The majority of the shipping-related literature has, until now, focused on the management perspective of shipping companies; either for firms which were or are still held private, or for those which are publicly listed in stock markets. Under this prism, performance of the companies has been vastly researched (inter alia, Panayides et al. 2011; Merika et al. 2015; Lambertides and Louca 2008). While performance, and more precisely financial performance, is not always a direct paragon of stock market returns, it nevertheless surely serves as a good proxy (DeBondt and Thaler 1985).
On a more stock price-oriented stream of literature, shipping stocks have been found to exhibit some unique characteristics when compared to stocks from other economic sectors, primarily due to the globalized nature of the sector’s operations. More precisely, previous researchers have found that market betas are of little importance for shipping stocks (Tezuka et al. 2012), leading to the conclusion that country-specific systematic risk is not particularly important for investors in shipping companies. The low importance of country-specific factors in the stock performance of shipping companies is even more evident when foreign exchange rates are considered. Given that US dollars are the predominant currency that it is used by shipping companies, the majority actively uses currency hedging techniques that ultimately leave companies indifferent from fluctuations in the currency markets (El-Masry et al. 2010).
On the other hand, variables that are more closely related with world economic outlook, like the G-7 industrial production (Papapostolou et al. 2014), changes in oil prices (Drobetz et al. 2010) and economic crises (Papapostolou et al. 2016), play a more important role in assessing the risk outlook of the shipping companies and subsequently their stock market performance.
Taking the above into consideration leaves investors in shipping companies in a conundrum: which are the factors that one can rely upon when investment strategies are drawn? Perhaps unsurprisingly, Syriopoulos and Roumpis (2009) show that the most important factor that can explain and predict shipping stock performance is freight rates. This finding makes intuitive sense, given that, from a financial accounting perspective, freight rates will ultimately determine the amount of income received (sales) by any given shipping company.
Various researchers have tried to explore additional factors which are important in assessing stock price performance of shipping firms. Grammenos and Marcoulis (1996), in their seminal work, provide evidence that financial leverage and the average age of the fleet have an explanatory power over the shipping stock returns. Additionally, Kavussanos and Marcoulis (2000a, 2000b) provide evidence on the macro-economic factors that are affecting shipping stock returns. In their studies they record that shipping stock returns have a positive relationship with oil prices and a negative one with industrial production. Drobetz et al. (2010) further strengthen these findings as they demonstrate the strong relationship of oil prices and industrial production for the period 1997 to 2007. Finally, Grammenos and Arkoulis (2002) show that oil prices and laid up tonnage are inversely related to shipping stocks, whereas the exchange rate exhibits a positive relationship.
On a broader level, though, the financialization of the wider spectrum of commodities has raised the interest of the research community on the linkages that exist between commodities and the stock markets (Basak and Pavlova 2016). While oil remains one of the main factors affecting stock price volatility (Sadorsky 1999), other commodities are also exhibiting a high correlation with the stock market. More precisely, Creti et al. (2013), show that during times of economic stability the correlation between the major commodities and the S&P500 is evident. Furthermore, the authors show that oil, coffee and cocoa are acting as speculative alternative assets as their correlation with the markets is strengthened during bullish periods and diminished during bearish ones. Finally, gold exhibits a safe haven character since it exhibits a negative correlation over time with the stock markets.
In the current paper, we exploit the stream of literature that embraces econometric techniques in portfolio-oriented investment strategies so as to produce above-average returns from trading in shipping companies. We derive our technique from the seminal work of Alexander (1999), who uses cointegration as a trading technique so as to hedge three different stock portfolios from the volatility that exists in the global financial markets. Even before their stock market application, hedging techniques based on the cointegration approach had also been used in the futures (Ghosh 1991) and the currency markets (Kroner and Sultan 1993).
However, cointegration has not been used for stocks in the shipping sector. The most related studies to this one are Alizadeh and Nomikos (2006), who use a cointegration approach to time buying and selling of ships in the tanker market, and Andriosopoulos et al. (2013) who use h stochastic optimization (heuristic) methods to reduce shipping-related investment risks and achieve the equivalent performance as the shipping indices and the shipping stocks.
However, this paper differs significantly from the above two studies. In particular, the Alizadeh and Nomikos (2006) approach is not suitable for retail investors, or even the majority of institutional investors who do not possess knowledge in the shipping sector. Furthermore, the Andriosopoulos et al. (2013) approach focuses on reducing risk instead of increasing performance and uses a much more complicated strategy than the one employed here.
In this paper, we follow the suggestions of the previous literature that the stock performance of a portfolio of tanker shipping companies is only affected by freight rates and thus exclude any factors that are either irrelevant or difficult to include to a model. To make the model suitable for the portfolio of shipping companies we seek to trade, we use the Baltic Tanker Index as an indicator for market conditions.Footnote 1 We expect that, ceteris paribus, our portfolio’s returns and the Baltic Tanker Index will move together in the long-run and share a cointegrating relationship. Any deviations from the cointegrating equation will trigger our trading strategy which is discussed in the next section.