Herding in Imperial Russia: Evidence from the St. Petersburg Stock Exchange (1865–1914)

ABSTRACT

We present seminal empirical evidence on market-wide herding from historical markets for the St. Petersburg stock exchange between 1865 and 1914. Our findings indicate the presence of herding in Imperial Russia’s largest equity market, which tends to vary among industries and grow stronger during months of negative performance and declining volatility. Controlling for the 1893-reform that prompted wider social participation in equity trading, we find that herding surfaces exclusively in the post-reform years, with no evidence of herding arising pre-reform. Our results showcase that the behavior of investors in historical stock exchanges exhibits patterns similar to those of modern-day ones.

Keywords:

• Herding
• Behavioral finance
• St. Petersburg stock exchange

JEL CLASSIFICATION:

• G12
• G14
• G23

Notes

1 The only other study we are aware of that has empirically investigated herding for pre- World war two years is that by Bohl et al. (2012); using a very small sample (19) of listed stocks in Germany for the 1920–1923 period, they found that investors herded significantly towards them during that period; Herding is not the only behavioural trading pattern for which there exists scant empirical research regarding historical markets; almost no empirical work exists for any other trading pattern of investors’ behaviour for them. An exception to this is Pierdzioch (2004), who demonstrated that German investors exhibited significant feedback trading during the 1880–1913 period. Studies relying on ownership data (Acheson and Turner 2011; Rutterford, Sotiropoulos, and Van Lieshout 2017) have confirmed the presence of home bias in UK investors’ holdings in the 19th century, yet did not research the effect of home bias over their trading decisions (something important, since home bias can foster correlation in the trades of investors within the same region; Feng and Seasholes 2004).

2 This has culminated in a growing body of literature devoted to the empirical testing (and, largely, confirmation) of a variety of stylized return-patterns of modern stock exchanges in market settings from earlier centuries. Examples of such patterns include autocorrelation (Annaert and Van Hyfte 2006; Bassino and Lagoarde-Segot 2015), non-normality (Campbell et al. 2018), beta-instability (Mensah 2013, 2015), momentum (Annaert and Mensah 2014; Geczy and Samonov 2016; Goetzmann and Huang 2018), mean reversion (Goetzmann, Ibbotson, and Peng 2001; Annaert and Van Hyfte 2006), liquidity premium (Burhop and Gelman 2010; Moore 2010; Gernandt, Palm, Waldenström 2012), equity premium (Annaert, Buelens, and Deloof 2015) and asymmetric volatility (Goetzmann, Ibbotson, and Peng 2001).

3 For more on the potential reasons underlying industry-variations in herding, see Andrikopoulos et al. (2017) and the discussion in the section, Data and Methodology.

4 Herding can be asymmetric contingent on market/industry performance and volatility due to a confluence of reasons; for a detailed discussion of those see Gavriilidis, Kallinterakis, and Leite-Ferreira (2013a) and the discussion in the third section, Data and Methodology.

5 With respect to developed markets, much research hails from the US, with its findings being rather period-dependent. Earlier studies on US pension (Lakonishok, Shleifer, and Vishny 1992) and mutual funds (Grinblatt, Titman, Wermers 1995; Wermers 1999) covering the pre-2000 years found limited herding among US fund managers; conversely, studies including the post-2000 years (Sias 2004; Choi and Sias 2009; Liao, Huang, and Wu 2011; Celiker, Chowdhury, and Sonaer 2015; Cui, Gebka, and Kallinterakis 2019) have reported greater magnitudes of institutional herding. Possible explanations for this include growing indexing among fund managers (Stambaugh 2014) and reduction in skill over time (Barras Scaillet, and Wermers 2010). As far as other developed markets are concerned, evidence in favour of institutional herding has surfaced in Germany (Kremer and Nautz 2013; Walter and Weber 2006), Spain (Gavriilidis, Kallinterakis, and Leite-Ferreira 2013a), Portugal (Holmes, Kallinterakis, and Leite-Ferreira 2013; Gavriilidis, Kallinterakis, and Leite-Ferreira 2013b) and the United Kingdom (Wylie 2005; Blake, Sarno, and Zinna 2017).

6 Evidence of institutional herding in emerging markets has been documented in Chile (Olivares 2008), Poland (Voronkova and Bohl 2005), South Korea (Choe, kho, and Stulz 1999; Kim and Wei 2002a, 2002b) and Taiwan (Hung, Lu, and Lee 2010).

7 See the study by Economou et al. (2015b) for institutional herding in Bulgaria and Montenegro.

8 See, for example the evidence from African markets (Guney, Kallinterakis, Komba 2017), the Asia-Pacific region (Chiang et al. 2013), China (Tan et al. 2008), European market samples (Economou, Kostakis, Philippas 2011; Mobarek, Mollah, and keasey 2014), the Euronext-group (Economou et al. 2015a; Andrikopoulos et al. 2017), Poland (Goodfellow, Bohl, and Gebka 2009), Taiwan (Demirer, Kutan, and Chen 2010) and the global evidence by Chiang and Zheng (2010).

