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A structural model for forecasting the shipping market

This document outlines a structural model developed by Piraeus Bank to forecast new-build and second-hand ship prices, emphasizing the interplay between supply and demand factors in the cyclical shipping market. The model aims to assist banks in evaluating shipping loan collateral values and includes external variables like oil prices and currency exchange rates. It also examines historical data to assess the impact of economic conditions on shipping market trends and price movements.

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A STRUCTURAL MODEL FOR FORECASTING THE SHIPPING MARKET OCTOBER 2017 ECONOMIC ANALYSIS & INVESTMENT STRATEGY Bloomberg: Piraeus Bank Shipping Index: {PBGGSHPP Index<GO>} Piraeus Bank Dry Bulk Index: {PBGGSHDB Index<GO>} Piraeus Bank Container Index: {PBGGSHCN Index<GO>} Piraeus Bank Oil Tanker Index: {PBGGSHOT Index<GO>} Ilias Lekkos lekkosi@piraeusbank.gr Haris Giannakidis giannakidisch@piraeusbank.gr Rotsika Dimitria rotsikad@piraeusbank.gr
CONTENTS 2
AIM OF THE STUDY 3 The aim οf this study is to develop an econometric model describing the evolution of new-build and second-hand ship prices. While this model was developed originally to address internal needs within the Piraeus Bank Group, we believe that both our modelling methodology and the broader “philosophy” of our approach could be of wider interest. The ability to identify the factors that affect the shipping market can be used in a number of ways, such as: o Estimate the “fair” value in the new-build and second-hand market and assess current market pricing vs fair- valuation levels. o Allow banks to assess the future evolution of the value of shipping loan collaterals (i.e. the value of the ship underlying the loan). o To be used for risk management purposes by assessing the sensitivity of the collaterals under a series of explanatory factors. o Create long-term forecasts under alternative macroeconomic scenarios.
CHARACTERISTICS OF THE SHIPPING INDUSTRY 4 Modelling ship prices is extremely difficult since loosely speaking the price of a vessel can be thought of as a “derivative” contract upon another “derivative” contract. This means that the price of a vessel depends on expectations about the future evolution of freight rates which in turn are determined by the interplay between global demand for shipping services (as a result of global growth and commodity prices) and supply of shipping services (determined by the current transportation capacity of world fleet and demolition volumes). By nature, demand as a function of economic growth and commodity prices is extremely volatile and fast changing. On the contrary, supply can adjust only at a very gradual pace due to the natural time-lag between new orders for ships and actual delivery by shipyards. The interplay between a fast moving demand and a slow adjusting supply gives rise to the main characteristic of the shipping industry, which is none other than its extreme cyclicality.
SHIPPING CYCLE: THE RESULT OF A DEMAND-SUPPLY MISMATCH 1. Over tonnage results in a fall in freight rates 2. Low demand for shipping services marks increase in demolitions 3. Fleet development slows down 4. Excess demand characterizes freight rate recovery5. Increase in earnings and ship valuations 6. Increase in newbuilding ordering 7. Shipyards struggle to meet ordering levels 8. Excess supply starts to build up 9. Excessive supply leads to low utilization of merchant fleet 5
OUR APPROACH 6 Our approach towards modelling new-build and second-hand prices is to bypass the freight market and focus on the underlying forces of demand and supply. In particular, we devise proxies for the theoretical concepts of “demand” and “supply” of shipping services and express ship prices as a function of the imbalance between the two. Furthermore, to improve the statistical behaviour of the model we also include a few exogenous variables such as oil prices and the USD exchange rate. In turn, when it comes to modelling the price of new-build ships we find that developments in the second-hand markets also contain information we can exploit. The fact that vessel prices in our model are allowed to be driven by supply and demand factors makes our model one of the “structural” models of the global shipping industry.
NEW-BUILD & SECOND-HAND SHIP PRICES o The new-build market relates to ships that do not currently exist, but need to be ordered, built by the shipyards and then delivered with a two to three years lag. On the contrary, the second-hand market is a spot market conducted by dedicated brokers. Given the great variety of types (and age of second-hand ships) the price indices we use as dependent variables are an amalgamation of various prices and quotes. o Limited new-build demand and weak market sentiment have led to a sharp drop in new-build ship prices along with a steady pressure on the second-hand market. Underutilization of shipyards and unfavorable market conditions led to a significant decrease in shipyard negotiating position and therefore to low new-build prices. On top of that, recent dollar depreciation intensified the pressure on shipyard for lower pricing. The competition in the second-hand market has increased considerably over the past 7 years as the gap between new-build and second-hand prices keeps widening. In effect, second-hand prices declined faster in 2016 than new-build and reached levels close to 45% of the respective new-build average price. Cheaper second-hand prices are adding an extra pressure on shipyards to adjust their pricing policy in order to maintain their already weakened orderbook levels. Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Secondhand vs Newbuild Ratio 40 50 60 70 80 90 100 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 45 65 85 105 125 145 165 185 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 SH Price Index NB Price Index Secondhand and Newbuild Ship Prices 7
A NUMBER OF GLOBAL ACTIVITY INDICATORS… Crude Oil Prices stabilized at 50 $/bl in 2017 despite efforts to curb production… Seaborne Trade Growth surprised on the upside in Q1 2017 at 3.94% on a YoY basis… -9 -4 1 6 11 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 World Real GDP World Seaborne Trade -5 -4 -3 -2 -1 0 1 2 3 4 0 20 40 60 80 100 120 140 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Crude Oil Excess Supply Index (RHS) Oil Prices Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network -6 -1 4 9 14 19 24 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 -7 -6 -5 -4 -3 -2 -1 0 1 2 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Global Financial Conditions more accommodative as liquidity constraints are gradually lifted… Trade Weighted Steel Production recovered following 2 years of depressed production growth… … point towards a mild recovery of global economic growth and trade. 8
