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. 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.

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

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 a specific period of time such as 6 months or a year, and the owners still manage the vessels but the charters select the ports and directions. The charters pay not only the freight calculated on a dollar per day basis, but also the fuel, crew and port expenses.

3. Trip charter. The ship is chartered for a specific period and it is comparatively short time charter. The charters pay the freight on a dollar per day basis. The ship owner controls the vessel and the charterer pays the voyage costs.

4. Contracts of affreightment (CoA). It is similar to a voyage charter on the cost allocation, but usually the cargo size excesses the ship’s capacity and needs more than one voyage for complementation. The owner offers a price at rate of per tonnage or per voyage and it’s particularly suits to bulk cargos.

2 Deadweight ton

5. Bare-boat charter. In this type of contract, the charters obtain full control of the ship along with the legal and financial responsibility. The charterer pays for all operating expenses, including fuel, crew, port expenses and P&I and hull insurance.

Voyage charter, trip charter and time charter are the most common types of contracts used in the dry bulk shipping market. Voyage and trip charter contracts are classified as short-term shipping contracts, since they cover only a single voyage or trip, while time charter contract is recognized as long-term contract.

2.2.2. Demand side key influences on freight rates As any other products or services in the market, dry bulk freight rates are determined by the demand and supply of shipping. In this part, I discuss the key factors on the demand first. Even though dry bulk freight rates are affected by many elements, an analysis on the key influences is not to suggest details should be ignored, but to make the study clearer.

Global commodity demand

Undoubtedly, the most important single influence on the shipping demand is the global dry bulk commodity demand, which is on behalf of the world economy. As the world economy is prosperous, the freight rate will go up, and Batrinca (2014) also examines empirical relationship between GDP and freight rate, concluding that 1% growth in GDP would generate about 5.4% increase in the freight rate.

Transport costs

Voyage costs make up 40% of costs for running ships, while 75% of voyage costs is oil (bunker fuel) cost and the rest 25% is the cost for port and others, which are relatively stable. So, as the oil price increase, it will reflected in the freight rate.

Average haul and ton miles

Average haul is generally referred to as the distance on the trade, and it is usual to measure sea transport demand. The effect on the shipping demand of changing the average haul has been illustrated several times in the history by the closure of the Suez Canal, which increased the

5 average distance by sea from the Arabian Gulf to Europe from 6,000 miles to 11,000 miles3. As a result of the sudden surge in shipping demand there was a freight market boom.

Random shocks

There are some sudden shocks may have temporary effect on the demand for ships, such as weather change, war, political regulations etc. however, such random shocks are not predictable and the consequences are not sure. Although, it is unusual for these shocks to happen, they could generate huge impacts on the shipping markets. For example, the financial crisis in 2008 results in a drastic decline of freight rates and time charter rates4

2.2.3. Supply side key influences on freight rates The supply of shipping is controlled, and influenced, by four groups of decision-makers: ship- owners, shippers/charterers, the bankers who finance shipping, and the various regulatory authorities who make rules for safety5. Ship-owners are the primary decision-makers who order new ships, scrap old ones and layup tonnage. Shippers can be ship-owners themselves by issuing time charters. The shipping industry is a capital intensive industry, so bank lending has a great influence on the supply of ships and usually it is bankers who post financial pressure on ship demolition in a weak shipping market. Safety or environmental legislation made by regulators also affect supply on transport capacity of the fleet. These decisions are implemented through four markets, which are newbuilding market, the freight market, the sale and purchase market and the demolition market. So to analyze the influence on the supply, discussions on these four markets and on the way they impact the freight rate are necessary.

The freight market

The freight market is the place where the supply and the demand meet, and where the freight rate is determined. Although the fleet size is fixed, the productivity is quite flexible in order to adjust to the demand on shipping. The productivity of ships measured in ton miles per deadweight depends on four main elements: speed, port time, deadweight utilization and loaded days at sea. As the demand for shipping rise, ship-owners can escalate the speed or make full use of

3 Maritime Economics 3rd edition Martin Stopford 4 Počuča and Zanne (2009) 5 Maritime Economics 3rd edition Martin Stopford

6 deadweight utilization to meet the requirement. On contrary, if the market is weak, shippers slow down the operation to save oil cost, or even keep vessels in idle.

Newbuiding market

In principal, the level of output for the shipbuilding industry could meet the changes on shipping demand, but this happens only over long period. Shipbuilding takes a long time to complete and the time-lag between ordering and delivering a ship ranges from one to four years. So, more time is needed for the order of bigger size vessels. However, it is usually the problem that after the new ships put into operation, the demand of the market has changed.

The sale and purchase market

The second-hand market for ships has close relationship with the freight market, and the price for second-hand ships depends mainly on freight rates. If the freight rate is high, existing ship- owners and other investors want to join the market, and buying a second-hand ship is the most convenient way. Nevertheless, as more ships are available for transport, the freight rate will decline.

The demolition market

Age is the primary factor that leads to scrap vessels and on average, the economic life of a ship is about 25 years, so normally only a small proportion of the fleet is scrapped each year. Apart from that, technical obsolescence, scrap price, current earning and market expectations can also influence the decision of demolition.

According to the analysis above, it can be found that the supply of shipping is relatively stable and have some delay compared to the demand. This property of the dry bulk shipping market lays the foundation of the dry bulk freight rate as an indicator of the economy and stock market returns.

2.3. Equilibrium freight rate of four sub-markets In this part, the processes of how the freight rate come into being in four sub-markets will be compared. As all price mechanism, the price for the dry bulk market is determined by an equilibrium of shipping demand and supply.

Stopford (2007) made a thorough analysis on the shipping demand and supply model. It maintains that the slope for demand curve is almost vertical (see Figure 1, panel a.), and this is mainly because a lack of any competing transport mode. The situation is even severe for dry bulk commodities. On the one hand, once the cargo needs to be shipped and no matter how expensive the rate, the exporters will accept the price to deliver goods. On the other hand, cheap rates will not tempt shippers to transport more commodities. The point that the freight occupies only a small proportion of material costs reinforces this argument.

Figure 1. Demand and supply curve of the dry bulk shipping market

However, the supply curve has a character of “J” shape (Figure 1, panel b.). To the convenience of analysis, the supply curve is divided into four parts and labeled I, II, III and IV (the same below). Each part describes a typical case in reality according to the number of available ships. As the freight rate is at a relative low level in the first part (the weak market case), there are just a few efficient ships willing to provide service, moving on very slow speed to cut fuel cost. In the second part, the price gradually go up, as long as it can cover operation cost for the voyage, more shippers are willing to make a trip instead of leaving ships in idle (the rising market case).

The steeper slope (shown in part III) follows since the oldest ships require very high operating costs and it can be called the boom market case. Finally, if the price is high enough to stimulate all ships participating and working at the fastest speed, the supply curve goes to part IV and becomes vertical (the extreme market case).

The supply curves on different size of vessels or different sub-markets are not the same. More specifically, capesize vessels have steepest supply curve and as the size of vessels decrease, the supply curve would be gentler (see Figure 2). The difference is because of two reasons. The operation flexibility comes first. The majority of capesize vessels are engaged in the transportation of iron ore and coal in some fix routes, and due to their deep draught and limited number of commodities they transport, the operation of these vessels in terms of trading routes and ports they can approach is restricted. Panamax and surpramax vessels, used primarily in coal, grain and to some extent in iron ore transportation, are more flexible than capesize vessels, but a lack of cargo handling gears and also a deep draught make these two types of vessels less flexible than handysize vessels. Therefore, as the freight rate goes up continually, it is much easier for bigger size vessels step to the extreme market period, owing to limited substitutions. Capacity diversification is another factor resulting in different supply curves. Capesize vessels have the largest capacity, which is about two time that of panamax, three time that of surpramax and four times that of handysize vessels. The crossovers of the larger vessels to markets for smaller vessels may have greater impact on the supply in markets for smaller vessels compared to the impact on that in the markets for larger vessels caused by the move of smaller vessels to the markets for larger vessels6. So, there would be more potential supply of ships for markets of smaller vessels.

6 Grammenos (2013)

Figure 2. Supply curves of different dry bulk vessels

Figure 3 below illustrates the equilibrium freight rate happens on the intersection of supply and demand curve, where Q stands for the quantity of available ships and F denotes the freight rate.

If the supply curve remains stable, the demand curve moves from D to D1 with the freight rate almost unchanged, which is usually the situation on a weak market. From D1 to D3, as the demand increases, the freight rate would rise and more cargoes are involved in the markets. When the demand upsurges continually, there are no ships available anymore in the short term, and the freight rate could go extremely high (the demand curve beyond D3). Conversely, if the demand curve remains constant, the supply curve moving to right would decrease the freight rate, and it would increase the freight rate as moving to the left side, except in the weak market case.

However, in the real economy, usually it is the demand curve that moves while the supply curve remains stable because of long time to constructing ships and high cost of demolishing.

Figure 3. The equilibrium freight rate

Moreover, It can be discovered that in the weak market case (the freight rate hovering at very low level) the change of dry bulk demand could not reflected into the freight rate due to the horizontal supply curve. Only in other three cases, the adjustment of dry bulk demand could influence the freight rate. Note that, since the diversification of supply curves among all kinds of vessels, it is more difficult for markets of smaller vessels to come into being extreme high equilibrium freight rates, which means the freight rates in markets of bigger vessels are more volatile and this property will be discussed further in next part.

2.4. The BDI and its sub-indices The Baltic Dry Index (BDI) is recognized as a barometer for the global dry bulk market, and at the beginning it was published as the daily freight index by Baltic Exchange, called the Baltic Freight Index (BFI). BFI is based on a weighted average of shipping costs on 13 trade routes: grain (five routes), coal (three routes), iron ore (one route) and general charter (four routes). In 1999, the BFI was replaced by the BDI. In October 2001, the BDI underwent major expansion to cover 26 shipping routes and four vessel sizes: capesize, panamax, supramax and handysize, which is classified regarding the ability to pass the Panamax Canal. After several time of

11 modifications (see Table 2 in Appendix), currently, the BDI is the weighted average of these four kinds of time charter averages and multiplied by a coefficient, and the calculation is:

Capesize_TCA + Panamax_TCA + Supramax_TCA + Handysize_TCA 퐵퐷퐼 = [ ] × 0.113473601 4

Where TCA is Time Charter Average

The BDI is authoritative because of several reasons. It covers not only rich historical data, large underlying membership and daily frequency of time charter rates but the large scope including geographical distance and types of vessels. Veracity is another competitive advantage, since the calculation is based on the real shipping activity happened between shippers and the ship owners. Therefore, the price only reflects the real demand and supply of the freight market without speculation.

“Capesize_TCA” and others above in the formula implies that only time charter rates of four types of vessels are included in the BDI. There are also sub-indices specifically designed to capture the freight rates in four sub-markets: the Baltic Capesize Index (BCI), the Baltic Panamax Index (BPI), the Baltic Surpramax Index (BSI) and the Baltic Handysize Index (BHSI). The BCI consists of five voyage routes and four trip-charter routes and covers the four major trading routes, which are Atlantic Trade, Pacific Trade, trips from the Continent to the Far East and trips back from the Far East to the Continent7. The composition of routes for the BPI and the BSI are broadly similar to that of the BCI, while the BHSI consists of six trip-charter routes that equally cover cargo movement in the Atlantic basin and in the Pacific basin. Therefore, the base regarding the voyage time of these four indices are different. Precisely, capesize and panamax vessels have longest average voyage time, usually more than 60 days, while 40 days for surpramax and 30 days for handysize vessels. Table 3 in the Appendix provides detailed information on these four indices.

Figure 4 below shows the historical prices for the five indices. Not surprisingly, owing to different volatility of the freight rates among all kinds of vessels, as discussed in Section 2.3, fluctuation of the BCI is the most tremendous, whereas that of the BPI is milder compared with the BCI and the other two indices are much more temperate. Because the BDI is the weighted

7 Batrinca (2013)

12 average of these four indices, it is reasonable that the value lies in between the biggest and smallest index. Moreover, it illustrates that, from 2006 to 2008, all indices increase drastically, especially for the BCI, BPI and BDI, but during the crisis from 2008 to the end of 2009, those three indices lose around 95% of their values. After 2009, all indices fluctuate more gently than before. However, in January 2007, the factor of BDI was revised from 0.998007990 to 1.192621362, which could be part of the reason of an increase on the index value, and in July 2009, the BDI was made a great modification with the multiplier of 0.113473601 and the drop of voyage charter included in the BCI.

