impact of exchange rates and oil prices on the …

S I L E S I A N U N I V E R S I T Y O F T E C H N O L O G Y P U B L I S H I N G H O U S E

SCIENTIFIC PAPERS OF THE SILESIAN UNIVERSITY OF TECHNOLOGY 2019

ORGANISATION AND MANAGEMENT SERIES NO. 137

http://dx.doi.org/10.29119/1641-3466.2019.137.8 https://www.polsl.pl/Wydzialy/ROZ/Strony/Zeszytynaukowe.aspx

IMPACT OF EXCHANGE RATES AND OIL PRICES 1

ON THE VALUATION OF COMPANIES FROM THE TSL SECTOR 2

Mateusz MIERZEJEWSKI1*, Jakub GARNCARZ2 3

1 Cracow University of Economics, Department of Organisation Development, Cracow; 4 [email protected], ORCID: 0000-0001-8542-2373 5

2 Cracow University of Economics, Department of Organisation Development, Student Institute of Economic 6 Analysis, Cracow; [email protected], ORCID: 0000-0003-0166-9403 7

* Correspondence author 8

Abstract: The transport, forwarding and logistics (TSL) sector in Poland has seen dynamic 9

growth in recent years. Great attention is paid to road freight transport, which in 2011-2017 10

increased by almost 37%. The aim of the work was to examine the impact of macroeconomic 11

variables, such as oil prices and USD / PLN and EUR / PLN exchange rates on the valuation of 12

companies from the TSL sector in 2010–2018. For this purpose, spectral analysis was used. 13

It was noted that companies most often reacted to changes in the USD / PLN exchange rate, 14

and the least was observed in the case of changes in oil prices. 15

Keywords: TSL sector, cross-spectral analysis, exchange rates, crude oil. 16

1. Introduction 17

The valuation of a company’s shares does not depend solely on the internal situation of the 18

company, as external factors have a significant influence on it. One of the most important 19

elements that shapes the share price of a listed company is the situation on world markets, 20

as well as macroeconomic variables that affect profits generated by a company’s operations. 21

The problems of exchange rates, prices of raw materials and foreign trade are usually analysed 22

in relation to countries (Gawlikowska-Hueckel, and Umiński, 2013). Adopting a given sector 23

with potentially strong connections with the respondents draws attention to the occurrence of 24

additional phenomena. 25

Economic growth, growing international trade exchange and the high level of foreign 26

investments create excellent prospects for the development of the Polish market of transport 27

and logistics services. After 2004, the moment in which Poland joined the European Union, 28

an increase in the activity of Polish transport companies on the international arena was observed 29

116 M. Mierzejewski, J. Garncarz

– about half of Polish companies from this industry operated on international markets three 1

years after Poland’s accession to the community (Ślubowski, 2007). In addition, it is worth 2

emphasising that the industry itself is of exceptional importance in creating many jobs and 3

providing huge revenues to the State budget due to high taxes imposed mainly on fuels 4

(Garncarz, 2015). The transport, forwarding and logistics (TSL) market is therefore an 5

interesting space for reflection on the impact of changes in macroeconomic factors on a given 6

economic sector. 7

This study researched the impact of changes in USD/PLN and EUR/PLN currency exchange 8

rates and the costs of oil barrels on the valuation of shares of Polish companies in the transport, 9

forwarding and logistics sectors. The selected variables are global, which means that a given 10

country has no direct influence on the way they will be shaped, but they depend on many factors 11

and the current political and economic situation in the world. 12

2. Purpose and methods 13

The aim of the study was to assess the impact of global variables on the prices of TSL 14

index share prices. The data used in the study included the period from December 2010 15

to October 2018. The length of individual time series used for the analysis was determined by 16

the availability of data on specific companies, and therefore the results of spectral analysis for 17

individual relationships between selected variables and companies have different 18

interpretations of the frequency of changes. Ten time series were used in the work: exchange 19

rates USD/PLN, EUR/PLN, crude oil prices and valuations of seven companies from the TSL 20

industry: PKP Cargo S.A., AVIA Solutions Group, EnterAir S.A., KDM Shipping Public Ltd., 21

OT Logistic S.A., Trans Polonia S.A. and Zastal S.A. 22

Spectrum estimation is based on a set of empirical observations for the time series 23

