Investigating The History Of Probability And Statistics Essay Example

Bertrand Russell defined mathematics as “ not just an inflexible application of rubrics to pointless symbols but something much more creative and an art in itself (“Famous Mathematicians,” 2012, para 1).” He compared it to painting and how as painters make patterns with careful strokes of their brushes, mathematics can be described as a significant study of patterns as well. Like Bertrand, Blaise Pascal, Jakob Bernoulli, C.R Rao and Leonhard Euler felt passionate about mathematics and made great contributions to the mathematical world, particularly in the fields of probability and statistics.


“Probability had its origin in the study of gambling and insurance in the 17th century, and it is now an indispensable tool of both social and natural sciences”( Probability and statistics, examples and facts, n.d). Simply put it is the extent or likelihood of an occurrence. Most are familiar with the marbles in a bag example. There is a set number of marbles in a bag, there are four different colors, what is the probability of grabbing a yellow marble. This is probability. Two mathematicians that helped shape probability into what we know and use today are Blaise Pascal and Jakob Bernoulli.

Pascal was born June of 1623 in Clermont-Ferrand France. His mother died when he was just three years of age and his father, a presiding judge, was also a mathematician and devoted his time to teaching his children. He was the only formal educator of Pascal due to Pascal’s poor health that he suffered from most of his life. At sixteen he went on to create and write on Pascal’s Theorem and Pascal’s Line (three intersections on opposite sides of a single line, inscribed in a circle and in a hexagon). Pascal is best known for his work the Pascal Triangle. He used the triangle to later help solve problems in probability theory. At age twenty-four, Blaise Pascal could no longer tolerate food and gained nutrients only in liquid form drop by drop. His family moved often because of financial and political reasons. Much of Pascals early work was in natural and applied sciences. He has a physical law named after him as well as the international unit for pressure. He went on through life believing it was better to bet than not to. While living in Paris Pascal wrote Essai pour les coniques and in 1645 he developed a prototype of a calculating machine and began experiments with mercury barometers. After working and experimenting with mercury barometers Pascal went on to publish Expériences nouvelles touchant la vide in 1647. In 1654 Pascal was in correspondence with Pierre Fermat about calculating probabilities associated with gambling. He proposed to solve problems in terms of quantity. He summarized his finding in a book called Traité du triangle arithmétique, which was published after his death. In 1665 Pascal worked on the arithmetic triangle and showed how to calculate numbers of combinations and how to solve basic gambling problems. Unfortunately, Pascals contribution to probability theory wasn’t recognized until the eighteen century when it was used by another mathematician. His probability theory took place in 1654 and was designed to solve a gambling problem related to expected outcomes. He wanted to figure out the best time to bet on a dice game, and how to fairly divide the stakes if the game was stopped midway through.

Jakob Bernoulli was born January of 1655. He is known for introducing the first principals of calculus of variation and the Bernoulli numbers concept. Along with his brother, he was one of the pioneers of Leibnizian form of the calculus. He formulated and proved the weak law of large numbers (also known as Bernoulli’s theorem), the foundation of modern probability and statistics. He is of Swiss descent whose family were drug merchants. Originally, Bernoulli was interested in studying theology, but he soon became interested in mathematics. In 1690 and 1691 Bernoulli studied catenary, “the curve formed by a chain suspended between two extremities” (‘Jakob bernoulli,’ n.d). He then became the first to use the term integral when analyzing a curve of decent. In 1695 he used calculus to aid in the design of bridges. After he died, many of his theories were published in “The Art of Conjecturing.” The publication included his theory of permutations and combinations (‘Jakob bernoulli,’ n.d.), Bernoulli numbers, his treatment of mathematical and moral predictability and the Bernoulli Law of Large Numbers (‘Jakob bernoulli,’ n.d.).


“Statistics, is the science of collecting, analyzing, presenting, and interpreting data” (statistics, n.d.) Statistics has been around since the beginning of civilization. Early empires often collated samples of the population or recorded trade in various commodities. The Roman Empire was among the first to extensively gather data to identify the size of the empire’s population, geographical area and wealth. The earliest writing on statistics was found in a 9th-century Arabic book called Manuscript on Deciphering Cryptographic Messages, written byAl-Kindi and the original scope of statistics may have been to use data for governance, it was extended to many other fields of a scientific or commercial nature during the 19th century. Two mathematicians that helped shape statistics into what we know and use today are C.R Rao and Leonhard Euler.