Figure 4. Prices of the BDI and its sub-indices

25000

20000

15000

10000

5000

BDI BCI BPI BSI BHSI

3. Hypothesis and Description of Data In this section, the hypotheses will be presented based on existing literatures, and I have certain expectations regarding to the analysis. Afterwards, the descriptions of data used in this study and also the basic econometric model will be introduced.

3.1. Hypotheses There is an economic common sense that the dry bulk freight rate delivers a message about the global economy, since it tracks the cost of shipping iron ore, coal, grain and other raw materials, which are important inputs for infrastructure and energy intensive industry. As the world economy is prosperous, the demand for dry bulk commodities will increase, and freight rates go up as well. As for the relationship between economy and the stock markets, Mohtadi and Agarwal (2001) and Antonios (2010) show a positive relation between stock market development and economic growth. The main reason is the stock market provides liquidity to the economy, which can promote the efficiency of resource allocation, and in turn, the economic development is to consolidate growth in the financial system. This paper does not examine economic growth as an intermediate step, and I look at the dry bulk freight rate as a predictor of the stock market directly.

The dry bulk freight rate as an indicator of the stock market return relies on two key assumptions. The first assumption is that the supply of dry bulk vessels is inelastic, which leads to the change of freight rate reflecting the change of the demand on dry bulk materials. Bakshi (2011) and Zuccollo (2013) argue that high cost and a large amount of time are required to construct ships, which makes the supply of ships relatively inflexible. Thus, the information contained in the freight rate is important for people to judge the real demand of dry bulk materials, the economic situation and the stock market returns. The Efficient Market Hypothesis (EMH) states that all the available market information is incorporated in the stock prices immediately, and if it is true, the predictive effect of the freight rate on stock market returns would be very little. However, in real life it is usually not the case. Hong, Torous and Valkanov (2007) suggests that stock markets react with a delay to information contained in industry returns about their fundamentals and that information diffuses only gradually across markets. Alizadeh and Muradoglu (2011) explain the gradual diffusion of information happens in the dry bulk market to the stock market. My study is based on these finding and the gradual diffusion of information on the stock market is the second

14 assumption of this paper. So, if these two assumptions hold, all these five shipping indices introduced in this study could forecast stock market returns.

However, due to different bases the predictive effect of these shipping indices must be distinct. More precisely, as discussed in the last section, larger vessels like capesize and panamax require about more than two months for shipping while supramax and handysize vessels are shorter concerning voyage time. It would affect the predictability because raw materials which arrive at the destination faster could put into production earlier and thus influence the stock market performance. Therefore, my conjecture is that it takes longer period for the BCI and the BPI to predict the stock market returns than the BSI and the BHSI (Hypothesis I), which means the information contained in the freight rates of capesize and panamax vessels would be reflected to the stock market later than surparmax and handysize vessels. Besides, the BDI, the BCI and the BPI are much more volatile than the BSI and the BHSI, which makes the former more difficult to be observed and predicted. Thus, it also influences their prediction of the stock market returns. As the external shocks come to the shipping markets, indices of larger size vessels would be more volatile and lose the predictive effect on the stock market. I expect that the BSI and the BHSI would have better predictive effect than the BDI, the BPI and the BHSI (Hypothesis II).

3.2. Data description 3.2.1. Shipping indices

The monthly freight rate data of the BDI and its sub-indices are collected from Thomson Reuters DataStream. The BDI is the earliest index, and it is available from 1985, while its sub-indices were published later in 1998(BPI), 1999(BCI), 2005(BSI), and 2006(BHSI) respectively. Because in Nov 1999, the BDI was largely revised and its sub-indices are only available from the end of 20 century, the analyses in this study start from January 2000 to June 2015, with at most 186 observations for each variable. Followed by Bakshi et al. (2011) and Oomen (2012), logarithmic returns of freight rates are used to depict the change for all indices. The log return of the BDI is defined as follows:

퐵퐷퐼 푔푡−푖 = ln (퐵퐷퐼푡−푖) − ln (퐵퐷퐼푡−푖−1)

Where 퐵퐷퐼푡−푖 is the BDI at month “t-i”, for i equals to 0,1,2,3 and 4, and the formula is also adapted to other shipping indices, such as the BCI, the BPI etc.

Table 4 below shows that even though the average (logarithmic) change of all shipping indices are negative, the volatility of the index is decrease with the size of vessels. The BSI and the BHSI display a higher kurtosis and lower skewness than other indices, and all indices point out their movement away from normality.

Table 4 Descriptive statistics of the beginning-of-the-month logarithmic returns of the BDI and all its sub-indices. The BDI, the BCI and the BPI are available from January 2000 until June 2015. The BSI and the BHSI were only introduced in July 2005 and June 2006.

No.of Minimum Maximum Mean Std.dev. Skewness Kurtosis observations BDI 186 -0.563 0.309 -0.002 0.102 -1.336 9.378 BCI 186 -0.631 0.440 -0.002 0.137 -0.902 6.761 BPI 186 -0.502 0.298 -0.002 0.109 -0.938 7.149 BSI 119 -0.667 0.469 -0.004 0.106 -1.613 17.770 BHSI 108 -0.606 0.319 -0.004 0.091 -2.520 20.540

3.2.2. MSCI stock market indices

Following Driesprong et al. (2008), Bakshi et al. (2011) and Oomen (2012), in this study, a series of Morgan Stanley Capital International (MSCI) indices are used to stand for stock market indices. MSCI Indices include three benchmark: MSCI World, MSCI Europe, and MSCI Pacific and 15 developed and undeveloped countries respectively. The selection of benchmarks and countries is mainly due to the representativeness and data availability. The logarithmic returns of the stock markets are calculated in the same way with the shipping indices, and the formula is as follows:

푀푆퐶퐼 푟푡 = ln (푀푆퐶퐼푡) − ln (푀푆퐶퐼푡−1)

Where 푀푆퐶퐼푡 is MSCI Indices at month “t”. The average (logarithmic) returns of MSCI Indices are presented below in Table 5. The statistics illustrate that compared with developed countries, undeveloped countries have higher average returns and are more volatile. The negative skewness shows that MSCI Indices have an asymmetrical distribution with a long tail to the left and the kurtosis displays a sharper peak and fatter tails than normal distribution.

Table 5 Descriptive statistics of the beginning-of-the-month logarithmic returns of MSCI Indices of the World, Europe, Pacific, 15 developed countries and 15 undeveloped countries. All series start from January 2000 and end on June 2015.

No.of Minimum Maximum Mean Std.dev. Skewness Kurtosis observations MSCI World 186 -0.091 0.059 0.001 0.022 -0.826 5.019 MSCI Europe 186 -0.101 0.068 0.000 0.026 -0.681 4.493 MSCI Pacific 186 -0.089 0.051 0.000 0.022 -0.551 3.782 Developed Countries Australia 186 -0.060 0.045 0.002 0.018 -0.578 3.448 Canada 186 -0.080 0.071 0.002 0.021 -0.829 5.448 Denmark 186 -0.081 0.062 0.003 0.024 -0.855 4.554 France 186 -0.080 0.049 0.000 0.023 -0.656 3.528 Germany 186 -0.099 0.074 0.001 0.028 -0.807 4.235 HK 186 -0.099 0.079 0.001 0.027 -0.483 4.408 Italy 186 -0.078 0.070 -0.001 0.026 -0.471 3.432 Japan 186 -0.109 0.063 0.000 0.024 -0.567 4.503 Netherlands 186 -0.108 0.049 0.000 0.025 -1.138 5.235 Singapore 186 -0.108 0.092 0.001 0.027 -0.633 5.406 Spain 186 -0.088 0.075 0.000 0.027 -0.343 3.849 Sweden 186 -0.081 0.080 0.001 0.028 -0.443 3.743 Switzerland 186 -0.066 0.053 0.001 0.019 -0.761 4.310 UK 186 -0.065 0.050 0.000 0.019 -0.720 3.958 USA 186 -0.081 0.063 0.001 0.021 -0.773 4.910 Undeveloped Countries Brazil 186 -0.116 0.069 0.003 0.030 -0.384 3.725 China 186 -0.130 0.086 0.002 0.036 -0.629 4.181 Colombia 186 -0.107 0.085 0.006 0.031 -0.198 3.875 Czech Republic 186 -0.121 0.078 0.003 0.029 -0.377 4.549 Egypt 186 -0.127 0.128 0.005 0.041 -0.120 3.708 Hungary 186 -0.168 0.090 0.001 0.035 -0.655 5.478 India 186 -0.108 0.118 0.004 0.035 -0.296 3.813 Indonesia 186 -0.129 0.089 0.005 0.034 -0.486 4.164 Korea 186 -0.095 0.097 0.002 0.031 -0.140 3.746 Malaysia 186 -0.065 0.060 0.002 0.020 -0.191 4.016 Mexico 186 -0.095 0.067 0.004 0.026 -0.391 3.902 Poland 186 -0.125 0.088 0.001 0.032 -0.269 4.186 Russia 186 -0.167 0.201 0.004 0.045 -0.247 5.887 South Africa 186 -0.072 0.062 0.004 0.023 -0.121 3.196 Taiwan 186 -0.101 0.098 0.000 0.030 -0.128 3.875

After I generate logarithmic returns of all variables, Dickey-Fuller tests are applied to check stationary of all variables. The tests show that all of them are stationary, and thus the further research can be conducted.

3.3. Econometric model Comparing extensive predictability literature8, I use a distributed lag model to examine the first conjecture stated in Section 3.1. In the model, both the current values of the shipping index variable and the lagged (past period) values of that are included to predict current values of the stock market return. To determine how many lagged variables should be included in the model, I apply the same method, polynomial transformation, introduced by Almon (1965) and find that four lagged variables of shipping indices should be involved. It is also in accord with the reality, since the longest voyage time of dry bulk vessels would not excess four months, and it implies that the change of freight rate beyond four months ahead have no effect on the stock market return of this month. Therefore, in this study the basic regression could be formed as follows:

푀푆퐶퐼 퐵 퐵 퐵 퐵 퐵 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 휀푡 (3.4.1)

푀푆퐶퐼 Where 푟푡 is the beginning-of-the-month logarithmic return of MSCI Indices, at time 푡. The 퐵 independent variable 푔푡 denotes the beginning-of-the-month logarithmic returns of five shipping 퐵 indices, at time t. The remaining independent variables are the different lag size of 푔푡 , lag size from one month to four months respectively. Moreover, 훼 is the constant and 휀푡 is the error term. In this study ordinary least squares (OLS) estimation is used to the linear regression model (3.4.1).

The individual coefficient is the marginal effect of a shipping index on the MSCI Index, and the sum of all coefficients is a long-term cumulative effect. By applying model (3.4.1), the pattern of how the shipping index affects the stock market return over time can be observed. Note that coefficient 훽0 captures the immediate effect, and coefficients from 훽1 to 훽4 capture the 퐵 cumulative lagged effects. Theoretically, 푔푡 is a contemporaneous variable without too much prediction, whereas the lagged variables have predictive effects. Therefore, according to the first hypothesis, I expect the sum of 훽1 to 훽4 is larger than 훽0 for all shipping indices, which means the lagged effect from month 푡 − 1 to 푡 − 4 is more important than the immediate effect at month 푡. Moreover, the coefficients 훽2, 훽3 and 훽4 should be more important than 훽0 and 훽1 for the regression of the BDI, the BCI and the BPI respectively, because the former denotes more

8 For example Lewellen (2004) and Driesprong et al. (2008)

18 than one-month delay of the stock market reacting to the shipping index. The opposite situation would be for that of the BSI and the BHSI.