{𝑦𝑡 : t = 1, 2,. . ., T}, on the basis of which the inference of the stochastic process 𝑌𝑡. is carried 24

out. One method of estimating the spectrum is to use the discrete Fourier transform, which for 25

the series {𝑦𝑡: t = 0, 1, ..., T − 1} is defined (and for s = 0, 1, …, T-1) as: 26

𝑥𝑠 = ∑ 𝑦𝑡𝑒−2𝜋𝑖𝑠𝑡/𝑇

𝑇−1

𝑇=0

27

After the Fourier transform, the Preseval equality was demonstrated (Talaga, Zieliński, 28

1986). Therefore, the periodogram function of the studied process can be described by the 29

following formula: 30

𝑓(𝑤𝑠) = 1

2𝜋(𝛾0 + 2 ∑ 𝛾𝑗

𝑇−1

𝑗=1

cos(𝑗𝑤𝑠)) 31

Impact of exchange rates and oil prices on the valuation… 117

where consecutive 𝛾𝑗 (for j = 0, 1, …, T-1) are values of autocorrelation coefficients in the 1

attempt. In order to examine the relationship between two variables, it is necessary to analyse 2

the stationary reciprocal spectrum (i.e. 𝑐𝑜𝑣 (𝑥𝑠; 𝑦𝑠+𝑗) = 𝑐𝑜𝑣 (𝑥𝑡; 𝑦𝑡+𝑗)∀(𝑠, 𝑡, 𝑗)) of the two-3

dimensional process [ 𝑥𝑡

𝑦𝑡 ], t∈N. The spectrum function of such a process has the form 4

(Łuczyński, 2015): 5

𝑓(𝜔) = [ 𝑓𝑥𝑥(𝜔) 𝑓𝑥𝑦(𝜔)

𝑓𝑦𝑥(𝜔) 𝑓𝑦𝑦(𝜔) ] =

1

2𝜋∑ 𝑒−𝑖𝜔𝑗Γ(𝑗)

∞

𝑗=−∞

6

Γ(𝑗) is the function of autocovarianity of the discussed process that equals [𝛾𝑥𝑥(𝑗) 𝛾𝑥𝑦(𝑗)

𝛾𝑦𝑥(𝑗) 𝛾𝑦𝑦(𝑗)] 7

where 𝛾𝑥𝑥(𝑗) = 𝑐𝑜𝑣(𝑥𝑡; 𝑥𝑡−𝑗), 𝛾𝑥𝑦(𝑗) = 𝑐𝑜𝑣(𝑥𝑡; 𝑦𝑡−𝑗), 𝛾𝑦𝑦(𝑗) = 𝑐𝑜𝑣(𝑦𝑡; 𝑦𝑡−𝑗), 𝛾𝑦𝑥(𝑗) =8

𝑐𝑜𝑣(𝑦𝑡; 𝑥𝑡−𝑗) (Łuczyński, 2015). The diagonals of the spectral function of the discussed 9

two-dimensional process are: the density of one-dimensional processes (𝑥𝑡, 𝑦𝑡), which take 10

the values respectively: 𝑓𝑥𝑥(𝑤) =1

2𝜋∑ 𝛾𝑥𝑥(𝑗)𝑒−𝑖𝑤𝑗∞

𝑗=−∞ , 𝑓𝑦𝑦(𝑤) =1

2𝜋∑ 𝛾𝑦𝑦(𝑗)𝑒−𝑖𝑤𝑗∞

𝑗=−∞ 11

and functions of mutual spectral density 𝑓𝑥𝑦(𝑤) =1

2𝜋∑ 𝛾𝑥𝑦(𝑗)𝑒−𝑖𝑤𝑗∞

𝑗=−∞ , 𝑓𝑦𝑥(𝑤) =12

1

2𝜋∑ 𝛾𝑦𝑥(𝑗)𝑒−𝑖𝑤𝑗∞

𝑗=−∞ . 13

The calculations also used a coefficient of multiple coherence allowing one to indicate the 14

frequency components of two-time series that are correlated with each other (Mierzejewski, 15

and Lampart, 2018). This takes values from 0 to 1. 16

In the next stage, the results of spectral density values of individual series were reviewed – 17

their high value indicates the importance of a given frequency in shaping the overall dynamics 18

of a given series. The last step was to calculate the values of time shifts for selected frequencies, 19

which made it possible to assess the causality of the analysed relationships. 20

3. The role and impact of macroeconomic variables on the valuation 21

of Polish companies in the TSL sector 22

This paper uses macroeconomic variables, such as the cost of a barrel of crude oil, as well 23

as exchange rates USD/PLN and EUR/PLN, which may have a direct impact on the results of 24

operations of enterprises providing transport, forwarding and logistics services. 25


