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Investor sentiment and aggregate stock returns: the role of investor attention

Review of Quantitative Finance and Accounting volume 53, pages 397–428 (2019)Cite this article

Abstract

We build on the intuitive, albeit overlooked, relationship between investor attention and investor sentiment to explore the open question of the impact of investor sentiment on aggregate stock returns. We find that investor attention causes changes in sentiment but not vice versa. Moreover, the effect of attention on sentiment is short-lived for medium and large stocks although persists for small stocks. We also document the existence of an important mediating role of attention in the link between sentiment and aggregate stock returns. Investor attention alters the predictability value of sentiment in future aggregate returns, providing new insight into the information content of investor sentiment as it relates to investor attention. We find that sentiment caused by investors’ inattentiveness mainly drives the underlying potent relationship between investor sentiment and aggregate stock returns. Our results accord with the notion that investor attention generally improves market efficiency.

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Notes

  1. See, for example, Baker et al. (2012), Hribar and McInnis (2012), Stambaugh et al. (2012), Neely et al. (2014), Hur and Mbanga (2015) and Baek (2016).

  2. Huang et al. (2015) provide a lucid description of the correction process.

  3. While Li and Yu (2012) find this evidence consistent with the overreaction to prolonged news as discussed in Griffin and Tversky (1992), they contemplate if their results are also consistent with a rational model of mean-reversing state variables. They conclude that an unobservable mean-reverting state variable cannot fully account for the predictive power of nearness to the historical high.

  4. For example, Mondria et al. (2010), Joseph et al. (2011), Dzielinski (2012), Schmidt (2012), Vlastakis and Markellos (2012), Da et al. (2011a, 2011b, 2013) and Ding and Hou (2013).

  5. Sourced from Search Engine Watch, https://searchenginewatch.com/2016/08/08/what-are-the-top-10-most-popular-search-engines/.

  6. See Vozlyublennaia (2014) for a detailed discussion of these keywords along with their advantages and potential limitations.

  7. See https://cfe.cboe.com/products/vx_qrg.pdf.

  8. While Baker and Wurgler (2007) recognize the volatility index as a proxy for investor sentiment (p. 135), they also suggest (p. 138) that data availability going back to 1960’s is the primary reason for the exclusion of this variable from their study. Da et al. (2013) build on Baker and Wurgler’s (2007) suggestion and use the volatility index to measure sentiment.

  9. We thank Goufu Zhou for providing the necessary data.

  10. See, for example, Harris (1995), Bordo and Wheelock (2004) and Mbanga and Darrat (2016).

  11. Aggregate retail investor attention to medium and large stocks are both I(0) whereas aggregate investor sentiment, however measured, is I(1).

  12. The VAR model is appropriate for investigating the relationship between aggregate attention to medium and large stocks and aggregate investor sentiment.

  13. We focus on the Federal Reserve Financial Stress Index (FSI) in light of its strong correlation with VIX (above 89%). The VIX may also be inappropriate in the case of the SP500 index return. Nevertheless, using VIX to measure market sentiment (not reported to conserve space) yielded similar to those reported in Table 5 on the basis of FSI. These results are available upon request.

  14. It is plausible that our results could be influenced by a few large stocks since the index returns are weighted by firm values. Moreover, the measure of investor attention based on Google search volume can be affected by these large firms. In order to alleviate this concern, we use an alternative measure of investor attention based on trading volume, which has been widely accepted in other studies (e.g., Barber and Odean 2008; Gervais et al. 2001; Hou et al. 2009). We then use equally-weighted stock returns in the construction of index returns and equally-weighted aggregated trading volume for each index. Using the logged value of aggregated trading volumes as investor attention and the equally-weighted index returns, we find that the results generally hold (not tabulated).

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Acknowledgements

We would like to thank Cheng-Few Lee (the Editor), an anonymous referee, Mahtab Athari (discussant), Edward Chang (discussant), and seminar participants at the 2018 Academy of Finance Conference and the 2017 Southwestern Finance Association Annual Meeting for their helpful comments and suggestions. We are grateful to Goufu Zhou for sharing the investor sentiment index of Huang et al. (2015).

Author information

Authors and Affiliations

  1. Department of Finance and General Business, School of Business Administration, Missouri State University, Springfield, USA

    Cedric Mbanga

  2. Department of Economics and Finance, College of Business Administration, Louisiana Tech University, Ruston, USA

    Ali F. Darrat

  3. Department of Finance, Muma College of Business, University of South Florida, Tampa, USA

    Jung Chul Park

Authors
  1. Cedric Mbanga
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  2. Ali F. Darrat
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  3. Jung Chul Park
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Correspondence to Jung Chul Park.