To test Hypothesis II, all shipping indices and their lags are included in the regression as follows:

푀푆퐶퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐶퐼 퐵퐶퐼 퐵퐶퐼 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 훾0푔푡 + 훾1푔푡−1 + 훾2푔푡−2 + 퐵퐶퐼 퐵퐶퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푆퐼 퐵푆퐼 훾3푔푡−3 + 훾푔푡−4 + 훿0푔푡 + 훿1푔푡−1 + 훿2푔푡−2 + 훿3푔푡−3 + 훿4푔푡−4 + 휃0푔푡 + 휃1푔푡−1 + 퐵푆퐼 퐵푆퐼 퐵푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 휃2푔푡−2 + 휃3푔푡−3 + 휃4푔푡−4 + 휌0푔푡 + 휌1푔푡−1 + 휌2푔푡−2 + 휌3푔푡−3 + 휌4푔푡−4 + 휀푡

(3.4.2)

푀푆퐶퐼 Where 푟푡 is the beginning-of-the-month logarithmic return of MSCI Indices, at time 푡. The 퐵퐷퐼 퐵퐶퐼 퐵푃퐼 퐵푆퐼 퐵퐻푆퐼 independent variables 푔푡 , 푔푡 , 푔푡 , 푔푡 , and 푔푡 denote the beginning-of-the-month logarithmic returns of the BDI and its sub-indices respectively at time 푡 . The remaining independent variables are the different lag size of them, lag size from one month to four months respectively. Moreover, 훼 is the constant and 휀푡 is the error term. Linear regression model (3.4.2) is estimated by ordinary least squares (OLS) and based on the estimation results, I test whether all coefficients are significant different from zero. Since I also conjecture that smaller indices such as the BSI and the BHSI have better effects on the stock market return, the expectation is variables of the BSI and the BHSI remain significant and outperform other indices as variables.

4. Empirical Results In this section, I present the empirical results and try to accept or reject the hypotheses discussed in Section 3.1. Firstly, the analyses regarding to lag size of the BDI and its sub-indices are made by applying the basic model (3.4.1), and then I use the model (3.4.2) to compare the predictive effect on all shipping indices. F-test and robustness test are also included to confirm the findings based on the estimations and at the end of this part a conclusion will be made.

4.1. Analyses on the lag size

4.1.1. Lag size of the BDI Based on model (3.4.1), I apply model (4.1.1) below to analyze lags variables of the BDI as a predictor of the stock markets.

푀푆퐶퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 휀푡 (4.1.1)

The results are presented in Table 7 below, and it can be found that from time 푡 − 4 to 푡 coefficients of the BDI decrease and then increase for MSCI World. To be more precise, an increase of one standard deviation in the return of the BDI will four months later result in a growth of 0.42% (0.102×0.041) on the world stock market return, while an increase of one standard deviation in the return of the BDI will two month later affect the world stock market return by an increase of 0.18%. It means the four-month lag variable has a greater effect on the world stock market return than the two-month lag variable. All the coefficients on MSCI World are significant with t-values different from zero at the 10% level, and they are all positive with economic significance as well. For European area, one-month lag coefficient is not significant, but coefficients 훽0 and 훽4 are large, which is also the case in Pacific area.

As a whole, significant results of developed countries almost double those of undeveloped countries, and the average adjusted R-squared of the former is 3.16% which is about 1% higher than the latter. Moreover, two-month, three-month and four-month lag variables are more important than one-month and without lag variables in developed nations, while significant outcomes are dispersive among five coefficients in undeveloped countries.

Table 7

푀푆퐶퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 Estimation results of regression: 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 휀푡 for the period 2000-2015, the bold t-values are significant different from zero at the 10% level. Adjusted R-squared calculates the proportion of the variation in the dependent variable accounted by the explanatory variables and (1−푅2)(푁−1) the formula is 퐴푑푗푢푠푡푒푑 푅2 = 1 − Where: R2 = sample R-square; p = Number of variables; N = (푁−푝−1) total sample size.

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared MSCI World 0.037 0.025 0.018 0.022 0.041 6.58% 2.83 2.21 2.09 1.97 3.09

MSCI Europe 0.042 0.022 0.020 0.031 0.049 6.31% 2.72 1.65 1.94 2.34 3.16

MSCI Pacific 0.045 0.017 0.012 0.022 0.041 3.77% 2.71 1.24 1.08 1.54 2.47

Developed Countries

Australia 0.016 0.021 0.013 0.011 0.034 3.41% 1.30 2.04 1.61 1.06 2.86

Canada 0.038 0.024 0.017 0.020 0.034 5.62% 2.90 2.13 2.00 1.80 2.57

Denmark 0.028 0.013 0.020 0.031 0.028 2.71% 1.75 0.96 1.90 2.28 1.76

France 0.015 0.012 0.020 0.030 0.034 2.49% 0.95 0.91 1.91 2.22 2.11

Germany 0.011 0.022 0.026 0.030 0.040 2.77% 0.62 1.45 2.21 1.96 2.23

HK 0.048 0.030 0.018 0.012 0.014 1.47% 2.22 1.60 1.22 0.67 0.65

Italy 0.003 0.017 0.027 0.035 0.042 2.67% 0.18 1.03 2.16 2.18 2.20

Japan 0.049 0.018 0.016 0.024 0.020 3.09% 2.89 1.24 1.44 1.64 1.21

Netherlands 0.029 0.022 0.019 0.022 0.037 3.31% 1.84 1.66 1.80 1.68 2.36

Singapore 0.048 0.043 0.028 0.019 0.030 4.73% 2.63 2.76 2.30 1.18 1.65

Spain 0.024 0.023 0.025 0.032 0.045 3.11% 1.26 1.43 2.01 1.98 2.37

Sweden 0.040 0.018 0.021 0.028 0.018 1.71% 2.03 1.09 1.63 1.68 0.94

Switzerland 0.006 0.022 0.024 0.023 0.027 3.02% 0.46 1.87 2.63 1.91 1.97

UK 0.017 0.008 0.008 0.016 0.032 2.09% 1.30 0.75 0.96 1.50 2.56

USA 0.027 0.028 0.020 0.017 0.035 5.22% 2.08 2.56 2.32 1.56 2.71

Undeveloped Countries

Brazil 0.080 0.027 0.009 0.017 0.046 0.43% 1.95 0.78 0.31 0.48 1.11

China 0.070 0.039 0.018 0.011 0.022 1.97% 2.49 1.62 0.97 0.46 0.79

Colombia 0.065 0.015 0.009 0.017 0.012 1.88% 2.82 0.78 0.56 0.85 0.52

Czech Republic 0.030 0.035 0.027 0.022 0.035 2.55% 1.41 1.93 1.93 1.23 1.66

Egypt 0.044 0.029 0.059 0.085 0.056 7.82% 1.65 1.28 3.35 3.73 2.09

Hungary 0.035 0.030 0.026 0.028 0.042 1.03% 1.21 1.23 1.36 1.15 1.49

India 0.064 0.036 0.029 0.033 0.042 4.53% 2.75 1.84 1.88 1.69 1.81

Indonesia 0.044 0.054 0.043 0.034 0.047 2.73% 1.52 2.18 2.24 1.37 1.63

Korea 0.005 0.047 0.029 -0.002 0.007 0.49% 0.18 2.23 1.73 -0.08 0.27

Table 7-Continued

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared India 0.064 0.036 0.029 0.033 0.042 4.53% 2.75 1.84 1.88 1.69 1.81

Indonesia 0.044 0.054 0.043 0.034 0.047 2.73% 1.52 2.18 2.24 1.37 1.63

Korea 0.005 0.047 0.029 -0.002 0.007 0.49% 0.18 2.23 1.73 -0.08 0.27

Malaysia 0.040 0.022 0.018 0.021 0.021 0.87% 1.79 1.15 1.25 1.11 0.93

Mexico 0.054 0.020 0.007 0.016 0.049 2.80% 2.58 1.14 0.48 0.87 2.35

Poland 0.014 0.030 0.025 0.020 0.037 -0.28% 0.40 1.06 1.14 0.71 1.09

Russia 0.104 0.052 0.059 0.067 0.015 2.69% 2.26 1.32 1.95 1.71 0.33

South Africa 0.034 0.006 0.002 0.009 0.016 0.33% 2.01 0.44 0.15 0.60 0.93

Taiwan 0.041 0.035 0.033 0.031 0.029 1.53% 1.54 1.58 1.87 1.38 1.10

4.1.2. Lag size of the sub-indices The BCI

As mentioned above, the basic model (3.4.1) is also applied for all sub-indices, and the regression results of the BCI are shown in Table 8. The results indicate that all the BCI variables are significant on MSCI World. However, one-month lag of the BCI loses prediction on European Index and both one-month and two-month lag variables are not significant on Pacific Index. Predictive effects of three-month and four-month lags are larger than that of other lag sizes for three benchmark indices. Taking look at the cumulative predictive effect of the BCI on the world stock market (without immediate effect), I find that if the return of the BCI increased by one standard deviation every month during the last four month, the return of the world stock market would grow by 1.11% [0.137×(0.017+0.016+0.021+0.027)] at this month, which is also economic significance compared with the monthly average return (0.1%) of MSCI World.

Compared to developed countries, undeveloped countries have less significant results but higher average adjusted R-squared. Generally, significant results on 훽2, 훽3 and 훽4 are more than those on 훽0 and 훽1, even though all variables of the BCI are explanative on the stock markets of the USA and India. Developed countries such as France, Germany, and Japan are significant on coefficients 훽2, 훽3, but it is not the case on countries like Russia, Brazil and China belonging to undeveloped market with huge impact on raw materials import and export. In these undeveloped countries, the BCI is indicative only within two months.

Table 8

푀푆퐶퐼 퐵퐶퐼 퐵퐶퐼 퐵퐶퐼 퐵퐶퐼 퐵퐶퐼 Estimation results of regression: 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 휀푡 for the period 2000-2015, the bold t-values are significant different from zero at the 10% level. Adjusted R-squared calculates the proportion of the variation in the dependent variable accounted by the explanatory variables

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared MSCI World 0.031 0.017 0.016 0.021 0.027 6.67% 2.84 1.82 2.01 2.15 2.42

