Statistical Freight Rates Predictions
The question
that this paper intends to answer is whether freight rates can be predicted
accurately applying statistical and network methods?
I had the
pleasure of watching an online video lecture in Computer Science by Professor,
John Guttag, at the Massachusetts Institute of Technology: “Using Randomness to
Solve Non-random Problems”. He wrote a small program in Python to obtain the
ratio of Pi using the old problem of randomly dropping needles on a drawing of
a circle inside a square to obtain the ratio between the two drawings. This
experimental geometric probability problem was first proposed in 1777 by
Georges-Louis Leclerc, Comte de Buffon. He never obtained a good answer because
his sample of trials wasn’t big enough or his method of dropping needles was
poorly designed. A correct solution was given by Laplace in 1783 using binomial
distribution: np (1-p) and sqrt (np (1-p).
Professor’s
Guttag Python model simulated 20 trials starting with 1000 needles and doubling
the number of needles each time to obtain a Gaussian distribution. As it turned
out 16,000 needles provided a Pi ratio of 3.1413875, however, with 32,000
needles the ratio was 3.1457. While the program continued to double the number
of trials, the trial ratio did not improve its variance. On the other hand, the
standard deviation became smaller with each doubling of the needles from .012
and 16,000 needles to .008 with 32,000 needles. So, even when there was
variance in the trial ratios throughout the trials the standard deviation got
smaller every time where two standard deviations would be a very small fraction
and a confidence level of 99% could be attained.
As stated by
Lancaster University, UK: “In the 1940’s Ulam and Von Neumann suggested that
aspects of research into nuclear fission at Los Alamos could be aided by use of
computer experiments based on chance. The project was top secret so Von Neumann
chose the name Monte Carlo in reference to the Casino in Monaco”. And so, experiments
with randomness became better known as Monte Carlo simulations.
This begs
the question of whether building a model of normal distribution using published
liner shipping rates and comparing it with a model of random rates is accurate.
The answer should be yes. We know from statistics that real life events have a
normal distribution and when compared with a model of virtual reality there is only
negligible variance between the two models.
With this in
mind can freight prediction be made and plugged into a transportation network
of nodes and edges where the nodes have all the attributes embedded or extracted
from databases? This, of course, would be a challenging coding task but this
paper proposes that such project is doable!
To begin
with and in order to simplify building a model, only known variables in a rate
structure should be considered. For example, a typical freight rate structure
contains distance, dimensional weight and the rate per unit of cargo to be
shipped. This model will not include the occasional variables of weather,
accidents, labor strikes, port congestions and consider them as out layers. Also,
a stable model is assumed for a length of time (one year) where fuel prices,
route competition, crewing cost remain steady and there is fair play in the
market between shippers and carriers.
Fair play in
the market is the most difficult variable to predict because of its numerous
components. Just as in financial markets the better informed party wins the day
bearing in mind that better information empowers consumers in an impartial
market.
Estimating
the actual value of trade between countries is a strenuous effort not only for
regulators but also for national institutions responsible for measuring the
behavior of international trade and its effects on local economies. Among these
institutions are the UN, OECD, The World Bank, The IMF and others but they all
get their values as quantified by national governments, trade banks and others
such as Global Insight, Platts etc.
The task of estimating
the value of trade would easy if the same factors were applied globally in a
linear relationship. However, this not the case, as the same unit of cargo will
have a higher transportation cost from a landlocked country than from a
developed country with multiple port facilities. Also, asymmetry of trade must be factored in;
in addition, competition in the route or the lack thereof injects itself
between nominal and real prices.
One general
measurement assessed by Custom Unions is the difference existing between
clearance values from origin and destination countries. The problem with this
measure is that Customs depends on declared invoice value to make the
assessment of duties after discounting transportation costs. So, if a commodity
is purchased Free on Board basis (FOB) or Cost and Freight basis (C&F) the
difference between the two values most be transportation. Unfortunately, this
is not the case as declared values are manipulated by buyers, sellers and carriers
while Customs use tariffs to determine duties--transportation tariffs can be
ambiguous in a deregulated market. And so, the real cost of transportation is
at best, an estimated value for reporting institutions.
Moreover, Customs
looks to documentation provided by the shipping company, as opposed to the documentation
between the buyer and the seller. Information from the buyer and seller often
contains estimated transportation costs or charges, while documentation from
the shipping company contains the cost for the shipment. Notwithstanding, there
is no precedent which specifically address the issue of whether terminal
handling charges or other surcharges like fuel adjustment factor and value
added services are to be taken into account when determining the actual amount
paid for international transportation. The reason is that this additional cost
factors sometimes are included in the basic freight rate tariff and other times
is not as a way to achieve pricing differentiation between carriers.
One method
used by some NGOs for estimating transportation cost in international trade is
to fix a point for both origin and destination and assess the average increase
or decrease in cost of commodities going through the fixed point in multiple
directions. The Strait of Malacca has been used as a fixed point of reference
for this purpose.
In
conclusion, predicting freight rates using a combination of statistics and
networks based on published rates should provide a probability confidence of
95% with one standard deviation from the mean in a basket of rates offered by
other carriers in compatible routes is doable.
Alfonso
Llanes
July 4, 2015
References:
John Guttag. 6.00SC Introduction to Computer Science and
Programming, Spring 2011. (Massachusetts Institute of Technology: MIT
OpenCourseWare), http://ocw.mit.edu
Kady Schneiter Utah State University Kady.schneiter@usu.edu.
Exploring Geometric Probabilities with Buffon’s Coin Problem. Fall 2014.
Monte Carlo Simulation - A Brief History - Lancaster
University www.lancaster.ac.uk/pg/jamest/Group/intro2.html
Maritime Operations and Logistics Data and Analysis
METHODOLOGY
AND SPECIFICATIONS GUIDE https://www.platts.com/IM.Platts.Content/MethodologyReferences/MethodologySpecs/Freight-methodology.pdf
June 2015.
The World Customs Organization (WCO), established in 1952
as the Customs Co-operation Council (CCC) is an independent intergovernmental
body whose mission is to enhance the effectiveness and efficiency of Customs
administrations.
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