EFFECT OF THE PRICING OF FOREIGN EXCHANGE RISK ON STOCK
RETURNS AT NAIROBI SECURITIES EXCHANGE, KENYA.
BY
MMBAYIZA ANTONY MUSE
D58\CTY\PT\22356\2012
A THESIS SUBMITTED TO THE SCHOOL OF BUSINESS IN PARTIAL
FULFILMENT FOR THE AWARD OF THE DEGREE OF MASTERS OF SCIENCE
IN FINANCE OF KENYATTA UNIVERSITY
OCTOBER, 2015
i
DECLARATION
ii
DEDICATION
I dedicate the entire thesis work to my Parents who gave me love and literature, and to those who will find time to read and appreciate my contribution on this topic and the massive wealth of knowledge engrossed in my work. It is therefore in the same spirit I acknowledge that wealth is not found in groups unless the investor is in a capacity to assemble and or lead the group in question.
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ACKNOWLEDGEMENT
It is with gratitude that I express my acknowledgement to all those parties that have made it possible for me to write this thesis through their contributions in one way or the other. Since the entire process of thesis writing is a vigorous task that upon completion calls for participation and contributions from different scholars, I therefore find it befitting to acknowledge the contributions of other players to this work. To start with, I would like to express my thanks and praise to our Lord for granting me good health and divine protection all my entire life as a result, has seen me come this far without destructions caused by sickness. Secondly, I express my sincere gratitude to the exceptional guidance I received from my research supervisors; Mr Gerald Atheru and Mr James Muturi, putting in mind their continuous support, patience, enthusiasm and their kind heart for sharing their immense knowledge and experience towards this study. Thirdly, my family members for their moral support and motivation during the entire process of course work to thesis writing and not forgetting the following academicians; Dr. Odongo Kodongo, Dr. Ambrose Jagongo,
Dr. Aflonia Mbuthia, Dr. James Maingi and Dr. John Mungai for spending their time to read and make their personal contribution towards improving this study. Finally, I sincerely thank the academic fraternity at Kenyatta University for their guidance and coordination of the entire program.
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TABLE OF CONTENTS
DECLARATION ...... ii DEDICATION ...... iii ACKNOWLEDGEMENT ...... iv TABLE OF CONTENTS ...... v LIST OF FIGURES ...... viii LIST OF TABLES ...... ix OPERATIONAL DEFINITION OF TERMS ...... x ABBREVIATION AND ACRONYMS ...... xii ABSTRACT ...... xiii CHAPTER ONE ...... 1 INTRODUCTION ...... 1 1.1 Background of the Study ...... 1 1.1.1 The Currency risk and Kenyan Securities markets ...... 3 1.2 Statement of the Problem ...... 9 1.3 Objective of the Study ...... 9 1.3.1 The General Objective ...... 9 1.3.2 Specific Objectives ...... 10 1.3.3 Research Hypothesis ...... 10 1.4 Assumptions of the Study ...... 10 1.5 Significance of the Study ...... 11 1.6 Scope and Organisation of the Study ...... 11 1.7 Limitation of the Study ...... 13 CHAPTER TWO ...... 15 LITERATURE REVIEW ...... 15 2.1 Introduction ...... 15 2.2 Theoretical Review ...... 15 2.2.1 Arbitrage Pricing Theory ...... 15 2.2.2 Inflation rate differential and Purchasing Power Parity Theory ...... 18 2.2.3 Interest rate differential and International Fisher effect Theory ...... 19 2.2.4 Interest rate differential and Interest Rate Parity Theory ...... 21 2.2.5 The Expectation Theory and Current - Account deficit ...... 23 2.2.6 Summary of the Theoretical Review ...... 25 2.3 Empirical Literature Review ...... 25 2.3.1 The Pricing of Foreign exchange risk ...... 25 2.3.2 Foreign Investors participation at Nairobi securities Exchange ...... 31
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2.4 Summary of Literature ...... 33 2.5 Conceptual Framework ...... 37 CHAPTER THREE ...... 39 METHODOLOGY ...... 39 3.1 Introduction ...... 39 3.2 Research Philosophy ...... 39 3.3 Research Design ...... 40 3.4 Empirical Research Model: Unconditional International Arbitrage Pricing Model...... 40 3.4.1 Operationalization of the Variables in GMM model...... 41 3.4.1.1 Constructing Stock returns and or Market Abnormal returns ...... 42 3.4.1.2 Constructing the Explanatory Variables ...... 43 3.5 Target Population...... 44 3.6 Sampling Design and Sample Size ...... 45 3.7 Data collection Procedure ...... 46 3.8 Data analysis and Presentation ...... 46 CHAPTER FOUR ...... 49 RESULTS, INTERPRETATION AND DISCUSSION ...... 49 4.1 Introduction ...... 49 4.2 Preliminary Analysis ...... 49 4.2.1 Descriptive Analysis for the Risk factors ...... 49 4.2.2 Simultaneous Co – movement within the Variables ...... 56 4.2.3 White’s Specification test for Heteroscedasticity ...... 59 4.2.4 The Unit Root test Results on the Monthly Data ...... 60 4.2.5 The Co - integration test Results on the Monthly Data ...... 61 4.3 Empirical results for Unconditional Multi – factor Asset Pricing Model ...... 63 4.3.1 Test Results for the three factor Model at Nairobi Securities Exchange ...... 64 4.4 Hypothesis Testing ...... 67 CHAPTER FIVE ...... 68 SUMMARY, CONCLUSION AND RECOMMENDATIONS ...... 68 5.1 Introduction ...... 68 5.2 Summary of the Findings and Discussion ...... 68 5.3 Conclusions ...... 70 5.4 Policy Recommendation ...... 71 5.5 Recommendation for Practice ...... 72 5.6 Recommendations for Further Research ...... 73 REFERENCE ...... 74
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APPENDICES ...... 78 Appendix i: Data Collection Form A ...... 78 Appendix ii: Data Collection Form B...... 79 Appendix iii: Constructed Data for Analysis ...... 80 Appendix iv: Hypothesis testing ...... 83 Appendix v: Research Permit ...... 85 Appendix vi: Listed Companies at NSE ...... 87
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LIST OF FIGURES
Figure 2.1 Conceptual framework …………………………………………………………. 38 Figure 4.1 Normality Tests for Abnormal Return …………………………………………. 52 Figure 4.2 Normality Tests for Current account deficit……………………………………. 52 Figure 4.3 Normality Tests for Inflation rate differential (IFRD) ……….…………..…….. 53 Figure 4.4 Normality Tests for Interest rate differential (ITRD) …………………….……. 53 Figure 4.5 Movements between abnormal returns and the Instrumental Variables..………. 57 Figure 4.6 Simultaneous co-movement between Variables…..……………………………..58
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LIST OF TABLES
Table: 2.1 Summary of literature review ………………………………………………….. 35 Table: 3.1 Operationalization of the variables ………………………………………….…. 44 Table: 4.1 Descriptive Statistic for Instrumental Variables and Risk factors……..…….…. 50 Table: 4.2 Breusch – Godfrey Serial Correlation LM Test…………………………….…... 54 Table: 4.3 Correlation Structure for Instrumental Variables and Risk factors………….….. 55 Table: 4.4 The White’s Specification test for Heteroskedasticity……………………….…. 59 Table: 4.5 Unit root test results ……………………………………………..…….…….….. 60 Table: 4.6 Pairwise Granger Causality Test Results ………...……………………….…..… 62 Table: 4.7 GMM Regression Results for Three factors Asset Pricing model on NSE ….... 64 Table: 4.8 T-Tests Statistics for hypothesis testing...…...... 67
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OPERATIONAL DEFINITION OF TERMS
Asymptotic: Asymptotic is an adjective meaning of a probability distribution as some
variable or parameter of it.
Bretton wood agreement: A landmark system for monetary and exchange rate
management established in 1944, developed at United nations monetary and
Finance conference held in Bretton Woods, New Hampshire, from July 1 to
July 22, 1944.
Cooperative peg regime: A systems where there is different equilibrium allocation for each
contribution of exchange - rate levels and monetary policies.
Crawling peg: Exchange rate adjustment system where currency with fixed exchange rate is
allowed to fluctuate within a band of rates hence the par value of the currency
is adjusted frequently due to market factors leading to a significant
devaluation of currency.