9 Small-capitalization stocks entail lower analyst-following and, hence, higher informational uncertainty; investors wishing to tackle the latter may choose to mimic their peers’ trades, if they deem the latter’s content as informative enough. For empirical evidence on institutional herding among small stocks, see Lakonishok, Shleifer, and Vishny (1992), Wermers (1999), Sias (2004), Wylie (2005) and Hung, Lu, Lee (2010). For evidence of herding among smaller stocks at the macro level, see Chang, Cheng, and Khorana (2000) and Caparrelli, D’Arcangelis, and Cassuto (2004).

10 Funds may herd towards large capitalization stocks due to regulatory reasons (the case, e.g., of pension funds facing an institutionally restricted opportunity set of stocks to invest into; see Voronkova and Bohl 2005 and Olivares 2008) or indexing reasons (the case of fund managers’ performance being benchmarked against an index, which prompts them to mirror its composition in their portfolios and rebalance their portfolios accordingly following any changes in its composition). For more on this, see the discussion in Walter and Weber (2006) and Blake, Sarno, and Zinna (2017).

11 Industry herding has been reported at the micro (Choi and Sias 2009; Gavriilidis, Kallinterakis, and Leite-Ferreira 2013a; Celiker, Chowdhury, and Sonaer 2015) and macro (Zhou and Lai 2009; Gebka and Wohar 2013; Andrikopoulos, Gebka, and Kallinterakis 2021) levels; for more on the reasons motivating possible variations in industry herding see the discussion in Andrikopoulos et al. (2017).

12 Herding (primarily at the market-wide level, but occasionally also at the micro level) has been found to vary with market performance, market volatility, market volume and market sentiment. No uniform pattern has been identified thus far as regards these asymmetries (see e.g., the discussion in Guney, Kallinterakis, and Komba 2017).

13 Herding has occasionally been found to be stronger (Kim and Wei 2002a; Chiang and Zheng 2010; Mobarek, Mollah, and Keasey 2014; Cui, Gebka, and Kallinterakis 2019) and on other occasions weaker (Choe, Kho, and Stulz 1999; Hwang and Salmon 2004) following the outbreak of financial crises.

14 See, for example, the study of Chiang et al. (2013) on cross-market herding in the Asia-Pacific region.

15 No specific year is identified with its foundation, the latter being assumed to coincide with 1703 (the foundation year of St. Petersburg as a city); as Borodkin and Perelman (2011) note, the first formal reference to the St. Petersburg stock exchange in official documentation dates to 1721.

16 The April 1877–March 1878 Russo-Turkish war prompted a rise in the government’s budget deficit and the devaluation of the rouble, and was followed by a period of economic stagnation that lasted throughout the 1880s (Owen 2013).

17 The Crimean War (1853–1856) led to the proliferation of railroads, steamships, factories and banks across Russia (Owen 2013). For more on the evolution of companies’ incorporations in the 19th and early 20th centuries in Imperial Russia, see Borodkin and Perelman (2011).

18 These industries revolved mainly around textiles and beet sugar; see Owen (2013).

19 During this period, St. Petersburg banks engaged in extensive investment banking activities, with much of their revenue hailing from securities’ dealings (Borodkin and Perelman 2011).

20 Prior to the 1890s, equity trading was largely confined among members of the upper social classes and corporate insiders (Borodkin, Konovalova, and Perelman 2006; Goetzmann and Huang 2018).

21 For more information on the compilation of the database, please refer to Goetzmann and Huang (2018).

22 Factors that can render herding stronger during bullish markets include noise trading (unsophisticated investors tend to be attracted to market rallies – see e.g. Grinblatt and Keloharju 2001; Lamont and Thaler 2003), positive mood (it reduces the perception of riskiness, prompting investors to employ heuristics – such as, e.g. mimicking others - when processing risky decisions; Gavriilidis, Kallinterakis, and Ozturkkal 2020), optimistic sentiment (it motivates investors to jump on its bandwagon or, alternatively, prey on it; see e.g. Liao, Huang, and Wu 2011; Economou et al. 2015a, 2015b), external habit formation (the profits enjoyed by their peers during bullish markets can motivate investors to herd with them in order to avoid missing out – see e.g. Guney, Kallinterakis, and Komba (2017) and overconfidence (if investors realize profits during bullish markets, they may attribute them to their skills and, consequently, trade more aggressively in tandem; see e.g. Barber et al. 2007). On the other hand, herding can grow stronger during bearish markets due to risk aversion (the case of investors sell-herding with their peers during market slumps in order to curtail their losses) and reputational reasons (“bad” fund managers may mimic the trades of their “good” peers during market slumps, in order to demonstrate that any losses incurred are not due to their investment choices – as these would appear similar to those of their “good” peers – but rather the result of the market’s adverse movements; Scharfstein and Stein 1990).