C.R.( short for Calyampudi Radhakrishna) Rao was born September of 1920 in Karnataka. Early on, Rao showed interest in mathematics. In 1940 he earned his master’s in mathematics and decided to pursue a research career. He later became a technical apprentice where he taught and researched at the same time. He established the Theory of Estimation not long after. C. R. Rao organized research and training programs for students in India, resulting in the country having one of the best statistical systems. He founded the Indian Econometric Society, which has been promoting quantitative studies and the Indian Society for Medical Statistics. He’s held multiple international positions and promoted applications such as supervising doctoral research of 50 students. Rao is the author of 14 books and over 300 research papers in high impact journals. After retiring he worked for 25 years as a university professor. First, he worked at the University of Pittsburgh, then at the Pennsylvania State University as a Professor of Statistics. He retired from teaching at the age of 80 but works currently as the Director of the Center for Multivariate Analysis at Pennsylvania State University and the founder of the C. R. Rao Advanced Institute of Mathematics, Statistics and Computer Science . On June 29th 2011, the at the time, 91 year old was awarded the Guy Medal in Gold of the Royal Statistical Society for his fundamental contributions in “statistical theory and methodology, including unbiased estimation, variance reduction by sufficiency, efficiency of estimation, information geometry, as well as the application of matrix theory in linear statistical inference’ (About C.R Rao, by Marianna Bolla,).

Leonhard Euler was born April 1707 in Basel Switzerland. He was one of the founders of pure mathematics making contributions to the subjects: geometry, calculus, and number theory, among many others. He developed methods for problem solving in astronomy and would demonstrate various mathematic applications in public affairs and technology. In 1727 he became an associate of the Saint Petersburg Academy of Science and developed the theory of trigonometric and logarithmic functions. Euler lost sight in one eye in 1735 and in 1748 developed the concept of function in mathematical analysis. Today he is known in elementary geometry for the Euler line through orthocenter, the circumcenter, and the barycenter of a triangle. He also discovered imaginary logarithms of negative numbers and found that complex numbers had an infinite number of logarithms. He is responsible for introducing may current notations that we use today. For example: e for the base of natural logarithms, a,b,c, for the sides of a triangle along with A, B, C for opposing angles, and (f) to stand for function. Years later he lost sight in his good eye and spent his remaining years of life in total blindness. Yet, one of his greatest discoveries occurred in 1783 with his discovery of the law of quadratic reciprocity. Euler made many contributions to many areas of mathematics, including a few that specifically relate to statistics. Much of his work laid the foundation for probability and statistic. An example would be his work on Graeco- Latin square which are sometimes known as Euler squares. Euler squares are created when two squares are overlaid to include two attributes in each cell of the array. The squares ensure that each and every possible pairing of the two attributes appears exactly once in the array. Towards the eighteen century he contributed to the concept of maximum likelihood.

All four of these individuals played monumental roles in the way we use and are taught mathematics today.


About C. R. Rao, by Marianna Bolla. (n.d.). Retrieved from
Blaise Pascal (Stanford encyclopedia of philosophy). (n.d.). Retrieved from
Famous mathematicians and statisticians. (2015, August 24). Retrieved from
Famous mathematicians. (2012, January 20). Retrieved from
Jakob bernoulli. (n.d.). Retrieved from
Leonhard Euler. (n.d.). Retrieved from
Probability and statistics | History, examples, & facts. (n.d.). Retrieved from
Statistics. (n.d.). Retrieved from 

About Game Monopoly: A Markov Process


Monopoly is a board game that dates back to 1904. Elizabeth Magie created the game, The Landlord’s Game, to describe the e↵ect of land monopolism and the use of land value tax. Although some people enjoyed her game, it did not gain popularity until Charles Darrow reinvented it with new rules in 1935. He created the game Monopoly, that is sold in stores today. Eventually, a company called Parker Brothers bought the game from Darrow. Then, as of 1991, the company Hasbro acquired the game from Parker Bros. To this day, Hasbro still has ownership of the game.