MSCI Europe 0.038 0.018 0.019 0.028 0.033 6.94% 2.85 1.58 2.01 2.41 2.49

MSCI Pacific 0.028 0.012 0.012 0.019 0.024 4.32% 2.50 1.17 1.47 1.94 2.10

Developed Countries

Australia 0.007 0.011 0.011 0.012 0.022 2.72% 0.75 1.44 1.70 1.53 2.37

Canada 0.032 0.017 0.015 0.020 0.025 6.99% 3.04 1.83 2.03 2.18 2.30

Denmark 0.023 0.009 0.011 0.017 0.016 1.39% 1.84 0.85 1.28 1.60 1.24

France 0.018 0.008 0.015 0.023 0.017 1.87% 1.44 0.72 1.68 2.13 1.37

Germany 0.016 0.017 0.020 0.023 0.023 1.77% 1.06 1.29 1.92 1.79 1.53

HK 0.040 0.022 0.016 0.016 0.014 3.85% 2.83 1.75 1.57 1.25 0.98

Italy 0.013 0.008 0.015 0.026 0.034 3.37% 0.97 0.71 1.59 2.22 2.48

Japan 0.028 0.015 0.017 0.020 0.012 2.99% 2.24 1.41 1.90 1.84 0.97

Netherlands 0.026 0.015 0.014 0.017 0.022 2.43% 2.00 1.35 1.47 1.49 1.65

Singapore 0.027 0.031 0.022 0.013 0.019 3.92% 1.96 2.59 2.22 1.06 1.35

Spain 0.020 0.022 0.019 0.020 0.031 3.16% 1.41 1.74 1.89 1.60 2.14

Sweden 0.026 0.015 0.016 0.020 0.013 1.10% 1.74 1.10 1.51 1.47 0.82

Switzerland 0.012 0.020 0.019 0.015 0.016 4.20% 1.28 2.33 2.72 1.84 1.67

UK 0.016 0.003 0.007 0.016 0.017 1.63% 1.61 0.31 0.95 1.82 1.73

USA 0.027 0.018 0.015 0.017 0.024 4.92% 2.45 1.84 1.89 1.78 2.13

Undeveloped Countries

Brazil 0.058 0.028 0.015 0.013 0.018 6.74% 3.75 2.12 1.37 0.97 1.17

China 0.041 0.020 0.014 0.013 0.005 1.07% 2.15 1.19 1.04 0.78 0.28

Colombia 0.047 0.015 0.011 0.018 0.018 3.28% 2.92 1.07 0.97 1.27 1.10

Czech Republic 0.025 0.019 0.018 0.020 0.023 1.98% 1.66 1.45 1.68 1.52 1.50

Egypt 0.036 0.020 0.046 0.068 0.041 7.39% 1.71 1.08 3.05 3.68 1.92

Hungary 0.030 0.014 0.014 0.021 0.025 0.69% 1.60 0.84 1.06 1.29 1.30

India 0.062 0.035 0.024 0.026 0.040 7.72% 3.45 2.23 1.86 1.68 2.23

Indonesia 0.035 0.041 0.027 0.020 0.046 6.03% 2.01 2.69 2.17 1.29 2.59

Korea 0.000 0.026 0.021 0.005 -0.001 0.08% 0.02 1.82 1.80 0.36 -0.07

Table 8 - Continued

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared

Malaysia 0.019 0.016 0.014 0.013 0.012 1.69% 1.71 1.62 1.76 1.32 1.04

Mexico 0.034 0.024 0.014 0.015 0.038 5.95% 2.57 2.02 1.45 1.31 2.81

Poland 0.019 0.019 0.018 0.019 0.026 0.98% 1.11 1.30 1.49 1.28 1.55

Russia 0.078 0.028 0.037 0.052 0.022 6.63% 3.31 1.38 2.18 2.50 0.94

South Africa 0.021 0.001 0.005 0.014 0.006 0.08% 1.70 0.07 0.63 1.32 0.47

Taiwan 0.011 0.024 0.025 0.019 0.012 1.07% 0.67 1.69 2.19 1.38 0.72

The BPI As for BPI (results displayed in Table 9), MSCI Word and Europe benchmark illustrate the same pattern, in which only coefficients of three-month lag are not significant, and one-month lag has largest predictive effect on the stock market return. In Pacific area, only two-month lag and without lag variables are significant. More accurately, if we take a look at the total effect of the BPI on the world stock market (MSCI World), it shows increases of one standard deviation of the BPI once a month for five times would result in a raising of the stock market return by 1.17% [0.109×(0.028+0.031+0.024+0.024)], which is more than five times of monthly average return of the BPI.

Generally, developed and undeveloped countries have the same number of significant results on all different lag size. It is easy to find that 훽1 and 훽2 are significant for almost all countries, while three-month and four-month lags of the BPI are predictive on the stock market returns only for just a few countries like Italy, Egypt and Hungary. Adjusted R-squared is about 4% for all countries.

Table 9

푀푆퐶퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 Estimation results of regression: 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 휀푡 for the period 2000-2015, the bold t-values are significant different from zero at the 10% level. Adjusted R-squared calculates the proportion of the variation in the dependent variable accounted by the explanatory variables.

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared MSCI World 0.028 0.031 0.024 0.018 0.024 6.54% 2.08 2.64 2.60 1.52 1.74

MSCI Europe 0.032 0.033 0.026 0.022 0.031 5.98% 1.95 2.35 2.40 1.60 1.94

MSCI Pacific 0.034 0.020 0.018 0.019 0.017 3.93% 2.33 1.59 1.80 1.54 1.16

Table 9 - Continued

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared Developed Countries

Australia 0.020 0.019 0.017 0.016 0.017 4.54% 1.77 2.01 2.34 1.69 1.51

Canada 0.033 0.025 0.020 0.018 0.021 5.91% 2.45 2.17 2.20 1.60 1.57

Denmark 0.029 0.019 0.019 0.022 0.023 3.50% 1.92 1.47 1.80 1.64 1.49

France 0.011 0.022 0.021 0.020 0.027 2.86% 0.74 1.67 2.09 1.53 1.79

Germany 0.010 0.031 0.025 0.019 0.037 3.33% 0.57 2.00 2.09 1.22 2.06

HK 0.056 0.026 0.023 0.019 -0.011 4.98% 3.02 1.66 1.82 1.18 -0.62

Italy -0.004 0.023 0.027 0.024 0.029 2.97% -0.23 1.59 2.40 1.67 1.74

Japan 0.043 0.025 0.023 0.021 0.000 5.54% 2.86 1.89 2.29 1.64 0.02

Netherlands 0.023 0.027 0.023 0.019 0.022 3.19% 1.45 1.94 2.10 1.35 1.39

Singapore 0.045 0.043 0.029 0.015 0.013 6.31% 2.56 2.85 2.46 1.00 0.71

Spain 0.015 0.029 0.022 0.017 0.036 3.07% 0.87 1.89 1.87 1.15 2.03

Sweden 0.034 0.022 0.019 0.018 0.011 1.70% 1.84 1.40 1.55 1.13 0.60

Switzerland 0.012 0.025 0.022 0.016 0.021 4.69% 1.00 2.39 2.66 1.54 1.76

UK 0.017 0.013 0.011 0.013 0.020 1.94% 1.36 1.25 1.38 1.25 1.65

USA 0.022 0.033 0.024 0.014 0.022 5.69% 1.62 2.82 2.66 1.24 1.59

Undeveloped Countries

Brazil 0.077 0.028 0.006 0.003 0.011 6.47% 3.87 1.62 0.45 0.20 0.55

China 0.071 0.040 0.022 0.013 0.005 4.31% 2.93 1.91 1.38 0.63 0.21

Colombia 0.054 0.025 0.012 0.005 -0.009 2.04% 2.45 1.32 0.83 0.25 -0.41

Czech Republic 0.028 0.042 0.026 0.013 0.037 4.37% 1.43 2.54 2.00 0.79 1.92

Egypt 0.041 0.030 0.051 0.068 0.048 6.76% 1.57 1.35 2.88 3.02 1.83

Hungary 0.043 0.027 0.027 0.035 0.040 3.69% 1.84 1.35 1.75 1.74 1.73

India 0.046 0.046 0.035 0.025 0.028 5.18% 2.09 2.44 2.38 1.34 1.27

Indonesia 0.075 0.049 0.038 0.036 0.038 8.96% 3.25 2.49 2.44 1.80 1.63

Korea 0.007 0.052 0.028 -0.008 0.002 1.85% 0.32 2.72 1.87 -0.40 0.09

Malaysia 0.024 0.027 0.023 0.014 0.006 2.31% 1.51 2.01 2.13 1.04 0.36

Mexico 0.040 0.025 0.012 0.009 0.025 3.43% 2.39 1.73 1.02 0.60 1.48

Poland 0.012 0.039 0.024 0.010 0.039 2.14% 0.54 2.12 1.68 0.53 1.78

Russia 0.107 0.061 0.049 0.040 0.002 7.04% 3.32 2.21 2.26 1.43 0.05

South Africa 0.030 0.009 0.000 0.002 0.012 0.43% 1.96 0.69 0.03 0.15 0.79

Taiwan 0.030 0.042 0.028 0.010 0.011 2.79% 1.50 2.45 2.05 0.56 0.57

The BSI

In this part the result of the regressions on the BSI (shown in Table 10) is discussed. Two-month and three-month lags of the BSI have no effect on three benchmark indices. Precisely, if returns of the BSI increase once a month for five times, the stock market returns of the world (MSCI World), European countries (MSCI Europe) and countries in the Pacific region (MSCI Pacific) would increase by 1.47%, 1.7% and 1% respectively, which means the BSI have a greater impact on the stock market of European countries than other two regions.

Looking at countries in detail, the findings for developed and undeveloped countries are quite different. Most significant results obtained for developed countries are 훽0 and 훽4 , while for undeveloped countries are 훽0, 훽1 and 훽4. However, there is a common situation for almost all nations that three-month lag of the BSI as an indicator of the stock market is not significant. The adjusted R-squared varies among all countries. Canada, Singapore, Czech Republic, Indonesia and Russia show high adjusted R-square with more than 12%, but Hong Kong, Sweden, UK and China imply a very low R-square about 4%.

Table 10

푀푆퐶퐼 퐵푆퐼 퐵푆퐼 퐵푆퐼 퐵푆퐼 퐵푆퐼 Estimation results of regression: 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 휀푡 for the period 2000-2015, the bold t-values are significant different from zero at the 10% level. Adjusted R-squared calculates the proportion of the variation in the dependent variable accounted by the explanatory variables and (1−푅2)(푁−1) the formula is 퐴푑푗푢푠푡푒푑 푅2 = 1 − Where: R2 = sample R-square; p = Number of variables; N = (푁−푝−1) total sample size

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared MSCI World 0.042 0.041 0.021 0.015 0.056 11.19% 2.09 2.42 1.54 0.87 2.83

MSCI Europe 0.051 0.040 0.021 0.022 0.070 10.26% 2.09 1.95 1.32 1.09 2.92

MSCI Pacific 0.047 0.015 0.008 0.020 0.050 7.29% 2.39 0.95 0.58 1.23 2.61

Developed Countries

Australia 0.020 0.046 0.022 -0.001 0.029 8.13% 1.16 3.14 1.90 -0.05 1.66

Canada 0.044 0.040 0.018 0.010 0.049 12.52% 2.50 2.67 1.50 0.68 2.79

Denmark 0.053 0.025 0.016 0.024 0.046 8.90% 2.60 1.46 1.18 1.40 2.30

France 0.025 0.024 0.022 0.025 0.039 5.34% 1.21 1.41 1.65 1.49 1.95

Germany 0.024 0.033 0.027 0.027 0.050 7.00% 1.07 1.75 1.83 1.41 2.24

HK 0.059 0.023 0.020 0.027 0.020 4.77% 2.37 1.08 1.20 1.29 0.81

Table 10 - Continued

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared

Italy 0.007 0.028 0.028 0.029 0.053 5.52% 0.29 1.37 1.74 1.43 2.18

Japan 0.073 0.018 0.011 0.023 0.027 7.81% 3.22 0.93 0.71 1.23 1.19

Netherlands 0.049 0.027 0.014 0.020 0.053 8.58% 2.36 1.53 1.03 1.16 2.59

Singapore 0.072 0.044 0.022 0.021 0.053 13.93% 3.28 2.36 1.52 1.14 2.43

Spain 0.042 0.014 0.017 0.039 0.067 7.97% 1.73 0.69 1.07 1.91 2.77

Sweden 0.046 0.016 0.016 0.023 0.012 3.95% 2.22 0.95 1.19 1.31 0.59

Switzerland 0.016 0.033 0.024 0.017 0.039 10.93% 1.04 2.52 2.35 1.33 2.52

UK 0.021 0.019 0.011 0.013 0.041 4.88% 1.20 1.36 0.99 0.89 2.44

USA 0.031 0.048 0.024 0.007 0.050 11.08% 1.60 2.95 1.82 0.45 2.59

Undeveloped Countries

Brazil 0.089 0.031 -0.002 -0.002 0.039 8.97% 3.57 1.48 -0.14 -0.12 1.60

China 0.085 0.026 0.012 0.022 0.037 4.53% 2.64 0.98 0.57 0.83 1.17

Colombia 0.087 0.007 -0.024 -0.005 0.065 10.85% 3.76 0.35 -1.59 -0.28 2.85

Czech Republic 0.070 0.036 0.024 0.030 0.047 12.80% 3.16 1.94 1.66 1.61 2.14

Egypt 0.054 0.057 0.060 0.058 0.048 8.44% 1.50 1.92 2.54 1.95 1.36

Hungary 0.066 0.056 0.035 0.031 0.071 9.94% 2.16 2.19 1.73 1.20 2.34

India 0.080 0.054 0.038 0.036 0.053 11.60% 2.75 2.21 1.97 1.49 1.84

Indonesia 0.101 0.058 0.024 0.020 0.071 17.21% 3.86 2.64 1.35 0.91 2.72

Korea 0.046 0.056 0.011 -0.019 0.035 8.30% 2.09 3.03 0.77 -1.02 1.61

Malaysia 0.041 0.022 0.014 0.013 0.016 9.31% 2.96 1.89 1.52 1.13 1.13

Mexico 0.054 0.024 0.007 0.014 0.055 8.07% 2.59 1.38 0.51 0.79 2.67

Poland 0.061 0.034 0.027 0.032 0.042 8.32% 2.44 1.64 1.63 1.52 1.71

Russia 0.101 0.074 0.072 0.061 0.007 15.95% 2.93 2.55 3.13 2.11 0.20

South Africa 0.037 0.010 -0.005 0.004 0.045 4.67% 2.15 0.65 -0.40 0.29 2.63

Taiwan 0.054 0.033 0.032 0.027 -0.006 9.09% 2.49 1.80 2.18 1.46 -0.27

The BHSI

Interestingly, the results of the BHSI regressions are very consistent for all MSCI indices (shown in Table 11). 훽0 , 훽1 and 훽4 have huge impacts on the stock market return, for example, in European area, an increase of one standard deviation on the BHSI will four months later generate a growth on the European stock market return by 1%, which is also economic significance with

10 times of the average monthly return of MSCI World. However, two-month and three-month lags have no explanation on the stock market returns of almost all nations. The average adjusted R-square on all MSCI indices is about 16%, but in some countries like Colombia, Czech Republic and Hungary, the adjusted R-squares are more than 20%.