Current account deficit: Is a component of the balance of payment as identified in the
monthly economic review, prepared by the Central bank of Kenya.
Economic exposure / operating exposure: Is a kind of foreign exchange exposure brought
about by the effect of unexpected currency fluctuations on a company’s future
cash flows.
Floating exchange regime: A country’s exchange rate regime where its currency is set by
the foreign – exchange market through supply and demand of a particular
currency relative to other currencies.
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Foreign exchange risk: Is currency fluctuation/volatility between Kenyan shilling and the
U.S dollar
Inflation rate differential: Is the difference between Kenyan inflation rate and the
American inflation rate.
Interest rate differential: Is the difference between the Kenyan 91days Treasury bills
interest rates and the American 91days Treasury bills interest rates.
Linearity principle: A principle maintains that two or more solutions to linear equations can
be added together so that their sum is also a solution.
Orthogonality: The variation between the actual percentage change in a risk factor ( ) and
its estimated value (
Probability density function: It is a statistical measure that expresses probability
distribution for a random variable denoted as f(x). Graphically PDF function
portrays the area under the graph which indicates the interval within which the
variable falls.
Smithsonian agreement: Agreement signed by ten countries (G10) in 1971 that ended
the fixed exchange rate system established under Britton Wood agreement,
hence re-established an international system of fixed exchange rate without
backing of silver or Gold. This allowed devaluation of U.S dollar by 8%.
Transaction exposure : This is the risk where companies are faced by involving into
international trade, and that the currency exchange rate will change after
companies have entered into a financial obligation leading to major losses.
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ABBREVIATION AND ACRONYMS
ADF Augmented Dickey – Fuller approach
APM Arbitrage Pricing Model
CAD Current Account Deficit
CBK Central bank of Kenya
GMM Generalized Methods of Moments
IAPM International Arbitrage Pricing Model
IFC International Finance Corporation
IFRD Inflation Rate Differential
IPP Interest Power Parity
ITRD Interest Rate Differential
OLS Ordinary least square
NSE Nairobi Securities Exchange
PDF Probability density function
PPP Purchasing Power Parity
SDR Special Drawing Right
SURM Seemingly Unrelated Regression Model
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ABSTRACT
The exits of purchasing power parity stipulates that different countries differ in prices for goods when a common digit is applied and therefore, random changes in exchange rate has often been implicated to be the key cause of fluctuations in prices leading to more risk in the assets pricing models. This implies that Exchange rate is a crucial variable that affect both the international competiveness of both multinationals and investor’s wealth as they participate in international stock markets. It then follows that investors and other players who participate by investing in stocks at the Nairobi Securities Exchange are not exceptional and therefore, this research seeks to empirically ascertain effects of the pricing of foreign exchange risk on stock returns on listed securities trading at Nairobi Securities Exchange, Kenya. The study covered a period of ten years starting from January 2003 to December 2012, with specific interest being to determine the degree at which inflation rate differential affected stock returns at Nairobi Securities Exchange, to examine the magnitude at which interest rate differential affected stock returns at Nairobi Securities Exchange and to analyze the extent to which current account deficit affected stock return at the Nairobi Securities Exchange. This research therefore employed monthly time series data while adopting the use of Unconditional three factor international arbitrage pricing model which formed the backbone on which the empirical model was based on. Moreover, all the sixty one (61) listed securities on Nairobi securities Exchange formed the population of this study. However by employing inclusion - exclusion criteria for survivorship biasness, only those securities that were in trade for the entire period formed a sample of thirty six (36) securities summing up to (59.02%) of the entire population. Empirically this study made use of linear regression equations that is orthogonalized based on actual values of the underlying factors. However, the researcher adopted the use of Generalized Method of Moments estimation technique to empirically analyze the data corresponding to the entire period under the study. This was supported with inbuilt E–Views computer software for data analysis. Since Generalized Method of Moments posits many advantages that make it free from Ordinary Least Square problems. It then follows that, by employing the services of Generalized Method of Moments, the data was presented in form of figures and tables with much emphasis being on descriptive analysis, testing of normality, stationarity and the prevalence of Ordinary Least Square problems. Using F and t– Statistics, Null hypothesis was tested at 5% confidence levels. This study is of critical use to academicians as it forms a foundation for future studies, to economists to draw implications of macroeconomic policy, investors to allocate appropriate risk level to their investment opportunities in Kenyan securities market as well as by multinational firms in diversifying their portfolio risk globally. The result revealed that foreign exchange risk was weakly priced and there existed a long run relationship between the interest rate differential and abnormal return, Current account deficit and Interest rate differential and between Interest rate differential and inflation rate differentials respectively. This meant that policy makers should consider regulating the interest rates and capping the inflation rate since they affect the purchasing power negatively. Moreover the CBK should consider empowering other currencies like Euros, yen, sterling pounds to diversify the impact of currency risk and integrate the Nairobi Securities Exchange to rest of the world securities markets.
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CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Foreign exchange risk is an institutional exposure to the prospective impact of movements in foreign exchange rates. The risk is caused by unfavourable movements in exchange rates that may amount in loss of value of a country’s currency terms to its institutions. Foreign exchange rates results from different foreign exchange rate regimes: fixed exchange rate regime, intermediate exchange rate regime and floating exchange rate regime which may be evaluated and compared in terms of efficiency specification monies, meaning the money of each country may be used to transact in its commodity market and its currency denominated bonds - with clear predictions only when all equilibrium allocations are pareto efficient.
Foreign exchange risk is created by currency mismatch in an institutional assets and liabilities both on and off the balance sheet as well as currency cash flow mismatch (Bank of
Jamaica, 1996). Incorporating the standard de jure classification approach, fixed exchange rate regimes are allied with changes in international reserves aimed at plummeting the instability in nominal exchange rate, flexible exchange rate regimes which are considered with significant volatility in nominal rate with fairly stable reserves and finally the intermediate exchange rate regimes ( conventional pegs that includes; one – sided peg regime and cooperative peg regime) as the exchange rate stabilizing authority breaks even over a period of time (Helpman, 2013). In equilibrium allocation, exchange rate regimes are described according to the behaviour of the classified variables. The variables may include
1 the variation in nominal exchange rate, the instability of those changes and the volatility of international reserves.
The systems involved in foreign currencies have evolved from the gold standard (1876 –
1913), in which exchange rates were dictated by gold standards while exchange rates between two currencies were determined by relative convertibility rate per ounce of gold. In
1944, the Bretton Woods Agreement was adopted until the year 1971. During this period government could intervene to prevent exchange rates from fluctuating above and below the established exchange rate level by 1 percent. The floating exchange rate system was adopted in 1973 - after the Smithsonian agreement of 1971, the government faced challenges in maintaining the exchange rate within the stated boundaries.
Since the end of Bretton woods fixed exchange system in 1973, exchange rate fluctuation has been of great concern to both financial analysts and investors. Therefore the process of pricing foreign exchange risk has been the most long – lived anomalies in open macroeconomic and international finance brainteaser that has triggered diverse scholarly arguments on whether or not currency risk is priced on equities listed on stock markets worldwide (Tai, 2010). Major studies that have been carried out on American stock markets converge at a common point that under deviations from purchasing power parity (PPP), currency risk ought to be priced as illustrated in the findings of solnik (1974), Adler and
Dumas (1983), Himmel (2002), Carrieri, Errunza and Majerbi (2006) respectively.
More so, other research studies that were carried out in American market, Canada, Japan and some parts of Europe, argued that exchange-rate movements affected corporate expected cash flows, and hence stock returns. This was caused by fluctuations in the home currency
2 value in respect to foreign currency denominated revenues and the terms of competition for multinationals firms with international activities. In a circumstance where purchasing power parity is violated, it may lead to exchange rate exposure that is not limited to firms with direct foreign trade activities. Therefore this necessitates the growing emphasis on exchange risk management which entails the extensive use of foreign currency derivatives and other hedging instruments by corporations to circumvent the effects of foreign exchange risk and hence implying that the market value of firms are sensitive to exchange rate uncertainty.
Empirical evidence has produced mixed results on related studies, according to Choi and
Prasad ( 1995) study posited a significant exchange rate exposure that contradicted with
(Jorion, 1990) study.
