The first hypothesis concerns the asset relatedness assumption of the limited-information models. In their articles, Hong, Torous and Valknanov (2007) and Menzly and Ozbas (MO, 2010) both derive their own limited-information models. Basically, both limited-information models predict that asset returns exhibit cross-predictability when two central assumptions are fulfilled: (i) the sets of assets such as stocks in different industries or market segments have correlated fundamentals and (ii) markets are informationally segmented as informed investors, to some degree, specialize along these boundaries in their information-gathering activities (MO, 2010). Under these assumptions value-relevant information diffuses slowly across the markets and causes returns on economically linked units to cross-predict each other.
Empirical evidence on assumption (ii) is provided in the literature review in subsection 2.2.2.1 and providing further proof of this assumption is out of the scope of this study. However, assumption (i) requires a closer examination in order to verify the validity of the empirical design for testing cross- predictability in this paper. As mentioned, this paper uses Eurostat consolidated input-output tables to determine industry relatedness. Since there is no earlier evidence on using Eurostat IO-tables in
cross-predictability analysis, it is important to confirm that they provide a meaningful description of industry relatedness for both Eurozone and EU27 samples. Furthermore, there are differences between the Benchmark Input-Output Surveys of the Bureau of Economic Analysis (BEA) that are used in previous studies on return cross-predictability (Shahrur, Becker and Rosenfeld, 2009; Menzly and Ozbas, 2010) and the Eurostat IO-tables. For example, the BEA IO-tables use different
industry classifications compared to the Eurostat input-output tables.28 In addition, there are
differences in the granularity of the tables: the BEA IO-tables contain 85 different industry accounts whereas the Eurostat tables contain 59 different industry accounts.
Another reason for studying industry relatedness in this paper is that, unlike previous research on the limited-information models, this paper examines return cross-predictability in a cross-country sample with data on the Eurozone and EU27 countries. Therefore, it needs to be verified that factors, such as national borders, do not blur the economic links between industries. For example, national borders might affect the way in which economic shocks are transmitted across industries in different countries. Importantly, earlier academic literature that compares within-country correlations of business cycles to cross-country correlations identifies a relation between national borders and business cycles, known as the border effect (see e.g., Wynne and Koo, 2000; Clark and van Wincoop, 2001). More specifically, the border effect refers to substantially lower business cycles correlations across countries than within countries. For example, Clark and van Wincoop (2001) document that business cycles in the US Census regions are substantially more synchronized than those of European countries. They also find that the lower level of trade between European countries, in comparison to US regions, accounts for majority of the observed border effect.
Another strand of literature decomposes the sources of within-country and cross-country fluctuations of business cycles into common, country-specific, region-specific, and industry-specific components. Clark and Shin (1998) review this literature and note that, in general, the evidence indicates that European business cycles are less synchronized than those of US regions. They also report that country-specific and idiosyncratic components are responsible for more than two-thirds of total variation in business cycles across countries and show that across US regions the common component is much larger than in Europe. They attribute these findings to the border effect and argue that economic borders that divide nations are greater than economic borders that separate
28 The Eurostat input-output tables are based on the General Industrial Classification of Economic Activities within the European
Communities revision 1.1 (NACE rev 1.1) whereas BEA input-output tables are based on the Standard Industrial Classification (SIC).
regions within nations.29 In other words, lower economic borders are associated with a lower
importance of country-specific disturbances and greater importance of common and industry- specific factors as drivers of business cycles.
There remains a great amount of uncertainty as to whether business cycles within Europe are converging or diverging. On one hand, integration in Europe is increasing as fiscal policies are becoming more coordinated and barriers to cross-border flows of goods, capital and labor are being removed (Clark and van Wincoop, 2001). Particularly, the formation of the Economic and Monetary Union (EMU) and the adoption of the single currency may have lowered economic borders between the euro countries and thus, increased business cycle correlations. On the other hand, for example, Clark and Shin (1998) question the divergence of business cycles within the euro area and argue that despite the creation of EMU, it is likely that the economic borders between EMU-member nations still remain higher than those in the US. De Haan, Inklaar and Jong-A-Pin (2008) provide an extensive review of the literature on business cycle synchronization in the Economic and Monetary Union. They note that business cycles in the euro area have experienced periods of both convergence and divergence and there is no monotone movement towards the emergence of a European business cycle.
Despite the mixed empirical evidence, the recent European sovereign debt crisis has clearly shown that European countries are in very different economic positions. While the Eurozone’s southern periphery countries have struggled with out-of-control government debts and high unemployment rates, other euro countries, most importantly Germany, have weathered the crisis much better. Practical observations and empirical evidence on the border effect in Europe highlight the need to verify the limited-information model assumption of correlated fundamentals in the European cross- country sample. The main concern here is that if national borders significantly decrease the correlations between industries across European countries, it may be that return cross-predictability does not exist because the units of analysis are not economically linked. To address this concern, an industry relatedness analysis is performed similar to Menzly and Ozbas (2010). Given the findings by Menzly and Ozbas, it is expected that industry fundamentals in Europe are positively correlated, as defined by the consolidated Eurostat input-output tables.
29 Factors such as independent monetary and fiscal policies of different nations and restrictions on labor, trade and capital flows
create economic borders between nations. Also cultural differences and language strengthen economic borders between nations. This suggests that the US states, which are regions within a nation, are likely to have lower economic borders than the European nations studied in this paper. Even though each US state determines their local fiscal policies they are also affected by national monetary and fiscal policies. Moreover, there are practically no restrictions on trade and capital flows within the US and also cultural differences are small. (Clark and Shin, 1998.)
Building upon the previous literature and the limited information models, the first hypothesis of this study is:
H1: Industry fundamentals are positively correlated over and above the market as measured by firm-, industry- and market-level measures of profitability.