OUR PREFERRED PROXY FOR SHIPPING SERVICES DEMAND IS … … the Global Seaborne Trade growth, which reflects the dynamics on aggregate trade flows of major commodity shipments. As already mentioned, seaborne trade momentum exhibits substantial volatility around its long-term average with substantial declines in times of global economic slowdowns such as the Asian crisis of ‘97-’98, the Dotcom bubble, the Sept/11 terrorist attacks in 2001 and the Global Financial Crisis in 2008-2009. More information can be extracted by decomposing the index in four phases, namely an expansion phase with above average levels and positive momentum; a downswing phase with above average levels but negative momentum; a contraction phase with below average levels and negative momentum; and an upswing phase with below average levels but positive momentum. Currently, in full accordance with the rest of our indicators, our demand proxy lies in the “upswing” phase. Demand Tracer Q1 2003 Q1 2017 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 Level Momentum ExpansionDownswing UpswingContraction Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Shipping Demand Index -10 -5 0 5 10 15 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 9
AT THE SAME TIME … Ordering in 2017 is significantly lower and is anticipated to decline further in the following years… In Q1 2017, Total Ship Deliveries were 2.54 million dwt lower compared to the same quarter of the previous year … Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Shipyard Capacity remains low increasing ship owners’ negotiating power… Post-crisis Aggregate Earnings ranged between 9,400-15,500 $/day … -100 -50 0 50 100 150 200 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total Deliveries Total Demolitions Change in Merchant Fleet 0 20 40 60 80 100 120 140 0% 10% 20% 30% 40% 50% 60% 70% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Orderbook % Fleet Change in Fleet (RHS) 8,000 13,000 18,000 23,000 28,000 33,000 38,000 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 $ / Day -25 -20 -15 -10 -5 0 5 10 15 20 25 0 1 2 3 4 5 6 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Forward Cover Shipyard Output Gap (RHS) … a number of factors that could signal a pickup in the availability of shipping tonnage remain muted. 10
OUR PREFERRED PROXY FOR SUPPLY OF SHIPPING TONNAGE IS … … the Merchant Fleet growth, which is driven by new vessel deliveries minus demolitions and losses. Given the natural constraints that this variable is subject to, i.e. shipyard capacity for new deliveries, its volatility is much more constrained with a notable exception in the mid-90s. Decomposing our tonnage supply proxy into its 4 phases, signals that in Q1-2017 the proxy was still in the contraction phase of the cycle due to the significant decline in new-build ship deliveries. Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Supply Tracer Q1 2003 Q1 2017 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 Level Momentum ExpansionDownswing UpswingContraction Shipping Supply Index 0 2 4 6 8 10 12 14 16 18 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 11
PUTTING EVERYTHING TOGETHER: THE SHIPPING IMBALANCE INDEX o Shipping cycles are defined by five interrelated markets: the commodity market, the secondhand market, the shipbuilding market, the demolition market and the funding market. Interaction among those five markets contributes to the evolution of aggregate demand and supply in the shipping sector. Constructing a model that takes into account all the interactions and features of each market is a non-trivial task. Instead, we model shipping market cycles by focusing only in two aggregate indicators of supply and demand proxied by merchant fleet and seaborne trade growth rates respectively. o Having already identified a demand and supply proxy it is natural to proceed to the approximation of the shipping sector business cycle as the gap between these new variables depicted by our Shipping Imbalance Index. Given that the demand and supply proxies are expressed in % rates of change, the Shipping Imbalance Index does not detect imbalances between the current stock of available shipping tonnage and the level of seaborne trade, but rather approximates more closely the trending behaviour of those quantities. In that way it provides a forward-looking measure of the anticipated imbalances in the shipping sector. o After 6 years of excess supply in the shipping market, stronger seaborne trade growth in Q1 2017 signals a momentum reversal in the balance in the maritime sector. More specifically, supply momentum stabilized at 3.4% per annum while seaborne trade growth picked up in 2016 and reached 3.9% in Q1 2017 on a YoY basis. Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network -15 -10 -5 0 5 10 15 20 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Supply Growth Indicator Demand Growth Indicator -10 -5 0 5 10 15 20 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Excess Demand Shipping Imbalance IndexShipping Supply & Demand Indicators Excess Supply 12
SHIPPING SECTOR STRUCTURAL MODEL: ESTIMATION & FORECASTING 13 In order to forecast the new-build and second-hand ship prices we developed a simple structural model for the aggregate shipping market. The forecasting process can be split into the following 4 stages: 1. First, we create a separate model for both Demand and Supply proxies. We express demand as a function of global GDP growth and oil prices as well as an ARMA(2,1) specification. In parallel, we express supply as a function of orderbook size (with a 2-year lag) and an ARIMA (4,1,2) term. 2. We express the price of second-hand ships as a function of the Shipping Imbalance Index, assuming that the second-hand market is the first to adjust to departures from an equilibrium level. 3. We proceed to modeling the price of new-build ships as a function of the imbalance index in conjunction with the prices of second-hand ships, the USD exchange rate and an AR(1) term. 4. We use the equations in steps 1 & 2 to generate 3-year forecasts for the explanatory variables which in turn are used as inputs in our second-hand and new-build price models to generate forecasts over a 3-year horizon.