USD/PLN

EUR/PLNDec-10

Jul-11Feb-12

Sep-12Apr-13

Nov-13Jun-14

Jan-15Aug-15

Mar-16Oct-16

May-17Dec-17

Jul-18

2,4

2,6

2,8

3,0

3,2

3,4

3,6

3,8

4,0

4,2

4,4

4,6

Exch

an

ge

Ra

tes

1

Figure 1. Exchange rates USD/PLN and EUR/PLN. Source: Own study based on data from the 2 Financial Portal Investing.com, data accessed: 01 December 2018. 3

Figure 1 presents the exchange rates of USD/PLN and EUR/PLN in the period from 4

December 2010 to October 2018. In December 2010, USD 1 was equal to PLN 2.965, 5

and the highest value in the given period was in November 2016, when USD 1 was equal to 6

PLN 4,2059, while at the end of 2018, the exchange rate fluctuated around 3.8 USD/PLN. 7

Significantly lower fluctuations were observed in the EUR/PLN exchange rate from December 8

2010 to October 2018, where it rose only from 3.9665 to 4.3281 EUR/PLN. 9

Dec-10

Jul-11Feb-12

Sep-12Apr-13

Nov-13Jun-14

Jan-15Aug-15

Mar-16Oct-16

May-17Dec-17

Jul-18

30

40

50

60

70

80

90

100

110

120

Cru

de

Oil

10

Figure 2. Prices of oil barrel. Source: own study based on data from the Financial Portal Investing.com, 11 data accessed: 01 December 2018. 12


Figure 2 shows the price formation per barrel of crude oil from December 2010 to October 1

2018. In December 2010, a barrel cost 91.37 USD, while in October 2018, only 67.59 USD. 2

The largest decrease was observed in the period from June to December 2014, when the price 3

fell by nearly 50% from USD 105.53 to USD 53.73. The reasons for this decline were described, 4

among others, in Baumeister and Kilian's Understanding the Decline in Price of June 2014, 5

mainly concerning supply shocks in Libya, Iraq and the United States, as well as the demand 6

shock caused by the unexpected weakening of the global economy towards the end 2014 7

(Baumeister, and Kilian, 2016). 8

In literature, much attention has been paid to the dependencies between oil and the transport 9

sector (Rickwood, 2010; Kozłowski, Gajewski, and Pilichowska, 2016). It has been noticed that 10

with the drop in fuel prices, the prices of transport services are decreasing. It was also noticed 11

that crude oil, without significant prior investments, cannot be easily replaced by other liquid 12

fuels in the short term, and it was concluded that the risk of possible oil price surges should be 13

reduced in the future. 14

The TSL sector is one of the fastest growing in Poland and directly contributes to the steady 15

growth of revenues from trade in services and the achievement in 2016 of a positive balance of 16

the balance sheet at a level of PLN 61.4 billion. In this year, revenues obtained from providing 17

services performed by Polish enterprises abroad reached a level of PLN 196.4 billion. 18

The greatest impact on achieving this result was due to transport services, and in 2016, 19

they increased to a level of PLN 53.2 billion, which constituted over 27% of the total in the 20

structure of revenues from international trade in services provided by Polish enterprises 21

(NBP, 2017). 22

The Polish transport sector has been particularly dynamic since 2014 (GUS, 2018). 23

The chart below presents the volume of transported loads in tonne-kilometres by Polish 24

transport companies. 25

Figure 3 illustrates the volumes of transported loads by Polish enterprises in 2011-2017. 26

In 2011, Polish enterprises transported nearly 320,000 (million tonne-kilometres), while in 27

2017, this number increased to nearly 435,000, an increase of approximately 37% in just 28

