Herd Behaviour in the Drybulk and Tanker Marker: Decision to Invest in New and Retire in Fleet Company

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1. Introduction

Investment decisions in shipping for newbuildings are predominantly stratified into replacement, expansion and new entrance opportunities.  As far as replacement investment is concerned, it includes the allotment of capital for substituting vessels that no longer fulfil the company’s requisites and are, thus, available for demolition. Vessels are mainly demolished due to technical obsolescence, age, poor overall condition, specific market conditions, implementing new international regulations or following company policies. Secondly, expansionary investment decisions comprise of capital disbursement for actualizing the company’s growth strategy as a response to the present or upcoming market conditions. Lastly, the case of a new entrance investment embodies a substantial amount of capital that newcomers bring into the industry by purchasing newbuilding vessels. Companies decide to expand their fleet capacity primarily due to freight market conditions in order to maintain or increase their industry market share. (Engelen, Meersman, & van De Voorde, 2006), (Adland & Strandenes, 2007) (Stopford, 2009) (Greenwood & Hanson, 2015). The decision for a newbuilding order is immensely affected by the relation between the second-hand and the newbuilding prices (Merikas et al., 2008), (Stopford, 2009)due to lags in construction (Kalouptsidi, 2014) as usually shipping investors require instantaneous vessel delivery to benefit from high-level freight rates.

On the other hand, the decision to scrap a vessel, which is rather important as a capital-intensive asset will be permanently disposed, as previously mentioned is based on particular vessel characteristics, specific market conditions or due to implementation of international regulations. In general, higher scrapping levels are noticed for older vessels in poor condition for which both employment potential and capital appreciation scope are narrow. Furthermore, technical obsolescence usually increases running costs for vessels leading them earlier to the scrapyard. In addition, new regulations lead a vessel to a compulsory demolition whereas poor market conditions leave the option up to the owner to either operate the vessel at a loss, lay-up or scrap it. Nevertheless, while deciding to operate at a loss and lay-up are reversible choices depicting a hope for future profitability, the decision to scrap is permanent and that is why ship-owners historically incline to avoid it.  Scrapping is related to the freight rate second-hand and scrap market levels.

Buxton (1991) underlined that the economic rationale in operating a vessel or selling her in the second-hand market is little when both markets have decayed significantly. Knapp et al. (2008) proved an inverse connection between vessel profitability and the demolition probability, found a positive link between scrap prices and demolition probability, and detected no particular relation between flag, ownership or safety and scrapping. In relation to that, Alizadeh et al. in 2016 after investigating the capacity retirement in the drybulk sector confirmed both the aforementioned inverse relationship between earnings and demolition and the fact that increased demolition prices motivate scrapping activity. Another finding of the study was also that the probability for scrapping rises with freight volatility, interest rates and age (Alizadeh, et al., 2016). Finally, as Stopford states (2009) market expectations play a vital role under freight income uncertainty to shipping investment/divestment decisions and the real options theory should be adopted by investors in their decision making as it enhances the company flexibility (Dixit and Pindyck, 1994; Dikos, 2008; Gkochari, 2015; Kyriakou et al., 2017).

Investing or divesting decisions regarding vessels may often be the result of ship-owners altering their market perspective due to the actions of other peers, i.e. there is a degree of herd behaviour.

As it is stated in Hwang and Salmon’s article (2004) “herding arises when investors decide to imitate the observed decisions of others or movements in the market rather than follow their own beliefs and information.” Due to this trading behaviour, investors might be guided to move in the same direction and, consequently, herding can result in asset prices differ from their fundamental values (Bikhchandani et al., 1992; Nofsinger and Sias, 1999). Hence, herd behaviour examination might lead to an understanding of its effect on asset values (Chang et al., 2000) as for instance, shipping investors could be benefited from profitable opportunities that might arise because of this group activity. Moreover, Scharfstein and Stein (1990) argued that despite that the classical economic theory proposes that investment decisions are made by utilizing all the available information efficiently, group psychology often drives investments and therefore undermining the relation between information and market outcomes.

The objective of this paper is to examining some of the forces that may contribute in herd behaviour in the shipping markets.  The existent literature on herding is primarily focused on herding between institutional/retail investors (Sias, 2004; Kumar and Lee, 2006) or towards the market consensus consensus (e.g., Christie and Huang, 1995; Chang et al., 2000). Falling into the latter strand, this paper examines herding behaviour in both newbuilding and scrap markets of drybulk and tanker vessels.

To achieve the latter, an enhanced herding equation was used to examine whether investors decide based on shared common elements or they just copy the decisions of few distinguished investors because of an informational drawback. Herding in general was examined and decomposed into intentional and unintentional.  Thereupon, separately for each sector, asymmetric herd behaviour effects were tested regarding the contraction and expansion phases of the market and lastly whether there are any spill-over herding effects from one market to another.

Overall, the results show that shipping investors herd unintentionally in their decision to order or scrap in both the newbuilding and scrap markets and intentionally in decisions for scrapping. Moreover, there is evidence of asymmetric herding since herding is more likely to be present in the newbuilding market during profitable market conditions and in the scrap market during poor conditions; a finding not supported by Wald Test. Finally, spillover effects of unintentional herding are evident from the newbuilding market to scrapping.

This paper is structured as follows. Section 2 discusses total herding, how it is measured and detected , Section 3 includes the decomposing of herding into intentional and unintentional and Section 4 provides the result for the asymmetric effects of herding during the ups and downs of the market. Herding spill-over effects evidences are presented in Section 5 and finally Section 6 concludes this paper.