AGGREGATE SHIPPING MODEL OUTLINE Demand 𝑫 𝒕 = 𝒇 𝑮𝑫𝑷𝒕−𝟏 , 𝑶𝒊𝒍 𝒕 , 𝑫 𝒕−𝒍 Supply 𝑺 𝒕 = 𝒇 𝑶𝒃𝒌 𝒕−𝟖 , 𝑺 𝒕−𝒍 Shipping Imbalance Index 𝑰𝒎𝒃 𝒕 = 𝑺𝒕 − 𝑫 𝒕 Secondhand Market 𝑺𝑯 𝒕 = 𝒇 𝑰𝒎𝒃 𝒕 , 𝑺𝑯 𝒕−𝒍 Newbuilding Market 𝑵𝑩 𝒕 = 𝒇 𝑰𝒎𝒃 𝒕 , 𝑫𝒐𝒍𝒍𝒂𝒓 𝒕, 𝑺𝑯 𝒕 , 𝑵𝑩 𝒕−𝒍 Where: Dt: World Trade Growth St: Fleet Development Obkt: Orderbook Imbt: Shipping Imbalance Index GDPt: World Real Output Growth Oilt: Oil Price Growth Dollart: Trade Weighted Dollar Rate SHt: Secondhand Prices Growth NBt: Newbuilding Prices Growth l: Number of Lags for Dynamic Model 14
AGGREGATE SHIPPING MODEL FINDINGS Demand Growth Model 𝐷𝑡 = 𝛽 𝐷,0+𝛽 𝐷,1 ∗ 𝐺𝐷𝑃𝑡−1+𝛽 𝐷,2 ∗ 𝑂𝑖𝑙 𝑡 + ARMA terms + 𝜀 𝐷,𝑡 Supply Growth Model 𝑆𝑡 = 𝛽𝑆,0+𝛽𝑆,1 ∗ 𝑂𝑏𝑘 𝑡−8 + ARMA terms + 𝜀 𝑆,𝑡 Secondhand Market Model 𝑆𝐻𝑡 = 𝛽𝑆𝐻,0+𝛽𝑆𝐻,1 ∗ 𝐼𝑚𝑏𝑡 + ARMA terms + 𝜀 𝑆𝐻,𝑡 Newbuilding Market Model 𝑁𝐵𝑡 = 𝛽 𝑁𝐵,0+𝛽 𝑁𝐵,1 ∗ 𝐷𝑜𝑙𝑙𝑎𝑟𝑡+𝛽 𝑁𝐵,2 ∗ 𝐼𝑚𝑏𝑡+𝛽 𝑁𝐵,3 ∗ 𝑆𝐻𝑡+𝛽 𝑁𝐵,4 ∗ 𝑁𝐵𝑡−1 + 𝜀 𝑁𝐵,𝑡 Dependent Variables Explanatory Variables 𝑫 𝒕 𝑺𝒕 𝑺𝑯 𝒕 𝑵𝑩 𝒕 𝐺𝐷𝑃𝑡−1 0.26 (2.36) 𝑂𝑖𝑙 𝑡 0.05 (5.00) 𝑂𝑏𝑘 𝑡−8 5.37 (1.61) 𝐼𝑚𝑏𝑡 -1.73 -0.17 (3.39) (2.43) 𝐷𝑜𝑙𝑙𝑎𝑟𝑡 -0.10 (2.50) 𝑆𝐻𝑡 0.13 (4.33) 𝑁𝐵𝑡−1 0.30 (2.50) 𝒂𝒅𝒋. 𝑹 𝟐 56% 42% 89% 54% Model Estimates o The MA and AR order in each ARMA model is chosen through the use of the AIC, BIC and Root Mean Squared Error (RMSE) criteria. o In the supply model the order of integration is chosen after we check for unit roots using the Augmented Dickey Fuller and Phillips-Perron tests. o t-statistics robust to autocorrelation and heteroscedasticity are reported in parenthesis under each coefficient and are computed using the HAC estimator. 15
MODEL FIT FOR NEWBUILD AND SECONDHAND PRICES 16Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Newbuilding Prices Model Fit -20 -16 -12 -8 -4 0 4 8 12 16 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Errors NB Prices QoQ% Estimate -8 -4 0 4 8 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Errors SH Prices QoQ% Estimate -48 -36 -24 -12 0 12 24 36 Newbuild Prices Model Fit Secondhand Prices Model Fit o The model seems to fit the actual ship price evolution reasonably well with adjusted R squared coefficients above 50% of total variation in new-build and second-hand prices respectively. Moreover, the coefficient estimates in each series have the anticipated sign. In particular, both shipping price indices are negatively correlated with the imbalance index, with second- hand prices being relatively more sensitive to market disequilibrium. In addition, the dollar’s value is negatively related to new-build prices as a dollar appreciation gives flexibility to shipyards to adjust their pricing policy on new-build contracts. o According to the model, current conditions in the shipping sector render actual new-build price growth undervalued relative to its “fair” estimate. On the contrary, the abrupt increase in actual second-hand prices in the first quarter of 2017 is higher compared to the growth rate that would be proper according to the model.