6 years. What is important is that car transport companies providing the above services have to 29

cope with numerous difficulties, such as EU regulations that directly affect Polish transport 30

companies and a significant deficit of drivers on the Polish labour market. According to PWC 31

estimates, there are around 600-650 thousand professionally active drivers in Poland, of which 32

500-550 thousand work in the road freight transport sector. The deficit is estimated at around 33

20%, which means that between 100 and 110 thousand drivers are missing. Every year, 34

the inflow of new drivers oscillates between 5-7%, while abandoning the profession of a driver 35

is planned by about 7% of employees, which means that the inflow of new drivers is 36

insufficient, even to balance the departure from this profession (PWC, 2016). Given the steady 37

growth of the transport sector, this problem is likely to increase. Another blow aimed at Polish 38

car transport companies was legislation adopted on 3 December 2018 by the Council of the 39


European Union – the Mobility Package. As stated by the Polish Minister of Infrastructure, 1

Andrzej Adamczyk, this Act contains disproportionate and protectionist regulations that are 2

directed against international road transport, in particular at Polish carriers (Wawryszuk, 2018). 3

4

2011 2012 2013 2014 2015 2016 2017300000

320000

340000

360000

380000

400000

420000

440000

Ca

rgo

tra

ffic

(m

ln t•km)

5

Figure 3. Cargo transport carried out by Polish enterprises (in million tonne-kilometres). Source: own 6 study based on data from the Polish Central Statistical Office, data accessed: 01 December 2018. 7

Recently, environmental issues that have often been raised may hinder the further dynamic 8

development of transport in Poland. In scientific literature, a lot of research has been devoted 9

to the impact of transport on the environment (Badyda, 2010; Walendzik, Łepkowski and 10

Nowacki, 2016; Puscaciu V., Mihalache and Puscaciu R., 2014). The authors of the research 11

papers agree that transport is an indispensable part of the economic development of the world, 12

but they also mention negative consequences for the environment, including air pollution, noise, 13

the greenhouse effect and taking up more and more land, often included in valuable natural 14

areas. 15

https://www.diki.pl/slownik-angielskiego?q=Polish+Central+Statistical+Office


PKP Cargo

AVIA SG

Enter Air

KDM

OTS

TRNP

Zastal

Dec-10

Jul-11

Feb-12

Sep-12

Apr-13

Nov-13

Jun-14

Jan-15

Aug-15

Mar-16

Oct-16

May-17

Dec-17

Jul-18

-20

0

20

40

60

80

100

Valu

ation o

f com

pany s

hare

s

1

Figure 4. Valuation of shares of Polish companies from the TSL sector. Source: own study based on 2 data from the Financial Portal Investing.com, data accessed: 01 December 2018. 3

Chart 4 presents the valuations of Polish companies from the TSL sector. From among 4

selected companies, the smallest fluctuations were recorded by companies that are listed on the 5

Warsaw Stock Exchange in the longest period: ZASTAL and TRNP. Most changes are 6

observed in the case of PKP Cargo and AVIASG, while the shortest on the stock exchange, 7

since only from February 2016, is the company EnterAir. The selected companies specialise in: 8

PKP Cargo S.A. – logistics services, rail freight transport, freight forwarding. 9

AVIA Solutions Group AB – aircraft servicing, pilot training, airport management. 10

EnterAir S.A. – air transport services, pilot training. 11

KDM Shipping Public Ltd. – sea and river transport services. 12

OT Logistic S.A. – water and rail transport services, logistics, forwarding. 13

Trans Polonia S.A. – car transport services of liquid fuels, chemicals, gases; logistics. 14

Zastal S.A. – transport and forwarding services, production of containers, rolling stock, 15

truck service. 16

4. Results of causality of selected macroeconomic variables 17

Based on the aforementioned dependencies, macroeconomic variables such as crude oil 18

prices and USD/PLN and EUR/PLN foreign exchange rates were selected, which were used to 19

assess the impact on the valuation of stocks of companies from the TSL sector. 20