2. Herding Measurement and Detection

The fact that investors might overlook former heterogeneous beliefs in order to adopt correlated patterns around the market conduct is the insight behind the following herding measure (Christie & Huang, 1995) (Chang, et al., 2000). Thus, herding was investigated by using broadly two methods. In their article, Christie and Huang (1995) applied the cross-sectional standard deviation (CSSD) of stock returns to detect herding behaviour and based their empirical evaluation on rational asset pricing models and herding during bear market periods. They suggested that rational asset pricing models foresee that as the absolute values of market returns increase so does the cross-sectional returns dispersion because of the fact that investors’ trading behaviour is based on private and diverse information. Nevertheless, in times of market stress, lower return dispersions are realised due to the tendency of investors to imitate collective actions in the market disregarding their private information. Hence, herding is more extensive in extreme market movements that result in aberrant returns on the market portfolio. To tailor the assumptions to the appropriate scenarios, they detached the upper and lower tails of the returns distribution and investigated whether these deviate from the average level of dispersion.

Furthermore, herding can be tested by examining the cross-sectional absolute deviaton (CSAD) of stock returns that Chang et al. proposed (2000). In their article, herding is regarded as a function of the dispersion variable which is either non-linearly decreasing or peaks at a certain value of the expected absolute market return and descends thenceforth. This paper examines the existence of herding in the bulker and tanker sector by utilizing an adjusted CSAD for both contracting and scrapping markets. Hence, the CSAD used for this purpose incorporates the number of the vessels that have been demolished and the new vessels that have been contracted instead of the return on the market and the asset. The equation used for the analysis is the following:

                              =1 ∑ =1 | , − |,  { , }                ( 1 )

Where CSADtθ is the cross-sectional absolute deviation of contracting ( θ=C) and scrapping ( θ = S) of vessels, , is the number of vessels in the ith sector of the market that are contracted or scrapped at time and = ∑ =14 , 4 is the cross-sectional average number of vessels contracted or scrapped.

As it concerns the dry-bulk market, in the equation 1 i represents the capesize, panamax, handymax or handysize sectors and C was estimated by using  data provided by Clarksons Shipping Intelligence Network for the period January 1996 to May 2019.  

The development of C  for bulker contracting and scrapping in the examined period is presented in Figure 1. Generally, the CSAD measure for contracting can be considered comparatively constant with three outstanding oddities where the values deviate fairly from the market consensus. Firstly and more evidently, there is the peak of all-time high for the dry-bulk market in 2007-2008, next there is 2010 where investors assumed that the market would boom again and lastly in 2013-2014 again there was a positive sentiment among the market participants for the future. On the contrary, CSAD s is characterized by great volatility and big fluctuations from the average market behaviour can be detected.  These include:  the Russian and Asian crises (1996-1999), the internet bubble (2000-2002), the financial crisis of 2008 and the events that followed it and lastly the monumental freight dives during the period 2011-2013.

Figure 1: Cross-sectional absolute deviation ( ) for contracted and scrapped vessels in the bulker market (1996-2019)

As it concerns the dry-bulk market, in the equation 1 i represents the U/VLCC, suezmax or aframax sectors and   was estimated by using  data provided by Clarksons Shipping Intelligence Network for the period January 1996 to May 2019.  The development of C  for tanker contracting and scrapping in the examined period is presented in Figure 2. In general, C  is presented relatively stable with two outstanding deviations from the average. Firstly, the peak of the tanker market during 2006-2008 where it reached the highest level historically and secondly during 2013-2014 where the positive sentiment of the investors dominated the market.  On the other hand, CSADS shows noticeable volatility and deviates from the market consensus repeatedly. These include  the Asian crisis  (1998-1999), the internet  bubble (2000-2002) , the transition from single-hulled tanker vessels because of the Oil Pollution Act of 1990 when Europe banned these vessels(2003)and lastly due to new IMO regulations and continuously low freights 2018 which was a year with increased scrapping activity.

Figure 2: Cross-sectional absolute deviation ( ) for contracted and scrapped vessels in the tanker market (1996-2019)

Comparing the two markets, it can be concluded that investors in both markets act in a similar way. As it regards the contracting, while in both sectors the deviations from the market consensus exist in the same periods the tanker market seems more volatile. Investors decide to allocate capital to newbuilding vessels in times where the overall sentiment of the market is positive. On the other hand, as scrapping is concerned the picture is different. Besides the fact that dry-bulk and tanker owners decide to scrap their vessels in different periods triggered by different events, tanker scrapping deviations are also more frequent and erratic.

In order to examine if herding activity exists, the non-linear OLS specification that Chang et al. (2000) proposed is used and the link between the cross-sectional absolute deviation (CSAD) and the overall market average of vessel contracting or scrapping, respectively, was estimated[1].

= 0 + 1 | | + 2 ( )2 + , ( 2 )

Chang et al. (2000) indicated that, if investors are inclined to adopt the behaviour of the market during times of large price volatility, then the rational asset pricing models that indicate the linear and increasing relation between dispersion and return no longer hold and this relation might become non-linearly increasing or decreasing. The positive coefficient γ1 illustrates the linear part of the above relation, while the non-linear part by coefficient γ2. If γ2 is negative (γ2 < 0) , then the cross-sectional deviation of contracting or scrapping grows less than linearly or even declines, when the overall absolute market average is large. This is explained as evidence of existent herding behaviour and, therefore, the coefficient of the non-linear term should be negative and statistically significant. Thus, this paper assumes that if herding behaviour exists for either contracting or scrapping, then coefficient γ2 has to be negative and statistically significant.