BASELINE SCENARIO FOR 3-YEAR FORECASTS 17 The baseline scenario is constructed upon the assumptions that: 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 2012 2013 2014 2015 2016 2017 2018 2019 2020 World Real GDP Growth Baseline Forecast 0 20 40 60 80 100 120 140 2012 2013 2014 2015 2016 2017 2018 2019 2020 Oil Prices Baseline Forecast 0 10 20 30 40 50 60 70 80 90 100 2012 2013 2014 2015 2016 2017 2018 2019 2020 Trade Weighted Dollar Baseline Forecast 0% 10% 20% 30% 40% 50% 60% 2012 2013 2014 2015 2016 2017 2018 2019 2020 Lagged Orderbook Ratio Baseline Forecast World real GDP growth accelerates in 2017 and stabilizes after 2018… Oil prices follow a slow 3-Year path towards 65 $/bl… Dollar depreciates by 13% over the next 3 years… Orderbook will remain depressed at 12% of merchant fleet... Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network -3 -2 -1 0 1 2 3 4 5 6 7 2012 2013 2014 2015 2016 2017 2018 2019 2020 Imbalance Index Baseline Forecast Excess demand increases over the next three years reaching a peak during 2018…
3 YEAR FORECAST – GRADUAL INCREASE OF PRICES o The baseline scenario refers to a recovery in global output growth and therefore seaborne trade is expected to reverse the downward path of ship prices. Second-hand prices are expected to rise first in response to anticipated fleet shortages sector since the orderbook adjustment over the past years contributed to low capacity utilization of shipyards. New-build prices will gradually increase over the next three years, with their growth rate expected to reach a peak in 2019. o Despite the fact that economic activity in the US, Europe and emerging markets started to gain momentum, this development is still associated with substantial downside risks around the global macroeconomic environment. Shipping Prices Base Scenario Forecast 2016 2017e 2018f 2019f 2020f Newbuilding Prices (%) -6.38 -1.42 1.62 3.16 0.88 Secondhand Prices (%) -19.66 15.73 4.35 0.22 2.64 Trade Growth (%) 3.35 4.11 4.54 3.52 4.01 Fleet Growth (%) 3.34 3.61 3.32 3.13 3.15 Imbalance Index -0.01 -0.50 -1.22 -0.39 -0.86 3-Year Projections under the Baseline Scenario Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network -100 -80 -60 -40 -20 0 20 40 60 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 SH Price Growth Actual (YoY) NB Price Growth Actual (YoY) NB Forecast (Base Scenario) SH Forecast (Base Scenario) 18
Disclaimer: This document is produced by the Economic Research & Investment Strategy Department of Piraeus Bank (hereinafter "the Bank"), which is supervised by the European Central Bank (ECB), in collaboration with the Bank of Greece and is sent or provided to third parties, without any obligation of its author. This document or any part of it should not be duplicated in any way without the prior written consent of its author. The information or opinions included in this document are addressed to existing or potential clients in a general manner, without taking into account the particular circumstances, the investment objectives, the financial ability, the experience and/or knowledge of the potential recipients of this document and, as a result, they do not constitute or should not be considered neither as a solicitation or offer for the conduct of transactions in financial instruments or currencies nor as a recommendation or advice for decision making in relation to those. Taking into account the aforementioned, the recipient of the information contained in this document should proceed with his/her own research, analysis, and confirmation of the information which is included in this document and seek for independent and professional legal, tax and investment advice, before proceeding with any investment decision making. The information depicted in this document is relied on sources that the Bank considers to be reliable and is provided on an "as is" basis, however, the Bank cannot warrant as to their accuracy and completeness. The opinions and estimates herein are related to the trend of the local and international financial markets at the indicated date (prices at closing time) and are subject to changes without any prior notice. Notwithstanding the above, the Bank might include in this document investment researches, which have been conducted by third persons. In this case, the Bank does not modify those researches, but it presents them on an "as is" basis, therefore, no responsibility is assumed in relation to the content of the aforementioned investment researches. The Bank is under no duty to update the information contained in this document. Considering the above, the Bank, the members of its Board of Directors and the relevant persons assume no responsibility for the information included in the present document and/or for the outcome of any investment decisions made according to such information. Piraeus Bank Group is an organization with a significant presence in the Greek market and an increasing one in the international markets providing a wide range of investment services. In the context of investment services offered by the Bank and/or any other Piraeus Group companies in general, there might be cases whereby conflict of interests may arise in relation to the information provided herein. Reference should be made to the fact that the Bank, the relevant persons and/or other Piraeus Group companies indicatively: a. Are not subject to any prohibition in relation to trading on own account or in the course of providing portfolio management services prior to the publication of this document or the acquisition of any shares prior to any public offering or the acquisition of any other securities. b. May offer upon remuneration investment banking services to issuers for whom this document may contain information. c. May participate to the issuers' share capital or acquire other securities issued by the aforementioned issuers or attract other financial interests from them. d. Might provide market making or underwriting services to issuers that might be mentioned in this document. e. Might have published papers the content of which is different or incompatible to the information presented herein. The Bank as well as the other Piraeus Group's companies have enacted, implement and maintain an effective policy, which prevents circumstances that may give rise to conflicts of interests and the dissemination of any information among the departments ("Chinese walls") and they also constantly comply with the provisions and regulations relevant to inside information and market abuse. Also, the Bank confirms that it doesn't have any kind of interest or conflict of interest with a) any other legal entity or person that could have participated in the preparation of the present document and b) with any other legal entity or person that couldn't have participated in the preparation of the present document, but had access to it before its publication. It is duly stated that: the investments described in the present document include investment risks, among which the risk of losing the entire capital invested. In particular, it is stated that; a. The figures presented herein refer to the past and that the past performance is not a reliable indicator of future performance. b. In case the figures refer to simulated past performance, that past performance is not a reliable indicator of future performance. c. The return on investments might be positively or negatively affected as a result of currency fluctuations, in case the figures are denominated in a foreign currency (other than Euro). d. Any forecasts in relation to future performance, May not be a reliable indicator of future performance. e. The tax treatment of the information as well as transactions pertained in this document, depends on each investor's individual circumstances and may be subject to change in the future. As a result, the recipient should seek for independent advice in relation to the applicable tax legislation. The distribution of the present document outside Greece and/or to persons governed by foreign law May be subject to restrictions or prohibitions according to the applicable legislation. Therefore, the recipient of the present should seek for independent advice in relation to the applicable legislation, in order to look into such restrictions and/or prohibitions. 19

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A structural model for forecasting the shipping market

  • 1.