Square of coherence: USD/PLN - ZASTAL

0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

-0,1

0,2

0,5

0,8

Square

Square of coherence: USD/PLN - ENTER AIR

0,00 0,06 0,13 0,19 0,25 0,31 0,38 0,44 0,50-0,1

0,1

0,3

0,5

0,7

Square

Square of coherence: USD/PLN - TRNP

0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

-0,1

0,2

0,5

0,8

Square

Square of coherence: USD/PLN - AVIASG

0,00 0,05 0,09 0,14 0,18 0,23 0,27 0,32 0,36 0,41 0,45 0,50-0,10,10,30,50,70,9

Square

Square of coherence: USD/PLN - OTS

0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 0,45 0,50-0,1

0,2

0,5

0,8

Square

Square of coherence: USD/PLN - PKP Cargo

0,00 0,05 0,10 0,16 0,21 0,26 0,31 0,36 0,41 0,470,0

0,2

0,4

0,6

Square

Square of coherence: USD/PLN - KDM

0,000,04

0,090,13

0,170,21

0,260,30

0,340,39

0,430,47

-0,1

0,1

0,3

0,5

0,7

Square

1

Figure 5. Square of coherence of changes in the exchange rate of USD / PLN and changes in share 2 prices of selected companies. Source: own study based on data from the Financial Portal Investing.com, 3 data accessed: 01 December 2018. 4

5


0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

0,00

0,01

0,02

0,03

0,04

0,05

0,06U

SD

/PLN

-0,5

0,5

1,5

2,5

3,5

TR

NP

0,00 0,06 0,13 0,19 0,25 0,31 0,38 0,44 0,500,0120,0160,0200,0240,0280,0320,0360,040

US

D/P

LN

-2024681012

EN

TE

RA

IR

0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

0,00

0,01

0,02

0,03

0,04

0,05

0,06

US

D/P

LN

0,0

0,4

0,8

1,2

1,6

2,0

ZA

ST

AL

0,000,05

0,090,14

0,180,23

0,270,32

0,360,41

0,450,50

0,00

0,02

0,04

0,06

0,08

US

D/P

LN

0

20

40

60

80

100

120

AV

IAS

G

0,000,05

0,100,15

0,200,25

0,300,35

0,400,45

0,500,0100,0140,0180,0220,0260,0300,0340,038

US

D/P

LN

-10

0

10

20

30

40

50

OT

S

0,000,05

0,100,16

0,210,26

0,310,36

0,410,47

0,0050,0100,0150,0200,0250,0300,0350,040

US

D/P

LN

20

40

60

80

100

120

PK

P C

AR

GO

0,000,04

0,090,13

0,170,21

0,260,30

0,340,39

0,430,47

0,0050,0100,0150,0200,0250,0300,0350,040

US

D/P

LN

0

4

8

12

16

20

24

KD

M

TRNP

ENTERAIR

ZASTAL AVIASG USD/PLN

OTS PKP CARGO

KDM

1

Figure 6. Spectral density of changes in the exchange rate of USD / PLN and changes in share prices of 2 selected companies. Source: own study based on data from the Financial Portal Investing.com, data 3 accessed: 01 December 2018. 4

The results presented in fig. 5 show that only two price variables (PKP Cargo and OTS) do 5

not show any reaction to changes in the exchange rate of USD/PLN. In other cases, it is possible 6

to indicate the significance of various change frequencies on stock exchange fluctuations. After 7

combining the frequencies with the highest values of the coherence square and appropriately 8

distinguishing values of density (figure 6) spectral, one can indicate the following time 9

dependencies between the exchange rate of USD/PLN and the individual variables of share 10

prices: 11

with a frequency of 0.01 and 0.1 for AVIASG (7-year and 10-month fluctuations), 12

with a frequency of 0.28 for ENTERAIR (4-month fluctuations), 13

with a frequency of 0.24 for the company KDM (4-month fluctuations), 14

with a frequency of 0.06 for TRNP (1.5-year fluctuations), 15

with a frequency of 0.11, 0.15 and 0.24 for ZASTAL (10-, 7- and 4-month fluctuations). 16


