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Do the Sub-Indices of the BDI Have Better Predictability for Stock Market Returns Than the BDI?

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Do the Sub-Indices of the BDI Have Better Predictability for Stock Market Returns Than the BDI? Master Thesis Finance Department of Finance – Tilburg University Do the sub-indices of the BDI have better predictability for stock market returns than the BDI? August 26, 2015 Author: Bingqian Luo ANR: 780063 Supervisor: Dr. P. C. de Goeij Table of Contents 1. Introduction ............................................................................................................................... 2 2. Dry Bulk Market ....................................................................................................................... 3 2.1. Seaborne trade ...................................................................................................................... 3 2.2. Freight rates .......................................................................................................................... 4 2.2.1. Freight rates based on contracts ..................................................................................... 4 2.2.2. Demand side key influences on freight rates ................................................................. 5 2.2.3. Supply side key influences on freight rates ................................................................... 6 2.3. Equilibrium freight rate of four sub-markets ....................................................................... 8 2.4. The BDI and its sub-indices ............................................................................................... 11 3. Hypothesis and Description of Data ...................................................................................... 14 3.1. Hypotheses ......................................................................................................................... 14 3.2. Data description.................................................................................................................. 15 3.3. Econometric model ............................................................................................................ 18 4. Empirical Results .................................................................................................................... 20 4.1. Analyses on the lag size ..................................................................................................... 20 4.1.1. Lag size of the BDI ...................................................................................................... 20 4.1.2. Lag size of the sub-indices .......................................................................................... 22 4.1.3. F-tests on the lag size................................................................................................... 29 4.2. Best indicator...................................................................................................................... 31 4.3. Robustness analysis ............................................................................................................ 33 5. Conclusion ............................................................................................................................... 36 Appendix:..................................................................................................................................... 37 References .................................................................................................................................... 45 1 1. Introduction Prediction of stock market returns has been a long-time attractive topic to researchers from different fields, and because of close relationships among international trading, world economy and the stock market, people start to look at freight rates of the dry bulk market as an indicator of stock market returns. Usually, people take the Baltic Dry Index (the BDI) as a representation of the dry bulk market performance. At first, people only investigate the relationship between dry bulk freight rates and the economy, for example Stopford (2009) who claims that the world economic activity is the most important single impact on the dry bulk shipping demand and that the freight rates are demand driven. Stopford (2009) also shows that the supply of ships is inelastic, which laid a solid foundation for the research of this topic. Kilian (2009) adapts dry cargo bulk freight rates to identify periods of low and high economic activity. Bakshi et al. (2011) is the first paper to investigate that the dry bulk shipping freight rate predicts global stock returns, commodity returns and global economic activity, demonstrating the argument through in-sample test and out-of-sample statistics. Alizadeh and Muradoglu (2011) takes this topic further by showing that predictability is not due to time-varying risk premia, but due to the gradual diffusion of information from shipping sector to the investors in other sectors. Oomen (2012) empirically examines that an increase of one standard deviation (16.2%) in the BDI return will one month later result in an increase in the MSCI World Index return of 0.78%. My study is based on Alizadeh and Muradoglu (2011) and Oomen (2012). Apart from researchers thinking about the dry bulk shipping market as a whole, Jing et al. (2008) by applying GARCH model investigates the characteristics of volatility in dry bulk freight rates of different vessel sizes (capesize, panamax and handysize) and finds that the freight rate of smaller vessels are less volatility due to its flexibility on operation. Chen et al. (2011) and Thalassinos and Politis (2014) follow and focus on different vessels in dry bulk market, trying to model and forecast the BDI and its sub-indices. However, no one inspect sub-indices as predictors of stock market returns, and it may mainly because people only concentrate on the representativeness of the BDI. Actually, during 2000 to 2008 it was a good indicator of the stock market, but after the crisis, the BDI shows a negative relationship with S&P 500 and other stock indices. Oomen (2012) also concludes that after 2008, the BDI is not a good indicator of stock market returns. Therefore, further investigations are needed to find better dry bulk freight rates indicator of stock market returns. 2 In this study, the dry bulk market is divided into four sub-markets by vessel size, because different vessel sizes get involved in different commodity trades and routes/regions of the world. Capesize, Panamax, Surpramax and Handysize Indices are used on behalf of freight rates on different sub-markets. In this paper, I try to find what main drivers of all indices are, and whether we should take certain sub index as predictor instead of the BDI. The rest of the paper continues as follows. Section 2 describes the dry bulk market and analyzes the difference among four sub-markets of dry bulk shipping. Section 3 contains the hypotheses and data descriptions of the whole data set. In Section 4 the results of the empirical research are presented and discussed, and Section 5 contains the conclusion, points out some limitations on this paper and suggests some areas for future research. 2. Dry Bulk Market 2.1. Seaborne trade Maritime transport is essential to the world’s economy as over 90% of the world’s trade is carried by sea and it is the most cost-effective way to move masse goods and raw materials around the world. Dry bulk shipping, as one of the most vital ways of transportation, accounts for more than half of seaborne trade. More importantly, dry bulks such as iron ore and coal are largely used in manufacturing steel and generating electricity, which is the foundation of economic activities, especially for countries going through industrial process. Dry bulks can be divided into two main categories: 1. The five major bulks: referred as iron ore, coal, grain, phosphates and bauxite. 2. Minor bulks: steel products, steel scrap, cement, gypsum, non-ferrous metal ores, sugar, salt, sulphur, forest products, wood chips and chemicals etc.1 Iron ore, coal and grain represent about 70% shipping volume in dry bulk market, with 30%, 30% and 10% respectively. In dry bulk trading activities, some countries or area play important or even determinant roles, for example, China, Japan, Australia, Brazil and European Union (see Table 1 in Appendix). 1 Maritime Economics 3rd edition Martin Stopford 3 In the bulk carrier market four sizes of ships can be identified from large to small, which are capesize (more than 100,000 dwt2), panamax (60,000-99,999 dwt), supramax (40,000-59,000 dwt) and handysize (10,000-39,999 dwt). Capesize vessels carry mostly iron ore and coal and sometimes grain. Panamax vessels, in addition to these cargoes are also sometimes employed to transport met coke, pet coke, fertilizers, sulphur, salt, bauxite, alumina and steel slabs. The smaller vessel sizes are supramax and handysize, and in addition to the above they are often employed in carrying loads such as steel products, scrap and sugar. Among these four types of vessels, capesize accounting for 62% of dry bulk traffic is the busiest vessels. 2.2. Freight rates 2.2.1. Freight rates based on contracts Usually, vessels are operated by different kinds of charter contract, and each of contract has different freight rate calculation basis. Five types of contracts can be identified as follows: 1. Voyage charter (spot charter). It’s the hiring of vessels and crew for a voyage between a load port and a discharge port with a specific cargo. Under this contract, the freight paid by charters is based on a dollar per ton, while the owners pay the port, fuel and crew costs. 2. Time charter. The vessels are chartered for
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