Wang_2006_International-Journal-of-Forecasting

The relationships between sentiment,returns and volatility

Yaw-Huei Wang a ,Aneel Keswani b ,Stephen J.Taylor c ,*

a

National Central University,Taiwan b

Cass Business School,City University,UK

c

Department of Accounting and Finance,Lancaster University,Lancaster LA14YX,UK

Abstract

Previous papers that test whether sentiment is useful for predicting volatility ignore whether lagged returns information might also be useful for this purpose.By doing so,these papers potentially overestimate the role of sentiment in predicting volatility.In this paper we test whether sentiment is useful for volatility forecasting purposes.We find that most of our sentiment measures are caused by returns and volatility rather than vice versa.In addition,we find that lagged returns cause volatility.All sentiment variables have extremely limited forecasting power once returns are included as a forecasting variable.D 2005International Institute of Forecasters.Published by Elsevier B.V .All rights reserved.

JEL classification:G12;G14

Keywords:Causality;Investor surveys;Market based sentiment measures;Realized volatility;Stock index returns

1.Introduction

Whilst earlier papers have underplayed the impor-tance of noise traders,more recent analysis has dis-cussed how such traders acting on a noisy signal,such as sentiment,can induce systematic risk and affect asset prices in equilibrium.For example,De Long,Shleifer,Summers,and Waldmann (1990)demon-strate that if risk averse arbitrageurs know that prices may perge further away from fundamentals before they converge closer,they may take smaller positions

when betting against mis-pricing.Thus if such unin-formed noise traders base their trading decisions on sentiment,then measures of it may have predictive power for asset price behavior.

Most papers that test whether sentiment can predict returns or volatility motivate the relationship through the role of noise traders who respond to changes in sentiment influencing subsequent returns and volatil-ity.If this is in fact what happens in practice,then it might be possible to use sentiment to forecast returns and volatility.1

0169-2070/$-see front matter D 2005International Institute of Forecasters.Published by Elsevier B.V .All rights reserved.doi:10.1016/j.ijforecast.2005.04.019

*Corresponding author.Tel.:+441524593624;fax:+441524847321.

E-mail addresses:yhwang@mgt.ncu.edu.tw (Y .-H.Wang),a.keswani@city.ac.uk (A.Keswani),s.taylor@lancaster.ac.uk (S.J.Taylor).

1

Forecasting realized volatility is important for a number of reasons.Firstly,the future behavior of realized volatility has an impact on current derivatives prices.Secondly,it is a required input for many models that calculate value at risk.For example,Risk-metrics requires a volatility estimate to calculate value at risk.

International Journal of Forecasting 22(2006)109–

123

http://doc.guandang.net/locate/ijforecast

Causality must run from sentiment to market be-havior if we accept the noise trader explanation.If we step back from the noise trader framework,however, and ask how sentiment might be generated,it is quite natural to expect that market behavior should influ-ence sentiment.Evidence of this was found by Brown and Cliff(2004)and Solt and Statman(1988)who document the fact that returns cause sentiment rather than vice versa.If returns have a strong impact on sentiment then it is also possible that volatility influ-ences sentiment as well.If this is the case we might observe a much stronger link between sentiment and returns or volatility if we do not assume that sentiment is the causal variable.Thus it is clearly important to test for the direction of causality.

A failure to recognize the impact of market behav-ior on sentiment may also explain why all previous studies that test the predictive power of sentiment fail to include lagged volatility when predicting returns and omit lagged returns as an additional variable when predicting volatility.However,if sentiment responds to lagged volatility or lagged returns then it makes sense to include these variables to supplement any forecasting tests of sentiment.Doing so is likely to avoid overestimating the true forecasting power of sentiment.

We test these ideas at a market-wide level by first looking at whether aggregate sentiment measures cause the returns and the realized volatility of the S&P100index as predicted by the noise trader liter-ature or whether sentiment simply responds to market behavior.In addition we test whether returns cause volatility.2After deciding on the variables that cause returns and volatility,we use these variables for fore-casting.This allows us to determine the incremental contribution of sentiment for forecasting.

The analysis is conducted on both a daily and weekly basis.In the daily analysis,the sentiment indicators used include the S&P100(OEX)put–call trading volume ratio(PCV),the OEX put–call open interest ratio(PCO),and the NYSE ARMS index.3In the weekly analysis,the sentiment indicators used consist of PCO,PCV and two sentiment ratios gath-ered through surveys by two different investment information providers.4As a number of papers have found a significant relationship between changes in sentiment and returns or volatility,we investigate both sentiment and its first differences.

Overall it is found that all sentiment measures are Granger-caused by returns and that many measures of sentiment are also caused by realized volatility.We show that the one sentiment measure,the ARMS index,which appears to consistently Granger-cause volatility,has only limited predictive power once returns are included.

This study makes two particular contributions. Firstly,it indicates that research that seeks to exploit the potential market impact of noise traders is unlikely to be successful for returns and vola …… 此处隐藏：52006字，全部文档内容请下载后查看。喜欢就下载吧 ……