Do Behavioral Biases Affect Prices?

Coval, Joshua D.; Shumway, Tyler

doi:10.2139/ssrn.269113

Cited by 105 publications

(111 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Big losses were followed by playing less cautiously, with players tending to be more aggressive compared to their behaviour after big wins. Similar behaviours have been found in sports gambling (Xu & Harvey, 2014) and in financial markets (Coval & Shumway, 2005;Garvey, Murphy, & Wu, 2007). Here we investigate whether forecasters' behaviour in relation to judgmental adjustments is affected by the experience of previous poor interventions and, if the effect is damaging to accuracy, how this might be mitigated.…”

Section: Introductionsupporting

confidence: 69%

Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?

Petropoulos

Fildes

Goodwin

2016

European Journal of Operational Research

View full text Add to dashboard Cite

a b s t r a c tThe behaviour of poker players and sports gamblers has been shown to change after winning or losing a significant amount of money on a single hand. In this paper, we explore whether there are changes in experts' behaviour when performing judgmental adjustments to statistical forecasts and, in particular, examine the impact of 'big losses'. We define a big loss as a judgmental adjustment that significantly decreases the forecasting accuracy compared to the baseline statistical forecast. In essence, big losses are directly linked with wrong direction or highly overshooting judgmental overrides. Using relevant behavioural theories, we empirically examine the effect of such big losses on subsequent judgmental adjustments exploiting a large multinational data set containing statistical forecasts of demand for pharmaceutical products, expert adjustments and actual sales. We then discuss the implications of our findings for the effective design of forecasting support systems, focusing on the aspects of guidance and restrictiveness.

show abstract

Section: Introductionsupporting

confidence: 69%

Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?

Petropoulos

Fildes

Goodwin

2016

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…However, in contrast to the proxy used above, it is calculated at each point in time only using a rolling window of past data. This indicator is highly correlated with the aggregate sentiment measure used before and we find very similar results: Again, there is no strong predictive power of sentiment for future aggregate returns or return spreads.29 Studies that find signs of irrational behavior among professional investors includeHaigh and List (2005),Coval and Shumway (2005), Glaser et al(2010), and Puetz and Ruenzi (2011).…”

supporting

confidence: 67%

The impact of investor sentiment on the German stock market

2012

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper develops a broad-based sentiment indicator for Germany and investigates whether investor sentiment can explain stock returns on the German stock market. Based on a principal component analysis, we construct a sentiment indicator that condenses information of several well-known sentiment proxies. We show that this indicator explains the return spread between sentiment sensitive stocks and stocks that are not sensitive to sentiment fluctuations. Specifically, stocks that are difficult to arbitrage and hard to value are sensitive to the indicator. However, we do not find much predictive power of sentiment for future stock returns. JEL-Classification Codes: G12, G14 AbstractThis paper develops a broad-based sentiment indicator for Germany and investigates whether investor sentiment can explain stock returns on the German stock market. Based on a principal component analysis, we construct a sentiment indicator that condenses information of several well-known sentiment proxies. We show that this indicator explains the return spread between sentiment sensitive stocks and stocks that are not sensitive to sentiment fluctuations. Specifically, stocks that are difficult to arbitrage and hard to value are sensitive to the indicator. However, we do not find much predictive power of sentiment for future stock returns. JEL-Classification Codes: G12, G14

show abstract

“…They find evidence that traders with cumulative morning losses increase risk taking in the afternoon, and perhaps most interestingly, that those morning-losers exacerbate the noise in afternoon market prices with excessively aggressive trading. The findings of Coval and Shumway (2005) and Haigh and List (2005) offer a challenge to the notion that professional traders dampen the emotions that lead to costly behaviors.…”

Section: Introductionmentioning

confidence: 93%