Industry investor sentiment in the global shipping industry

ABSTRACT

This article investigates the influence of industry investor sentiment on excess stock returns of matched, global listed shipping and non-shipping firms. We document a weak insignificant association of industry investor sentiment on excess stock returns for shipping firms and a strong significant positive association for non-shipping peers. These results are mimicked by the trading behavior of both groups. As shipping is not a frequently traded asset with a concentrated, predominantly professional ownership, arbitrageurs limit the influence of noise traders and are the major driver in the formation of stock prices. On the contrary, the noise trader hypothesis holds for non-shipping peers due to a relatively dispersed ownership of non-shipping peers.

KEYWORDS:

• Investor sentiment
• shipping industry
• noise trader
• behavioral finance

Acknowlegement

Ehlert is a doctoral candidate of Hamburg Business School’s doctoral program and with KPMG AG WPG. We thank Prof Li, Wolfgang Drobetz, Henning Schröder, and the three anonymous referees for valuable comments and suggestions. Furthermore, we would like to thank Refinitv for serving us with data on investor sentiment sourced from their commercial data service Refinitiv MarketPsych.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/03088839.2022.2087237.

Notes

1. Prominent examples are ’GameStop’ and ’AMC Entertainment’. The share price of GameStop Corp., a US-retail company providing computer games and entertainment software, rose more than $1,000\mathrm{\%}$ in the beginning of 2021 due to the intervention of individual investors on hedge funds’ short-selling, while the share price of AMC Entertainment Inc., an international operator of movie theaters, grew to almost eight times its pre-pandemic value due to its hype on social media. Information is taken from the article ’GameStop: Professional throws in the towel, exasperated’ of ’Der Aktionär’ from 28 June 2021 (Source: https://www.deraktionaer.de/artikel/aktien/gamestop-profi-wirft-entnervt-das-handtuch-20232792.html) and ’The End of Stock Market Fundamentals?’ of the ’The New York Times’ from 3 June 2021 (Source: https://www.nytimes.com/2021/06/03/business/dealbook/amc-meme-stocks.html).

2. Shiller (2015) keeps records of those systemic events. He summarizes the swing up of periods and markets to extreme heights and the bursting of their bubbles.

3. Shleifer and Summers (1990), 30

4. Da, Engelberg, and Gao (2015), 26

5. Dividend-Earning and Dividend-Price ratios are taken from stock market data used in Shiller (2015) and updated regularly by Robert J. Shiller on http://www.econ.yale.edu/ shiller/data.html.

6. Shipping is known to be an industry closely linked to international trade and the up and down of the global economy (Drobetz, Schilling, and Tegtmeier 2010).

7. We use 50 replications by default.

8. For convenience to the reader, we do not report the detailed results but would make them available upon request. Nevertheless, the $\mathit{Mean}\mathit{VIF}$ of our return regressions (Table 4) range from $1.68$ to $1.80$ while the test statistic of our trading regressions (Table 6) is slightly higher with values form $2.54$ to $2.50$ due to the higher $\mathit{VIF}$ for $\mathit{Sentimen}{t}^{\mathit{filtered}}$ with $4.56$ and $D^{\mathit{Shipping}}$ $\times$ $\mathit{Sentimen}{t}^{\mathit{filtered}}$ with $4.60$.

9. Syriopoulos and Bakos (2019) highlight four distinctive economic sub-periods between $2000$ and $2016$ where three mark periods of economic crunch and only one flags economic offspring. The sub-period $2005-2007$ presents an era of growth and prosperity, especially for shipping firms, followed by the financial crisis in $2008-2010$ and market imbalances of supply and demand in $2011-2014$ and $2015-2016$ resulting in high volatility, low freight rates, and low profitability.

10. In our analysis, we also estimated the joint regression for the shipping and non-shipping sample which supports the recent findings. In light of our hypothesis and to demonstrate the distinctive effect of industry investor sentiment per industry, we thought a split presentation of the results would be more to the point. However, the additional results could be shared upon request.

11. In particular, higher payout ratios ($\mathit{DE}\mathit{ratio}$) are associated with higher excess returns for $\mathit{t}$ while higher payout ratios predict lower future excess returns for $\mathit{t}+1$ to $\mathit{t}+3$ except for the insignificant results in the non-shipping panel. The theoretical view of the payout ratio on future stock returns is not unequivocal. Empirical findings suggest that an increase of the payout ratio in light of an experienced persistent increase in earnings (resulting in an initial decline of the payout ratio) might signal good news on the future performance of the firm (DeAngelo, DeAngelo, and Skinner 1996; Benartzi, Michaely, and Thaler 1997). Other findings suggest no informational content in the ratio of dividends and earnings. The coefficients of dividend yield ($\mathit{DP}\mathit{ratio}$) are positive and significant at the $1\mathrm{\%}$ level across all columns. Dividend yields are attributed to contain information about the future economic outlook of a firm such that higher payout ratios are followed by higher future excess stock returns (Ang and Bekaert 2007). The opposite holds for market volatility (Fernandes, Medeiros, and Scharth 2014) and our documented results on $\mathit{Vix}$. Lower (Higher) market volatility is associated with higher (lower) future excess returns. Baker and Wurgler (2007) quote the market volatility index as the ’investor fear gauge’. Estimates of economic uncertainty show counter intuitive results despite the small coefficient of $0.002$. An increase in economic uncertainty is expected to increase market volatility and harm economic performance instead of leading to an increase of excess stock returns.

12. We follow Campbell, Grossman, and Wang (1993) in the construction of trading volume where we remove low-frequency variation through logs and detrend the time-series by subtracting a lagged moving average of log-returns.

13. And an additional ’Thank you’ to the two anonymous referees to bring this topic to the table.

14. All utilized portfolio factors in this robustness test are drawn from Fama-French website (Source: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).

15. For smoother reading we only report the final estimation results but could share detailed results from all stages of the estimation process upon request.