The objective is to be the only player left in the game who is not bankrupt. It is unclear if there is one specific strategy that guarantees a victory in this game. Monopoly can be seen as a Markov Process, specifically a finite Markov Chain. The players’ placements can change within one turn depending on the outcome of rolling two dice. Therefore, it is di cult to predict where a player will be after a turn. If we study Monopoly through a Markov process, we can consider the likelihood of landing on a certain square after each turn.


This paper will explain the ways in which a Markov process can help a player understand the probabilities of landing on certain spaces after each roll of the dice. We will look at each individual property space on the board as a state when creating the Markov chain. For a model such as Monopoly to be Markovian, the probability of moving to a state must not depend on any previous states and only depend on the current state. Unfortunately, if a player ends their turn on the jail space they potentially to stay in that state for three consecutive turns. They have the opportunity to exit the Jail space if they roll a double or if they pay money to leave. Therefore, the probability of being in a future state depends on the players choice which is not Markovian. In order to make the process of the game Markovian, we will ignore this particular rule. Players will not have a choice, rather, they will leave jail on their first turn. Considering the objective of the game, it is beneficial for a strategic player to understand the properties that are most frequently landed on in order to increase income and decrease payout. The calculations completed in this paper will result in finding the long-term probability that a player will end on a specific state. Therefore, we will be able to recognize the most frequently visited spaces of the game and then create a plan to take ownership of those states and manage a player’s cash flow.


A Stochastic process is a sequence of events where the position at any stage depends on some probability.

The state space is the set of distinct values assumed by a stochastic process.

A Markov process is a stochastic process with the following characteristics:

(a) The number of possible states (outcomes) is finite.

(b) The probability to be at any state only depends on the current state.

(c) The probabilities are constant over time.

A finite Markov chain is a Markov Process such that the transition probabilities pij(n), which are the probabilities of going from state i to state j at time n, do not depend on any previous situations.

A transition matrix is an n ⇥ n matrix P , whose entries are pij , that describes the transition probabilities of a Markov chain.

An eigenvalue of a square matrix P is a number, , such that there exists some nonzero vector v that satisfies Pv = v. An eigenvector is a vector, v, which corre- sponds to the eigenvalue.

A steady state is the long-term probability that a particular state is active.

Basic Example:

We will look at a basic example to explain the main concept of a finite Markov chain. Consider a four square board with each square labeled one through four.

At time 0 a person is on space 1 and will flip a fair coin to see if he goes to space 2 or space 4. If the coin lands on heads he will go clockwise and if it lands on tails he will go counterclockwise. Let Xn denote the square the player is on at time n. 4 Hence, (X0,X1,X2,…,Xn) is a random process with state space {1,2,3,4} that can be considered a Markov process since a future state only depends on the current state.

Considering the player starts on 1 at time 0 we have P(X0 = 1) = 1. When he decides toflipthecoinandmove,wehaveP(X1 =2)= 1,P(X1 =4)= 1,andP(X1 =3)=0 22 because the probability that he lands on heads is 1 and the probability he lands on 2 tails is also 1. There is no chance that he can land on space 3 in the first step. 2 Computing the distribution of Xn for n 2 is more complex, so it will be useful to consider conditional probabilities. Suppose at time n the player is on square 2 then the conditional probabilities are P(Xn+1 = 1|Xn = 2) = 1 and P(Xn+1 = 3|Xn = 2) = 1 . 22

When we calculate the probabilities from time 0 all the way up to time n we get P(Xn+1 = 1|X0 = i0,X1 = i1,…,Xn 1 = in 1,Xn = 2) = 1 and P(Xn+1 = 3|X0 = i0,X1 = i1,…,Xn 1 = in 1,Xn = 2) = 1 2

The coin-flip at time n + 1 is independent of all the previous coin-flips which makes this example is a random process with Markovian property. This example is also a finite Markov chain that has a transition matrix P with transition probabilities pij. The transition probabilities are calculated using

pij = P(Xn+1 = sj|Xn = si) where the state space is S = {s1,…,sk}. In knowing that the state space for this example is S = {1, 2, 3, 4} we create the transition matrix to be 260 1 0 137 6227

61 0 1 07 P=62 2 7

60 1 0 17 6227

41 0 1 05 22

Another important characteristic of a Markov chain, informing us how the chain starts, is the initial distribution. We denote the initial distribution as the row vector μ(0) This vector shows the probabilities that the player is on a square at time 0. Considering weknowthatP(X0 =1)=1wehave

μ(0) =(1,0,0,0)

We denote μ(1), μ(2), …, μ(k) as the distributions of the Markov chain at times 1, 2, …, k.