Table 11

푀푆퐶퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 Estimation results of regression: 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 휀푡 for the period 2000-2015, the bold t-values are significant different from zero at the 10% level. Adjusted R- squared calculates the proportion of the variation in the dependent variable accounted by the explanatory (1−푅2)(푁−1) variables and the formula is 퐴푑푗푢푠푡푒푑 푅2 = 1 − Where: R2 = sample R-square; p = Number of (푁−푝−1) variables; N = total sample size

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared MSCI World 0.086 0.050 0.005 0.001 0.093 20.58% 3.56 2.49 0.29 0.07 3.84

MSCI Europe 0.104 0.046 0.002 0.010 0.112 19.33% 3.59 1.91 0.09 0.43 3.86

MSCI Pacific 0.084 0.020 -0.005 0.013 0.075 15.04% 3.64 1.01 -0.34 0.66 3.25

Developed Countries

Australia 0.042 0.059 0.016 -0.011 0.055 14.73% 2.01 3.39 1.16 -0.64 2.66

Canada 0.076 0.050 0.009 0.001 0.071 20.87% 3.65 2.86 0.68 0.04 3.37

Denmark 0.079 0.044 0.011 0.009 0.068 15.26% 3.22 2.17 0.72 0.46 2.76

France 0.063 0.033 0.009 0.014 0.070 11.54% 2.56 1.60 0.58 0.69 2.86

Germany 0.071 0.040 0.013 0.017 0.084 13.41% 2.60 1.79 0.72 0.76 3.08

HK 0.096 0.037 0.007 0.011 0.055 9.70% 3.13 1.45 0.35 0.43 1.78

Italy 0.053 0.041 0.008 0.009 0.102 11.19% 1.78 1.64 0.39 0.37 3.40

Japan 0.109 0.022 0.002 0.017 0.034 13.03% 4.02 0.95 0.11 0.75 1.24

Netherlands 0.095 0.036 -0.004 0.004 0.087 18.62% 3.89 1.78 -0.24 0.18 3.59

Singapore 0.117 0.050 0.009 0.013 0.084 23.40% 4.52 2.34 0.53 0.61 3.25

Spain 0.085 0.014 -0.004 0.027 0.104 13.13% 2.86 0.57 -0.22 1.09 3.49

Sweden 0.079 0.022 0.007 0.016 0.030 9.00% 3.22 1.09 0.46 0.77 1.20

Switzerland 0.041 0.036 0.015 0.011 0.059 14.68% 2.21 2.32 1.23 0.70 3.11

UK 0.052 0.027 -0.002 0.002 0.070 12.07% 2.54 1.54 -0.11 0.11 3.41

USA 0.070 0.061 0.008 -0.009 0.087 19.90% 2.99 3.10 0.54 -0.46 3.72

Table 11 - Continued

훽 훽 훽 훽 훽 0 1 2 3 4 Adjusted R-squared Undeveloped Countries

Brazil 0.131 0.045 -0.015 -0.018 0.061 17.57% 4.52 1.84 -0.78 -0.76 2.09

China 0.144 0.024 -0.008 0.018 0.072 11.11% 3.74 0.75 -0.32 0.56 1.88

Colombia 0.128 0.018 -0.040 -0.022 0.099 25.40% 5.39 0.92 -2.65 -1.12 4.15

Czech Republic 0.115 0.040 0.007 0.020 0.081 21.53% 4.43 1.84 0.45 0.94 3.11

Egypt 0.117 0.065 0.042 0.047 0.079 14.20% 2.89 1.93 1.62 1.40 1.95

Hungary 0.142 0.070 0.010 0.015 0.138 23.23% 4.03 2.39 0.44 0.50 3.92

India 0.138 0.054 0.020 0.030 0.076 18.87% 4.11 1.91 0.93 1.07 2.25

Indonesia 0.154 0.056 0.011 0.022 0.093 25.39% 5.01 2.20 0.54 0.85 3.04

Korea 0.090 0.064 -0.009 -0.037 0.073 17.82% 3.50 3.02 -0.53 -1.73 2.83

Malaysia 0.058 0.031 0.012 0.008 0.023 13.67% 3.37 2.17 1.13 0.55 1.35

Mexico 0.097 0.018 -0.012 0.010 0.084 16.28% 3.96 0.86 -0.75 0.49 3.40

Poland 0.118 0.036 0.004 0.023 0.095 19.54% 4.11 1.52 0.22 0.95 3.31

Russia 0.171 0.092 0.060 0.049 0.031 23.85% 4.23 2.73 2.32 1.45 0.76

South Africa 0.069 0.010 -0.017 -0.001 0.067 14.43% 3.66 0.65 -1.38 -0.08 3.54

Taiwan 0.099 0.033 0.019 0.024 0.011 14.88% 3.82 1.53 1.16 1.09 0.43

4.1.3. F-test on the lag size From the analysis above, the number of significant results illustrates that for the BDI, the BCI and the BPI, two-month, three-month and four-month lags are more important, while for the BSI and the BHSI, one-month and four-month lags and without lag variables dominate. It seems discovery is consistent with the first hypothesis that coefficients 훽2 , 훽3 and 훽4 are more important than 훽0 and 훽1 for the BDI, the BCI and the BPI, but 훽0 and 훽1 are more important for the BSI and the BHSI. Furthermore, I conduct F-tests regarding to part of the regressions in Section 4.1.2 to determine whether these findings are robust.

Table 12

푀푆퐶퐼 퐵 퐵 퐵 퐵 퐵 P-values of F-test on the regression: 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 휀푡. The first row represents null hypotheses. The bold P-values are significant different from zero at the 10% level.

(1) (2) (3) (4)

훽0 = 훽1 + 훽2 + 훽3 + 훽4 훽1 = 훽2 + 훽3 + 훽4 훽1 + 훽4 = 훽2 + 훽3 훽0 + 훽1 + 훽4 = 훽2 + 훽3 BDI World 0.057 0.030 0.456 0.451 Europe 0.047 0.013 0.534 0.281 Pacific 0.252 0.018 0.883 0.341 BCI World 0.073 0.032 0.799 0.457 Europe 0.062 0.017 0.956 0.329 Pacific 0.188 0.031 0.972 0.355 BPI World 0.059 0.063 0.807 0.538 Europe 0.057 0.041 0.763 0.399 Pacific 0.367 0.038 0.699 0.528 BSI World 0.019 0.395 0.079 0.025 Europe 0.028 0.249 0.116 0.036 Pacific 0.226 0.125 0.310 0.069 BHSI World 0.127 0.628 0.004 0.000 Europe 0.176 0.394 0.010 0.000 Pacific 0.621 0.256 0.059 0.003

It reports that except the BHSI, for MSCI World and Europe, the cumulative lagged effect of all shipping indices overtakes their immediate effect, which can be concluded from column (1) with p-value close to zero. Moreover, column (2) displays that for the BDI, the BCI and the BPI, the sum of coefficients 훽2 , 훽3 and 훽4 is significant different from 훽1 , which means two-month, three-month and four-months lags as a whole have a large predictive effect on the stock market return. For the BSI and the BHSI, column (3) and (4) show that the sum of coefficients 훽0, 훽1, and 훽4 is significant different from the others, which implies two-month and three-month lag variables are less important.

Therefore, the results are generally consistent with my conjecture that the cumulative lagged effects of shipping indices are more important than immediate effects, even though the result for the BHSI is not significant. The insignificant result for the BHSI could be resulted from the monthly data involved in this study, and if it takes the BHSI shorter than one month to predict the stock market return, the lagged effect would be incorporated in coefficient 훽0, recognized as immediate effect. Moreover, according to the first hypothesis in Section 3.1, the BCI and the BPI should have larger lag size than the BSI and the BHSI cannot be approved, since four-month lag size is also significant for the BSI and the BHSI.

4.2. Best indicator As stated earlier, all shipping indices and their lagged values are included as independent variables on the regression (4.2.1) below to investigate which shipping index is the best indicator of the stock market return.

푀푆퐶퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐶퐼 퐵퐶퐼 퐵퐶퐼 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 훾0푔푡 + 훾1푔푡−1 + 훾2푔푡−2 퐵퐶퐼 퐵퐶퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푆퐼 + 훾3푔푡−3 + 훾푔푡−4 + 훿0푔푡 + 훿1푔푡−1 + 훿2푔푡−2 + 훿3푔푡−3 + 훿4푔푡−4 + 휃0푔푡 퐵푆퐼 퐵푆퐼 퐵푆퐼 퐵푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 + 휃1푔푡−1 + 휃2푔푡−2 + 휃3푔푡−3 + 휃4푔푡−4 + 휌0푔푡 + 휌1푔푡−1 + 휌2푔푡−2 퐵퐻푆퐼 퐵퐻푆퐼 + 휌3푔푡−3 + 휌4푔푡−4 + 휀푡

(4.2.1)

To save the space, the estimations are shown in Table 13 in Appendix. It displays that current values of the BDI, the BCI and the BPI have a good explanation on the stock market return with about 22 out of 33 MSCI Indices significant, but their lagged variables are only significant for a few nations. Lagged variables of the BDI, the BCI and the BPI are more important in prediction of the stock market return in developed countries like Denmark, Italy and Netherland than in undeveloped countries. The BSI and its lagged variables are not significant on the regression, which means the BSI is not a good predictor compared with other shipping indices. However, current value and one-month lagged value of the BHSI are significant for almost all countries, especially developed countries, while four-month lag size is a good indicator on undeveloped countries.

Although the regression results seem to illustrate that the BHSI outperforms other shipping indices, it cannot be concluded that sub-indices of the BDI are better indicators of the stock market return than the BDI. Therefore, I conduct different F-tests regarding to the regressions above to examine whether the predictive effect on sub-indices is better than that of the BDI. The null and alternative hypotheses are presented below:

4 4 4 4 H0: ∑푖=0 훽푖 − ∑푖=0 훾푖 = 0 H1: ∑푖=0 훽푖 − ∑푖=0 훾푖 ≠ 0

4 4 4 4 H0: ∑푖=0 훽푖 − ∑푖=0 훿푖 = 0 H1: ∑푖=0 훽푖 − ∑푖=0 훿푖 ≠ 0

4 4 4 4 H0: ∑푖=0 훽푖 − ∑푖=0 휃푖 = 0 H1: ∑푖=0 훽푖 − ∑푖=0 휃푖 ≠ 0

4 4 4 4 H0: ∑푖=0 훽푖 − ∑푖=0 휌푖 = 0 H1: ∑푖=0 훽푖 − ∑푖=0 휌푖 ≠ 0 31

The results of F-tests are reported in Table 14, and the p-values in the last column illustrate that sub-indices of the BDI have no difference on the BDI in prediction of the stock market return can be statistically rejected with p-values below 0.1. Although the significant results are not for all countries included in this research, the benchmark indices (MSCI Word and Europe) indicate that the BHSI is better indicator of the stock market than the BDI. Nevertheless, there is no evidence support that the BCI, the BPI and the BSI are better predictors.

Table 14. P-values of F statistics

4 4 4 4 The table lists p-values of F statistics on testing null hypotheses: ∑푖=0 훽푖 = ∑푖=0 훾푖 , ∑푖=0 훽푖 = ∑푖=0 훿푖 , 4 4 4 4 ∑푖=0 훽푖 = ∑푖=0 휃푖 and ∑푖=0 훽푖 = ∑푖=0 휌푖 . The bold P-values are significant different from zero at the 10% level.