    A STRUCTURAL MODELFOR FORECASTING THE SHIPPING MARKET OCTOBER 2017 ECONOMIC ANALYSIS & INVESTMENT STRATEGY Bloomberg: Piraeus Bank Shipping Index: {PBGGSHPP Index<GO>} Piraeus Bank Dry Bulk Index: {PBGGSHDB Index<GO>} Piraeus Bank Container Index: {PBGGSHCN Index<GO>} Piraeus Bank Oil Tanker Index: {PBGGSHOT Index<GO>} Ilias Lekkos lekkosi@piraeusbank.gr Haris Giannakidis giannakidisch@piraeusbank.gr Rotsika Dimitria rotsikad@piraeusbank.gr
  • 2.
  • 3.
    AIM OF THESTUDY 3 The aim οf this study is to develop an econometric model describing the evolution of new-build and second-hand ship prices. While this model was developed originally to address internal needs within the Piraeus Bank Group, we believe that both our modelling methodology and the broader “philosophy” of our approach could be of wider interest. The ability to identify the factors that affect the shipping market can be used in a number of ways, such as: o Estimate the “fair” value in the new-build and second-hand market and assess current market pricing vs fair- valuation levels. o Allow banks to assess the future evolution of the value of shipping loan collaterals (i.e. the value of the ship underlying the loan). o To be used for risk management purposes by assessing the sensitivity of the collaterals under a series of explanatory factors. o Create long-term forecasts under alternative macroeconomic scenarios.
  • 4.
    CHARACTERISTICS OF THESHIPPING INDUSTRY 4 Modelling ship prices is extremely difficult since loosely speaking the price of a vessel can be thought of as a “derivative” contract upon another “derivative” contract. This means that the price of a vessel depends on expectations about the future evolution of freight rates which in turn are determined by the interplay between global demand for shipping services (as a result of global growth and commodity prices) and supply of shipping services (determined by the current transportation capacity of world fleet and demolition volumes). By nature, demand as a function of economic growth and commodity prices is extremely volatile and fast changing. On the contrary, supply can adjust only at a very gradual pace due to the natural time-lag between new orders for ships and actual delivery by shipyards. The interplay between a fast moving demand and a slow adjusting supply gives rise to the main characteristic of the shipping industry, which is none other than its extreme cyclicality.
  • 5.
    SHIPPING CYCLE: THERESULT OF A DEMAND-SUPPLY MISMATCH 1. Over tonnage results in a fall in freight rates 2. Low demand for shipping services marks increase in demolitions 3. Fleet development slows down 4. Excess demand characterizes freight rate recovery5. Increase in earnings and ship valuations 6. Increase in newbuilding ordering 7. Shipyards struggle to meet ordering levels 8. Excess supply starts to build up 9. Excessive supply leads to low utilization of merchant fleet 5
  • 6.
    OUR APPROACH 6 Our approachtowards modelling new-build and second-hand prices is to bypass the freight market and focus on the underlying forces of demand and supply. In particular, we devise proxies for the theoretical concepts of “demand” and “supply” of shipping services and express ship prices as a function of the imbalance between the two. Furthermore, to improve the statistical behaviour of the model we also include a few exogenous variables such as oil prices and the USD exchange rate. In turn, when it comes to modelling the price of new-build ships we find that developments in the second-hand markets also contain information we can exploit. The fact that vessel prices in our model are allowed to be driven by supply and demand factors makes our model one of the “structural” models of the global shipping industry.
  • 7.
    NEW-BUILD & SECOND-HANDSHIP PRICES o The new-build market relates to ships that do not currently exist, but need to be ordered, built by the shipyards and then delivered with a two to three years lag. On the contrary, the second-hand market is a spot market conducted by dedicated brokers. Given the great variety of types (and age of second-hand ships) the price indices we use as dependent variables are an amalgamation of various prices and quotes. o Limited new-build demand and weak market sentiment have led to a sharp drop in new-build ship prices along with a steady pressure on the second-hand market. Underutilization of shipyards and unfavorable market conditions led to a significant decrease in shipyard negotiating position and therefore to low new-build prices. On top of that, recent dollar depreciation intensified the pressure on shipyard for lower pricing. The competition in the second-hand market has increased considerably over the past 7 years as the gap between new-build and second-hand prices keeps widening. In effect, second-hand prices declined faster in 2016 than new-build and reached levels close to 45% of the respective new-build average price. Cheaper second-hand prices are adding an extra pressure on shipyards to adjust their pricing policy in order to maintain their already weakened orderbook levels. Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Secondhand vs Newbuild Ratio 40 50 60 70 80 90 100 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 45 65 85 105 125 145 165 185 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 SH Price Index NB Price Index Secondhand and Newbuild Ship Prices 7
  • 8.
    A NUMBER OFGLOBAL ACTIVITY INDICATORS… Crude Oil Prices stabilized at 50 $/bl in 2017 despite efforts to curb production… Seaborne Trade Growth surprised on the upside in Q1 2017 at 3.94% on a YoY basis… -9 -4 1 6 11 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 World Real GDP World Seaborne Trade -5 -4 -3 -2 -1 0 1 2 3 4 0 20 40 60 80 100 120 140 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Crude Oil Excess Supply Index (RHS) Oil Prices Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network -6 -1 4 9 14 19 24 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 -7 -6 -5 -4 -3 -2 -1 0 1 2 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Global Financial Conditions more accommodative as liquidity constraints are gradually lifted… Trade Weighted Steel Production recovered following 2 years of depressed production growth… … point towards a mild recovery of global economic growth and trade. 8
  • 9.
