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\def\makelabel##1{\upshape ##1}}% } {\end{list}\end{raggedright}} \newenvironment{specHead}[2]% {\vspace{20pt}\hrule\vspace{10pt}% \phantomsection\label{#1}\markright{#2}% \pdfbookmark[2]{#2}{#1}% \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}% }{} \def\TheFullDate{2012-03-15 (revised: 15 March 2012)} \def\TheID{\makeatother } \def\TheDate{2012-03-15} \title{Impact of the Macroeconomic Variables on the Stock Market Returns: The Case of Germany and the United Kingdom} \author{}\makeatletter \makeatletter \newcommand*{\cleartoleftpage}{% \clearpage \if@twoside \ifodd\c@page \hbox{}\newpage \if@twocolumn \hbox{}\newpage \fi \fi \fi } \makeatother \makeatletter \thispagestyle{empty} \markright{\@title}\markboth{\@title}{\@author} \renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@ \belowdisplayskip \abovedisplayskip \abovedisplayshortskip \z@ plus2\p@ \belowdisplayshortskip 4\p@ plus2\p@ minus2\p@ \def\@listi{\leftmargin\leftmargini \topsep 2\p@ plus1\p@ minus1\p@ \parsep 2\p@ plus\p@ minus\p@ \itemsep 1pt} } \makeatother \fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm} \fancyhf{} \setlength{\headheight}{14pt} \fancyhead[LE]{\bfseries\leftmark} \fancyhead[RO]{\bfseries\rightmark} \fancyfoot[RO]{} \fancyfoot[CO]{\thepage} \fancyfoot[LO]{\TheID} \fancyfoot[LE]{} \fancyfoot[CE]{\thepage} \fancyfoot[RE]{\TheID} \hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true} \fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}} \date{} \usepackage{authblk} \providecommand{\keywords}[1] { \footnotesize \textbf{\textit{Index terms---}} #1 } \usepackage{graphicx,xcolor} \definecolor{GJBlue}{HTML}{273B81} \definecolor{GJLightBlue}{HTML}{0A9DD9} \definecolor{GJMediumGrey}{HTML}{6D6E70} \definecolor{GJLightGrey}{HTML}{929497} \renewenvironment{abstract}{% \setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Mahedi Masuduzzaman} \affil[1]{ Finance Division, Ministry of Finance, Bangladesh} \renewcommand\Authands{ and } \date{\small \em Received: 12 February 2012 Accepted: 29 February 2012 Published: 15 March 2012} \maketitle \begin{abstract} This paper strives to investigate the long-run relationship and the short-run dynamics among macroeconomic fundamentals and the stock returns of Germany and the United Kingdom. Each case was examine individually, by applying Johansen co-integration, error correction model, variance decomposition and impulse response functions, in a system incorporating the variables such as consumer price index (CPI), interest rates, exchange rates, money supply and industrial productions between the periods February 1999 to January 2011. The Johansen co-integration tests indicate that the UK and German stock returns and chosen five macroeconomic variables are co-integrated. The findings also indicate that there are both short and long run causal relationships between stock prices and macroeconomic variables. The results imply the existence of short-term adjustments and long-term dynamics for both the UK and the German stock markets returns and the certain macroeconomic fundamentals. The results of the study also indicate that the variables employed in the VARs explain some of the variation of the stock market indices, while the intensity and the magnitude of the responses are comparable for the US and the German stock markets. \end{abstract} \keywords{Macroeconomic variables, Investor, Germany, UK, Returns, Stock markets} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \section[{Introduction}]{Introduction}\par acroeconomic variables play an important role in the performance of stock market returns. Numerous studies document that there are link between macroeconomic variables and equity returns. It is found that changes in the macroeconomic environment affect the price of share. According to the arbitrage pricing theory the relation between stock returns and certain macroeconomic variables has been established {\ref (Ross-1976}). In addition, some studies concerning multifactor models frequently incorporate certain macroeconomic variables as explanatory factor of the expected returns \hyperref[b2]{(Bilson et. al. 2001)}. A potential investor and portfolio manager looks at such a stock market where macroeconomic variable are moves sense of direction. It is very interesting to invest stock market but a very risky trench of investment. So, potential investors always try to predict the trends of stock market prices to obtain maximum benefits and minimize the E-mail : mahedimasuduzzaman@yahoo.com future risks. Being concerned with the relationship between stock market returns and macroeconomic variables, investors might guess how stock market behaved if macroeconomic indicators such as exchange rate, industrial productions, interest rate, consumer price index and money supply fluctuate \hyperref[b13]{(Hussainey and Ngoc, 2009)}. Macroeconomic indicators are compositions of data which frequently used by the policy makers and investors for gathering knowledge of current and upcoming investment priority. The present studies have concentrated on two developed countries' stock markets such as Germany and the United Kingdom and will try to find out the relationship between stock market returns and certain macroeconomic variables in Frankfurt stock exchange and the London stock exchange.\par The rest of the study is structured as follows: section two highlights on related literature, section three concentrates on methodology and description of the dataset, section four discusses the empirical results and finally, section five draws a conclusion to the study. \section[{II.