3. Decomposing Herding into Intentional and Unintentional

Herding behaviour can be divided into intentional and unintentional herding. Regarding intentional herding, investors imitate other investors’ decisions with intent. This kind of conduct is ordinarily seen in less sophisticated investors who seek to mimic respectable or well-established investors, since acquiring the complete information of these market players would be costly. In general, intentional herd behaviour is defined by professional, as a result of ability or reputation, or informational asymmetry, when investors believe they have an informational disadvantage. (Devenow and Welch, 1996) This is frequently seen in hedge funds’ or financial intermediaries’ managers who are constantly evaluated and therefore less qualified managers may imitate the actions of the more sophisticated or more reputable peers. (Scharfstein and Stein, 1990; Villatoro, 2009).  In contrast, unintentional herd behaviour is observed when because of a sharing element in their environment investors come to similar investment decisions Hirshleifer et al., 1994; Bikhchandani and Sharma, 2000). Share elements may contain characteristic trading (Bennett et al., 2003) and relative homogeneity (Teh and de Bondt, 1997). Characteristic trading refers to investment decisions taken on the basis of certain characteristics of the assets, which in the course of time result in style investing (e.g income, momentum, growth, industry trading) whereas relative homogeneity refers to the way investors process financial information or signals (e.g. financial ratios) received from the market in a similar way because of the fact they share similar analytical skills or academic backgrounds (Wermers, 1999).

To decompose the cross-sectional absolute deviaton CSADϑ t into intentional and unintentional components, three parameters that sufficiently capture crucial shipping information, are similar to all market players and might influence the choice to order a new or demolish an old vessel were used. The fact that there are many other parameters affecting this decision is recognised nevertheless. These three parameters cover major areas of the shipping market and are divided into: valuation (price-earnings ratio) and market conditions (secondhand-newbuilding price ratio and Clarksea Index). All the required data that were used for the estimation of the variables are provided by by Clarksons Shipping Intelligence Network for the period January 1996–May 2019.

The first estimated parameter is the price-to-earnings ratio (PE) for vessels: PEi,t = PSH it − Eit, where PSHit is the log-price of the 5-year old secondhand vessel and Eit the log-earnings (Clarksea Earnings for bulkers and tankers respectively)[2] in sector i and month t. This ratio is applied to predict subsequent asset returns (Campbell & Shiller, 1998), (Rangvid, 2006), (Alizadeh & Nomikos, 2007) and points out the relative level of overvaluation or undervaluation in asset prices. This estimate is forwarded-looking and points out the expected earnings from an one year operation of the vessel from the point of valuation .i.e. high(low) P/E ratios are interpreted as high(low) present vessel prices compared to the one-year earnings. Papapostolou et al. (2014) in their article underline that high P/E ratios are linked with low investment sentiment levels which might lead to higher levels of demolition activity and low investment in contracting newbuilding vessels.

The second parameter, which is part of the market conditions category, is the secondhand-to-newbuilding price (SNB) ratio: SNBi,t = PSHit − PNBi,t , where PNB i,t is the log newbuilding vessel price. It need to be underlined that because newbuilding vessels have longer economic lives than similar secondhand vessels of certain age (e.g., five-year-old vessels), they are, in general, more expensive. However, it is important to realize that during bullish and profitable freight rate markets, investors in order to take advantage of these favourable conditions immediately prefer to purchase secondhand vessels to bypass the construction lags of newbuilding vessels. This leads to a delivery premium which drives the SNB ratio to higher levels. As Papapostolou et al. (2014)report , the SNB ratio is connected with investors sentiment for the shipping market and ,therefore, a higher ratio is expected to lead to less scrapping and more newbuilding orders.

Finally, the second parameter of the market conditions category is the Clarksea Index: C.I= Cit-Ct-12 where Cit is the log Clarksea Index level in month t.  Similarly to SNB ratio, it is expected that higher freight market rates can lead investors to order new vessels while scrapping remains at the minimum possible level.

To provide evidence that the aforementioned parameters contain useful information that may influence the decision to contract or to scrap vessels, the following regression is estimated:  Iϑt = β0 +β1Xt +υϑt ; where Iϑt =P4i=1Iϑi,t is the total number of vessels contracted or scrapped and Xt includes the aggregate metrics P/Et , SNBt and the CSIt.To calculate the aggregate metrics P/Et and SNBt for the drybulk and tanker market, weights ωi,t were assigned to each metric of sector i based on the market share (in terms of deadweight tonnage) of the sector in the total drybulk and tanker fleet respectively: PEt = P4i=1ωi,tP Ei,t and SNBt = P4i=1ωi,tSNBi,t. The results are presented in Appendix A and suggest that the proposed parameters explain a significant proportion of the variance of the number of vessels contracted or scrapped.

Finally, in order to decompose CSAD into intentional and unintentional herding the following regression is estimated:               = 0 + 1 + (3)

Intentional herding is defined as , = and unintentional herding is given by the difference between total herding and intentional herding

                                                   CSADϑ,Ut = CSADϑt − CSADϑ,I (4)

Thus, υϑt term can be defined as a measure of clustering due to the fact that investors respond to uncorrelated information, whereas CSADϑ,U t as a measure of clustering due to the fact that correlated information is analyzed in a similar manner.