This gives us

μ(n) = ⇣μ(n), μ(n), …, μ(n)⌘ 12k

In knowing the initial distribution μ(0) and the transition matrix P we now calculate all the distributions for this chain.

Theorem: For a Markov chain (X0,X1,…,Xn) with state space {s1,…,sk}, initial distribution μ(0), and transition matrix P, we have for any n that the distribution μ(n) at time n satisfies

μ(n) =μ(0)Pn

Knowing a Markov chain’s characteristics allows us to predict where a player might land at a given time.

Relation to Monopoly:

When looking at a model such as Monopoly, it is useful to create a finite Markov chain including the transition matrix in order to understand the long-term probability of a player landing on each square.

Each property square will be classified as the states as shown in the figure.

To begin creating the transition matrix, we recall that there are 40 squares on the board, each square is represented as a state of its own and so the transition matrix is a 40 ⇥ 40 matrix with the first row of the matrix being

0,0, 1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 1,0,0,…,0 36 36 36 36 36 36 36 36 36 36 36

This initial distribution vector shows us the probabilities a player has when moving from the GO square, which is the initial state, to any of the other squares when rolling two six-sided dice. The second row will be the probabilities a player has when moving

from the second square to any other square on the board and the pattern continues 7

up to the 40th state. Hence, there are 1,600 entries total. The second row of the transition matrix is

0,0,0, 1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 1,0,…,0 36 36 36 36 36 36 36 36 36 36 36

Another concept of a Markov Chain is calculating the probability of going from state i to state j at a given time step. We have already described the transition matrix at time step 1. If we want to know what the probability is for going from state i to state j at time step 2 there is an equation that will be used. For example, to calculate the probability at time step 2 from leaving state 1 and going to state 3 we use the equation

p(2)=p p +p p +p p 13 11 13 12 23 13 33

This is the dot product of two vectors, specifically the first row of matrix P and the third column of P . In a general case, if the Markov chain has r states then

(2) Xr

pij = pikpkj


The reason we want to look at future time steps is to calculate the long-term probability of ending a turn on a state. Using the matrix we calculate the steady state of our Monopoly Markov process.

According to the relative frequencies, a player will most likely end a turn on the Jail square which makes logical sense since there are multiple ways a person can end up in Jail. If we were to ignore Jail, the next square a person is most likely to end their turn on would be Illinois.

One may ask ”why is it important to know these long-term probabilities?” When properties are obtained by others and a player ends up on someone’s property they will have to pay that person the rent. Strategic players will want to determine which properties they desire to obtain in order to increase their cash flow and eventually win the game. Hence, a player who is first to obtain Illinois is o↵ to a good start.


Isaacson, D. L., Madsen, R. W. (1985). Markov Chains: Theory and Applications. Malabar, FL: R.E. Krieger Pub.
Kemeny, J. G., Snell, J. L. (1981). Finite Markov Chains. New York: Springer.
Ash, R. B., Bishop, R. L. (1972). Monopoly as a Markov Process. Mathematics Magazine, 45(1), 26. doi:10.2307/2688377
Bilisoly, R. (2014). Using Board Games and Mathematica to Teach the Fundamen-
tals of Finite Stationary Markov Chains. https://search-ebscohost-com.proxy- livescope=site
Johnson, R. W. (2003). Using Games to Teach Markov Chains. PRIMUS, 13(4),
337–348. livescope=site

Probability In Cross Border M&A: The Case Of Shipping Industry After Global Crisis Of 2008


The shipping industry is one of the leading mode of transportations that moves more than 80% of the goods transactions generated around the world. The technological advances increase the number and size of vessels, reducing operational costs generating great economies of scales. However, the high operational costs affect the profitability when crisis comes and open a field to improves M&A on the industry. On this study we analyze the probability of M&A after the economic crisis of 2008 and test the hypothesis through the data bases analysis of the sector.