The BDI VS The BCI The BDI VS The BPI The BDI VS The BSI The BDI VS The BHSI

MSCI World 0.453 0.682 0.931 0.076 MSCI Europe 0.647 0.969 0.759 0.074 MSCI Pacific 0.638 0.771 0.965 0.179 Australia 0.751 0.905 0.564 0.110 Canada 0.777 0.744 0.831 0.126 Denmark 0.130 0.138 0.032 0.071 France 0.674 0.563 0.356 0.288 Germany 0.802 0.658 0.402 0.297 HK 0.807 0.971 0.605 0.103 Italy 0.434 0.215 0.405 0.520 Japan 0.630 0.468 0.881 0.364 Netherlands 0.395 0.420 0.240 0.593 Singapore 0.664 0.879 0.831 0.058 Spain 0.279 0.210 0.613 0.517 Sweden 0.669 0.767 0.286 0.458 Switzerland 0.576 0.494 0.914 0.209 UK 0.639 0.660 0.771 0.144 USA 0.348 0.470 0.763 0.088 Brazil 0.845 0.362 0.602 0.305 China 0.739 0.627 0.642 0.386 Colombia 0.395 0.993 0.661 0.036 Czech Republic 0.861 0.879 0.737 0.414 Egypt 0.464 0.698 0.605 0.232 Hungary 0.979 0.879 0.651 0.063 India 0.479 0.799 0.583 0.322 Indonesia 0.176 0.193 0.538 0.048 Korea 0.669 0.951 0.682 0.303 Malaysia 0.853 0.890 0.394 0.365 Mexico 0.974 0.563 0.867 0.546 Poland 0.778 0.789 0.816 0.110 Russia 0.677 0.330 0.284 0.133 South Africa 0.418 0.283 0.252 0.710 Taiwan 0.585 0.925 0.930 0.149

4.3. Robustness analysis In previous part, comparisons are made and the BHSI is discovered to be the best indicator of the stock market return, but in this part, other common predictors would be incorporated into the robust test to investigate whether the shipping index have better predictive effect compared with others.

To test for predictability in the stock market returns, most studies in predictive literature look into several economic variables. Choudhury (2003) concludes that dividend yield could forecast stock market returns by performing time series and cross section analyses on the international markets. Deaves (2008) find that if the high price-earnings ratios have been high, stock prices have usually grown slowly both in the short term and the following decade. Sun (2008) and Kheradyar (2011) examine stock market returns predictability by including plenty of financial variables and the former find that earning yield, term spread, yield spread and inflation rate are good indicators, while the latter concludes the book-to-market ratio has the higher predictive power than dividend yield and earning yield.

In this study, in line with Sun (2008) and Oomen (2012), four common stock market return indicators are included next to the BHSI variables on the stock market of the USA to examine whether they remain significant and I employ regressions of the form:

푈푆퐴 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 훽5푋푡−1 + 휀푡

The dependent variable is the logarithmic return of the MSCI Index for the USA, and independent variables are current logarithmic return of the BHSI and lagged logarithmic return of it and 푋, while independent variables include four lags of the BHSI and one lag of 푋. 푋 is a vector of the logarithmic change in the following independent variables.

Dividend Yield: Difference between dividends and the lagged prices with the S&P500 as underlying9. The increase of dividends yield would have negative effect on the stock market return in the short run, since the corporation holds less cash after paying out dividends and is worth less, which would affect the performance on the stock market.

9 http://www.multpl.com/s-p-500-dividend-yield/table?f=m

Price Earnings Ratio (P/E Ratio): The log of prices divided by the log of earnings with the S&P500 as underlying and the data is collected from the same source as dividend yield above. The expectation is that the increase of the P/E ratio would indicate a decrease of the stock market return since the higher P/E ratio is a signal of market overpricing10.

Term Spread: Difference between the long term yield on government bond (10-year Government Bond) and short-term Treasury bill (6-month Government Bond). Data is collected from Federal Reserve Economic Data and the term spread could be calculated. The term spread could reflect the confidence of investors on the economy and the stock market between short-term and long- term period. So, I expect a negative coefficient of term spread on the stock market return.

Default Yield Spread: Difference between the yields of investment grade (Moody’s AAA) and below investment grade (Moody’s BAA) bonds. Data is collected from Federal Reserve Economic Data and the default yield spread could be calculated. The default yield spread would go up as the default risk increase or capital market is illiquidity, which have negative influences on the stock market return.

Table 15 below is the descriptive statistics of these four variables. It shows the term spread is the most volatile with highest average logarithmic return, and all of these variables are not normal distributed.

Table 15 Descriptive statistics of the beginning-of-the-month logarithmic returns of dividend yield (DY), price-earnings ratio (PE), term spread (TS) and default risk spread (DS). All series start from January 2000 and end on June 2015.

No.of Minimum Maximum Mean Std.dev. Skewness Kurtosis observations DY 186 -0.058 0.097 0.001 0.017 1.059 8.194 PE 186 -0.296 0.227 -0.001 0.039 -1.566 27.600 DS 186 -0.125 0.195 0.001 0.042 1.014 7.427 TS 186 -0.540 1.322 0.019 0.147 4.154 38.130

The regression results are presented in Table 16 below and the results illustrate that the BHSI is a good indicator, even though other common indicators are included. More specifically, regression (1) shows both dividend yield and the BHSI have a predictive effect on the US stock market

10 Shen (2000)

34 return, with coefficients of -0.61 and 0.124 (0.06+0.064) respectively. From regression (3), it can be found that one standard deviation growth of the default yield spread would one month later result in a decrease of 1.08% on the stock market return, but the BHSI increases by one standard deviation will four months later increase the stock market by 1.54% (0.091× 0.169). Regressions (2) and (4) illustrate the P/E ratio and the term spread don’t have significant effects on the stock market. Regression (5) shows consistent findings by including all variables, and it displays that the dividend yield and the default yield spread have a better predictive effect than the shipping index whereas shipping index outperforms the P/E ratio and the term spread as indicators. Moreover, one-month and four-month lags of the BHSI dominant the predictive effect, which is consistent with the findings in previous section.

Table 16

푈푆퐴 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 Estimation of regression: 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 훽5푋푡−1 + 휀푡 with changing independent variables from 2000 to 2015. The first row of a regression presents the coefficient and the lower number is the t-value, and the bold t-values are significant different from zero at the 10% level. N is the number of observations and R-sq denotes R-squared.

_cons 훽0 훽1 훽2 훽3 훽4 DY PE DS TS N R-sq.

(1) 0.003 0.033 0.060 -0.018 -0.007 0.064 -0.610 104 0.45

1.67 1.57 2.96 -0.91 -0.34 3.05 -6.05

(2) 0.003 0.061 0.074 -0.018 0.007 0.075 -0.025 104 0.25

1.49 2.47 3.04 -0.73 0.32 2.86 -0.51

(3) 0.001 0.035 0.086 -0.024 0.006 0.083 -0.257 73 0.51

0.64 1.42 3.68 -1.01 0.26 3.42 -5.09

(4) 0.004 0.067 0.076 -0.016 0.010 0.085 -0.001 101 0.26

1.63 2.72 3.18 -0.66 0.40 3.45 -0.05

(5) 0.002 0.029 0.076 -0.029 -0.003 0.077 -0.350 -0.013 -0.170 -0.003 70 0.58 0.81 1.14 3.11 -1.22 -0.14 2.94 -2.50 -0.27 -2.81 -0.10

5. Conclusion This study examines five dry bulk shipping indices (the BDI, the BCI, the BPI, the BSI and the BHSI) as indicators of the stock market return by looking at MSCI Indices. Distributed lag models are used to illustrate how these indices affect the stock market return as time goes by. Moreover, comparisons between the shipping index and other four common indicators are made to test the effect of predictability of the stock market returns.

The main findings presented in this paper show that dry bulk shipping indices have a positive effect on the stock market returns. More precisely, for the BDI, the BCI and the BPI, two-month, three-month and four-month lagged values have more predictive effect on the stock market returns, while for the BSI and the BHSI, the current value and one-month and four-month lagged values are more important in prediction. Furthermore, compared with the BDI, only the BHSI could outperforms the BDI as a predictor of the stock market return. Compared with other stock market indicators, the shipping index is better than the price-earnings ratio and the term spread, whereas it overtaken by the dividend yield and the default spread. Therefore, this study suggests that people should take the BHSI into consideration as making investment decisions or constructing portfolios.

However, there are some limitations in this study. Firstly, the relationship between shipping indices and the stock market is investigated only in a broad perspective without analysis on certain countries or sectors. What’s more due to the data of the BSI and the BHSI is only available after 2005 and 2006, sub period test could not be operated. Lastly, in this passage, I could not examine why there are so many significant results of four-month lag size on the BHSI, which is not completely consistent with one of my conjectures. So, I recommend further studies to look at these four sub-markets in more detail and could replenish this topic.

Appendix: Table 1.Main producers, users, exporters and importers of dry bulks11

Some major dry bulks and steel: Min producers, users, exporters and importers (Percentage world market share) Iron Ore exporters Iron Ore importers Grain exporters Grain importers Australia 49 China 67 United States 19 Asia 31 Brazil 27 Japan 11 Argentina 12 Developing America 21 South Africa 5 European Union 9 European Union 11 Africa 20 Canada 3 Republic of Korea 5 Australia 10 Western Asia 18 Sweden 3 Other 8 Ukraine 9 Europe 7 Other 13 Canada 8 Transition economies 3 Other 31 Coal exporters Coal importers Steel producers Steel users Indonesia 34 China 19 China 49 China 47 Australia 32 Japan 17 Japan 7 European Union 10 United States 9 European Union 16 United States 5 North America 9 Colombia 7 India 16 India 5 Transition economies 4 Russia Federation 7 Republic of Korea 11 Russia Federation 4 Developing America 3 South Africa 6 Taiwan 5 Republic of Korea 4 Western Asia 3 Canada 3 Malaysia 2 Germany 3 Africa 2 Other 2 Thailand 2 Turkey 2 Other 22 Other 12 Brazil 2 Ukraine 2 Other 17

11Source: Data source: UNCTAD Review of MaritimeTransport2014

Table 2. Modifications of BDI12

Time Modifications 1 Nov.1999 BDI replaced BFI. BDI was calculated as an equally weighted index of BPI, BCI and BHI. The factor of BDI was 0.998007990. 2 Jan. 2001 BHMI replaced BHI. So, BHMI was used for the calculation of BDI. 3 Jan. 2006 BSI replaced BHMI. So, BSI was used for the calculation of BDI. 2 Jan. 2007 BHSI was used for the calculation of BDI. The multiplier of BDI changed from 0.998007990 to 1.192621362. 1 July 2009 BDI calculation procedure changed. - BDI has been comprised solely of time charter routes, no longer including capsize voyage routes. - So, the formula has become as follows: BDI = {(Capesize_TCA+ Panamax_TCA + Supramax_TCA + Handy Size_TCA)/4}×0.113473601, Where TCA = Time Charter Average. The multiplier (0.113473601) was first applied when the BDI replaced BFI, and has changed over the years as the contributing indices and the methods of calculation have been modified.

12Source:Ko B W. An Application of Dynamic Factor Model to Dry Bulk Market [J]. KMI International Journal. of Maritime Affairs and Fisheries, 2011, 3(1): 69-81.