    OUR PREFERRED PROXYFOR SHIPPING SERVICES DEMAND IS … … the Global Seaborne Trade growth, which reflects the dynamics on aggregate trade flows of major commodity shipments. As already mentioned, seaborne trade momentum exhibits substantial volatility around its long-term average with substantial declines in times of global economic slowdowns such as the Asian crisis of ‘97-’98, the Dotcom bubble, the Sept/11 terrorist attacks in 2001 and the Global Financial Crisis in 2008-2009. More information can be extracted by decomposing the index in four phases, namely an expansion phase with above average levels and positive momentum; a downswing phase with above average levels but negative momentum; a contraction phase with below average levels and negative momentum; and an upswing phase with below average levels but positive momentum. Currently, in full accordance with the rest of our indicators, our demand proxy lies in the “upswing” phase. Demand Tracer Q1 2003 Q1 2017 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 Level Momentum ExpansionDownswing UpswingContraction Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Shipping Demand Index -10 -5 0 5 10 15 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 9
  • 10.
    AT THE SAMETIME … Ordering in 2017 is significantly lower and is anticipated to decline further in the following years… In Q1 2017, Total Ship Deliveries were 2.54 million dwt lower compared to the same quarter of the previous year … Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Shipyard Capacity remains low increasing ship owners’ negotiating power… Post-crisis Aggregate Earnings ranged between 9,400-15,500 $/day … -100 -50 0 50 100 150 200 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total Deliveries Total Demolitions Change in Merchant Fleet 0 20 40 60 80 100 120 140 0% 10% 20% 30% 40% 50% 60% 70% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Orderbook % Fleet Change in Fleet (RHS) 8,000 13,000 18,000 23,000 28,000 33,000 38,000 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 $ / Day -25 -20 -15 -10 -5 0 5 10 15 20 25 0 1 2 3 4 5 6 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Forward Cover Shipyard Output Gap (RHS) … a number of factors that could signal a pickup in the availability of shipping tonnage remain muted. 10
  • 11.
    OUR PREFERRED PROXYFOR SUPPLY OF SHIPPING TONNAGE IS … … the Merchant Fleet growth, which is driven by new vessel deliveries minus demolitions and losses. Given the natural constraints that this variable is subject to, i.e. shipyard capacity for new deliveries, its volatility is much more constrained with a notable exception in the mid-90s. Decomposing our tonnage supply proxy into its 4 phases, signals that in Q1-2017 the proxy was still in the contraction phase of the cycle due to the significant decline in new-build ship deliveries. Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Supply Tracer Q1 2003 Q1 2017 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 Level Momentum ExpansionDownswing UpswingContraction Shipping Supply Index 0 2 4 6 8 10 12 14 16 18 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 11
  • 12.
    PUTTING EVERYTHING TOGETHER:THE SHIPPING IMBALANCE INDEX o Shipping cycles are defined by five interrelated markets: the commodity market, the secondhand market, the shipbuilding market, the demolition market and the funding market. Interaction among those five markets contributes to the evolution of aggregate demand and supply in the shipping sector. Constructing a model that takes into account all the interactions and features of each market is a non-trivial task. Instead, we model shipping market cycles by focusing only in two aggregate indicators of supply and demand proxied by merchant fleet and seaborne trade growth rates respectively. o Having already identified a demand and supply proxy it is natural to proceed to the approximation of the shipping sector business cycle as the gap between these new variables depicted by our Shipping Imbalance Index. Given that the demand and supply proxies are expressed in % rates of change, the Shipping Imbalance Index does not detect imbalances between the current stock of available shipping tonnage and the level of seaborne trade, but rather approximates more closely the trending behaviour of those quantities. In that way it provides a forward-looking measure of the anticipated imbalances in the shipping sector. o After 6 years of excess supply in the shipping market, stronger seaborne trade growth in Q1 2017 signals a momentum reversal in the balance in the maritime sector. More specifically, supply momentum stabilized at 3.4% per annum while seaborne trade growth picked up in 2016 and reached 3.9% in Q1 2017 on a YoY basis. Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network -15 -10 -5 0 5 10 15 20 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Supply Growth Indicator Demand Growth Indicator -10 -5 0 5 10 15 20 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Excess Demand Shipping Imbalance IndexShipping Supply & Demand Indicators Excess Supply 12
  • 13.
    SHIPPING SECTOR STRUCTURALMODEL: ESTIMATION & FORECASTING 13 In order to forecast the new-build and second-hand ship prices we developed a simple structural model for the aggregate shipping market. The forecasting process can be split into the following 4 stages: 1. First, we create a separate model for both Demand and Supply proxies. We express demand as a function of global GDP growth and oil prices as well as an ARMA(2,1) specification. In parallel, we express supply as a function of orderbook size (with a 2-year lag) and an ARIMA (4,1,2) term. 2. We express the price of second-hand ships as a function of the Shipping Imbalance Index, assuming that the second-hand market is the first to adjust to departures from an equilibrium level. 3. We proceed to modeling the price of new-build ships as a function of the imbalance index in conjunction with the prices of second-hand ships, the USD exchange rate and an AR(1) term. 4. We use the equations in steps 1 & 2 to generate 3-year forecasts for the explanatory variables which in turn are used as inputs in our second-hand and new-build price models to generate forecasts over a 3-year horizon.
  • 14.
