Feedback Traders and Stock Return Autocorrelations: Evidence from a Century of Daily Data

Sentana, Enrique; Wadhwani, Sushil

doi:10.2307/2234525

Cited by 320 publications

(439 citation statements)

References 11 publications

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“…Then, the observed negative auto-correlation is completely explained by the bid-ask bounce effect, and we can rule out alternative explanations, like positive feedback trading (Sentana and Wadhwani, 1992). This result supports the efficiency of the Italian futures market.…”

Section: Preliminary Analysissupporting

confidence: 73%

Dynamics of intraday serial correlation in the Italian futures market

Bianco

Renò

2005

Journal of Futures Markets

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We study the serial correlation of high-frequency intraday returns on the Italian stock index futures (FIB30) in the period 2000-2002. We find that intraday autocorrelation is mostly negative for time scales lower than 20 minutes, mainly due to the bid-ask bounce effect. While this supports the efficiency of the Italian futures market, we also provide evidence that intraday serial correlation becomes positive in high volatility regimes. Moreover we find that it is mainly unexpected volatility to make serial correlation rise, and not its predictable part. Our results are supportive of the Chan (1993) model.

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Section: Preliminary Analysissupporting

confidence: 73%

Dynamics of intraday serial correlation in the Italian futures market

Bianco

Renò

2005

Journal of Futures Markets

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“…Our empirical design is based on the framework proposed by Sentana and Wadhwani (1992), according to which there exist two types of traders, rational speculators and feedback traders. Rational speculators aim at maximizing their expected mean-variance utility in the following demand function:…”

mentioning

confidence: 99%

“…An issue arises, however, with the extent to which the autocorrelation observed is due to feedback traders or extant market frictions, caused by for example non-synchronous and thin trading. To that end, Sentana and Wadhwani (1992) proposed the following specification of equation (5):…”

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confidence: 99%

“…Table 2 presents the estimation results for equations (6) and (10), i.e. the original Sentana and Wadhwani (1992) model. As the estimates from the conditional mean equation suggest, none of the four ETFs accommodated significant feedback trading during our sample period ( 1  is found to be insignificant for all of them), while they also appear to be unaffected by the presence of market frictions (the sole evidence of significant 0  is reported for the Brazilian ETF for the 10 percent level of significance).…”

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confidence: 99%

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Premiums, discounts and feedback trading: Evidence from emerging markets' ETFs

Charteris

Chau

Gavriilidis

et al. 2014

International Review of Financial Analysis

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(2014) 'Premiums, discounts and feedback trading : evidence from emerging markets' ETFs.', International review of nancial analysis., 35 . pp. 80-89. Further information on publisher's website:http://dx.doi.org/10.1016/j.irfa.2014.07.010Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in International Review of Financial Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. A de nitive version was subsequently published in International Review of Financial Analysis, 35, October 2014, 10.1016/j.irfa.2014.07.010. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.

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“…Because of the non-trading, the trading volume, which is strongly positive related with volatility, lowers. Thus, we have a market with low trade Kim [180], Sentana and Wadhwani [278] and Oedegaard [253] have also investigated the relationship between autocorrelation and volatility and found an inverse relation between volatility and autocorrelation. Moreover, Oedegaard [253] found that the evidence of autocorrelation, for the S&P500 daily index, decreased over time, possibly because of the introduction of financial derivatives (options and futures) on the index.…”

Section: Volatility and Serial Correlationmentioning

confidence: 99%

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

Degiannakis

Xekalaki

2004

Quality Technology & Quantitative Management

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Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been employed in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In this paper, a number of univariate and multivariate ARCH models, their estimating methods and the characteristics of financial time series, which are captured by volatility models, are presented. The number of possible conditional volatility formulations is vast. Therefore, a systematic presentation of the models that have been considered in the ARCH literature can be useful in guiding one's choice of a model for exploiting future volatility, with applications in financial markets.

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Feedback Traders and Stock Return Autocorrelations: Evidence from a Century of Daily Data

Cited by 320 publications

References 11 publications

Dynamics of intraday serial correlation in the Italian futures market

Dynamics of intraday serial correlation in the Italian futures market

Premiums, discounts and feedback trading: Evidence from emerging markets' ETFs

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

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