Table 3. Description of different kinds of vessels13

Vessel size Type Route Route description Cargoes (dwt) Delivery Gibraltar -Hamburg range, trans-Atlantic Transatlantic round voyage duration 30 -45 days, redelivery 172000/161000 Iron Ore, coal (TAC) Gibraltar-Hamburg range Delivery Amsterdam-Rotterdam-Antwerp range Fronthaul or passing Passero, redelivery China -Japan range, 172000/161000 Iron Ore, coal (FHC) Capesize duration about 65 days Transpacific Delivery China -Japan range, round voyage 172000/161000 Iron Ore, coal (TPC) duration 30-40 days, redelivery China-Japan range Delivery China -Japan range, redelivery Backhaul Amsterdam-Rotterdam-Antwerp range or passing 172000/161000 Coal (BHC) Passero, duration about 65 days A trans-Atlantic (including ECSA) round of 45/60 Transatlantic days on the basis of delivery and redelivery Skaw 74000/70000 Diverse (TAP) - Gibraltar range Basis delivery Skaw -Gibraltar range, for a trip to Fronthaul the Far East, redelivery Taiwan -Japan range, 74000/70000 Grain, iron ore, coal (FHP) duration 60/65 days Panamax Pacific round of 35/50 days either via Australia or Transpacific Grain, iron ore, coal, Pacific, delivery and redelivery Japan/South 74000/70000 (TPP) sulphur, bauxite Korea range Delivery Japan -South Korea range for a trip via Backhaul US West Coast-British Columbia range, Coal, cement clinker, 74000/70000 (BHP) redelivery Skaw-Gibraltar range, duration 50/60 coke days Delivery Antwerp/Skaw range for a trip of 60/65 Fronthaul days redelivery Singapore/Japan range including 45496/52454 grain, minor bulks (FHP) China Delivery South Korea/Japan range for 1 Surpramax Transpacific Australian or trans Pacific round voyage, for a Coal, iron ore minor 45496/52454 (TPP) 35/40 day trip, redelivery South Korea/Japan bulks range Backhaul Delivery South Korea/Japan range for a trip of 45496/52454 minor bulks (BHP) 60/65 days redelivery Gibraltar/Skaw range

13Source: MANUAL FOR PANELLISTS A Guide to Freight Reporting and Index Production November 2011

Table 3 - Continued

Delivery SE Asia for a trip via Australia, about 25/30 days, redelivery Singapore – Japan range 28000 including China. minor bulks Pacific basin Delivery S Korea – Japan range for a trip via Nopac of about 40/45 days, redelivery Singapore- 28000 Japan range including China. minor bulks Handysize Delivery Recalada – Rio de Janeiro for a trip about 35/45 days, redelivery Skaw – Passero 28000 Atlantic range minor bulks basin Delivery US Gulf for a trip about 35/45 days, via US Gulf or NC South America, redelivery Skaw – 28000 Passero range. minor bulks

Table 13

푀푆퐶퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐷퐼 퐵퐶퐼 Estimation results of regression: 푟푡 = 훼 + 훽0푔푡 + 훽1푔푡−1 + 훽2푔푡−2 + 훽3푔푡−3 + 훽4푔푡−4 + 훾0푔푡 + 퐵퐶퐼 퐵퐶퐼 퐵퐶퐼 퐵퐶퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푃퐼 퐵푆퐼 퐵푆퐼 훾1푔푡−1 + 훾2푔푡−2 + 훾3푔푡−3 + 훾푔푡−4 + 훿0푔푡 + 훿1푔푡−1 + 훿2푔푡−2 + 훿3푔푡−3 + 훿4푔푡−4 + 휃0푔푡 + 휃1푔푡−1 + 퐵푆퐼 퐵푆퐼 퐵푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 퐵퐻푆퐼 휃2푔푡−2 + 휃3푔푡−3 + 휃4푔푡−4 + 휌0푔푡 + 휌1푔푡−1 + 휌2푔푡−2 + 휌3푔푡−3 + 휌4푔푡−4 + 휀푡 for the period 2000-2015, the bold t-values are significant different from zero at the 10% level. Adjusted R-squared calculates the proportion of the variation in the dependent variable accounted by the explanatory variables.

(1) (2) (3) (4) (5) (6) (7) (8) (9) MSCI MSCI MSCI Australia Canada Denmark France Germany HK World Europe Pacific

훽0 -0.204 -0.255 -0.250 -0.183 -0.204 -0.208 -0.204 -0.191 -0.296 -1.92 -2.02 -2.43 -1.96 -2.17 -2.18 -1.88 -1.54 -2.27 훽1 -0.174 -0.162 -0.063 -0.028 -0.061 -0.101 -0.056 -0.003 -0.023 -1.7 -1.34 -0.64 -0.31 -0.68 -1.11 -0.53 -0.03 -0.18 훽2 0.112 0.169 0.108 0.137 0.156 0.237 0.183 0.123 0.063 1.05 1.34 1.04 1.46 1.66 2.48 1.68 0.99 0.48

훽3 0.004 0.009 0.040 0.048 0.015 0.322 0.161 0.158 0.009 0.03 0.07 0.39 0.52 0.16 3.42 1.51 1.29 0.07

훽4 0.056 0.120 0.032 -0.033 0.054 0.161 0.057 0.033 0.178 0.53 0.95 0.31 -0.35 0.57 1.68 0.52 0.26 1.36 훾0 0.094 0.124 0.096 0.065 0.091 0.083 0.091 0.095 0.131 2.12 2.35 2.24 1.69 2.31 2.09 2.01 1.84 2.4 훾1 0.057 0.055 0.010 -0.005 0.019 0.018 0.003 -0.008 -0.008 1.26 1.02 0.23 -0.14 0.46 0.43 0.07 -0.15 -0.15

훾2 -0.049 -0.064 -0.053 -0.049 -0.063 -0.112 -0.080 -0.045 -0.017 -1.04 -1.13 -1.16 -1.19 -1.51 -2.65 -1.66 -0.81 -0.29

훾3 0.017 0.025 0.003 -0.002 0.022 -0.112 -0.045 -0.046 0.005 0.37 0.45 0.08 -0.05 0.53 -2.65 -0.95 -0.84 0.08 훾4 0.000 -0.024 0.007 0.052 0.001 -0.059 -0.013 -0.003 -0.049 0 -0.42 0.14 1.24 0.01 -1.36 -0.27 -0.06 -0.82 훿0 0.099 0.108 0.137 0.102 0.086 0.106 0.080 0.065 0.168 2.08 1.91 2.98 2.46 2.04 2.5 1.65 1.18 2.89

훿1 0.025 -0.001 0.000 -0.013 -0.029 0.036 -0.008 -0.030 -0.012 0.45 -0.01 0.01 -0.27 -0.59 0.72 -0.14 -0.46 -0.17

훿2 -0.052 -0.087 -0.030 -0.054 -0.090 -0.103 -0.083 -0.065 -0.029 -0.88 -1.23 -0.52 -1.03 -1.7 -1.93 -1.36 -0.93 -0.39

훿3 -0.042 -0.069 -0.050 -0.025 -0.059 -0.125 -0.072 -0.076 -0.065 -0.77 -1.06 -0.95 -0.53 -1.22 -2.55 -1.29 -1.19 -0.97 훿4 -0.061 -0.090 -0.070 -0.024 -0.072 -0.077 -0.028 0.004 -0.151 -1.17 -1.46 -1.4 -0.54 -1.56 -1.67 -0.53 0.06 -2.37

휃0 -0.116 -0.159 -0.068 -0.015 -0.072 -0.106 -0.126 -0.185 -0.118 -1.36 -1.58 -0.82 -0.2 -0.95 -1.4 -1.45 -1.87 -1.13

휃1 -0.018 -0.056 -0.078 -0.059 -0.041 -0.247 -0.092 -0.096 -0.131 -0.2 -0.52 -0.9 -0.75 -0.52 -3.08 -1.01 -0.92 -1.19

휃2 -0.026 -0.052 0.003 -0.070 -0.058 -0.051 -0.028 -0.018 -0.059 -0.27 -0.45 0.03 -0.83 -0.68 -0.59 -0.28 -0.16 -0.5 휃3 0.033 0.014 0.008 -0.091 0.008 -0.176 -0.051 -0.039 0.016 0.35 0.12 0.09 -1.09 0.1 -2.05 -0.52 -0.35 0.13

휃4 -0.035 -0.057 0.023 -0.089 0.024 -0.033 -0.057 -0.055 -0.109 -0.39 -0.53 0.26 -1.11 0.3 -0.41 -0.61 -0.52 -0.98

휌0 0.252 0.347 0.215 0.100 0.223 0.256 0.273 0.341 0.279 2.88 3.35 2.55 1.32 2.89 3.28 3.07 3.35 2.61

휌1 0.182 0.233 0.155 0.189 0.184 0.354 0.205 0.185 0.185 1.98 2.13 1.73 2.35 2.26 4.29 2.18 1.72 1.64 휌2 -0.003 0.014 -0.056 0.011 0.050 0.053 -0.006 0.009 0.076 -0.03 0.12 -0.56 0.12 0.56 0.58 -0.05 0.07 0.61 휌3 0.022 0.079 0.057 0.098 0.047 0.125 0.046 0.032 0.094 0.23 0.68 0.61 1.14 0.55 1.43 0.46 0.28 0.78

휌4 0.125 0.147 0.068 0.159 0.053 0.052 0.100 0.090 0.181 1.5 1.48 0.84 2.18 0.72 0.69 1.17 0.92 1.76 _cons 0.002 0.001 0.001 0.001 0.002 0.005 0.001 0.003 0.004 1.16 0.59 0.65 0.75 1.25 2.62 0.5 1.1 1.41 N 104 104 104 104 104 104 104 104 104 R-sq 0.439 0.446 0.387 0.383 0.419 0.535 0.37 0.342 0.407

Table 13 - Continued

10 11 12 13 14 15 16 17 18 Italy Japan Netherlands Singapore Spain Sweden Switzerland UK USA

훽0 -0.271 -0.245 -0.155 -0.251 -0.182 -0.104 -0.081 -0.166 -0.158 -2.12 -2.04 -1.43 -2.11 -1.36 -0.93 -0.97 -1.84 -1.5

훽1 -0.040 -0.167 -0.022 -0.029 -0.018 -0.030 -0.255 -0.095 -0.209 -0.32 -1.45 -0.21 -0.25 -0.14 -0.28 -3.18 -1.1 -2.07

훽2 0.219 0.105 0.223 0.064 0.194 0.165 0.093 0.095 0.081 1.7 0.87 2.05 0.54 1.45 1.47 1.11 1.05 0.77

훽3 0.273 0.111 0.176 0.021 0.321 0.053 0.074 -0.020 -0.005 2.15 0.93 1.65 0.18 2.42 0.47 0.9 -0.22 -0.04 훽4 0.128 0.012 0.055 0.048 0.128 0.070 0.041 0.074 0.020 0.99 0.1 0.5 0.4 0.95 0.62 0.49 0.82 0.19 훾0 0.109 0.082 0.070 0.086 0.075 0.059 0.034 0.077 0.076 2.03 1.63 1.55 1.73 1.34 1.25 0.98 2.05 1.72

훾1 0.004 0.059 -0.001 0.005 0.016 -0.003 0.105 0.019 0.071 0.07 1.14 -0.02 0.1 0.27 -0.06 2.95 0.49 1.58

훾2 -0.107 -0.071 -0.107 -0.031 -0.093 -0.073 -0.042 -0.043 -0.041 -1.89 -1.34 -2.23 -0.58 -1.57 -1.46 -1.14 -1.07 -0.88 훾3 -0.083 -0.020 -0.056 0.006 -0.113 -0.008 -0.023 0.024 0.015 -1.46 -0.37 -1.16 0.12 -1.91 -0.15 -0.63 0.59 0.31 훾4 -0.023 0.002 -0.006 -0.003 -0.032 -0.017 -0.011 -0.016 0.013 -0.4 0.03 -0.12 -0.06 -0.52 -0.33 -0.29 -0.39 0.27

훿0 0.088 0.156 0.084 0.148 0.076 0.063 0.065 0.084 0.081 1.54 2.91 1.74 2.8 1.28 1.26 1.74 2.09 1.72

훿1 -0.045 0.057 0.011 -0.010 -0.019 0.016 0.080 0.019 0.047 -0.67 0.9 0.19 -0.16 -0.28 0.27 1.82 0.4 0.86 훿2 -0.113 0.032 -0.075 -0.042 -0.070 -0.039 -0.030 -0.032 -0.035 -1.58 0.48 -1.24 -0.63 -0.94 -0.62 -0.64 -0.63 -0.6 훿3 -0.165 -0.027 -0.058 -0.069 -0.153 -0.004 0.001 0.004 -0.022 -2.5 -0.44 -1.04 -1.13 -2.23 -0.07 0.02 0.09 -0.41