}]{II.} \section[{Review of the Literature}]{Review of the Literature}\par In globalized economy there are various ways financial market especially the stock market and the macro-economy have been related in the literature. In recent past, longstanding academic studies evidence that macroeconomic indicator affects stock prices. We find plenty of research on how the macroeconomic indicators affect the stock market. In 1981, Fama established a relationship among stock prices and macroeconomic indicators. He found that expected nominal inflation is negatively correlated in real activity and the reality is that the changing inflation has positive relation to returns on the stock market. Later studies support the \hyperref[b8]{Fama's (1981)} hypothesis. \hyperref[b10]{Geske and Roll (1983)} emphasized on the importance of policy responses in explaining stock returns. In 1987 Kaul also emphasized the same.\par Errunza and Hogan (1998) examined whether the variability of a set of monetary and real macroeconomic factors can explain the variation of the some European stock market volatility. Employing a Vector-auto Regression (VAR), they found evidence to support that monetary instability is a significant factor for \hyperref[b9]{Filis (2010)} found that there is no causal relationship between Greek stock market and industrial production during the period spanning from January 1996 to June 2008 using multivariate VAR model. He also argued, stock market and oil prices exercise a positive impact on Greek consumer price index in the long-run. \hyperref[b4]{Daly and Fayyad (2011)} examined, the relationship between Gulf Cooperation Council (GCC) countries, the UK and the US stock market returns and oil price by employing DCV and VAR analysis during the period September 2005 to February 2010 and find that when oil prices increase sharply it predicts the USA, UAE and Kuwait but not the UK, Oman, Bahrain and Qatar.\par There are little segmentation observed between emerging and developed market stock returns. The volatility of developed economies' stock returns is less than the volatility of emerging market stock returns. The volatility of emerging market is changed by local macroeconomic variables as well as international macroeconomic variables. \hyperref[b0]{Abugri (2008)} The correlations between stock market returns and the macroeconomic variables are different. A positive correlation is evident between the DAX30 and the macro-economic variables with the exception of bond;the correlation (table-1) between the UK price index and the macroeconomic variables are fairly strong with the exception of CPI and MS. In research, the data sources, data description and the methodology need to be specified. The methodology needs to be cautiously designed to obtain realistic results. The methodological design employed in this study consists of unit root tests; Johansen cointegration test, VECM based Granger causality, variance decomposition analysis and impulse response analysis.\par The empirical investigation has been carried out in the case of the United Kingdom and German stock market returns and selective macroeconomic variables. The data used under the study are monthly data from February 1999 to January 2011. The UK and German stock prices is the end-of-period closing share price indices.\par The stock indices are DAX30 of Frankfurt stock exchange and FTSE100 of London stock exchange. These stock price indices and the chosen macroeconomic variables such as broad money supply (MS), exchange rates, treasury bill rates (Representing interest rate for UK), bond rate (Representing interest rate for Germany) are obtained from the Data Stream.\par Consumer price index (CPI) representing the rate of inflation and Industrial Production Index (IP) representing the economic activity are sourced from OECD data bank.\par The stock market returns of Germany and the UK are shown a high level of time varying correlation. If we have a close look towards German and the UK stock markets return (figure-4.1), we observe that these two developed economies stock market returns are closely correlated in the sample period except late 2000. The first step of the methodological process involves a test for stationarity as the variables to be used in this paper are time series which are usually nonstationary. We employed Augmented Dickey-Fuller(ADF) and Phillips-Perron (PP) tests for unit root. If the variables are stationary in level, they are said to be integrated of order 0 that is I(0). On the other hand, if the said variables become stationary after first differencing are said to beI(1). c) Johansen Multivariate Co-integration Test:\par Co-ingration refers to the situation where the nonstationary time series of the same order exist a longrun relationship. After determing the order of integration of each variables, we perform Johansen co-integration tests whether there is a cointegrating relationship between stock returns and chosen five macroeconomic variables in Germany and the UK. The mathematical form of Johansen cointegration test is given below: Where = k vector of endogenous variables, a vector of deterministic variables, = a vector of innovations. The model (i) may be re-written as a vector auto regression (VAR) following way In equation (ii) the vector and are I(1) variables. Therefore, the long run relationship among will be determined by the rank of , if r= 0 the n the equation (ii) reduce to a VAR model of p-th order and in this case the macroeconomic variables in level do not have any co-integrating vector. On the other hand, If the rank 0