23 Rising volatility periods can motivate herding regardless of whether this volatility is the product of noise (noise traders would be likely to herd; see e.g. Barber and Odean 2013) or a higher information flow (if the informational environment grows too complex, investors may choose to herd with their peers to tackle this complexity). On the other hand, declining volatility periods render it easier for uninformed investors to track – and, potentially mimic – their informed peers’ trades (Holmes, Kallinterakis, and Leite-Ferreira 2013), while also making it easier for investors to decipher the market’s direction – and, again potentially, herd on it.

24 Industry-variations in herding may be the result of reputational reasons, risk aversion, sector indexing, fads and style rotation; for a detailed discussion of those factors, see Andrikopoulos et al. (2017).

25 In this case, $R_{m,t}$ would correspond to the industry’s average performance and its squared value would now signify industry volatility in Equation (4)(4) $${\mathit{CSAD}}_{m,t}=\mathrm{ }{\beta }_0+\mathrm{ }{\beta }_1{D}^{IV}\left|R_{m,t}\right|+\mathrm{ }{\beta }_2\left(1-\mathrm{ }{D}^{IV}\right)\left|R_{m,t}\right|+\mathrm{ }{\beta }_3{D}^{IV}{R}_{m,t}^2+\mathrm{ }{\beta }_4\left(1-D^{IV}\right)R_{m,t}^2+\mathrm{ }{e}_t$$(4) .

26 For the purposes of this study, statistical significance is established at the 10% level, i.e. for p-values less than 0.1.

27 The significantly positive ${\beta }_2$-values for these two industries indicate the presence of “counter herding”, whereby investors strongly deviate from the market’s consensus as far as these industries are concerned. Although it is impossible to assert the reasons underlying the lack of herding for Railways and Steamships, one may argue that their later appearance in Russian economic life (both as innovations and industries) may have led them to appear as less established (compared to Financials and Trade & Industrials) in the perception of investors, who may have viewed their prospects as less clear to herd on.

28 See e.g. Chang, Cheng, and Khorana (2000), Gavriilidis, Kallinterakis, and Leite-Ferreira (2013a), Economou et al. (2015a), Economou et al. (2015b) and Guney, Kallinterakis, and Komba (2017).

29 Such a convergence to the market’s consensus is less observed on the up-market side, whereby ${\beta }_3$ coefficients turn positive for Financials, Steamships and Trade-& Industrials, an indication of counter herding.

30 ${\beta }_3$ is positive for Railways and Steamships, an indication of counter herding during increasing volatility months; the negative ${\beta }_4$ coefficient for Steamships is statistically insignificant.

31 Similar to earlier research; see e.g. Gavriilidis, Kallinterakis, and Leite-Ferreira (2013a), Economou et al. (2015a), Economou et al. (2015b) and Guney, Kallinterakis, and Komba (2017).

32 Given the absence of institutional investors (key candidates for informed traders in modern financial markets) from the 19th-century St. Petersburg stock exchange, that would likely involve individual investors tracking the trades of corporate insiders.

33 ${\beta }_4$ is overwhelmingly positive, a reflection of counter herding during the pre-reform years.

34 Due to the small number of observations (595 monthly observations), we have not tested for asymmetric herding vis-à-vis market performance and market volatility (as this would allow for very few observations on either of the extreme tails of the market return distribution). Specifically conditioning herding on market performance would also be clearly inappropriate in the specific context of the Christie and Huang (1995) model, since the latter assesses herding during extreme positive and extreme negative market returns in the same specification (it would make no sense, for example, to test for herding in the extreme positive and negative tails of the market return distribution for months of positive market performance – since these months would entail no negative values).

35 See e.g., the references in Guney, Kallinterakis, and Komba (2017).

36 Hwang and Salmon (2004) proposed an alternative market herding model based on extracting herding via the cross-section of monthly firm-betas. The reason we cannot employ this model as a robustness check here hinges on the fact that our database does not contain high frequency (e.g., daily) data, from which to estimate monthly betas. The lack of data on a proper market index and a risk-free rate further deters us from utilizing this empirical design; Several herding studies (Galariotis, Rong, and Spyrou 2015; Cui, Gebka, and Kallinterakis 2019; Andrikopoulos, Gebka, and Kallinterakis 2021) have identified whether market herding is intentional or not using the Chang, Cheng, and Khorana (2000) model employed in this paper. The method basically involves partitioning CSADm,t into its fundamental and non-fundamental components, using each separately as the dependent variable for herding estimations. To the extent that this partitioning involves the employment of common risk factors (e.g., Fama-French five factors), this renders the examination of intentional v. spurious herding not feasible in the context of this study, in view of the complete absence of data on common risk factors for the St. Petersburg stock exchange during our sample period; Aside from not capturing non-linear dynamics, an additional issue identified with the Christie and Huang (1995) framework pertains to the susceptibility of CSSD to the presence of outliers (Economou, Kostakis, and Philippas 2011).

37 An example here would be frontier stock exchanges, whose regulatory frameworks are still at an evolutionary stage of development (Guney, Kallinterakis, and Komba 2017), similar to pre-20th century markets.