The maritime transportation belongs to a system that is divided basically into three types, depending over the surface the mobile transport asset travel: land, water, and air (Rodrigue, 2016). It is the oldest technological way of transport and the modal that carry more than 80% of the world trade goods transaction (Vince_V, 2003). Since 2000 the worldwide seaborne transportation rises in an average of 3,0% every year (UNCTAD, 2017) and still in expansion, this convert the maritime transport in the most used and globalized modal of transportation.

The shipping industry, that uses the maritime transport for mobility, rapidly growth due the increment of the load capacity of the vessels, generating economy of scale and reducing the transportation cost to decimal values. In 1995 the freight transport cost per Ton/Mile was calculated in $0,01, compared with $0,25 of Road Transportation and $0,59 of Air Transportation (Rodrigue, 2016). After this date a lot of investment and improvement on the sector was developed to increase the capability of the vessels and increment the power efficiency pushing the transport cost down. In the beginning of the vessel’s containerization process, the vessels barely carry up to 1,000 TEU’s*, in the 80’s the “Panamax” era push the capability to near 5,000 TEU’s per ship, the 90’s capability rises to almost 8,000 TEU’s per ship and the latest Mega-Ships released after 2013, can carry in average 19,000-20,000 TEU’s (Rodrigue, 2016). All this technological advance pushes the other actors of the shipping industry to improve their structure, such as ports, river channels, trucks and rail transport terminal sizes.

However, in the last decades the development of the industry brings to many ships into the water, operated by many operators, some of them with insignificant market share for the industry. The needs of survive, sparkling a war price spread all over the world forcing operators to down the prices to gain or simply keep markets. This action conduct to various operators to bankruptcy and create a wave of Mergers and Acquisition (M&A) as never happen in the industry before. The top twenty ocean carriers shrink to eleven, and it’s expected this number be smaller for the coming years (Costas Paris, 2018).

Despite the challenges, the shipping industry still growing. Between 1974-2014 the seaborne trade volumes increase in +3% in average annually (covering the negatives results of the 2008 global crisis). 2015 and 2016, increase +1,8% and +2,6% respectively and UNCTAD drives a projection of an average +3,2% from 2017 to 2022 (UNCTAD 2017). So far, the prediction was not wrong, the seaborn trade increase +4% effective in 2017 (UNCTAD 2018).

The sustainability of the shipping sector is not under question on this article, the numbers above mentioned proves the sector is a rich field of investment. What the authors want to demonstrate on the present research, is after the crisis that affects the seaborne trade, be created an open field to improves M&A on the industry. Through an investigation on the official databases, we will apply concepts of M&A to support the hypothesis of the present study.

In the coming sections we will expose the literature review, conceptual model, sample analysis source, discussion and conclusion. For the purpose of this article (easy) we will not develop the method, simply mention the sources.


The shipping industry is not immune from an economic recession. This fact was known from the global financial and economic crisis of 2008 and 2009, which had a strong impact on the shipping market (Kalgora and Christian, 2016) and other sectors of financial investment. The strong global crisis of 2008 and the worst global recession in 2009 caused a 4,5% decrease in maritime trade at that time, caused mainly by the collapse of economic and trade growth, according to data extracted from UNCTAD. As a result, this situation caused a slow development in the global economy and many companies have had to reduce the request for shipping industry transport and related services, for example, the case with many companies in the Asian continent.

We define M&A as a “M&A activity has become common in today’s economic environment and can vary in size from very small businesses to enterprise corporations. A company acts as seller or buyer for each transactional exchange and represents their interests depending on their position in a deal” (Grave, Vardiabasis and Yavas 2012). During the global crisis, M&A suffered the consequence of the economic changes that affected the environment, causing this type of acquisition to become a high-risk investment.

Therefore, the M&A contribute to increasing the benefits obtained through the relationship between the shipping industry providing new evidence that contradicts the negative results of a prior theory how concluded (Alexandrou, Gounopoulos and Thomas 2014). This theory provide evidence that the M&A in different types of situation, including the less favorable ones, such as global crisis would obtain additional benefits for the economy of both firms. (Grave, Vardiabasis and Yavas 2012) argued in their article “The Global Financial Crisis and M&A” that from crisis comes change and new behaviors. New participants, creative and innovative approaches and new industries and deal targets are designed to make M&A work for new reasons to support hyper competitive global resource re-balancing.