Appendices

Appendix 1: St. Louis Fed’s Financial Stress Index (STLFSI)

1.1 The list of factors in the St. Louis Fed’s Financial Stress Index (STLFSI)

Interest Rates:

  • Effective federal funds rate

  • 2-year Treasury

  • 10-year Treasury

  • 30-year Treasury

  • Baa-rated corporate

  • Merrill Lynch High-Yield Corporate Master II Index

  • Merrill Lynch Asset-Backed Master BBB-rated

Yield Spreads:

  • Yield curve: 10-year Treasury minus 3-month Treasury

  • Corporate Baa-rated bond minus 10-year Treasury

  • Merrill Lynch High-Yield Corporate Master II Index minus 10-year Treasury

  • 3-month London Interbank Offering Rate–Overnight Index Swap (LIBOR-OIS) spread

  • 3-month Treasury-Eurodollar (TED) spread

  • 3-month commercial paper minus 3-month Treasury bill

Other Indicators:

  • J.P. Morgan Emerging Markets Bond Index Plus

  • Chicago Board Options Exchange Market Volatility Index (VIX)

  • Merrill Lynch Bond Market Volatility Index (1-month)

  • 10-year nominal Treasury yield minus 10-year Treasury Inflation Protected Security yield (breakeven inflation rate)

  • Vanguard Financials Exchange-Traded Fund (equities)

1.2 Calculation of St. Louis Fed’s Financial Stress Index (STLFSI)

Each series is de-meaned and then divided by its sample standard deviation. The method of principal components is used to calculate the coefficients of the variables in the financial stress index (FSI). These coefficients are scaled to set the standard deviation of the index as one. The FSI for time t is the sum of each series multiplied by its respective adjusted coefficient. Higher and lower values of the FSI indicate a higher and lower degree of financial stress in the economy, respectively. For more detail information, refer to the file available on the site: https://research.stlouisfed.org/datatrends/pdfs/net/NETJan2010Appendix.pdf.

Appendix 2: The volatility index (VIX)

The volatility index (VIX) of the Chicago Board Options Exchange (CBOE) is comprised of options, with the price of each option reflecting the market’s expectation of future volatility. It is calculated as follows.

$$VIX = \sqrt {\frac{2}{T}\sum\limits_{i} {\frac{\Delta K}{{K_{i}^{2} }}e^{rT} Q\left( {K_{i} } \right) - \frac{1}{T}\left( {\frac{F}{{K_{0} }} - 1} \right)^{2} } } \div 100$$
(7)

where T = time to expiration is calculated by T =(Mc+ Ms+ Mo)/minutes in a year, where Mc is the number of minutes remaining until midnight of the current day, Ms is the number of minutes from midnight until 8:30 a.m. on settlement day, and Mo is the total number of minutes in the days between current day and settlement day. F = forward index level desired from index option prices. K0 = first strike below the forward index level (F). Ki  = Strike price of the ith out-of-the-money option; a call if Ki> K0, and a put if Ki < K0, both put and call if Ki = K0. ∆Ki = interval between strike prices—half the difference between the strike on either side of Ki. That is, ∆Ki  = (Ki+1+ Ki-1)/2. R = risk-free interest rate to expiration. Q(Ki) = the midpoint of the bid-ask spread for each option with strike Ki. For more detail information, refer to the file available on the CBOE site: https://www.cboe.com/micro/vix/vixwhite.pdf.

Appendix 3: Three digit SIC codes of high tech industries

Three-digit SIC codes

Industries

283

Drugs

357

Computer and office equipment

360

Electrical machinery and equipment, excluding computers

361

Electrical transmissions and distribution equipment

362

Electrical industrial apparatus

363

Household appliances

364

Electrical lighting and wiring equipment

365

Household audio, video equipment, audio receiving

366

Communication equipment

367

Electronic components, semiconductors

368

Computer hardware (including min, micro, mainframes, terminals, discs, tape drives, scanners, graphics systems, peripherals, and equipment)

481

Telephone communications

737

Computer programming, software, data processing

873

Research, development, testing services

  1. Using this classification, we obtain 721 firms in the Nasdaq Composite Index, 80 firms in the SP 500 Index and 8 firms in the Dow Jones Industrial Average

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Mbanga, C., Darrat, A.F. & Park, J.C. Investor sentiment and aggregate stock returns: the role of investor attention. Rev Quant Finan Acc 53, 397–428 (2019). https://doi.org/10.1007/s11156-018-0753-2

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  • DOI: https://doi.org/10.1007/s11156-018-0753-2

Keywords

  • Investor attention
  • Investor sentiment
  • Aggregate stock returns

JEL Classification

  • G12
  • G14