According to Carrieri and Majerbi (2006) the study compiled evidence from both South
American stock markets and Middle East stock markets, the study ruled that exchange rate was globally priced and commanded a significant unconditional risk premium in emerging stock markets. Although it noted that the findings were contrary to those studies that were earlier done in major developed markets as the hypothesis of zero exchange risk was not rejected in a fully integrated model. This made the study even more interesting for the topic to be researched on since the prior studies posited conflicting results.
1.1.1 The Currency risk and Kenyan Securities markets
Kenyan security market is a relatively small and speculative market that begun during colonialism in 1954. It acted as an overseas stock exchange operating under London stock exchange. Since 1974 to 1990’s, Kenyan stock market has undergone several phases of exchange regimes. For instance, in 1974, Kenyan shilling was pegged to the US dollar which was later reviewed to Special Drawing Right (SDR), before changing to crawling peg in
3
1982 up to 1990. It further moved to dual exchange rate regime that lasted till 1992. Kenya shilling then, was devalued hence marking the end of fixed exchange rate regime so was the instigation of floating exchange rate. This led to a policy that ensured shilling remained competitive and these efforts eventually were able to tame inflation rate and therefore ensured strict monetary standpoint and upholding of real interest rate at a positive digit
(Kodongo, 2011). Lately, NSE operates as the fourth- largest sub – Sahara’s exchange market with market Capitalization of approximately Ksh.1.90 trillion by the end of 2013
(Economic Survey, 2014). Nairobi securities market was opened to foreign investors in 1995 with maximum limit of 20% shareholding for institutions and 2.5% for individuals. It deals in equities exchange trades, Kenyan government bonds, corporate bonds, Treasury bills, commercial papers and the central depository system which has seen it automate all its operations, with an aim of speeding up clearing and settlement.
The effect of the pricing of foreign exchange risk on stock returns can be felt on any security market worldwide. This because domestic firms are affected by unexpected exchange-rate change or by alteration on aggregate demand hence, leading to current account deficits.
Study by Bartov and Bodnar (1994) indicated that failure of past studies to control firms’ associations to international conditions might have been the reason why they could not document a significant relation between exchange rate variations and stock returns.
Moreover, literature on the effect of the pricing of foreign exchange risk on stock returns could not be captured on Kenyan security market or African securities markets well due to limited information available on the same. It then followed that, since this problem was persistent and needed to be addressed, any relevant materials both locally and internally were incorporated to build both the background and the research gap.
4
In line with the above, the study by Jorion, (1991) indicated that correlation between stock returns and the value of dollar varied steadily a cross industries. The study also observed that active hedging policies could not affect the cost of capital and therefore the need for hedging could not be explained. The result further revealed the presents of unconditional small premium of 0.2 percent which were associated to foreign currency risk, as this was in line with early study of (Jorion, 1990). These results were relatively small and insignificant for the study. It then followed that on such circumstance, investors were not willing to pay premiums on firms with active hedging policies. This implied that currency risk was not diversified and therefore more stable earning could neither alter the cost of capital nor the firm’s capitalization value. This creates a limbo on whether currency risk is well priced or which effect currency risk has on stock returns.
Choi and Prasad (1995) study examined exchange risk sensitivity of 409 firms on U.S multinationals from 1978 to 1989. The study revealed that the pricing of currency risk was significant at sixty percent of the firms registered gains in respect to depreciation of US dollar hence exchange rate variations did not affect firm’s value due to depreciation of the dollar since there existed a positive correlation between the slops of exposures as measured in foreign sales . This results were in contradiction with the findings of the study done by
Dumas and Solnik (1995). Their study established the existence of foreign exchange risk with variation in the realized premiums. This implied that foreign exchange risk was priced since different countries were faced with different prices of goods hence risk premiums were based on covariance of assets exchange rates employed in the model. The study findings further indicated a significant variation of between negative (-0.48) coefficient and positive
(0.51) coefficient values. These significant variations clearly indicated that the level of
5 pricing of currency risk differed from one security market to another. This therefore leaves one wondering on the effect of the pricing of the currency risk to stock returns on any given security market. Following the negative and positive coefficients realized above, it is clear that the effects are never the same when two markets are compared at the same time. It hence leaves the researcher with the duty to ascertain the position taken at NSE decisively.
Brennan (2006) study argued that the relationship between foreign exchange risk premium, exchange rate volatility, and the volatilities of the pricing kernels for the underlying currencies, were derived under the assumption of integrated capital markets, and that foreign exchange risk premium were significantly related to both the estimated volatility of the pricing kernels and the volatility of exchange rates. It then followed that the risk premium notion in the foreign exchange markets varied with the general level of risk premium in the corresponding bond markets as expected in integrated capital markets. Therefore the results in the study supported the notion of forward premium puzzle. This findings were consistent with the findings of the study by Dumas and Solnik (1995). The emphasis on the pricing of foreign exchange risk and the effect it has on stock return can be depicted from this study.
These variations in the pricing of currency risk brings about variation in returns hence the speed of wealth accumulation varies from security to security, from one portfolio to the other and from one security market to another. Hence, it validates the need to ascertain the same on
NSE.
In addition to the above, study by Carrieri and Majerbi (2006) argued that by violating exchange rate caused a change in the value of multinational firms in foreign markets. This led to profit (pricing of currency risk) or loss (unprized currency risk), and eventually a change in stock prices when other factors were kept constant. The results showed that the
6 betas in respect to world market varied from – 0.04 for India to 0.89 for Mexico, the exchange rate betas were larger than world market beta’s as they ranged from 0.46 for
Zimbabwe to 3.18 for Argentina. The estimated world market premium was 0.11 percent against monthly risk factor premium of 0.73 percent; this was a significant premium to reckon against insignificant market premium. The presents of significant variations in the world market beta, exchange rate beta and world market premium against monthly risk premium results, presented ambiguity that needed to be addressed since investors would be left wondering which market is well priced and which effects do the pricing has on stock returns. Although these studies posited significant results on currency risk, the degree of significance also varied steadily, hence the need to remove ambiguity by ascertaining the levels at which currency risk was priced in other markets particularly the NSE was critical.
Moreover, (Kodongo, 2011), carried a study on seven African stock markets (Botswana,
Egypt, Ghana, Kenya, Morocco, Nigeria and South Africa), the study realized that foreign exchange risk was not priced unconditionally when returns were measured in US dollar, were weakly priced when returns were measured in Euros and were priced with time varying risk premium in conditional sense. The study also argued that African stock markets were partially integrated with the world markets. The study posited mixed result since the findings were unable to come to a definite conclusion. The confusion brought about by the findings can easily lead to investor’s indecision on which currency to use when investing since each currency has different effect on the pricing of foreign exchange risk when stock returns are used. The mixed results further creates dilemma on which security market should one invest on and which currency should be used. Therefore in such circumstance, further research on the same topic is important for conclusive result.
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Subsequently, a study by (Kodongo and Kalu, 2012) on examining currency risk and market integration in equity market of Nigeria and South Africa, using GMM with multi – beta asset pricing model and firms – level data, indicated that currency price was partly unconditionally priced in South Africa’s stock market and largely incorporated with the world equity market.
However this finding contradicted with the results on Nigerian stock market as the currency risk was neither priced nor integrated with world equity market since no evidence was staged. This was because portfolio beta varied from 0.5652 to 0.0560 on low market capitalization of South Africa and Nigeria respectively. More so, the estimated unconditional monthly risk premium for currency risk factors posited a statistically insignificant value to world market equity portfolio of 0.017 and unprized unconditionally premium of -0.0165 in
Nigeria’s equity market. It then follows that the cross sectional result on South African and
Nigerian stock markets posited contradicting findings on situations in the two markets. This contradicting result brings much confusion to investors, as one wonders on the state of the pricing of foreign exchange risk and the effect it has on individual stock returns. This necessitates the need to arrive to a precise result in future.
In a nutshell, the performance of many emerging markets currencies are driven by the behaviours of major currencies in developed markets and so do Kenyan currency. It is from these findings that foreign investments on African stock markets are restricted by factors such as foreign exchange risk, political risk, low liquidity, information asymmetry and institutional barriers. Keeping other factors constant, the paradox of effect of the pricing of foreign exchange risk in Kenyan security market posits a prominent argument since the need to examine stock prices and establish its relationship with currency exchange rates exposure
8 has been the researcher’s headache as they try to establish the effect of the pricing of currency risk on stock returns on the Kenyan Nairobi Security Exchange.