    AGGREGATE SHIPPING MODELOUTLINE Demand 𝑫 𝒕 = 𝒇 𝑮𝑫𝑷𝒕−𝟏 , 𝑶𝒊𝒍 𝒕 , 𝑫 𝒕−𝒍 Supply 𝑺 𝒕 = 𝒇 𝑶𝒃𝒌 𝒕−𝟖 , 𝑺 𝒕−𝒍 Shipping Imbalance Index 𝑰𝒎𝒃 𝒕 = 𝑺𝒕 − 𝑫 𝒕 Secondhand Market 𝑺𝑯 𝒕 = 𝒇 𝑰𝒎𝒃 𝒕 , 𝑺𝑯 𝒕−𝒍 Newbuilding Market 𝑵𝑩 𝒕 = 𝒇 𝑰𝒎𝒃 𝒕 , 𝑫𝒐𝒍𝒍𝒂𝒓 𝒕, 𝑺𝑯 𝒕 , 𝑵𝑩 𝒕−𝒍 Where: Dt: World Trade Growth St: Fleet Development Obkt: Orderbook Imbt: Shipping Imbalance Index GDPt: World Real Output Growth Oilt: Oil Price Growth Dollart: Trade Weighted Dollar Rate SHt: Secondhand Prices Growth NBt: Newbuilding Prices Growth l: Number of Lags for Dynamic Model 14
  • 15.
    AGGREGATE SHIPPING MODELFINDINGS Demand Growth Model 𝐷𝑡 = 𝛽 𝐷,0+𝛽 𝐷,1 ∗ 𝐺𝐷𝑃𝑡−1+𝛽 𝐷,2 ∗ 𝑂𝑖𝑙 𝑡 + ARMA terms + 𝜀 𝐷,𝑡 Supply Growth Model 𝑆𝑡 = 𝛽𝑆,0+𝛽𝑆,1 ∗ 𝑂𝑏𝑘 𝑡−8 + ARMA terms + 𝜀 𝑆,𝑡 Secondhand Market Model 𝑆𝐻𝑡 = 𝛽𝑆𝐻,0+𝛽𝑆𝐻,1 ∗ 𝐼𝑚𝑏𝑡 + ARMA terms + 𝜀 𝑆𝐻,𝑡 Newbuilding Market Model 𝑁𝐵𝑡 = 𝛽 𝑁𝐵,0+𝛽 𝑁𝐵,1 ∗ 𝐷𝑜𝑙𝑙𝑎𝑟𝑡+𝛽 𝑁𝐵,2 ∗ 𝐼𝑚𝑏𝑡+𝛽 𝑁𝐵,3 ∗ 𝑆𝐻𝑡+𝛽 𝑁𝐵,4 ∗ 𝑁𝐵𝑡−1 + 𝜀 𝑁𝐵,𝑡 Dependent Variables Explanatory Variables 𝑫 𝒕 𝑺𝒕 𝑺𝑯 𝒕 𝑵𝑩 𝒕 𝐺𝐷𝑃𝑡−1 0.26 (2.36) 𝑂𝑖𝑙 𝑡 0.05 (5.00) 𝑂𝑏𝑘 𝑡−8 5.37 (1.61) 𝐼𝑚𝑏𝑡 -1.73 -0.17 (3.39) (2.43) 𝐷𝑜𝑙𝑙𝑎𝑟𝑡 -0.10 (2.50) 𝑆𝐻𝑡 0.13 (4.33) 𝑁𝐵𝑡−1 0.30 (2.50) 𝒂𝒅𝒋. 𝑹 𝟐 56% 42% 89% 54% Model Estimates o The MA and AR order in each ARMA model is chosen through the use of the AIC, BIC and Root Mean Squared Error (RMSE) criteria. o In the supply model the order of integration is chosen after we check for unit roots using the Augmented Dickey Fuller and Phillips-Perron tests. o t-statistics robust to autocorrelation and heteroscedasticity are reported in parenthesis under each coefficient and are computed using the HAC estimator. 15
  • 16.
    MODEL FIT FORNEWBUILD AND SECONDHAND PRICES 16Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network Newbuilding Prices Model Fit -20 -16 -12 -8 -4 0 4 8 12 16 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Errors NB Prices QoQ% Estimate -8 -4 0 4 8 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Errors SH Prices QoQ% Estimate -48 -36 -24 -12 0 12 24 36 Newbuild Prices Model Fit Secondhand Prices Model Fit o The model seems to fit the actual ship price evolution reasonably well with adjusted R squared coefficients above 50% of total variation in new-build and second-hand prices respectively. Moreover, the coefficient estimates in each series have the anticipated sign. In particular, both shipping price indices are negatively correlated with the imbalance index, with second- hand prices being relatively more sensitive to market disequilibrium. In addition, the dollar’s value is negatively related to new-build prices as a dollar appreciation gives flexibility to shipyards to adjust their pricing policy on new-build contracts. o According to the model, current conditions in the shipping sector render actual new-build price growth undervalued relative to its “fair” estimate. On the contrary, the abrupt increase in actual second-hand prices in the first quarter of 2017 is higher compared to the growth rate that would be proper according to the model.
  • 17.
    BASELINE SCENARIO FOR3-YEAR FORECASTS 17 The baseline scenario is constructed upon the assumptions that: 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 2012 2013 2014 2015 2016 2017 2018 2019 2020 World Real GDP Growth Baseline Forecast 0 20 40 60 80 100 120 140 2012 2013 2014 2015 2016 2017 2018 2019 2020 Oil Prices Baseline Forecast 0 10 20 30 40 50 60 70 80 90 100 2012 2013 2014 2015 2016 2017 2018 2019 2020 Trade Weighted Dollar Baseline Forecast 0% 10% 20% 30% 40% 50% 60% 2012 2013 2014 2015 2016 2017 2018 2019 2020 Lagged Orderbook Ratio Baseline Forecast World real GDP growth accelerates in 2017 and stabilizes after 2018… Oil prices follow a slow 3-Year path towards 65 $/bl… Dollar depreciates by 13% over the next 3 years… Orderbook will remain depressed at 12% of merchant fleet... Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network -3 -2 -1 0 1 2 3 4 5 6 7 2012 2013 2014 2015 2016 2017 2018 2019 2020 Imbalance Index Baseline Forecast Excess demand increases over the next three years reaching a peak during 2018…
  • 18.