Furthermore Andrade, Mitchell and Stafford in his article found that “If mergers come in waves, but each wave is different in terms of industry composition, then a significant portion of merger activity might be due to industry level shocks. Industries react to these shocks by restructuring, often via merger. These shocks are unexpected, which explains why industry-level takeover activity is concentrated in time, and is different over time, which accounts for the variation in industry composition for each wave.”, (2001). Therefore, this could mean that during the global crisis a restructuring forced by a cross-border M&A would result in a possible innovation and means the exit from the state of slow economic development because of the crisis in the shipping industry.

Álvarez-SanJaime, Cantos-Sánchez, Moner-Colonques and Sempere-Monerris discussed and analyze land and water transport. It studies the implications of a structural change in the shipping line industry. A theoretical model has been developed where the maritime sector, assumed oligopolistic, competes for freight transport with a competitive road transport industry (2013). The results establish enough conditions to ensure that user surplus and social welfare increase after M&A. this theory indicates that both M&A and shipping industry obtain mutual benefits by being developed together.

In another hand, distance has a negative effect on the takeover flows between pairs of regions because the search costs could be greater for potential acquisition firms that are located far away from the acquirer argued (Aschcroft, Coppins and Raeside 1994). This may be concluded that the greater the geographical distance, the higher the investment cost, the more difficult it is to control the operations of the company, as opposed to those where the geographic distance is relatively shorter.

Cross-border and short distance M&As will be supported if the target or an acquiring firm possesses an asset whose common utilization increases efficiency and profit, as indicate (Hee-Jung, 2013). This could mean that the greatest chance of success could result from cross-border M&A at the short geographic distance, which would indicate that the greater the geographic distance, the greater the risk of failure.

In addition, Hee-Jung (2013) found that distant M&As are riskier because they are based on more imprecise information compared to the close M&As. Ellison and Glaeser (1997) found that domestic mergers are an important factor, which affects the concentration of economic activity within industries. Therefore, both authors in their theories concluded that the greater the geographical distance, the greater the risk of failure between M&A and the shipping industry can be due to the lack of control of remote operations.

In contrast to the emerging market, multinationals have become far more assertive in their M&A activities on the global stage over the past decade. Save a dip during the global crisis in 2008 and 2009, the overall trend in cross-border M&A has been upward, especially in the post-dot-com period since 2001 by Mansoor Dalilami, Sergio Kurlat and Jamus Jerome Lim (2012), and this growth has had a positive impact on the emerging economies, starting restructuring of some companies, including the shipping industry, which could obtain high values.


In this study, we want to focus on the cross-border M&A in the shipping industry after de global crisis of 2008 until 2013. The impact that the recession has on the global economy and its influence in seeking new ways to overcome the economic difficulties presented by the deterioration of trade. Which factors influence cross-border M&A in a global crisis scenario?, what would be the probability of success of cross-border M&A within the shipping industry? and would be the consequences for the shipping industry of restructuring in order to integrate the cross-border M&A? dues to this question, we have developed the following research question:

“How could impact the probability in cross-border M&A in the shipping industry after the global crisis of 2008?”

Moreover, through an investigation of different theories of authors who have done previous research. (Álvarez-SanJaime, Cantos-Sánchez, Moner-Colonques and Sempere-Monerris, 2013), they argued that the investment and innovation of involving the M&A within the shipping industry sector in order to obtain development and added value. However, their results conclude that both factors developed and applying the strategies properly the shipping industry can obtain an economic development, a favorable diversification and continuous increase of the economic activity within the sector.

On the contrary, with the crisis factor presented and a period of economic recession applied worldwide where the market of values in shipping industry decade 22.9% in the period of 2008-2010 to its lowest level in a long time according to statistical data of UNCTAD, creating an unfavorable scenario to market trade. This leads us to develop our first hypothesis:


Due to the global crisis both the shipping industry and cross-border M&A underwent major changes, during the crisis a restructuring of both sectors could have a positive impact that includes, as a result, a probability of success. It is understandable that in this state of global crisis large companies refrain from making large investments. However, M&A has shown in the past to obtain benefits higher than expected and based on these results the probability of success of cross-border M&A within the shipping industry are greater regardless of the investment cost.