1.2 Statement of the Problem
The problem of the pricing of currency risk in stock markets has continuously become a contentious issue to financial analysts as argued in the past studies since the magnitude at which stronger currency affects weaker currency in respect to the pricing of foreign exchange risk differ from currency to currency with integral part being constituted by time variations.
Therefore, the need to evaluate the effect of the pricing of foreign exchange risk on stock returns at Nairobi Securities Exchange was triggered by the fact that most of the past studies had reported mixed findings since currency risk were either non priced when returns were measured in US dollar, were weakly priced when returns were measured in Euros or were priced with time (Kodongo, 2011).
It then follows that the failure of the past studies to give conclusive results substantiated the statement of the problem as the need to disclose the ambiguity in stock market environment was pledged on the fact that no such study had neither been conducted on Kenyan securities market nor had conclusively unlock the ambiguity experienced on the same and therefore the need to empirically evaluate how the pricing of foreign exchange risk affected returns on equity at Nairobi securities exchange was inevitable.
1.3 Objective of the Study
1.3.1 The General Objective
To find out the effect of the pricing of foreign exchange risk on stock returns at the Nairobi
Securities Exchange.
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1.3.2 Specific Objectives
The specific objectives were to:
i) Analyze the effect of current- account deficit on stock returns at NSE.
ii) Determine the effect of inflation rate differential on stock returns at NSE.
iii) Examine the effect of interest rate differential on stock return at NSE.
1.3.3 Research Hypothesis
Briefly the research hypotheses of the study are:
, Current - account deficit has no significant effect on stock returns at NSE.
, Inflation rate differential has no significant effect on stock returns at NSE.
, Interest rate differential has no significant effect on stock returns at NSE.
1.4 Assumptions of the Study
This study aimed at investigating how the pricing of foreign exchange risk affected stock returns on equities at Nairobi securities exchange. To justify how the independent variables forming the proxies of the study enabled the study to attain the general objective, several assumptions were made; the expected excess returns on asset i, is a linear function of the foreign exchange risk premium factors where is the exchange rate movements caused
by independent variables (Current-account deficits, Inflation rate differential and Interest rate
differential) other factors held constant, this assumption was substantiated by the volatility of
the three factors above.
It was assumed that Nairobi securities market does not operate as a closed market and
therefore foreign investors all over the world participates in the investment activities at NSE.
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This followed that their returns were susceptible to the impacts of exchange rate fluctuation i.e. since earning are denominated in foreign currency (translation exposure). The study also assumed that some of the players were multinational companies operating in more than one country and therefore exchange rate is a major source of their uncertainty as argued by
(Jorion,1990). Moreover the study assumed that all listed firms at NSE either imported inputs of production or used locally available inputs whose prices were determined in the international markets marking the effects of firms’ shareholding composition irrelevant since all the firms were susceptible to the effects of currency risk. Finally the study assumed that all investors had a short term investment plan and therefore the effect of dividend payout on returns was irrelevant in the study, meaning that investors were driven by capital gains rather than dividend payout.
1.5 Significance of the Study
The results in this study are useful to; economists to draw implications of macroeconomic policies, are also significant to investors to allocate appropriate risk level to their investment opportunities in Kenyan securities market, will also be significant to multinational firms in diversifying their portfolio risk globally and to the scholars as the study helps them fill the gap in the empirical literature on the effects of the pricing of Foreign exchange risk on stock returns at NSE sin relation to other markets world wide.
1.6 Scope and Organisation of the Study
This study focused on the effect of the pricing of foreign exchange risk on stock returns at
Nairobi Securities Exchange, with special take on the determinant proxy variables (current account deficit, inflation rate differential and interest rate differential), leading to currency risk. The study further chose the American Dollar as a hard currency to compare with
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Kenyan Shilling, while concentrating on the thirty six firms that were actively in trade for the entire period of ten years between; January 2003 to December 2012 with NSE – 20 index being used as a measure of transformation for abnormal returns. The firms under the study were; Kakuzi, Rea Vipingo Plantations, Sasin, Car and General (K) Ltd, Kenya Airways,
National Media Group, TPS Eastern Africa (Serena) Ltd, Barclays Bank, CFC Stanbic
Holdings, Diamond Trust Bank Kenya Ltd, Housing Finance Co. Ltd, Centum Investment
Company ltd, Jubilee Holdings Ltd, Kenya Commercial Bank Ltd, National Bank of Kenya,
NIC Bank Ltd, Pan African Insurance holdings Ltd, Standard Chartered Bank Ltd, Athi River
Mining, Bamburi Cement Ltd, British American Tobacco Kenya Ltd, Crown Berger Ltd,
Olympia Capital holdings Ltd, E.A. Cables Ltd, E.A Portland Cenment Ltd, East African
Breweries Ltd, Sameer Africa Ltd, Kenya Oil Co Ltd, Mumias Sugar Co. Ltd, Kenya Power
& Lighting Ltd, Total Kenya Ltd, Unga Group Ltd, City Trust Ltd, Express Ltd, Williamson
Tea Kenya Ltd and Standard Group Ltd. These were the only securities which conformed to survivorship bias criterion since they had all the data for the whole period under the study.
Since Scholars concurs with the past studies and observations that currency risk is usually priced in one or more sub sectors of financial markets of a country as well as by individual multinational firms as they aims at circumventing the adverse implication of foreign exchange risk. Although through the past studies, mixed findings have been realized over a long period of time. Our study attempted to address this issue in Kenyan equity market with aim to empirically justify the findings of the study. However no researcher has carried such a study on NSE due to availability of little or limited information.
To realise the above goals in this study, Chapter one introduced the fundamental and the gaps that necessitated the study. In a bid to meet the objective set, the rest of this thesis was
12 structured as follows. Chapter Two reviewed the existing theoretical and empirical literature as related to the study. Chapter Three reviewed the existing econometric models for the analysis of the pricing of currency risk relative to specific objectives of the study. Chapter
Four presented characteristically basic statistics of the data, as well as interpreted the most important empirical findings of the study. Chapter Five overhauled all the documents step by step as it critically concluded the research topics, the challenges as well as the achievements that had been realized.
1.7 Limitation of the Study
Study fields are prone to difficulties that need to be circumvented for the success of any study. Several limitations were expected in this study as follows:-
The study suffered from limited documented information on the previous studies done in the
African markets as well as Kenyan securities market in particular. Most researchers had not compiled all data on this area, thus made it impossible for the researcher to include all proxy variables for exchange rate based on available literature support. However, this was overcome by leaving out some proxy variables forming exchange rate the data corresponding to those variables could distort the findings.
Moreover, not all listed firms on Nairobi Securities exchange were active for the ten year period that was covered in this study. This led to a large amount of data which was missing or were inconsistent at firm’s level and on proxy independent variables for exchange rate
(public debt, terms of trade, political stability and economic performance) respectively. To address this problem, the study employed survival bias criterion that considered only those firms which had actively participated on NSE without being delisted, dormant or suspended
13 from active business for the entire period of ten years. The researcher also transformed the inconsistent data by using the three month moving average to get rid of the outliers that would distort the findings.
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CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
It is in financial interest that the analysis of the stocks return – generating process should be considered in line with: investor’s value of judgment, investor’s perspective, portfolio composition, investment returns, consumer preference, as well as time value of money.
Numerous studies have tried to ascertain the behaviour of exchange rates as well as understand the extent at which risk distribution can adequately be described.
Therefore, this chapter is organized as follows: section 2.2 concentrates at the theoretical review relating to the pricing of currency risk. Section 2.3, deals with empirical evidence on the pricing of foreign exchange risk and the proxy independent variables effect on investor’s excess returns 2.4 summarizes the literature and the gap identified 2.5, explores the conceptual framework to identify and understand the variables under the study.
2.2 Theoretical Review
The researcher was tasked to scrutinize a number of literature and studies which rendered relevancy to this study from various public and private libraries. The contributions of the valid literature are presented to this study as herein cited.
