title: The analysis of investment theories and application of Tobin-Q model to second-hand dry bulk and tanker ship investments
Creators: name: Celik Girgin, S
Publisher: name: University of Tasmania
Year: 2020

description: Ship investment has two essential components: (1) Purchasing and selling shipping assets (i.e., ships) to ensure a certain capacity of marketable shipping service while optimising the investment timing to achieve better deals in asset price arbitrage (e.g., counter-cyclical asset play); (2) operating ships to generate revenues from shipping services (carrying cargoes) while sustaining a strong relationship with charterers as cargo owners or traders. During economic downturns of the last few decades, many ship investors observed that the former component of revenue generation (asset price arbitrage) contributes a much significant fraction of net revenue comparing to the latter part (operating income). For example, a Capesize dry bulk carrier would be sold at US$140 million during June 2008, and the same asset would be valued at just US$40 million in December 2008, only six months later. The investor lost US$100 million amount of asset value, and that has not returned back yet (by October 2019). In other words, prices of the market (mark-to-market value) do not efficiently represent a robust and credible value of shipping assets, particularly in potential asset bubbles during the prosperity of freight markets. Accordingly, an investor would not be advised on investment timing based on market prices. For mitigating the biasedness of asset prices in the sale and purchase market, investors need a forward-looking instrument to shed light on the robustness and durability of ship prices at any time and to develop an asset management strategy in line with long-term prospects of the market. In this regard, a valuation test ratio, the shipping Q index, has been proposed to monitor asset value shortfalls as well as overvaluations and to identify mismatch of market prices and long-term values of shipping assets. In ship investment literature, investment decisions are mainly investigated from a perspective of the relationship among the shipping markets (newbuilding, second-hand, freight, and scrap) and their impacts on asset price valuation, the timing of investments and market entry and exit conditions. Although the shipping industry is highly capital intensive and attracts a high amount of investment, very limited research has been undertaken focusing on the discrepancy between market prices and the long-term nominal value of a ship reflecting any mispricing, which in turn sheds light on investment timing and market entry-exit decision. Therefore, this study investigated the major investment theories and their applicability to the ship investments in the context of maximizing the investment return by reaching the optimum level subject to investment return. Considering the fundamental characteristic of shipping markets, the Q theory of investment was adapted to the ship investment in this study. A “Shipping Q” indicator based on the Tobin-Q theory was developed for dry bulk and tanker carriers as the ratio of the nominal price of the ship to the market price. The computation of the nominal price of a ship was undertaken by calculating the cash flow for each vessel segment from 1990 to 2017. For each year in the life of the ship, the anticipated revenue and expenditure were determined. The net cash flow, which is revenue (time-charter rate) less expenditure (operational expenses), was then discounted by means of a discount factor (long-term government bond yields of 10 years). The value of the ship was calculated as the sum of each of the discounted cash flows plus the discounted residual value, which was the demolition value of the ship in this model. This study revealed two main findings. First, the results indicated that the Shipping Q indicator was a robust and significant tool to explore market entry and exit timings through the difference between market prices and the long-term nominal value of ships. In contrast to the mainstream belief, invest when freight rates are high, Shipping Q proved that the investment becomes less profitable after a certain level based on the ratio of market price to the nominal value of a ship. Second, the Shipping Q indicator was optimized subject to return maximization, which provided a certain level of investment return maximization. The optimization of the indicator created the opportunity for market participants to manage their shipping asset portfolio with better insights into the direction of second-hand ship prices. The findings of this study contribute to the asset valuation concept, both theoretically and empirically. Theoretically, it contributes to the literature of firm-level investments in fixed capital by producing an analytical indicator based on the empirical data for ship investment. Empirically, the Shipping Q is able to interpret the future second-hand ship market considering the changes in the time-charter rate and nominal value of a ship. Moreover, the optimized Shipping Q leads to a certain level of maximization of investment return. Having a robust tool to evaluate the future second-hand ship market could contribute to efficient asset management along with the efficient use of financial funds. The thesis has a number of limitations and recommendations for future studies; firstly, the SQ indicator inherently carries asymmetric information. Secondly, the sample data is confined to industry-level data due to unavailable firm-level data. Further studies regarding firm-level analysis are highly recommended in case of accessibility of the data. Thirdly, the thesis merely adapted the investment models to second-hand ships, has not suggested a new investment model. Fourthly, SQ indicator optimization analysis conducted for 5-year-old vessel types due to insufficient 10- and 15-year-old vessel data. In addition to the limitations, there are several recommendations to future studies, additional research on the back-testing the predictability of the SQ indicator with various methods that could be executed. Secondly, a study could analyse the effectiveness of the SQ indicator for different ship types, such as container ships.