Table 1:Herding behaviour in the drybulk market; intentional and unintentional herding

 

γ0

γ1

γ2

R2

Panel A: Total

CSADtC

CSADtS

 

1.3779α

0.2828α

0.0005

0.6597

 

(0.3166)

(0.0404)

(0.0008)

 

0.3019γ

0.7905α

-0.0260α

0.6516

(0.1572)

(0.0615)

(0.0044)

 

Panel B: Unintentional

CSADtC,U

CSADtS,U

 

2.6950α

0.2403α

-0.0022α

0.5183

 

(0.1922)

(0.0245)

(0.0005)

 

1.7400α

0.3442α

-0.0110α

0.4546

(0.1044)

(0.0408)

(0.0030)

 

Panel C: Intentional

CSADtC,I

CSADtS,I

 

-1.3171α

0.0425

0.0027α

0.3026

 

(0.3897)

(0.0497)

(0.0010)

 

-1.4381α

0.4464α

-0.015α

0.2812

(0.1914)

(0.0749)

(0.0054)

 

After decomposing the total herding behaviour into the unintentional and intentional derivatives, the regression model given by Equation (2) is calculated. The results for the drybulk market are illustrated by the OLS estimates (symbolised by γ0, γ1and γ2) in Table 1 and are classified in three different classes: total herding (CSADϑt), unintentional herding (CSADϑ,U,t) and intentional herding (CSADϑ,It).[3]In regard to total herding, the cross-sectional contracting and scrapping dispersions rise as the cross-sectional market average number of contracting and scrapping rises as well, a conclusion that is consistent with rational asset pricing models. However, herding activity is observed only in the scrap market as reflected by the negative and statistically significant value of γ2.  The results slightly differ qualitatively in the cases of unintentional and intentional herding. While, regarding unintentional herding, there is evidence of reduced cross-sectional dispersion around the market average both of contracting and scrapping, in the case of intentional herding this is observed only in the scrapping market. Consequently, it can be suggested that in the period January 1996-May 2019, investors, in the drybulk market, herded both intentionally and unintentionally regarding their decision to demolish older vessels but in the case of contracting new vessels they herded only unintentionally. Hence, scrapping herding behaviour is observed in every way (total, intentionally and unintentionally) and contracting herding behaviour is only observed unintentionally. The unconditional herding behaviour in scrapping and contracting markets can be associated with relative homogeneity. Particularly, these investment decisions were reached due to equivalent academic backgrounds and/or similar skills, which led investors to process the correlated information received in a similar way.

Table 2: Herding behaviour in the tanker market; intentional and unintentional herding

 

γ0

γ1

γ2

R2

Panel A: Total

CSADtC

CSADtS

 

0.5988α

0.3964α

0.0068β

0.6134

 

(0.1370)

(0.0466)

(0.0027)

 

0.1201α

0.6853α

-0.0557α

0.6461

(0.0390)

(0.0466)

(0.0098)

 

Panel B: Unintentional

CSADtC,U

CSADtS,U

 

1.8097α

0.1156α

-0.0037α

0.2318

 

(0.0454)

(0.0153)

(0.0009)

 

0.6404α

0.0694α

-0.0092α

0.0817

(0.0135)

(0.0166)

(0.0036)

 

Panel C: Intentional

CSADtC,I

CSADtS,I

 

-1.2153α

0.2827α

0.0104α

0.5188

 

(0.1592)

(0.0537)

(0.0030)

 

-0.5315α

0.6272α

-0.0462α

0.5812

(0.0407)

(0.0501)

(0.0107)

 

Accordingly, the results of the OLS estimates calculated by equation (2) for the tanker market are illustrated in Table 2. The results are quite similar for the two market sectors. In particular, in regard to total tanker herding the cross-sectional contracting and scrapping dispersions rise as the cross-sectional market average number of contracting and scrapping rises as well, a conclusion that, as previously mentioned, is consistent with rational asset pricing models. However, herding activity is, again, observed only in the scrap market as reflected by the negative and statistically significant value of γ2.  While, regarding unintentional tanker herding, there is again evidence of reduced cross-sectional dispersion around the market average both of contracting and scrapping, in the case of intentional herding this is observed only in the scrapping market. Therefore, it can be argued that in the period January 1996-May 2019, investors, in the tanker market, herded both intentionally and unintentionally regarding their decision to demolish older vessels but in the case of contracting new vessels they herded only unintentionally. Hence, scrapping herding behaviour is observed in every way (total, intentionally and unintentionally) and contracting herding behaviour is only observed unintentionally.

On the whole, shipping investors in the drybulk and the tanker sectors seem to herd in accordance. In both sectors relative homogeneity leads them, unintentionally, to take similar decisions in regards of contracting and scrapping. Of course, some less-sophisticated market players tend to intentionally mimic the decisions of well-established investors in the case of scrapping activity.

4. Asymmetric Herding during the Ups and Downs of the Market

In this section, the existence of an asymmetric relationship between CSADϑt and the cross-sectional market average of contracting and scrapping is examined. Researchers such as Christie and Huang (1995), Chang et al. (2000) and Demirer et al. (2010) argue that herding behaviour is expected to be more evident during downturn market periods. The assumption that shipping investors behave differently during periods when the freight rates are up compared to periods when  the freight rates are down, is tested by the approach  of Chiang and Zheng (2010) who use a single model with a dummy variable approach illustrated by :

= 0 + 1 (1 −  )| | + 2 | | + 3 (1 − )( )2 + 4 ( )2 + , ( 5 ) 

where d = 1 if CI,r < 0 (down market), and 0 otherwise (up market) for dry bulk and tanker markets.