2.2.1 Arbitrage Pricing Theory
When considering how managers determines financial risk exposures of both small and large projects, it is empirically confirmed that the prospective for risk transmission is an embryonic product of existing risk in identification methods and their consequential designs as well as lack of planned strategies for risk management intervention (Brookfield and Boussabaine,
15
2013). Investing in foreign market entails exposure to foreign exchange rate risk, it then follows that an investor willing to invest in foreign assets needs to assess the vulnerability of the foreign exchange risk since it comprises of amalgamation of investment performance of foreign exchange risk and investment performance of the domestic currency relative to foreign currency. According to Carrieri and Majerbi ( 2006) study, theoretical impacts of currency risk does not vanish in a well risk spread portfolio since the volatility of foreign exchange risk should earn a risk premium when PPP is not maintained. This eventually implies that the relative result differential in the net wealth is significant as it leads to a direct implication on the strategy to be employed when hedging, meaning that uncompensated risk factor must be hedged against. Financial economists believes that foreign exchange rate volatility, like a number of random economic variables, can be described by a normal probability distribution (Kodongo, 2011). In similarity, be it assets profits or expected future cash flows streams, - whether certain or not are susceptible to currency risk as long as currency movement changes for better or worse to its parent currency value. This is evidence though theories put forward in attempt to justify the results and explain the current situations in the markets.
Stephen Ross in the mid 1970’s developed an alternative Arbitrage pricing model (APT) and hence APT theory. Arbitrage pricing theory is realistically perceptive since it entails limited assumptions enabling it to incorporate multiple risk factors. It assumes that the market is perfectly competitive and that investors put much utility on more wealth than less wealth with certainty. Therefore Arbitrage pricing model assumes that returns on assets are linearly related to a set of risk factors that influence it. Chandra (2008) posits empirical evidence that contradicts to CAPM since APT does not specify in priority what the underlying risk factors
16 in the model are. Therefore a researcher needs to establish the underlying risk factors in the model before employing multivariate techniques when examining whether the basic factors of foreign exchange risk corresponds to some economic or behavioural factors. These econometric advantages of Arbitrage pricing model over the Capital asset pricing model marks the foundation on which the model to be used in analysing and interpreting the data in this study is based.
Following Choi et al., (1998) on a bid to understand the effect of the pricing of currency risk on Japanese stock market, the study employed a three factor model on both conditional and unconditional multi-factor international asset pricing model (IAPM) on return generating process when industry portfolio excess return on the risk free rate was a linear function of exchange risk factor achieved by running a side regression of actual percentage change to excess return on market factors. It adopted the use of orthogonalised exchange risk factor, orthogonalised interest rate risk factor and the market risk factor. Orthogonality is the variation between the actual percentage change in risk factor y and its estimated value (Elton and Grube, 1991). The study discovered that in totality exchange risk was priced in Japanese stock market, specifically by providing evidence on unconditional asset pricing model which stipulates that exchange risk was priced both in weak and strong yen period and when bilateral yen/Us dollar exchange rate was employed respectively.
In researcher’s opinion, the presents of investors excess return is an indication of a well priced foreign exchange rate risk, meaning that, the effects of both inflation rate differential, interest rate differentials, current account deficits amounting to currency risk have well been rewarded in the model used in such circumstance. The result is championed in an event where purchasing power parity, interest rate parity and Fisher effect are violated. Although
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Choi et al.. (1998) made use of exchange risk factor, interest rate risk factor and the market
risk factors, this study employed a closely related model while incorporating the use of
current account deficit, inflation rate differential and interest rate differential in respect to
APT theory.
2.2.2 Inflation rate differential and Purchasing Power Parity Theory
Long run determination of exchange rate may be made possible by using the law of one price
when cost is ignored. The law of one price stipulates that identical goods should sell at same
price regardless of where they are sold, as pardoned by the logic of purchasing power parity.
Since Purchasing power parity explains the correlation between a country’s foreign exchange rate and its national price level of fluctuation relative to that of a foreign country, absolute
Purchasing power parity (PPP) stipulates that the purchasing power of a unit of home currency should exactly be equivalent to that of foreign economy once it is converted into foreign currency at absolute purchasing power exchange rate (Coakley, Flood, Fuertes, and
Taylor, 2005). It then follows that if the price changes in one country and not in another, the exchange rate adjust to reflect the change, where if country A has a higher inflation rate than country B, then country A should depreciate its currency relative to country B’s currency.
Hence it can be observed that in long run, shifts in exchange rate would be due to the difference in inflation rate (Cecchetti, 2011).
In line with the above, Devereux and Engel (2002) explored the condition to which local- currency price induces a higher level of exchange rate volatility by impending the linkages of prices of goods a cross countries as well as local currency leading to deviation from purchasing power parity, in a bid to explain the high exchange rate volatility after insight of
Krugman. Their study noted that if international financial market allows for a full cross
18 boarder risk sharing then the exchange rate would be determined by a risk sharing requirement even if local currency price are autonomous of exchange rate. It is obvious that the linkage of assets prices through bonds markets imposes a tight limit on the degree of exchange rate movement leading to lack of international trade. This implies that local currency pricing do not guarantee high exchange rate volatility because of the wealth effect of exchange rate that changes firms profit limits and the degree to which exchange rate fluctuates.
2.2.3 Interest rate differential and International Fisher effect Theory
Championed by economist Irving Fisher, the theory stipulates that real interest rate equals the nominal interest rate less the expected inflation rate, hence the real interest rate decreases with increasing inflation rate, except when nominal rates increases with the same rate as inflation. It then follows that if an investor in country M had a saving account earning a nominal interest rate of say 7% and expected inflation rate of 5%, the investor would be subjected to savings with smaller real interest rate of 2%, meaning that the deposit would take a considerably longer time to grow from purchasing power perspective.
Therefore, by adopting International fisher effect, nominal interest rate differential must be equal to the expected inflation rate differential in two countries (Pandey, 2010). It then follows that differential in inflation rate would result due to variability in exchange rate hence foreign exchange rate risk. It is in financial interest that a highly competitive forex market with free floating regime should register both spot and forward rates that reflect the current expectation as well as future events. Therefore a change in the current expectation in the interest rate differential should rapidly be reflected in both spot and forward exchange
19 rates. The study by Pandey (2010) maintained that interest rate is influenced by inflation rate and that in the event where forex market is efficient then; interest rate, inflation rate, and exchange rate should posit an equilibrium relationship. Therefore, if the market is not efficient; then forex players are prone to register abnormal profits or losses as a result of arbitrage which eventually eliminates disparities in exchange markets in long run.
Lucas (2000) defines welfare cost of inflation as an area or a sector with inverse demand function on consumer surplus that can be achieved by reducing interest rate from point K to zero. Therefore the gains (excess returns) can be registered by reducing inflation rate from say 10% to zero. This should therefore equal an increase in real income of change below one percent and therefore making it clear that inflation rate immensely contributes to the effects brought about by the pricing of foreign exchange risk on investors returns on any given equity market and hence forming a proxy factor in the arbitrage pricing model to be employed in the above study.
Adrangi, Chatrath, and Shank (1999) examines the negative correlation between equity returns and inflation rate in market industrialized economy in Peru and Chile while incorporating Fama’s model, the study noted that the research did not provide a strong evidence of Fama’s hypothesis and that negative correlation between stock returns and inflation rate persisted even after purging inflation rate off the effect of real economic activities. It is from these observations that the researcher concludes that since they existed a negative relationship between the two variables, an increase in inflation rate would result to a decrease in stock return with time. Moreover on, lack of strong evidence to justify if currency risk is priced, leads to a gap that needs to be researched on.
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2.2.4 Interest rate differential and Interest Rate Parity Theory
Pandey (2010) argued that the parity between interest rate and exchange rate is the interest rate parity. The influence of interest rate parity stipulates that the relationship between interest rate and exchange rate of given two countries, is affected by interest rate differential.
It therefore implies that a currency of a higher interest rate country would be subjected to forward discount relative to the currency of a lower interest rate country. It is then clear that the exchange rate -spot and forward- differentials should be equal to interest rate differential between two countries. In an event that interest rate parity fails to hold, it then triggers arbitrage opportunities responsible for exchange rate variability leading to currency risk, this implicates the significance of interest rate differential as a core variable in this study.