훿4 -0.099 -0.051 -0.037 -0.102 -0.068 -0.016 -0.012 -0.027 -0.034 -1.59 -0.86 -0.71 -1.76 -1.04 -0.29 -0.29 -0.61 -0.66

휃0 -0.138 -0.040 -0.154 -0.105 -0.014 -0.097 -0.109 -0.095 -0.106 -1.35 -0.42 -1.78 -1.1 -0.13 -1.08 -1.63 -1.33 -1.26 휃1 -0.086 -0.084 -0.101 -0.095 -0.021 -0.103 0.045 -0.047 0.017 -0.79 -0.83 -1.11 -0.95 -0.18 -1.09 0.64 -0.62 0.19 휃2 0.029 -0.001 -0.035 -0.047 0.125 -0.039 -0.011 -0.005 -0.021 0.25 -0.01 -0.35 -0.43 1.03 -0.38 -0.15 -0.06 -0.22

휃3 0.006 -0.039 -0.019 0.013 0.062 -0.061 -0.042 -0.022 0.042 0.05 -0.36 -0.2 0.12 0.51 -0.61 -0.56 -0.27 0.44

휃4 -0.031 0.068 -0.045 -0.039 -0.043 -0.139 -0.055 -0.072 -0.045 -0.29 0.67 -0.49 -0.38 -0.37 -1.45 -0.77 -0.93 -0.5

휌0 0.335 0.184 0.291 0.278 0.152 0.186 0.139 0.193 0.202 3.19 1.87 3.27 2.85 1.38 2.02 2.03 2.61 2.35 휌1 0.225 0.161 0.166 0.193 0.092 0.167 0.080 0.139 0.163 2.02 1.55 1.77 1.87 0.79 1.72 1.11 1.79 1.79

휌2 -0.016 -0.105 -0.030 0.056 -0.201 -0.065 -0.017 -0.052 -0.005 -0.13 -0.92 -0.28 0.49 -1.57 -0.6 -0.21 -0.61 -0.05

휌3 -0.004 0.045 -0.025 0.098 -0.064 0.084 0.011 0.052 -0.017 -0.03 0.41 -0.25 0.9 -0.52 0.82 0.15 0.63 -0.17

휌4 0.109 -0.020 0.114 0.166 0.107 0.125 0.093 0.102 0.133 1.08 -0.21 1.33 1.77 1.02 1.41 1.42 1.44 1.61 _cons -0.001 0.001 0.002 0.002 0.001 0.003 0.001 0.001 0.003 -0.48 0.52 1.14 1 0.32 1.28 0.79 0.83 1.57 N 104 104 104 104 104 104 104 104 104 R-sq 0.41 0.381 0.411 0.414 0.359 0.311 0.387 0.391 0.415

Table 13 - Continued

19 20 21 22 23 24 25 26 Brazil China Colombia Czech Republic Egypt Hungary India Indonesia

훽0 -0.272 -0.290 -0.084 -0.333 -0.593 -0.367 -0.202 -0.349 -2.06 -1.7 -0.78 -3 -3.52 -2.49 -1.31 -2.45

훽1 -0.039 0.030 -0.057 -0.060 -0.022 -0.264 0.007 -0.051 -0.31 0.19 -0.55 -0.56 -0.14 -1.87 0.05 -0.37

훽2 0.202 0.177 -0.017 0.123 0.136 0.048 0.055 -0.022 1.52 1.03 -0.16 1.11 0.81 0.32 0.35 -0.15

훽3 0.126 -0.010 -0.026 0.201 0.132 0.190 -0.036 0.046 0.96 -0.06 -0.25 1.83 0.79 1.31 -0.24 0.33 훽4 0.123 0.234 0.023 0.020 0.029 0.300 -0.081 -0.175 0.93 1.36 0.21 0.18 0.17 2.03 -0.52 -1.23 훾0 0.123 0.106 0.067 0.125 0.250 0.130 0.130 0.135 2.24 1.48 1.5 2.71 3.55 2.11 2.03 2.28

훾1 0.013 -0.055 0.032 0.021 -0.031 0.083 0.000 0.027 0.22 -0.75 0.69 0.44 -0.43 1.32 0 0.44

훾2 -0.061 -0.070 0.040 -0.067 -0.035 -0.051 -0.019 -0.005 -1.04 -0.92 0.83 -1.37 -0.46 -0.78 -0.27 -0.07 훾3 -0.024 0.014 0.050 -0.052 -0.026 -0.097 0.017 -0.020 -0.41 0.19 1.05 -1.06 -0.35 -1.5 0.25 -0.32 훾4 -0.016 -0.086 0.024 0.003 0.027 -0.141 0.059 0.100 -0.26 -1.11 0.48 0.06 0.35 -2.1 0.84 1.54

훿0 0.103 0.202 0.038 0.106 0.203 0.174 0.039 0.182 1.75 2.66 0.78 2.14 2.71 2.65 0.56 2.88

훿1 -0.057 -0.036 -0.015 -0.009 -0.079 0.074 -0.048 -0.024 -0.83 -0.4 -0.27 -0.16 -0.89 0.96 -0.6 -0.32 훿2 -0.149 -0.063 -0.053 -0.070 -0.072 -0.038 -0.050 0.006 -2.02 -0.66 -0.87 -1.12 -0.76 -0.46 -0.58 0.08 훿3 -0.111 -0.095 -0.056 -0.126 -0.077 -0.071 -0.044 0.000 -1.63 -1.09 -1 -2.21 -0.89 -0.94 -0.55 -0.01

훿4 -0.132 -0.201 -0.072 -0.018 -0.030 -0.141 0.004 0.034 -2.05 -2.42 -1.36 -0.34 -0.37 -1.96 0.05 0.49

휃0 -0.058 -0.164 -0.026 0.086 0.060 -0.228 -0.187 -0.047 -0.55 -1.2 -0.3 0.97 0.45 -1.94 -1.52 -0.42 휃1 -0.031 -0.053 -0.120 0.036 0.034 -0.031 0.066 -0.021 -0.28 -0.37 -1.31 0.39 0.24 -0.25 0.51 -0.17 휃2 -0.142 -0.042 -0.093 0.089 0.056 0.007 0.056 0.033 -1.18 -0.27 -0.95 0.88 0.36 0.06 0.4 0.26

휃3 0.024 0.023 -0.044 0.042 0.046 0.008 0.191 -0.069 0.2 0.15 -0.45 0.42 0.3 0.06 1.37 -0.54

휃4 0.006 -0.016 -0.112 -0.118 -0.083 -0.177 0.034 -0.015 0.05 -0.11 -1.22 -1.24 -0.58 -1.41 0.26 -0.12

휌0 0.296 0.373 0.156 0.161 0.282 0.491 0.416 0.275 2.73 2.66 1.76 1.77 2.04 4.07 3.29 2.36 휌1 0.178 0.088 0.206 0.079 0.192 0.205 0.019 0.136 1.55 0.59 2.21 0.83 1.31 1.61 0.14 1.11

휌2 0.163 0.029 0.078 -0.096 -0.134 0.061 -0.003 -0.046 1.29 0.18 0.75 -0.9 -0.83 0.44 -0.02 -0.34

휌3 -0.027 0.155 0.097 -0.040 0.026 0.017 -0.127 0.102 -0.22 0.99 0.98 -0.4 0.16 0.13 -0.9 0.78

휌4 0.092 0.107 0.246 0.220 0.151 0.272 0.068 0.155 0.88 0.8 2.9 2.53 1.14 2.35 0.56 1.38 _cons 0.002 0.005 0.003 0.001 0.004 0.000 0.005 0.007 0.9 1.36 1.36 0.23 1.07 0.13 1.67 2.36 N 104 104 104 104 104 104 104 104 R-sq 0.38 0.362 0.438 0.476 0.454 0.513 0.378 0.419

Table 13 - Continued

27 28 29 30 31 32 33 Korea Malaysia Mexico Poland Russia South Africa Taiwan

훽0 -0.245 -0.088 -0.073 -0.238 -0.394 -0.046 -0.255 -2.03 -1.09 -0.67 -1.93 -2.43 -0.55 -2.15

훽1 -0.048 0.045 -0.054 -0.074 -0.028 0.091 -0.034 -0.41 0.58 -0.52 -0.62 -0.18 1.12 -0.3

훽2 0.126 -0.011 0.144 0.101 0.263 0.128 0.079 1.04 -0.14 1.31 0.81 1.61 1.5 0.66

훽3 0.017 0.112 0.040 0.024 0.195 0.036 0.007 0.15 1.39 0.37 0.2 1.21 0.42 0.06 훽4 0.011 0.003 0.006 0.123 0.230 0.026 0.040 0.09 0.04 0.05 0.99 1.41 0.3 0.33 훾0 0.077 0.054 0.045 0.101 0.182 0.034 0.103 1.53 1.6 0.99 1.96 2.68 0.97 2.08

훾1 0.015 -0.019 0.035 0.019 -0.007 -0.054 0.002 0.28 -0.55 0.74 0.36 -0.1 -1.48 0.05

훾2 -0.047 0.004 -0.051 -0.029 -0.114 -0.033 -0.022 -0.87 0.1 -1.06 -0.53 -1.59 -0.88 -0.42 훾3 0.015 -0.048 0.008 0.008 -0.013 0.004 0.014 0.28 -1.34 0.16 0.15 -0.17 0.12 0.26 훾4 0.011 0.009 0.041 -0.021 -0.058 0.002 0.004 0.2 0.24 0.82 -0.38 -0.78 0.06 0.07

훿0 0.069 0.050 0.048 0.064 0.194 0.033 0.092 1.29 1.39 0.98 1.16 2.68 0.88 1.73

훿1 -0.020 -0.012 -0.033 0.008 -0.055 -0.039 -0.043 -0.32 -0.27 -0.57 0.12 -0.64 -0.87 -0.69 훿2 -0.097 0.019 -0.096 -0.092 -0.170 -0.056 -0.057 -1.44 0.43 -1.56 -1.33 -1.87 -1.18 -0.85 훿3 -0.052 -0.045 -0.059 -0.095 -0.187 -0.034 -0.058 -0.84 -1.09 -1.05 -1.5 -2.24 -0.78 -0.95

훿4 -0.069 0.003 -0.052 -0.082 -0.155 -0.038 -0.052 -1.17 0.07 -0.98 -1.36 -1.95 -0.92 -0.9

휃0 -0.034 -0.071 -0.067 -0.045 -0.275 -0.074 -0.122 -0.35 -1.09 -0.76 -0.45 -2.12 -1.09 -1.28 휃1 0.034 -0.084 0.074 -0.021 -0.100 -0.036 -0.030 0.34 -1.23 0.8 -0.2 -0.73 -0.5 -0.3 휃2 0.024 -0.050 -0.012 -0.002 -0.133 -0.105 0.015 0.22 -0.67 -0.12 -0.02 -0.9 -1.37 0.14

휃3 0.069 -0.018 0.063 0.088 0.063 0.021 0.042 0.63 -0.25 0.64 0.79 0.43 0.27 0.39

휃4 0.012 -0.059 -0.086 -0.226 -0.151 -0.052 -0.017 0.11 -0.85 -0.91 -2.14 -1.09 -0.72 -0.17

휌0 0.264 0.132 0.159 0.264 0.563 0.144 0.343 2.67 1.98 1.77 2.6 4.22 2.07 3.52 휌1 0.087 0.091 0.049 0.141 0.319 0.060 0.132 0.83 1.29 0.52 1.32 2.26 0.81 1.28

휌2 -0.009 0.071 -0.046 0.012 0.204 0.033 0.008 -0.08 0.91 -0.44 0.1 1.31 0.41 0.07

휌3 -0.082 -0.002 -0.012 0.016 0.041 -0.006 0.040 -0.74 -0.02 -0.12 0.14 0.27 -0.08 0.36

휌4 0.115 0.074 0.185 0.324 0.154 0.133 0.026 1.21 1.16 2.15 3.33 1.2 1.99 0.28 _cons 0.003 0.003 0.004 0.001 0.001 0.005 0.002 1.08 1.92 1.87 0.39 0.34 2.73 0.86 N 104 104 104 104 104 104 104 R-sq 0.338 0.297 0.394 0.456 0.551 0.373 0.354

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