStrk1 + βAW2AWStrk2

        • + βOpenOpen + ε, where

        • HWStrk1 = home’s W strk. if home’s W strk. < 3

        • = 3 if home’s W strk. ≥ 3

        • HWStrk2 = 0 if home’s W strk. < 3

        • = home’s W strk. – 3 if

        • home’s W strk. ≥ 3


    Spline transformation of streak length l.jpg

    HWS2

    HWS1

    Change in Spread

    3

    3

    3

    HLS1

    Home Team’s

    Streak Length

    HLS2

    3

    Spline Transformation of Streak Length


    Do investors use streak lengths30 l.jpg

    Do Investors Use Streak Lengths?

    • Streak-Based Tests (cont’d)

      • … and AWStrk1 & AWStrk2 defined similarly

    • Null hypothesis: βi = 0 for all i

    • Alternative hypothesis: βi > 0 for i = HW1, HW2, HL1, HL2 and βi < 0 for i = AW1, AW2 , AL1, AL2


    Do investors use streak lengths31 l.jpg

    Do Investors Use Streak Lengths?

    • Alternative hypothesis (predicted by BSV): βi > 0 for i = HW1, HW2, HL1, HL2; βi < 0 for i = AW1, AW2, AL1, AL2

    • Results: βHW1>0, βHW2<0, βLW1>0, βLW2<0 … all stat.-sig.

    • Interpretation: Bettors expect short streaks to continue & longer streaks to reverse

    • Similar results based on losing streaks


    Spline transformation of streak length32 l.jpg

    0.131

    –0.091

    3

    3

    Home Team’s

    Streak Length

    Spline Transformation of Streak Length

    HLS2

    Change in Spread

    0.363

    HWS1

    –0.115

    0.188

    HWS2

    HLS1

    0.574


    Do investors use streak lengths33 l.jpg

    Do Investors Use Streak Lengths?

    • Alternative hypothesis (predicted by BSV): βi > 0 for i = HW1, HW2; βi < 0 for i = AW1, AW2

    • Results: βHW1>0, βHW2<0, βLW1>0, βLW2<0 … all stat.-sig.

    • Interpretation: Bettors expect short streaks to continue & longer streaks to reverse

    • Similar results based on losing streaks


    Conclusions l.jpg

    CONCLUSIONS

    • Performance (against point spreads) is random

      • Consistent with Assumption of BSV Model

  • Football bettors are relatively insensitive to the frequency of recent performance reversals

    • Inconsistent w/ Primary Premise of BSV Model

  • Bettors expect: ― continuations in short-run performance ― reversal in performance as streak length grows (or exceeds 3)

    • Consistent w/ Belief in Regimes, but not as hypothesized by BSV


  • Contact information l.jpg

    CONTACT INFORMATION

    • GREG DURHAM

      • Assistant Professor of Finance

      • Montana State University

      • Phone: (406) 994-6201

      • E-mail: [email protected]


    ad