Square of coherence: EUR/PLN - ZASTAL

0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

-0,1

0,2

0,5

0,8

Sq

ua

reSquare of coherence: EUR/PLN - ENTERAIR

0,00 0,06 0,13 0,19 0,25 0,31 0,38 0,44 0,500,05

0,15

0,25

0,35

0,45

0,55

Sq

ua

re

Square of coherence: EUR/PLN - TRNP

0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

0,0

0,2

0,4

0,6

0,8

Sq

ua

re

Square of coherence: EUR/PLN - AVIASG

0,000,05

0,090,14

0,180,23

0,270,32

0,360,41

0,450,50

-0,1

0,1

0,3

0,5

0,7

0,9

Sq

ua

re

Square of coherence: EUR/PLN - OTS

0,000,05

0,100,15

0,200,25

0,300,35

0,400,45

0,50-0,1

0,1

0,3

0,5

0,7

Sq

ua

re

Square of coherence: EUR/PLN - PKP CARGO

0,00 0,05 0,10 0,16 0,21 0,26 0,31 0,36 0,41 0,47-0,2

0,2

0,6

1,0

Sq

ua

re

Square of coherence: EUR/PLN - KDM

0,000,04

0,090,13

0,170,21

0,260,30

0,340,39

0,430,47

-0,2

0,2

0,6

1,0

Sq

ua

re

1

Figure 7. The square of coherence of changes in the exchange rate of EUR/PLN and changes in share 2 prices of selected companies. Source: own study based on data from the Financial Portal Investing.com, 3 data accessed: 01 December 2018. 4


0,00

0,04

0,09

0,13

0,17

0,21

0,26

0,30

0,34

0,38

0,43

0,47

-0,002

0,004

0,010

0,016

0,022

0,028

EU

R/P

LN

0,0

0,4

0,8

1,2

1,6

2,0

ZA

ST

AL

0,00 0,06 0,13 0,19 0,25 0,31 0,38 0,44 0,500,0000,0020,0040,0060,0080,0100,0120,014

EU

R/P

LN

-2024681012

EN

TE

RA

IR

0,00

0,04

0,09

0,13

0,17

0,21

0,26

0,30

0,34

0,38

0,43

0,47

-0,002

0,004

0,010

0,016

0,022

0,028

EU

R/P

LN

-0,5

0,5

1,5

2,5

3,5

TR

NP

0,00

0,05

0,09

0,14

0,18

0,23

0,27

0,32

0,36

0,41

0,45

0,50

0,000

0,010

0,020

0,030

0,040

EU

R/P

LN

0

20

40

60

80

100

120

AV

IAS

G

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

0,45

0,500,000

0,004

0,008

0,012

0,016

0,020

EU

R/P

LN

-10

0

10

20

30

40

50

OT

S

0,00

0,05

0,10

0,16

0,21

0,26

0,31

0,36

0,41

0,47

0,000

0,004

0,008

0,012

0,016

0,020

EU

R/P

LN

20

40

60

80

100

120

PK

P C

AR

GO

0,00

0,04

0,09

0,13

0,17

0,21

0,26

0,30

0,34

0,39

0,43

0,47

-0,002

0,004

0,010

0,016

0,022

EU

R/P

LN

0

6

12

18

24

KD

M

EUR/PLN

ZASTAL

ENTERAIR

TRNP

AVIASG

OTS

PKP CARGO

KDM

1

Figure 8. Spectral density of changes in the exchange rate of EUR/PLN and changes in the share prices 2 of selected companies. Source: own study based on data from the Financial Portal Investing.com, data 3 accessed: 01 December 2018 4

In the case of the study on the dependence of changes in share prices on changes in the 5

EUR/PLN exchange rate, it was observed that as many as three of the surveyed lines 6

(ENTERAIR, OTS and KDM) indicate no relationship between variables (low levels of the 7

coherence square depicted in Figure 7). In other cases, after taking into account the values of 8

spectral densities (figure 8), the following relationship between share prices and the exchange 9

rate surveyed was recorded: 10

with a frequency of 0.02 for the company PKP Cargo (5-year fluctuations), 11

with a frequency of 0.11 for the company AVIA SG (9-month fluctuations), 12

with a frequency of 0.03 for the company TRNP (2.5-year fluctuations), 13

with a frequency of 0.01, 0.1, 0.14 and 0.24 for ZASTAL (8-year, 10-, 7- and 4-month 14