Table 3: Herding behaviour under up and down dry bulk market conditions

 

γ0

γ1

γ2

γ3

γ4

R2

Wald Test

Panel A: Total

CSADtC

CSADtS

 

1.5414α

0.2412α

0.2389α

0.0036γ

     

0.0011

0.6716

[1.7127]

(0.3405)

(0.0681)

(0.0423)

(0.0020)

(0.0008)

   

0.2256

0.8196α

0.9274α

-0.0275α

-0.0527α

0.6549

[1.9495]

(0.1703)

(0.0652)

(0.1302)

(0.0047) 

(0.0188)

   

Panel B: Unintentional

CSADtC,U

CSADtS,U

 

2.5809α

0.2654α

0.2775α

-0.0045α

 

   

-0.0027α

0.5505

[2.4264]

(0.2033)

(0.0406)

(0.0252)

(0.0012)

(0.0005)

   

1.8740α

0.4258α

0.1262

-0.0179α

0.0003

0.5483

[2.7509]γ

(0.1034)

(0.040)

(0.0790)

(0.0029)

(0.0114)

   

Panel C: Intentional

CSADtC,I

CSADtS,I

 

-1.0395β

-0.0242

-0.0387

0.0081

     

0.0039α

0.3572

[3.4619]γ

(0.4097)

(0.0819)

(0.0509)

(0.024) 

(0.0010)

   

-1.6484α

0.3938α

0.8013α

-0.0096γ

-0.0530β

0.3141

[4.0448]β

(0.2035)

(0.0779)

(0.1556)

(0.0056) 

(0.0224)

   

Table 3 illustrates the OLS estimates of the model presented by equation 5 for total, unintentional and intentional herding ( CSADϑt, CSADϑ,Ut, CSADϑ,It respectively) under asymmetric market conditions for the dry-bulk market. Γ3 and γ4 coefficients values confirm the results presented in Section 3. More specifically, unintentional contracting herding is observed irrespectively of whether the market is up or down as both Γ3 and γ4coefficients are negative and statistically significant. The results show stronger herding behaviour in months of rising freight rates as contracting a newbuilding vessel may be more attractive due to the fact that charterers prefer to be associated with top tonnage and thus newbuildings may be first in line for competitive charter bidding.  The chartering side can be proved really important as it can enable newbuilding transactions to be financially viable through the existence of quality time-charter parties from prominent charterers and thereby provide a level of security to lenders or providers of credit. During poor market times, the decision for contracting newbuildings might be proved advantageous for investors as, due to construction lags, they can contract for new tonnage at a lower price and hope to benefit from an improved market on the time of delivery.

Moreover, unintentional herd behaviour in the drybulk sector is observed in the scrap market only in up freight markets. In order to examine the equality of the herding coefficients between up and down markets, a Wald test is conducted to test the null hypothesis that Ho: γ3 = γ4. The chi-square statistic in the case of unintentional herding behaviour in the scrap market confirms the asymmetric herd behaviour described above while in the case of the contracting market it provides no evidence.

Finally, total and intentional herding are present in the scrap market during both up and down market conditions but  this finding is confirmed by the Wald Test only for intentional herding activities.

Table 4: Herding behaviour under up and down tanker market conditions

 

γ0

γ1

γ2

γ3

γ4

R2

Wald Test

Panel A: Total

CSADtC

CSADtS

 

0.4981α

0.5495α

03492α

0.0013

     

0.0105

0.6337

[2.0382]

(0.1557)

(0.0683)

(0.0730)

(0.0032)

(0.0067)

   

0.1269α

0.6960α

0.5979α

-0.0611α

-0.0158

0.6520

[4.4796]β

(0.0390)

(0.0521)

(0.0708)

(0.0108)

(0.0207)

   

Panel B: Unintentional

CSADtC,U

CSADtS,U

 

1.7976α

0.1022α

0.1480α

-0.0029α

 

   

-0.0070α

0.2459

[4.1297]β

(0.0491)

(0.0216)

(0.0230)

(0.0010)

(0.0021)

   

0.6423α

0.1189α

0.0081

-0.0157α

-0.0008

0.2213

[4.5165] ]β

(0.0127)

(0.0170)

(0.0231)

(0.0035)

(0.0067)

   

Panel C: Intentional

CSADtC,I

CSADtS,I

 

-1.2995α

0.4472α

0.2012β

0.0041

     

0.0176β

0.5419

[3.5976]γ

(0.1698)

(0.0745)

(0.0797)

(0.0034)

(0.0073)

   

-0.5153α

0.5771α

0.5898α

-0.0453γ

-0.0149

0.5967

[1.8324]

(0.0409)

(0.0547)

(0.0744)

(0.0114)

(0.0217)

   

Table 4 illustrates the OLS estimates of the model presented by equation 5 for total, unintentional and intentional herding ( CSADϑt, CSADϑ,Ut, CSADϑ,It respectively) under asymmetric market conditions for the tanker market. In this case, again, 3 and γ4 coefficients values confirm the results presented in Section 3. More specifically, unintentional contracting herding is observed irrespectively of whether the market is up or down as both Γ3 and γ4 coefficients are negative and statistically significant. The results show stronger herding behaviour in months of rising freight rates as contracting a newbuilding tanker might be more attractive for the same reasons mentioned above. The chartering side in the tanker market plays a key role, maybe greater than the dry bulk market, as time-charters are essential for operating efficiently vessels like U/VLCCs. During poor market time tanker investors can also be benefited by the existing construction lags.

Moreover, unintentional herd behaviour in the tanker sector is observed in the scrap market both for up and down freight markets. The existence of herding in down freight markets is consistent with the negative relation between scrapping and market conditions (Buxton, 1991; Knapp et al., 2008; Alizadeh et al., 2016) i.e. freight rates are such that it is not economically viable to operate older vessels. The chi-square statistics point towards rejection of the null hypothesis of the Wald test, confirming the asymmetric herd behaviour in both the contracting and the scrapping markets.Finally, total and intentional herding are present in the scrap market during both up and down tanker market conditions but  this finding is confirmed by the Wald Test only for total herding.