On the other hand, geographic distance plays an important role in the success of cross-border. Other theories proved negative effect in relation to long geographic distances and the cross-border M&A, due to the inability to handle the right information. However, in an economy facing a global crisis where similar markets no longer represent an economic benefit, the need to expand the maritime industry can find other results by including large geographical distances in their economies of scale. Shipping Industry deal with geographic distances since remote times, and it’s correct to say the global market is the field of the shipping industry, this mean factors like culture, developing markets and foreign resources are common routines of vessel operators. The theories insist that geographic distance has meant a factor that influences the success of cross-border M&A, but at a time of global crisis and where new markets can be an advantage to take and control new areas over the competitors, this leads us to develop our second hypothesis:



In this section, we will describe in a short and summarized way how we could obtain the necessary data for our research. We would like that the acquirer target is a public listed in the stock market and the firm size will be taken by the value of the total assets and the statistic value of the shipping industry. We want to focus on the impact of the shipping industry after the global crisis of 2008 and the probability of cross-border M&A success.

We will test our first hypothesis by analyzing the values of the cross-border M&A with success or failure event after the crisis for the next 5 years (2008-2013). The net value of the acquirer, public debt and the profitability will be our control variables on the analysis to determine if the high value of the transaction matter at the time to close the cross-border M&A. For our second hypothesis, we will analyze which factors of geographic distance between acquirer and target firms could influence the success of failure that exists within the shipping industry and the cross-border M&A.


In order to collect the data to our study, we will collect information of cross-border M&A and shipping industry from 2008 to 2013, based on the following data bases:

Refinitiv, a Thomson Reuters’ Merger & Acquisitions Database (
Federal Communications Commission, agency dependent of US Congress (
The Institute for Mergers, Acquisitions and Alliances (IMAA) (
UNCTAD databases


The shipping industry is an important field of study in constant development due technological advances and new commercial rules often revised to attend a globalized market. This mean that every study is not a final true and need to be further and deeply analyzed again. Some authors as Yeo, 2013 that study cross-border M&A on shipping industry during 2006-2007, evidence that “geographical closeness is a characteristic of great importance for M&A”. Penayides, 2011 that study strategic alliances during 2008 and 2010, evidence alliances between distant shipping lines to can achieve “geographic diversification”.

On the same rational line, Alexandrou, 2014 analyses the value and number of global shipping M&A from 1984 to 2011 an evidence a significant decrease during 2008 and 2009, but immediately increase during 2010 and 2011 taking the same level of the previous year before global crisis.

This simply analysis, contributes with more data about the behavior of shipping industry on M&A and gives to lecturer a slight idea about the synergies between shipping lines to gain competitiveness and diversify the market service. The shipping industry involves alliances and co-operations and not all the activities drives to a M&A. This article requests further development with more dept study on M&A during and after crisis and economy deceleration. Many authors that study shipping industry agrees that more empirical analysis with multiples context need to be done.


In this study, we are focus in the effects that the global crisis after 2008 had in the shipping industry and the role of the cross-border M&A. We based our research on the necessity of restructuring the shipping industry to avoid the increasing deterioration of the economic. The shipping industry involve the most important sector in the global economy, that means that its growth and development is of great importance to the world economy.

The effects of the global crisis began the increase of new strategies that favor the recovery of the economy, this includes maritime trade which increased its participation in M & A. In the beginning, this business strategy provided positive results in the strategies of economies of scale applied by maritime trade and as in its article “Competition and Horizontal Integration in Maritime Freight Transport”, (Álvarez-SanJaime, Cantos-Sánchez, Moner-Coloques and Sempere-Monerris 2013) proved that cross-border M & A obtained results above what was expected in an integrated business structure. To conclude that in effect, an increase in M & A investment would provide a successful recovery from the existing crisis.

On the other hand, in relation to geographical distance we can conclude that our results in this time of crisis can be favorable, this is thanks to the fact that crossing long distances would provide us with new investment routes, technological advantages and resources with unique qualities that in this period of recession would provide an economic advantage within our market as well as that emerging economies are becoming an attractive market to make investments that would have widely tested positive results within the shipping industry.


YEO Heejung, (2012), “Impacts of the board of directors and ownership structure on consolidation strategies in shipping industry” The Asian Journal of Shipping and Logistic, Vol 28, no. 1 p.019-040
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