Recognizing ‘post’ Keynesian views, the future spot rate cannot predict forward exchange rate. Sharpe (2003) carried a research on imperfect asset substitutability, speculations of fixed exchange rate and the impact of that speculation in forward exchange market - in the view that uncovered interest parity holds – the study recognised that the differential in interest rate and the differential between forward and spot rate, using graphs to demonstrate how current spot rate could be influenced by the interest rate differentials. It then followed that some kind of currency premium would be required in circumstance where interest rate of one currency was different from that of another currency only when premiums depends on the accumulated net foreign debt. This is because, it is believed that, real exchange rate is in the principle of monetary variable and hence prone to policy manipulation of an incredible fixed exchange rate regime, and hence interest rate discrepancies should not occur. The study then concludes that in circumstances that monetary policies are manipulated and that free
21 floating exchange rate regime is adopted by the government, then interest rate discrepancies would be registered hence, leading to exchange rate fluctuation.
In recent years, portfolio theories have demonstrated that investors are willing to pay or encounter extra cost (premiums) for firms which engages in active hedging polices only when foreign exchange risk can’t be diversified a way. In Arbitrage pricing model forwarded by Ross in 1996, it is argued that should the economy be described by a small number of all- encompassing factors, then the factors should be priced since the investor is ready and willing to circumvent the risks imposed by those factors by paying a premium and in circumstances where home currency is anticipated to appreciate and domestic nominal interest rate exceed foreign interest rate leading to a well documented market empirical finding, then the slope coefficient from a linear projection would be a shift in the foreign exchange rate and on interest rate differential between home and foreign countries and hence, negatively significant.
(Wu, 2007) carried a study on the interest rate risk and forward factors that predict the excess bond returns, the study incorporated interest rate risk (uncovered interest parity) in a joint restriction on exchange rate and interest rate imposed by dynamic terms structured model. In conclusion, the study posited that exchange market and bond market were not fully integrated. The intuition for the remarks was from the presumption that international investors expects better interest on the value of a depreciating currency as no investor would gamble with his premium by taking it to the market where interest rate is low and inflation is high.
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2.2.5 The Expectation Theory and Current - Account deficit
The use of current account deficit as an independent variable forming one of the pillar that the study reckons the relevancy of expectation theory of exchange rate which states that the expected future spot rate depends on the expectation of forex market participation. By hedging forex risk or in an event of risk neutrality, then the forward rate must be equivalent to expected future spot rate. By incorporating the relevancy of economic exposure, which refers to the change in the value of a firm due to the unexpected change in the exchange rate
(operational exposure/ long term cash flow exposure), leads to current account deficit hence exchange rate risk.
Although Fama’s hypothesis posits a converse to Phillips curve that recognizes the existence of a negative relationship between inflation and real economic activity in which stock returns are directly related to the real economic activities. It therefore follows that an increasing inflation rate reduces real economic activities and demand for money hence negatively affecting firm’s profits and stock prices and eventually resulting to a negative relationship between stock returns and inflation rate. It is also known from fisher hypothesis of stock returns that stock markets are efficient and inflation rate vary independently to each other
(Fama & French, 1993).
Blanchard and Giavazzi (2005) developed a simple model of exchange rate and current account determinable with an aim of interpreting the past and exploring the future. The study made an assumption of imperfect substitutability not only on the U.S and the foreign goods but also between U.S and the foreign assets hence enabling them to rely on the effect of change in relative demand of goods abroad (export) and the change in relative demand for assets (import), these eventually led to depreciation in the domestic currency against foreign
23 currencies. They concluded that current account deficit led to a substantial depreciation in currency value. By borrowing from the law of demand and supply, it’s a general knowledge that when the demand for foreign currency increases due to more importation of foreign goods then, the price for a foreign currency increase hence depreciation of domestic currency, and when the demand for local products by foreign customers increases the demand for a domestic currency increases and therefore an appreciation on domestic currency value against foreign currency when other factors are held constant. It is therefore significant to argue that current account deficit leads to exchange rate fluctuation and hence currency risk.
Obstfeld and Rogoff (2005) study on how U.S return leads to current account deficit to balance, the study acknowledged that the burden of adjusting current account deficit is allocated across Europe and Asia and posit a massive implication for global exchange rate. It is from growing financial globalization and financial integration that can facilitate gradual current account and exchange rate adjustment in long run. Therefore becoming an obvious fact that current account deficit directly affects exchange rate fluctuation. In attempt to fill the gap between the current account and real exchange rate, Lee and Chinn (2006) while seeking to utilize one of chronicle implication of temporal approach to current account, where temporal shocks have no long run effect on either variables. The study made an assumption that global shocks have neither effect on real exchange rate nor effect on the variables but only a country’s specific variables affects them. From common knowledge we anticipates that, the results and the impulse response function should be in line with model prediction to avoid a case where a permanent shock e.g. technology innovation induces a permanent appreciation of the real exchange rate. The study further outlined that some visible
24 effects on current account were statistically insignificant hence realizing the temporal shock related to monetary innovation that leads to a temporal depreciation of real exchange rate and current improvements in current account
2.2.6 Summary of the Theoretical Review
From theoretical point of view, it has been demonstrated how this research is governed by several closely related theories ranging from the Purchasing power parity to the Fisher effect theory, to interest rate parity and finally the expectation theory. It then follows that this study borrows and rely heavily on the principles and the fundamentals of Purchasing power parity.
However this study won’t ignore any effects and belligerents that will be championed by other closely related theories as stated above.
2.3 Empirical Literature Review
2.3.1 The Pricing of Foreign exchange risk
The paradox of foreign exchange rate risk has to be unmasked to help investors understand the importance of this study in the foreign exchange markets. As many researchers tries to find whether currency risk is priced in many different foreign markets, The most outstanding research gap has been the failure of the past studies to reach to a common conclusion on whether or not foreign exchange risk is being priced, the level at which it is priced and the effects of the study on both small, medium and multinational firms worldwide. Bekaer ,
Robert and Hodrick (1992) acknowledged that excess returns in regard to diversity of assets could be predictable due to the methods of evaluation (dividend yield, short term interest rate, structural interest rate yield and default spread) as outlined in foreign exchange markets.
However, the predictability of returns is evaluated through forward premiums as indicators of
25 currency risk. It is from this observation in line to foreign exchange risk that Asset pricing model helps predict the expected return on asset that moves proportionately with different beta, in respect to the underlying factors.
Following the theoretical sides taken on the debate in which the international assets pricing model corresponding to Solnik (1974) and the optimizing portfolio positions as contained in forwards contract on equal foreign currency denominated hedging bills, (Jorion, 1990) and
(Jorion, 1991) studied on the pricing of exchange rate risk by incorporating both two factor model, (on the market and exchange rates) and multi – factor arbitrage pricing model made up of the value- weighted stock market return, industrial production growth, change in expected inflation, unexpected inflation, risk premium and term structure. On adopting a two factor Arbitrage pricing model that incorporated the stock market return orthogonal composition and innovation in a trade weighted exchange rate, the study propounded that the correlation between the dollar value and stock returns varied steadily across industries without a clear indication that foreign exchange rate risk was priced in stock markets as the unconditional premium attached to foreign exchange exposure appeared considerably small.
Although the study concluded that active hedging policies adopted by managers were neutral to the cost of capital and that hedging were motivated by other factors due to little evidence implying that foreign exchange risk was being priced. The study presumed that in the above case, the excess market return was an indicator of priced foreign exchange risk. However, lack of clear indictors on whether foreign exchange risk was priced and the presents of a small unconditional premium relating to foreign exchange exposure, creates an ambiguous situation on the position in other markets and therefore makes it necessary to test the same in other markets.
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It must be understood that, in a scenario where foreign exchange exposure is considerably small as in one of the case above, is an indication that either the risk is not correctly priced; it is under priced or experiences other factors that are not captured in the model. Therefore the need to carry further research on the topic in other markets worldwide is necessary to ascertain the level at which the currency risk is priced when adopting an enlarged factor based model. Consequently, using eight industry portfolios from Mexico, Bailey and Chung
(1995) tested for a conditional multi-factor model with time-varying risk premium. The estimation results of the study showed that none of the eight industry portfolios had significant peso/$ risk exposure and that the peso/dollar exchange rate risk was not priced both unconditionally and conditionally. This creates a limbo in the research knowledge on the same. It is therefore necessary to understand with facts why all the eight industry portfolios do not come clear on the topic under the study in Mexican market. To achieve that objective and despair the limbo realised before; a comparison study on the same should therefore be conducted on similar markets elsewhere in the world and that permit the study in question to ride on that.