On the whole, shipping investors in the drybulk and the tanker sectors seem to be in accordance even in their asymmetric herding behaviour. They particularly base their decisions to scrap their vessels based on the conditions of the market and they unintentionally contract newbuildings due to relative homogeneity as previously mentioned.

5. Herding Contagion; the Spill-Over Effect

Stopford (2009) argues that the shipping industry can be devided into four separate but interrelated markets: the newbuilding market, the sale and purchase market, the freight market and the demolition market. On the other hand, an alternative classification was suggested by Wijnolst and Wergeland (1996) which categorizes the shipping industry into: the real market for vessels (newbuilding and demolition) and spot freight rates, and the auxiliary markets for timecharters and secondhand vessels. The dynamics of these markets are closely related as in both cases the same investors participate in trading in all markets. In addition, herding may also be a result of social interaction and social mood. In relation to that, Olson (2006) argues that people participating in a group are prone to contagion by emotions and therefore the overall behaviour of the group is directly affected.  Social interaction among investors with their peers, shipowners and intermediares is rather important in shipping as word of mouth information is vital when dealing with niche sectors of the drybulk and tanker markets.

Hence, in an integrated market like thr drybulk or tanker sector, the efficient information flow and processing lead to investment and divestment activities that are interrelated, particularly when a company should make decisions on both contracting new vessels and scrap older ones according with its fleet. To examine the possibility of herding contagion. i.e. spill-over effects, from the newbuilding to the scrap market and vice versa, a modified herding measure of Equation (2 ) which assumes a closed system with no effects from one market to the other, is estimated. This measure is described by Equations (6) and (7).

= 0 + 1| | + 2(   2 + 3( )2 + tC  , ( 6 )

  = 0 + 1| | + 2( )2 + 3( )2 + , ( 7 )

All variables are defined in Section 2 and principal herding equation (Equation (2)) is adjusted to take into account the fact that participants in the scrap (newbuilding) market may actually exhibit herd behaviour as a result of extreme movements in the newbuilding (scrap) market.

Table 5: Herding contagion in the drybulk market.

 

γ0

γ1

γ2

γ3

R2

Wald Test

Panel A: Total

CSADtC

CSADtS

 

1.1935α

0.2847α

0.0006

0.0047

   

0.6624

[1.6316]

(0.3392)

(0.0403)

(0.0008)

(0.0031)

   

0.2751

0.7954α

-0.0262α

0.0001

0.6517

[33.72] α

(0.1727)

(0.0629)

(0.0045)

(0.0001)

   

Panel B: Unintentional

CSADtC,U

CSADtS,U

 

2.9701α

0.2374α

-0.0022α

-0.0070α

   

0.5415

[6.1917] β

(0.2017)

(0.0240)

(0.0005)

(0.0019)

   

1.9744α

0.3015α

-0.0087α

-0.0004α

0.5031

[8.3732] α

(0.1095)

(0.0399)

(0.0029)

(0.0001)

   

Panel C: Intentional

CSADtC,I

CSADtS,I

 

-1.7766 α

0.0473

0.0028 α

 0.0117 α

   

0.3253

[5.1443] β

(0.4124)

(0.0490)

(0.0010)

(0.0038) 

   

-1.6993α

0.4939α

 -0.0175α

  0.0005

0.3048

[11.0067]α

(0.2069)

(0.0754)

(0.0054)

(0.0001) 

   

The regression OLS estimates (symbolised by ̂ 0 , ̂ 1 , ̂ 2 and ̂ 3) for the regression given by equations (6) and (7) are presented in Table 5 for the drybulk sector. As mentioned, this model examines for possible herding spill-over effects between the newbuilding and scrapping markets. In harmony with earlier findings, total and intentional herding activity is observed in the scrapping market while unintentional herding is observed in both markets as reflected by the negative and statistically significant γ2 values. More specifically, in terms of total and intentional herding no evidence of spill-over effects is observed. However, as unintentional herding is concerned, adding ( )2 into Equation (6) or ( )2 into Equation (7) improves the explanatory power of the model as suggested  by the higher 2. Moreover, the negative and statistically significant values of γ3 reveal spill-over herding effects. This effect is observed  in both markets ,i.e. running from the newbuilding to the scrap market and vice versa and it is also indicated by the results of the Wald test. It must be noted, though, that herding contagion from the scrapping market to the contracting market is more intense. This findings can explained due to the social interplay (hence, social mood driving herding) among market players as the decision to order a newbuilding and scrap an older vessel is taken by investors who are operating in both markets. Additionally, the scrap-and-build scheme underlines that scrapping a vessel should take place first and the order confirmation for a new replacement vessel second so this could be a factor leading to the spill-over effects found.

Table 6: Herding contagion in the tanker market.

