Market-wide impact of the disposition effect: evidence from IPO trading volume

Kaustia, Markku

doi:10.1016/j.finmar.2003.11.002

Cited by 102 publications

(67 citation statements)

References 41 publications

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“…In fact, evidence for the Disposition Effect has been found among individual investors in the stock market (e.g. Schlarbaum et al (1978a), Ferris et al (1988), Odean (1998), Odean (1999) and others), in financial advice of stock brokers (Shapira and Venezia (2001)), in the behavior of future trades (Heisler (1994), Frino et al (2004), Coval and Shumway (2005) as well as Locke and Mann (2005)), IPO trading volume (Kaustia (2004a)), real estate markets (Genesove and Mayer (2001)), insurance contracts (i.e. Camerer and Kunreuther (1989), Schoemaker and Kunreuther (1979)) and observed risk behavior in laboratory environments for stocks (see Camerer (1998), Chui (2001), Dhar and Zhu (2006), Vlcek and Wang (2007) and Talpsepp et al (2014)).…”

Section: Prospect Theory: Fit For Finance? a Brief Reflection Of Relementioning

confidence: 99%

Taming Models of Prospect Theory in the Wild? Estimation of Vlcek and Hens (2011)

2016

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Abstract. Shortcomings revealed by experimental and theoretical researchers such as Allais (1953), Rabin (2000) and Rabin and Thaler (2001) that put the classical expected utility paradigm von Neumann and Morgenstern (1947) into question, led to the proposition of alternative and generalized utility functions, that intend to improve descriptive accuracy. The perhaps best known among those alternative preference theories, that has attracted much popularity among economists, is the so called Prospect Theory by Kahneman and Tversky (1979) and Tversky and Kahneman (1992). Its distinctive features, governed by its set of risk parameters such as risk sensitivity, loss aversion and decision weights, stimulated a series of economic and financial models that build on the previously estimated parameter values by Tversky and Kahneman (1992) to analyze and explain various empirical phenomena for which expected utility doesn't seem to offer a satisfying rationale. In this paper, after providing a brief overview of the relevant literature, we take a closer look at one of those papers, the trading model of Vlcek and Hens (2011) and analyze its implications on Prospect Theory parameters using an adopted maximum likelihood approach for a dataset of 656 individual investors from a large German discount brokerage firm. We find evidence that investors in our dataset are moderately averse to large losses and display high risk sensitivity, supporting the main assumptions of Prospect Theory.

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Section: Prospect Theory: Fit For Finance? a Brief Reflection Of Relementioning

confidence: 99%

Taming Models of Prospect Theory in the Wild? Estimation of Vlcek and Hens (2011)

2016

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“…The formulation of trading volume activity by Lo and Wang (2000) is presented in Equation (4). Most of the previous studies used formula presented in Equation (4) (Chordia & Swaminathan, 2000;Kaustia, 2004;Lynch & Mendenhall, 1997).…”

Section: The Testing Of the Effect Of Jii Selection Restriction On Pementioning

confidence: 99%

How Performance of Jakarta Islamic Index (Jii) Stocks Relative to Other Stocks?

Listyaningsih

Krishnamurti

2015

JDM

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This study was conducted to assess the performance of Jakarta Islamic Index (JII) stocks and also investigate whether there was an ethical effect (JII selection restriction) and compare it with non-Sharia stocks. The main model used in this study was the Capital Asset Pricing Model (CAPM) single index model extended to the Fama and French three factors. This study employs elaborate matching data. The data used in this study was split into two periods: the 2005-2007 periods which consists of two groups: JII and non-JII and the 2008-2012 periods which consists of three groups: JII, Sharia and non-Sharia based on industry sector. This study found that basically there was no difference on performance between JII and non-JII stocks. Therefore, this result supports the previous studies in which there were no significant differences between Sharia and conventional investment.

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“…4 Other studies focus on the interdependence between Prospect Theory and option exercise behavior (Heath et al (1999), Poteshman and Serbin (2003)), the behavior of futures traders in real markets (Locke and Mann (2000), Locke and Mann (2005) and Coval and Shumway (2005)) and experiments 3 For a critique on the empirical findings for risk aversion based on their inconsistency with the equity premium, see Mehra and Prescott (1985), Mankiw and Zeldes (1991), Benartzi and Thaler (1995), Blake (1996), Kocherlakota (1996), Goetzman and Ibbotson (2005) and Mehra (2008) 4 If individual preferences follow the predictions of Prospect Theory, phenomena such as the Disposition Effect should be observable in other environments. In fact, evidence for the Disposition Effect has been found among individual investors in the stock market (Schlarbaum et al (1978), Ferris et al (1988), Odean (1998), Weber and Camerer (1998), Odean (1998), Odean (1999), Garvey and Murphy (2004), Jordan and Diltz (2004), Lehenkari and Perttunen (2004), Frazzini (2006), Dhar and Zhu (2006)) and other environments, such as in the financial advice of stock brokers (Shapira and Venezia (2001)), the behavior of futures trades (Heisler (1994), Frino et al (2004), Coval and Shumway (2005) as well as Locke and Mann (2005)), IPO trading volume (Kaustia (2004)), real estate markets (Genesove and Mayer (2001)), insurance contracts (i.e. Schoemaker and Kunreuther (1979), Camerer and Kunreuther (1989)), and observed risk behavior in laboratory environmen...…”

Section: Preferences In Financial Markets and Experimentsmentioning

confidence: 99%

On the Applicability of Maximum Likelihood Methods: From Experimental to Financial Data

Jakusch

2013

SSRN Journal

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Abstract. This paper addresses whether and to what extent econometric methods used in experimental studies can be adapted and applied to financial data to detect the best-fitting preference model. To address the research question, we implement a frequently used nonlinear probit model in the style of Hey and Orme (1994) and base our analysis on a simulation stud. In detail, we simulate trading sequences for a set of utility models and try to identify the underlying utility model and its parameterization used to generate these sequences by maximum likelihood. We find that for a very broad classification of utility models, this method provides acceptable outcomes. Yet, a closer look at the preference parameters reveals several caveats that come along with typical issues attached to financial data, and that some of these issues seems to drive our results. In particular, deviations are attributable to effects stemming from multicollinearity and coherent under-identification problems, where some of these detrimental effects can be captured up to a certain degree by adjusting the error term specification. Furthermore, additional uncertainty stemming from changing market parameter estimates affects the precision of our estimates for risk preferences and cannot be simply remedied by using a higher standard deviation of the error term or a different assumption regarding its stochastic process. Particularly, if the variance of the error term becomes large, we detect a tendency to identify SP T as utility model providing the best fit to simulated trading sequences. We also find that a frequent issue, namely serial correlation of the residuals, does not seem to be significant. However, we detected a tendency to prefer nesting models over nested utility models, which is particularly prevalent if RDU and EXP O utility models are estimated along with EU T and CRRA utility models.

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Market-wide impact of the disposition effect: evidence from IPO trading volume

Cited by 102 publications

References 41 publications

Taming Models of Prospect Theory in the Wild? Estimation of Vlcek and Hens (2011)

Taming Models of Prospect Theory in the Wild? Estimation of Vlcek and Hens (2011)

How Performance of Jakarta Islamic Index (Jii) Stocks Relative to Other Stocks?

On the Applicability of Maximum Likelihood Methods: From Experimental to Financial Data

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