According to Roon and Frank de jong, (2001) study on Expected returns in emerging markets, the study employed a single model where the expected return in 30 emerging markets relied on their systematic risk measured in beta relative to world portfolio and the level of integration in the market. Since integration is a time varying variable dependable on market value of an asset, the study noted an existence of strong relationship to the level of both integration and market segmentation on the expected returns in emerging markets. The study therefore concluded that the expected returns are dependable on both the level of integration and on regional segmentation level. It follows that for this result to hold more
27 study need to be done on individual market segments to clarify this finding and also ascertain the degree at which pricing of exchange rate risk affects stock returns.
Himmel, (2002) carried a study on the pricing of exchange risk using the two international asset pricing model (IAPM), where a single currency index alongside world market factor
(CI – IAPM) was employed on one side and the three exchange risk variable were adopted to check the triad region by the (Triad – IAPM) on the other side. The study posited a mixed result with time variation for both sub periods and the moving window periods. More so contrary to CI – IAPM, the Triad – IAPM provided systematic exchange risk evidence in more recent time with a more differentiated picture on the pricing of exchange risk since
IAPM uses a single currency index. It is critical to realise that no academic researcher can use such findings as back up his or her argument in favour of the position taken by any given market in the world, since the mixed findings leaves one wondering whether currency risk is priced or not. Lack of conclusive result on the same creates a situation where there is no precise position taken in the study as from the evidence of mixed results brought forward in the study above. This therefore, necessitates the need to carrying a similar study elsewhere.
Yang and Shuh (2004) explored nature of volatility transmission mechanism between stock and foreign exchange markets on the G – 7 countries while adopting the weekly (Friday) closing exchange rate and market indices of Toronto 300 composite, Paris CAC 40,
Frankfurt DAX, Millan stock index, Mikkei 225, FT – 100 and S & P 500 of DataStream international. By applying the Vector autoregressive (VAR) model, where the conditional variance of volatility spill over takes a version of Nelson’s, (1991) exponential GARCH
(EGARCH) model, in volatility transmission mechanism. The study posited empirical evidence that asymmetric volatility spill over affected stock price movement in near future, a
28 very crucial evidence for international portfolio manager when adopting hedging and diversification technique for portfolios.
Following the study done by Carrieri and Majerbi (2006), the study reckons the earlier studies done by Carrieri, Errunza, and Majerbi (2003), Blanchard , Filipa and Giavazzi
(2005) as well as Phylaktis and Ravazzolo (2005) while studying nine foreign stock markets which included; Argentina, Brazil, Chile, Mexico, Greece, India, Korea, Thailand and
Zimbabwe. The study found that by employing the unconditional capital asset pricing model
(ICAPM), exchange rate was globally priced and commanded a significant unconditional risk premium in emerging stock markets. It also acknowledged that there was a controversy between its findings and those studies that were done earlier in major developed markets as the hypothesis of zero exchange rate risk was not rejected in a fully integrated model. It is from researcher’s perspective that this study posited a valid finding since; the model adapted only incorporated one factor whose effect was implicated in the findings. In the assumption that the study complied with Ceteris Paribus , other factors held constant, then the study was valid. However, in the interest of common knowledge, foreign markets are affected by other volatile factors that may adversely affect the outcome of such a study should they be incorporated. Therefore it becomes necessary to carry a similar study on other domestic markets when adopting a multi-factor Asset pricing model which incorporates more factors to enhance the complexity and the reliability of the model.
Moreover, Tai ( 2010) studied on currency risk on Japanese stock Markets and reckons that the debates on the linkage between exchange rate fluctuations and pricing of foreign exchange risk in domestic markets was prone to continue since the study acknowledged the existence of past diverging results from different market environment. This is after the result
29 of the study showing that currency risk was not priced in Japanese stock market. The above study creates a research gap from the fact that why does its findings contradict with early results of other studies? Why does it acknowledge that there is a need for further study? This may imply that the study had not done enough to conclusively exhaust the debate on the same. (Kodongo, 2011) studied on seven major African stock markets including; Botswana,
Egypt, Ghana, Kenya, Morocco, Nigeria and South Africa from the year 1997 to 2009. Using asset pricing model, the study noted that foreign exchange risk was not priced unconditionally when returns were measured in US dollar, was weakly priced when returns were measured in Euros and was priced with time varying risk premium in conditional sense and hence concluding that African stock markets were partially integrated with the rest of the world. In the researcher’s opinion, this may be attributed by the fact that weak currency markets tends to be influenced by strong currency market behaviours and so is the currency risk they experience. Despite that, due to the presence of arbitrage, the excess returns were wiped away in long run hence implying that the mean ought to be zero in long runs too. It is obvious the study gives mixed results and fails to give a conclusive finding on whether or not, and the degree to which the foreign exchange risk was priced. One wonders how similar conditions tested on the same markets with three different currencies would give mixed results. This ambiguity in the study simply creates a research gap that the study seeks to fill.
According to (Kisaka and Mwasaru, 2012) study, the data comprised of monthly NSE stock price index and the nominal Kenyan shilling per US dollar. The result stipulated that both foreign exchange rate and stock prices were nonstationary in both first differences and second level of integration as well as co-integration. It is clear that further studies should be conducted to establish if the integration results indicated the pricing of the currency risk.
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(Du, 2012) recognises international capital asset pricing model of Sonilk (1974) and Sercu
(1980) by reiterating that the covariance of stock return with foreign exchange change should be priced when purchasing power parity is violated. (Kodongo and Kalu, 2012) posited conflicting results on the study of South Africa stock markets and Nigerian stock market. The study propounded that currency risk was priced in South African stock market and that the market was integrated to the rest of the world. Contrary to the fact that currency risk was neither priced at Nigerian stock market nor was the market integrated to the rest of the world.
If same conditions experienced in two different markets at same time posit contradicting results, then it leaves a question of what would be the situation in other markets not under the study. Therefore, lack of a precise answer for the above question leaves a gap and opens a door for further studies to be conducted on the same. The study findings also contradict with study by Carrieri and Majerbi (2006), study that beside working on nine emerging markets;
Argentina, Brazil, Chile, Mexico, Greece, India, Korea, Thailand and Zimbabwe; the study realized that foreign exchange risk was priced and (Tai, 2010) study, that worked on
Japanese markets and realized that currency risk was priced, although, it acknowledge that the research was to be carried also on other markets to compare the study results.
2.3.2 Foreign Investors participation at Nairobi securities Exchange
The effect of the pricing of foreign exchange risk must be managed when a firm profitability position is adversely hit by the effects of foreign exchange rate in line with the nature of the business. Foreign exchange risk is derived from; transaction exposure, translation exposure and economic exposure therefore affecting the investors returns. The need to evade these effects leads to trade communities undergoing monetary integration by adopting a single currency with intention to further integrate their respective markets to realize the resource
31 allocation efficiency, eliminate bilateral nominal exchange rate volatility hence reducing uncertainties and risk involved in the trade. The American dollar therefore has been used as a global currency by many investors worldwide and so makes it relevant in the pricing process at NSE.
By examining foreign investors’ contributions in the pricing of foreign exchange risk on stock returns at Korea’s stock market from November 30, 1996 to Dec 1997, (Choe, Kho, and Stulz, 1999) portrayed a stronger indication of positive feedback trading and herding by foreign investors before Korea’s economic crisis, after which followed the herding fall within the crisis period. The study confirmed that trades by foreign investors didn’t have a destabilizing effect on the market stock returns. Dornbusch and Park ( as cited in Choe et al.,
1999) argued that foreign investors deployed schemes that made stock price to go over the top due to change in fundamentals and so do Nairobi securities exchange.
However, the data at Nairobi Securities Exchange reveal the levels of operation and trade performance on annual basis. According to NSE annual reports, the trading performance has for years experienced fluctuating trends on shareholding composition and participation on stock security. Following (Nairobi Securities Exchange [NSE], 2009) annual report, equity turnover rose by 135% from Ksh. 38.16 billion to Ksh. 89.7 billion with domestic investors actively operating on NSE at 70% against foreign investors operating level of 30% (Nairobi
Securities Exchange, 2014). Moreover, (NSE, 2010) annual report indicated that, there was a global financial crisis leading to the prevalence of economic recession that precipitated to market downturn resulting in a swing by investors from equity security to relatively safety fixed income securities. This led to a fall in equity turnover by 60.87% from 2009, with
39.42% domestic investor’s participation against 60.58% foreign investor’s operational level.