 

γ0

γ1

γ2

γ3

R2

Wald Test

Panel A: Total

CSADtC

CSADtS

 

0.5545α

0.4001α

0.0067 β

0.0135

   

0.6147

[0.2255]

(0.1445)

(0.0468)

(0.0027)

(0.0140)

   

0.1148 α

0.6871α

-0.0559α

0.0002

0.6463

[32.18]α

(0.0411)

(0.0469)

(0.0099)

(0.0004)

   

Panel B: Unintentional

CSADtC,U

CSADtS,U

 

1.8224α

0.1141α

-0.0036α

-0.0043

   

0.2339

[0.0138]

(0.0479)

(0.0154)

(0.0009)

(0.0052)

   

0.6550α

0.0640α

-0.0086α

-0.0005α

0.1200

[5.4470]β

(0.0139)

(0.0164)

(0.0035)

(0.0001)

   

Panel C: Intentional

CSADtC,I

CSADtS,I

 

-1.2542α

0.2871 α

0.0103α

 0.0130

   

0.5197

[0.0213]

(0.1683)

(0.0541)

(0.0030)

(0.0182) 

   

-0.5544α

0.6355α

 -0.0471α

  0.0007 γ

0.5859

[19.95]α

(0.0427)

(0.0501)

(0.0107)

(0.0004) 

   

Table 6 illustrates the OLS estimates (symbolised by ̂ 0 , ̂ 1 , ̂ 2 and ̂ 3)  for the examination of possible herding spill-over effects between the newbuilding and scrapping markets in the tanker sector. Again, in accordance with earlier findings total and intentional herding activity is observed in the scrapping market while unintentional herding is observed in both markets as reflected by the negative and statistically significant γ2 value. Particularly, in terms of total and intentional herding no evidence of spill-over effects is observed. However, as unintentional herding is concerned, adding ( )2 into Equation (6) or ( )2 into Equation (7) improves again the explanatory power of the model as suggested  by the higher 2. Furthermore, the negative and statistically significant value of γ3 reveal spill-over herding effects from the newbuilding to the scrapping market, a fact which is also confirmed by the Wald test at 5% significance level. This indicates that tanker investors do not decide to scrap a vessel unless they have already signed for a newbuilding. Therefore, the tanker market seems to behave differently than the drybulk market disregarding the scrap-and-build scheme mentioned above.

Hence, one, in order to analyse herding behaviour in the shipping industry, cannot overlook the fact that the different markets are fully integrated within the industry and interact with each other. Spill-over effects in the shipping industry have been previously exhibited in terms of volatility (Kavussanos, 2003; Chen et al., 2010; Drobetz et al., 2012; Tsouknidis, 2016) and more specifically spill-over effect in terms of how markets interact with each other in the drybulk sector have initially been established by Papapostolou et al. (2017). On the whole, spill-over effects seem to be present in unconditional herding behaviour in both markets indicating that shipping investors act similarly regardless the sector they operate in.

6. Conclusion

This paper contributes to the literature by providing evidence of herd behaviour in the dry bulk and tanker market and supporting previous similar findings established in the papers of Papapostolou, et al. (2017) and Liadi (2017). Moreover, this study outlines the importance of decomposing herding into intentional and unintentional in order to examine its effects on these two sectors of the shipping industry. Taken together, the results would seem to suggest that investors in the dry bulk and the tanker market act similarly in their decision to herd. In both sectors, unintentional herding was detected in both contracting and scrap markets, indicating that investors herd due to the fact they share common elements and not because they mimic the actions of well-established and respectable investors. This behaviour, i.e. intentional herding, was only observed in the scrap market for both sectors.

In terms of up and down freight markets, evidence of similar asymmetric effects in the herding behaviour of the investors exists in both the drybulk and the tanker sector. Regarding the drybulk sector, the findings confirm in the scrapping market clear unintentional herding behaviour during months of increasing freight rates, and in periods of decreasing freight rates stronger intentional herding activity. As far as the tanker market is concerned, there is clear evidence that unintentional herding behaviour is present in both the contracting and the scrapping market during up and down freight markets. However, while asymmetric behaviour in the contracting market is stronger during months of decreasing freight, the case in the scrapping market is the opposite.

