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- Hassan Afrouzi & Spencer Yongwook Kwon & Augustin Landier & Yueran Ma & David Thesmar (2020): Overreaction in Expectations: Evidence and Theory
We find that forecasts display significant overreaction to the most recent observation. Moreover, overreaction is especially pronounced for less persistent processes and longer forecast horizons. We also find that commonly-used expectations models do not easily account for these variations in the degree of overreaction across different settings. To explain the observed patterns of overreaction, we develop a tractable model of expectations formation with costly information processing.
RePEc:hal:wpaper:hal-03885149 Save to MyIDEAS - Afrouzi, Hassan & Kwon, Spencer Yongwook & Landier, Augustin & Ma, Yueran & Thesmar, David (2021): Overreaction in Expectations: Evidence and Theory
We find that forecasts display significant overreaction to the most recent observation. Moreover, overreaction is especially pronounced for less persistent processes and longer forecast horizons. We also find that commonly-used expectations models do not easily account for these variations in the degree of overreaction across different settings. To explain the observed patterns of overreaction, we develop a tractable model of expectations formation with costly information processing.
RePEc:ebg:heccah:1444 Save to MyIDEAS - Guglielmo Maria Caporale & Alex Plastun (2018): On the Frequency of Price Overreactions
This paper explores the frequency of price overreactions in the US stock market by focusing on the Dow Jones Industrial Index over the period 1990-2017. It uses two different methods (static and dynamic) to detect overreactions and then carries out various statistical tests (both parametric and non-parametric) including correlation analysis, augmented Dickey–Fuller tests (ADF), Granger causality tests, and regression analysis with dummy variables. The following hypotheses are tested: whether or not the frequency of overreactions varies over time (H1), is informative about crises (H2) and/or price movements (H3), and exhibits seasonality (H4). ... On the whole it appears that the frequency of overreactions can provide useful information about market developments and for designing trading strategies.
RePEc:ces:ceswps:_7011 Save to MyIDEAS - Carlos Marcelo Lauretti & Eduardo Kazuo Kayo & Emerson Fernandes Marçal (2009): Market Overreaction to Intangible Information
Academic studies have shown that returns show reversion effects, which has often been explained as market overreaction to firms past performance. ... More recent studies in the U.S. market showed that these observations stem from the same phenomenon: the overreaction to the intangible information, that is, information that is not present in accounting performance statements,
RePEc:brf:journl:v:7:y:2009:i:2:p:215-236 Save to MyIDEAS - Guglielmo Maria Caporale & Alex Plastun & Viktor Oliinyk (2018): Bitcoin Fluctuations and the Frequency of Price Overreactions
This paper investigates the role of the frequency of price overreactions in the cryptocurrency market in the case of BitCoin over the period 2013-2018. Specifically, it uses a static approach to detect overreactions and then carries out hypothesis testing by means of a variety of statistical methods (both parametric and non-parametric) including ADF tests, Granger causality tests, correlation analysis, regression analysis with dummy variables, ARIMA and ARMAX models, neural net models, and VAR models. Specifically, the hypotheses tested are whether or not the frequency of overreactions (i) is informative about Bitcoin price movements (H1) and (ii) exhibits seasonality (H2).
RePEc:ces:ceswps:_7280 Save to MyIDEAS - Guglielmo Maria Caporale & Alex Plastun & Viktor Oliinyk (2019): Bitcoin fluctuations and the frequency of price overreactions
This paper investigates the role of the frequency of price overreactions in the cryptocurrency market in the case of BitCoin over the period 2013–2018. Specifically, it uses a static approach to detect overreactions and then carries out hypothesis testing by means of a variety of statistical methods (both parametric and non-parametric) including ADF tests, Granger causality tests, correlation analysis, regression analysis with dummy variables, ARIMA and ARMAX models, neural net models, and VAR models. Specifically, the hypotheses tested are whether or not the frequency of overreactions (i) is informative about Bitcoin price movements (H1) and (ii) exhibits no seasonality (H2).
RePEc:kap:fmktpm:v:33:y:2019:i:2:d:10.1007_s11408-019-00332-5 Save to MyIDEAS - Borgards, Oliver & Czudaj, Robert L. (2021): Features of overreactions in the cryptocurrency market
This paper examines features of overreactions that are able to enhance the prediction quality for twelve cryptocurrencies compared to the US stock market. For this purpose, we perform random forest classifications on the basis of all feature combinations and a customized performance metric to predict overreactions on interday and various intraday price levels. We find that features describing the price development prior to the overreaction have the highest ability to classify an overreaction for different frequencies, indicating volatility clustering and framing effects. During an overreaction, the duration and the price steadiness are important features describing the overreaction itself. ... In addition, our results show for all assets and frequencies that the prediction results are slightly higher for positive overreactions compared to negative overreactions.
RePEc:eee:quaeco:v:80:y:2021:i:c:p:31-48 Save to MyIDEAS - Constantin Bürgi & Julio L. Ortiz (2022): Overreaction through Anchoring
We further document evidence of individual overreaction at the quarterly frequency and a lack of overreaction at the annual frequency.
RePEc:ces:ceswps:_10193 Save to MyIDEAS - Caio Machado (2023): Managing Overreaction During a Run
This paper studies whether suspensions intended to provide a time-out for agents to digest incoming information attenuate runs, under the assumption that agents overreact to news and need time to properly process it. ... I show that during bad times, when bad public news arrives and/or investment returns are low, such policy actually amplifies runs, even in cases where almost all investors are receiving negative news and temporarily overreacting to it.
RePEc:ioe:doctra:574 Save to MyIDEAS - Machado, Caio (2023): Managing Overreaction During a Run
This paper studies whether suspensions intended to provide a time-out for agents to digest incoming information attenuate runs, under the assumption that agents overreact to news and need time to properly process it. ... I show that during bad times, when bad public news arrives and/or investment returns are low, such policy actually amplifies runs, even in cases where almost all investors are receiving negative news and temporarily overreacting to it.
RePEc:pra:mprapa:117896 Save to MyIDEAS