fluctuations). 15


Square of Coherence: Oil Prices - ZASTAL

0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

-0,1

0,1

0,3

0,5

0,7

0,9

Sq

ua

re

Square of Coherence: Oil Prices - ENTERAIR

0,00 0,06 0,13 0,19 0,25 0,31 0,38 0,44 0,50-0,1

0,1

0,3

0,5

0,7

Sq

ua

re

Square of Coherence: Oil Prices - TRNP

0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

-0,1

0,1

0,3

0,5

0,7

0,9

Sq

ua

re

Square of Coherence: Oil Prices - AVIA SG

0,000,05

0,090,14

0,180,23

0,270,32

0,360,41

0,450,50

-0,1

0,1

0,3

0,5

0,7

Sq

ua

re

Square of Coherence: Oil Prices - OTS

0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 0,45 0,50-0,1

0,1

0,3

0,5

0,7

Sq

ua

re

Square of Coherence: Oil Prices - PKP CARGO

0,00 0,05 0,10 0,16 0,21 0,26 0,31 0,36 0,41 0,470,0

0,1

0,2

0,3

0,4

0,5

Sq

ua

re

Square of Coherence: Oil Prices - KDM

0,000,04

0,090,13

0,170,21

0,260,30

0,340,39

0,430,47

-0,1

0,1

0,3

0,5

0,7

Sq

ua

re

1

Figure 9. The square of coherence of changes in oil prices and changes in share prices of selected 2 companies. Source: own study based on data from the Financial Portal Investing.com, data accessed: 3 01 December 2018. 4


0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

0100200300400500600

Oil

Pri

ce

0,0

0,4

0,8

1,2

1,6

2,0

ZA

ST

AL

0,00 0,06 0,13 0,19 0,25 0,31 0,38 0,44 0,500

200

400

600

800

Oil

Pri

ce

-2024681012

EN

TE

RA

IR

0,000,04

0,090,13

0,170,21

0,260,30

0,340,38

0,430,47

0100200300400500600

Oil

Pri

ce

-0,5

0,5

1,5

2,5

3,5

TR

NP

0,000,05

0,090,14

0,180,23

0,270,32

0,360,41

0,450,50

-100

100

300

500

Oil

Pri

ce

020406080100120

AV

IA S

G

0,000,05

0,100,15

0,200,25

0,300,35

0,400,45

0,500

100200300400500600

Oil

Pri

ce

-1001020304050

OT

S

0,000,05

0,100,16

0,210,26

0,310,36

0,410,47

0100200300400500600

Oil

Pri

ce

20

40

60

80

100

120

PK

P C

AR

GO

0,000,04

0,090,13

0,170,21

0,260,30

0,340,39

0,430,47

0

100

200

300

400

500

Oil

Pri

ce

0

6

12

18

24

KD

M

ROPA

ZASTAL

ENTERAIR

TRNP

AVIA SG

OTS PKP CARGO

KDM

1

Figure 10. Spectral density of changes in oil prices and changes in share prices of selected companies. 2 Source: own study based on data from the Financial Portal Investing.com, data accessed: 01 December 3 2018. 4

As illustrated in figure 9, the impact of changes in crude oil prices on international markets 5

also showed the weakest impact on the shaping of selected share prices – as many as four of 6

the seven surveyed companies (PKP Cargo, Avia SG, ENTERAIR and OTS) did not show any 7

dependence on changes in prices of this commodity. In the case of the remaining companies, 8

after taking into account the values of the spectral densities presented in figure 10, the following 9

relationships were observed: 10

with a frequency of 0.28 for ZASTAL (4-month fluctuations), 11

with a frequency of 0.01 for TRNP (8-year fluctuations), 12

with a frequency of 0.24 for KDM (4-month fluctuations). 13

It is worth noting, however, that in the case of all the selected relationships, the values of 14

spectral densities indicated a weak relation between variables. 15

16


Table 1. 1 Delay values of selected dependencies (in months) 2

Cycle

length

(months)

PKP

Cargo

AVIASG ENTERAIR KDM OTS TRNP ZASTAL

US

D/P

LN

4 3 -4 2

7 -3

9 7

10 10

16 5

88 69

EU

R/P

LN

4 3

7 -3

9 -8

10 8

31 13

58 56

94 85

OIL

4 1 -3

94 -77

* Positive values mean that the change in the macroeconomic variable preceded the change in the stock value;

analogically, the negative values mean that the change in the stock value preceded the change in the

macroeconomic variable.