Bibliography

  • Adland, R. & Strandenes, S., 2007. A discrete-time stochastic partial equilibrium model of the spot freight market. Journal of Transport Economics and Policy 41, pp. 189-218.
  • Alizadeh, A. & Nomikos, N., 2007. Investment timing and trading strategies in the sale and purchase market for ships. Transportation Research Part B: Methodological 41, pp. 126-143.
  • Alizadeh, A., Strandenes, S. & Thanopoulou, H., 2016. Capacity retirement in the dry bulk market: A vessel based logit model. Transportation Research Part E: Logistics and Transportation Review 92, pp. 28-42.
  • Bennet, J., Sias, R. & Starks, L., 2003. Greener pastures and the impact of dynamic institutional preferences. Review of Financial Studies 16, pp. 1203-1238.
  • Bikhchandani, S., Hirshleifer, D. & Welch, I., 1992. A theory of fads, fashion, custom, anf cultural change as informational cascades. Journal of Political Economy 100, pp. 992-1026.
  • Bikhchandani, S. & Sharma, S., 2000. Herd behavior in financial markets. IMF Staff Papers 47, pp. 279-310.
  • Buxton, I., 1991. The market for ship demolition. Maritime Policy & Management 18, pp. 105-112. Campbell, J. & Shiller, R., 1998. Valuation ratios and the long-run market outlook. Journal of Portfolio Management 24, pp. 11-26.
  • Chang, E., Cheng, J. & Khorana, A., 2000. An examination of herd behaviour in equity markets: An international perspective. Journal of Banking & Finance 24, pp. 1651-1679.
  • Chen, S., Meersman, H. & van de Voorde, E., 2010. Dynamic interrelationships in returns and volatilities between Capesize and Panamax markets. Maritime Economics & Logistics 12, pp. 65- 90
  • Chiang, T. & Zheng, D., 2010. An empirical analysis of herd behavior in global stock markets. Journal of Banking & Finance 34, pp. 1911-1921.
  • Christie, W. & Huang, R., 1995. Following the pied piper: Do individual returns herd around the market? Financial Analysts Journal 51, pp. 31-37.
  • Demirer, R., Kutan, A. & Chen, C., 2010. Do investors herd in emerging stock markets?: Evidence from the Taiwanese market. Journal of Economic Behavior & Organization 76, pp. 283-295.
  • Devenow, A. & Welch, I., 1996. Rational herding in financial economics. European Economic Review 40, pp. 603-615.
  • Dikos, G., 2008. Real options econometrics for aggregate tanker investment decisions. International Journal of Ocean Systems Management 1, pp. 31-44.
  • Dixit , A. & Pindyck, R., 1994. Investment Under Uncertainty. Princenton(New Jersey): Princenton University Press.
  • Drobetz, W., Richter, T. & Wambach, M., 2012. Dynamics of time-varying volatility in the dry bulk and tanker freight markets. Applied Financial Economics 22, pp. 1367-1384
  • Economou , F., Kostakis, A. & Philippas, N., 2011. Cross-country effects in herding behaviour: Evidence from four south European markets. Journal of International Financial Markets, Institutions and Money 21, pp. 443-460.
  • Engelen, S., Meersman, H. & van De Voorde, E., 2006. Using System dynamics in maritime economics: An endogenous decision model for shipowners in the dry bulk sector. Maritime Policy & Management 33, pp. 141-158.
  • Galariotis, E., Rong, W. & Spyrou, S., 2015. Herding on fundamental information: A comparative study. Journal of Banking & Finance 50, pp. 589-598.
  • Gkochari, C., 2015. Optimal investment timing in the dry bulk shipping sector. Transportation Research Part E: Logistics and Transportation Review 79, pp. 102-109.
  • Greenwood , R. & Hanson, S., 2015. Waves in ship prices and investment. The Quarterly Journal of Economics 130, pp. 55-109.
  • Hirshleifer, D., Subrahmanyam, A. & Titman, S., 1994. Security analysis and trading patterns when some investors receive information before others. The Journal of Finance 49, pp. 1665-1698.
  • Hwang, S. & Salmon, M., 2004. Market stress and herding. Journal of Empirical Finance 11, pp. 585-616.
  • Kavussanos, E., 2003. Time varying risks among segments of the tanker freight markets. Maritime Economics & Logistics 5, pp. 227-250.
  • Knapp, S., Kumar, S. & Remijn, A., 2008. Econometric Analysis of the ship demolition market. Maritime Policy 32, pp. 1023-1036.
  • Kyriakou, I., Pouliasis, P., Papapostolou, N. & Nomikos N.K, 2017. Income uncertainty and the decision to invest in bulk shipping. European Financial Management , Issue forthcoming
  • Lakonishok, J., Shleifer, A. & Vishny, R., 1992. The impact of institutional trading on stock prices. Journal of Financial Economics 32, pp. 23-43.
  • Merikas , A., Merika, A. & Koutroubousis, G., 2008. Modelling the investment decision of the entrepreneur in the tanker sector: Choosing between a second-hand vessel and a newly built one. Maritime Policy & Management 35, pp. 433-447.
  • Nofsinger, J. & Sias, R., 1999. Herding and feedback trading by institutional and individual investors. The Journal of Finance 54, pp. 2263-2295.
  • Olson, K., 2006. A literature review of social mood. Journal of Behavioral Finance 7, pp. 193-203. Papapostolou , N., Nomikos, N., Pouliasis, P. & Kyriakou, I., 2014. Investor sentiment for real assets: The case of dry bulk shipping market. Review of Finance 18, pp. 1507-1539.
  • Papapostolou, N., Pouliasis, P. & Kyriakou, I., 2017. Herd behavior in the drybulk market: An empirical analysis of the decision to invest in new and retire existing fleet capacity. Transportation Research Part E: Logistics and Transportation Review, 104, pp. 36-51.
  • Rangvid, J., 2006. Output and expected returns. Journal of Financial Economics 81, pp. 595-624.
  • Scharfstein, D. & Stein, J., 1990. Herd behaviour and investment. American Economic Review 80, pp. 465-479.
  • Sias, R., 2004. Institutional herding. Review of Financial Studies 17, pp. 165-206.
  • Stopford, M., 2009. Shipping Economics. New York: Routledge.
  • Teh, L. & de Bondt, W., 1997. Herding behavior and stock returns: An explanatory investigation. Swiss Journal of Economics and Statistics 133, pp. 293-324.
  • Tsouknidis, D., 2016. Dynamic volatility spillovers across shipping freight markets. Transportation Research Part E: Logistics and Transportation Review 91, pp. 90-111.
  • Villatoro, F., 2009. The delegated portfolio management problem: Reputation and herding. Journal of Banking & Finance 33, pp. 2062-2069.
  • Wermers, R., 1999. Mutual fund herding and the impact on stock prices. The Journal of Finance 54, pp. 581-622.
  • Wijnolst, N. & Wergeland, T., 1996. Shipping. The Netherlands: Delft University Pres

[1] The empirical results of the OLS estimation are presented as in Hwang & Salmon, 2004, Chiang & Zheng, 2010, Economou , et al., 2011, Galariotis, et al., 2015 and Papapostolou, et al., 2017 among others.

[2]  Fixed freight rate per day (US$/ day) annualised.

[3] It is important to mention that Newey-West t-statistics (with a lag of 12) are shown in all tables and superscripts , , designate significance of variables at the levels: 1%, 5% and 10% respectively.

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