    3 YEAR FORECAST– GRADUAL INCREASE OF PRICES o The baseline scenario refers to a recovery in global output growth and therefore seaborne trade is expected to reverse the downward path of ship prices. Second-hand prices are expected to rise first in response to anticipated fleet shortages sector since the orderbook adjustment over the past years contributed to low capacity utilization of shipyards. New-build prices will gradually increase over the next three years, with their growth rate expected to reach a peak in 2019. o Despite the fact that economic activity in the US, Europe and emerging markets started to gain momentum, this development is still associated with substantial downside risks around the global macroeconomic environment. Shipping Prices Base Scenario Forecast 2016 2017e 2018f 2019f 2020f Newbuilding Prices (%) -6.38 -1.42 1.62 3.16 0.88 Secondhand Prices (%) -19.66 15.73 4.35 0.22 2.64 Trade Growth (%) 3.35 4.11 4.54 3.52 4.01 Fleet Growth (%) 3.34 3.61 3.32 3.13 3.15 Imbalance Index -0.01 -0.50 -1.22 -0.39 -0.86 3-Year Projections under the Baseline Scenario Source: Piraeus Bank Research, Clarksons Shipping Intelligence Network -100 -80 -60 -40 -20 0 20 40 60 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 SH Price Growth Actual (YoY) NB Price Growth Actual (YoY) NB Forecast (Base Scenario) SH Forecast (Base Scenario) 18
  • 19.
    Disclaimer: This documentis produced by the Economic Research & Investment Strategy Department of Piraeus Bank (hereinafter "the Bank"), which is supervised by the European Central Bank (ECB), in collaboration with the Bank of Greece and is sent or provided to third parties, without any obligation of its author. This document or any part of it should not be duplicated in any way without the prior written consent of its author. The information or opinions included in this document are addressed to existing or potential clients in a general manner, without taking into account the particular circumstances, the investment objectives, the financial ability, the experience and/or knowledge of the potential recipients of this document and, as a result, they do not constitute or should not be considered neither as a solicitation or offer for the conduct of transactions in financial instruments or currencies nor as a recommendation or advice for decision making in relation to those. Taking into account the aforementioned, the recipient of the information contained in this document should proceed with his/her own research, analysis, and confirmation of the information which is included in this document and seek for independent and professional legal, tax and investment advice, before proceeding with any investment decision making. The information depicted in this document is relied on sources that the Bank considers to be reliable and is provided on an "as is" basis, however, the Bank cannot warrant as to their accuracy and completeness. The opinions and estimates herein are related to the trend of the local and international financial markets at the indicated date (prices at closing time) and are subject to changes without any prior notice. Notwithstanding the above, the Bank might include in this document investment researches, which have been conducted by third persons. In this case, the Bank does not modify those researches, but it presents them on an "as is" basis, therefore, no responsibility is assumed in relation to the content of the aforementioned investment researches. The Bank is under no duty to update the information contained in this document. Considering the above, the Bank, the members of its Board of Directors and the relevant persons assume no responsibility for the information included in the present document and/or for the outcome of any investment decisions made according to such information. Piraeus Bank Group is an organization with a significant presence in the Greek market and an increasing one in the international markets providing a wide range of investment services. In the context of investment services offered by the Bank and/or any other Piraeus Group companies in general, there might be cases whereby conflict of interests may arise in relation to the information provided herein. Reference should be made to the fact that the Bank, the relevant persons and/or other Piraeus Group companies indicatively: a. Are not subject to any prohibition in relation to trading on own account or in the course of providing portfolio management services prior to the publication of this document or the acquisition of any shares prior to any public offering or the acquisition of any other securities. b. May offer upon remuneration investment banking services to issuers for whom this document may contain information. c. May participate to the issuers' share capital or acquire other securities issued by the aforementioned issuers or attract other financial interests from them. d. Might provide market making or underwriting services to issuers that might be mentioned in this document. e. Might have published papers the content of which is different or incompatible to the information presented herein. The Bank as well as the other Piraeus Group's companies have enacted, implement and maintain an effective policy, which prevents circumstances that may give rise to conflicts of interests and the dissemination of any information among the departments ("Chinese walls") and they also constantly comply with the provisions and regulations relevant to inside information and market abuse. Also, the Bank confirms that it doesn't have any kind of interest or conflict of interest with a) any other legal entity or person that could have participated in the preparation of the present document and b) with any other legal entity or person that couldn't have participated in the preparation of the present document, but had access to it before its publication. It is duly stated that: the investments described in the present document include investment risks, among which the risk of losing the entire capital invested. In particular, it is stated that; a. The figures presented herein refer to the past and that the past performance is not a reliable indicator of future performance. b. In case the figures refer to simulated past performance, that past performance is not a reliable indicator of future performance. c. The return on investments might be positively or negatively affected as a result of currency fluctuations, in case the figures are denominated in a foreign currency (other than Euro). d. Any forecasts in relation to future performance, May not be a reliable indicator of future performance. e. The tax treatment of the information as well as transactions pertained in this document, depends on each investor's individual circumstances and may be subject to change in the future. As a result, the recipient should seek for independent advice in relation to the applicable tax legislation. The distribution of the present document outside Greece and/or to persons governed by foreign law May be subject to restrictions or prohibitions according to the applicable legislation. Therefore, the recipient of the present should seek for independent advice in relation to the applicable legislation, in order to look into such restrictions and/or prohibitions. 19