Source: own study based on data from the Financial Portal Investing.com, data accessed: 01.12.2018. 3

Table 1 shows the delay values of selected dependencies. Negative values in cells mean the 4

company's reaction to a given variable change following the specified time, whereas a positive 5

value means a change in the company's share price before the change in the variable. 6

The highest number of dependencies was demonstrated by ZASTAL, as changes in its value 7

were delayed on average by 3 months from changes in EUR/PLN in the 3-month series, 8

over 8 months in the 10-month series and 7 years in the 8-year series, and a 7-month change in 9

the valuation of shares preceded the change of the exchange rate by an average of 3 months. 10

Changes in the USD/PLN exchange rate also influenced the valuation of the company's shares; 11

2 months in the 4-month series, 7 months in the 9-month series, while in the case of a 7-month 12

series, the change in the share valuation preceded the change in the exchange rate by an average 13

of 3 months. In the discussed company, changes in the price of crude oil influenced changes in 14

the valuation of shares, and in a 4-month period, the share price changed on average 3 months 15

after the change in the price of oil. The valuations of Avia SG reacted only to the EUR/PLN 16

exchange rate in a 10-month period and showed two positive dependencies on the USD/PLN 17

exchange rate, while TRNP stock valuations reacted on a 8-year basis to changes in oil prices 18

and showed positive dependencies in the 2.5-year series in the case of EUR/PLN exchange rate 19

and 16-month to the USD/PLN exchange rate. PKP Cargo and EnterAir showed only one 20

dependency; PKP Cargo to change the EUR / PLN rate in 5-year series, and EnterAir to change 21

the USD/PLN rate in a 4-month series. OTS was the only company that did not show any 22

dependencies between macroeconomic changes. 23

24


The emergence of environmental barriers to economic growth forced companies from the 1

discussed industry to reorient the efficiency of transport, as a result of which they began to take 2

into account the volatility of external factors in their costs more and more often (Michałowska, 3

2012). This information was included in annual reports. As presented in Table 1, the majority 4

of the analysed relations did not indicate the existence of a relationship between the valuation 5

of companies and the changes in the observed macroeconomic indicators. This may result from 6

the proper flow of information to investors about the company's activities and actions taken to 7

safeguard them against threats to macroeconomic changes (e.g. keeping factoring in current 8

operations (Kręczmańska-Gigol, and Pajewska-Kwaśny, 2011)). 9

5. Summary 10

The aim of the study was to research the impact of macroeconomic variables, such as the 11

price of crude oil and USD/PLN and EUR/PLN exchange rates, on the valuation of shares of 12

companies from the TSL sector. The research was carried out using cross-spectral analysis and 13

coefficient of multiple corelation, and the following conclusions were obtained: 14

1. In the case of the USD/PLN exchange rate, only PKP Cargo and OTS did not show any 15

reactions to changes in the exchange rate, while three companies showed one 16

dependence, Avia SG showed two dependencies, and ZASTAL three. 17

2. ZASTAL also reacted three times to changes in the EUR/PLN rate. PKP Cargo, Avia 18

SG and TRNP showed one dependence, while the remaining three companies did not 19

show any dependence. 20

3. The lowest prices of the company's shares were influenced by oil prices. KDM, TRNP 21

and ZASTAL showed one dependence, while the remaining four companies showed no 22

reaction to changes in crude oil prices. 23

4. ZASTAL (seven reactions) turned out to be the most susceptible to changes in 24

macroeconomic variables, while the weakest was OTS (no reaction) and PKP Cargo 25

(one reaction). 26

5. The results of the study showed a very weak occurrence of a company's price 27

dependence on macroeconomic variables, which may indicate that the related risks are 28

taken into account in the valuation of shares by investors. 29

The conducted research showed, despite the intuitive relationship between the TSL market 30

and selected variables, that there are no clear relationships determining the valuation of the 31

surveyed companies. The lack of this relationship may, however, result not only from the lack 32

of dependencies, but also from the very nature of the valuation of listed companies, which, 33

assuming complete information, will also include changes in exchange rates and commodity 34

prices. This assumption may be the basis for continuing research on the subject. 35


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