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In the year 2011, equity turnover sunk from previous year by 13% up from Ksh. 179.5
Billions down to Ksh. 156.1 billions. The market participation stood at 50.5% for foreign investors with domestic investors operating level of 49.5% respectively (Nairobi Securities
Exchange, 2012). In addition, by the end of December 2012, equity turnover was up by 11%, down from Ksh. 156.1 billion in 2011 to Ksh. 173.6billion. Foreign investors occupied
49.2% of total equity operations against domestic investor’s participation of 51.8% respectively (Nairobi Securities Exchange, 2014). The 2012 annual report also reckons that the shareholding composition at NSE can be displayed by the proportionality of the investor’s participation at the equity markets. However, the level of operations for foreign investors (30% in 2009, 60.58% in 2010, 50.5% in 2011and 49.2% in 2012), is not reflective in the effect of the pricing of foreign exchange risk on stocks returns at NSE, since the data for analysis was not computed based on the level of shareholders participation. It then makes it clear that although; foreign investor’s control a significant portion of the NSE market, their relevance was not validated due to the general objective of the study.
2.4 Summary of Literature
Market segmentation is a significant area of the study as much as international asset pricing is concerned. Diversifying risks are investor’s avenues since conglomerate investments drawn from different boundaries with their heterogeneous portfolios affect the position of assets pricing relations. It is argued that while investors depends on imported goods in current market settings, inflationary exchange risk is an essential determinant of real equity price, since inflationary exchange rate affects the investors real purchasing power of current money, where the value of the firms is likely to be affected. It then should be noted that foreign exchange rate is influenced by the level of speculation and integration of risk
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management (Aabo, Hansen, & Pantzalis, 2012). Therefore the impact of unexpected change
in exchange rate exposure on firm’s stock return can produce a significant impact on stock
return of large firms than in medium and small firms (El-Masry and Abdel-Salam, 2007).
Adrangi et al., (1999) investigated the negative relationship between stock return and
inflation rate in market industrialized economy while incorporating Fama’s model, the study
noted a strong evidence of Fama’s hypothesis and a negative correlation between stock
returns and inflation rate which endured even after purging inflation rate off the effect of real
economic activities. It then follows that when an economy is described by a small value of all
encompassing factors then, the factors must be priced simply because investors are willing to
circumvent the effects of the risk exposure by paying a premium.
The table below summarises the literature conducted by other studies on related topics in
other part of the world hence triggering the need for this research in Kenyan market context.
Table: 2.1 Summary of literature review
Author (s) Area of the study Research Gap
1. (Jorion, 1991) The pricing of Exchange Rate Risk The study was carried in U.S. stock markets. The result
in the Stock Markets. posited that the relationship between the dollar value and
stock returns varied steadily across industries. The
unconditional premium in respect to currency risk was
significantly small and unable to precisely portray the
degree at which currency risk was being priced.
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2. (Choi, Elyasiani, The Sensitivity of Bank Stock The study adopted a multi – factor regression model on
and Kopecky, return to Market, interest and individual banks stock returns to market. The finding
1992) Exchange rate risk was; exchange rate had a significant influence on banks
stock returns free from other markets and interest rate
factors.
3. (Roon. Frank de Expected Returns in Emerging The result exhibited a strong relationship on the level of
jong, 2001) Markets: Time – Varying Market both integration and expected returns in emerging
Integration and Expected Return in markets, concluding that expected returns depend on
Emerging Markets, both the level of integration and on regional
segmentation level.
4. Yang, C. Shuh, Price and Volatility Spillovers It was evidence that asymmetric volatility spill over
(2004) between Prices and Exchange affects stock price movement in near future, hence the
Rates: Empirical Evidence from the study did not specify whether or not currency was being
G – 7 Countries. priced
5. Blanchard and The U.S. Current Account and the It was noted that current account deficit leads to a
Giavazzi (2005) Dollar substantial depreciation in currency value. The study
concluded that it was important for Yen and the
Renminbi to be depreciated against the Euros.
6. Carrieri and The Pricing of Exchange Risk in The study affirmed that currency risk was globally priced
Majerbi,( 2006) Emerging Stock Markets and commanded a significant unconditional risk
premium in emerging stock markets a contradiction of
the past studies.
7. (Wu, 2007) Interest Rate Risk and the Forward The study concluded that exchange market and bond
Premium Anomaly in Foreign market are not fully integrated and therefore not fully
exchange Markets priced. The study also failed to ascertain the level at
which the exchange rate market is integrated and priced
8. (Tai, 2010) Foreign exchange risk and risk Using industry data, currency risk was not priced based
exposure in the Japanese Stock on unconditional two factor Asset pricing model. The
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Market. study concluded that the debate on the pricing of foreign
exchange risk is prone to continue on individual markets
segments.
9. Kodongo (2011) Foreign Exchange risk and the flow The study found that currency risk was either not priced,
of international Portfolio Capital: weakly priced when returns were measured in US dollar
Evidence from Africa’s Capital and Euro respectively or was priced with time varying
Markets. risk hence the African market was partially integrated to
the rest of the world.
10. (Du, 2012) Foreign exchange Volatility and Empirical results posited that currency risk as a pricing
Stock returns factor could not explain either time – series or cross
section return. Hence suggesting for further research on
foreign exchange risk since currency risk causes firms
cash flow fluctuation and should be considered as a
pricing factor.
11 (Kodongo and A comparative examination of The findings were conflicting as currency risk on South
Kalu, 2012) Currency risk pricing and Market Africa’s stock market was priced and the market
integration in the stock Markets of integrated to the rest of the world, contrary to Nigerian
Nigeria and South Africa stock market which was neither priced nor integrated to
the rest of the world markets too.
Source (Author, 2014)
The above literature can be summarised by acknowledging Tai (2010) that the linkage
between exchange rate fluctuations and pricing of foreign exchange risk in domestic markets
is subject to greater debates in future studies. In addition, a study by (Kodongo, 2011)
produced mixed results while the study conducted on South Africa and Nigeria stock markets
by (Kodongo & Kalu, 2012) posited conflicting results.
36
This research therefore, seeks to fill the gap in the study done by (Carrieri and Majerbi, 2006) which posited results with varying level at which currency risk was priced from one equity market to another since, It is important for a study to give precise direction on the degree at which currency risk is priced as well as the trend taken and so, this is also applicable is in the
Kenyan Nairobi Securities Market context.
2.5 Conceptual Framework
(Padmar Gutur, 1985) acknowledges that inconsistent patterns of exchange rate movements leads to a widespread in interest rate to the level impacting the movement of trade hence increasing risk due to exchange rate volatility as well as the uncertainty of international transaction leading to influence in trade and investment flow. Although the study failed to demonstrate on empirical basis how exchange rate volatility discourages international trade, majority of the studies were also unable to establish systematic significant association between quantifiable exchange rate variability and their degree of trade.
For the model and the methodology in this research to be logical and effective, the following two assumptions were considered; the expected excess returns on asset i, listed on a security market (NSE), is a linear function of the foreign exchange risk premium factors;
, , and which represents current account deficit, inflation rate differential and interest rate differential respectively. Moreover the model should rule out the possibilities of other factors that can override or interfere with the relationship between the antecedent and consequent. Therefore the researcher acknowledges Ceteris Paribus assumptions of other factors held constant in respect to this study as prescribed in the conceptual framework below:
37
Figure 2.1 Conceptual Framework
Independent Variables Dependent Variables
Inflation rate differential
Stock Returns Interest rate differential • Abnormal Returns
Current account deficit
Source (Author, 2014)
38
CHAPTER THREE
METHODOLOGY
3.1 Introduction
This chapter outlined and elucidated the rationale of appropriate models on which the methods of inquiring rested on as the researcher tried to establish the impact of the hypothesized variables under this study as well as suggesting appropriate scientific models to test the effectiveness of the general and specific hypothesis and their noteworthy. The chapter went further to present the target population, sampling design and procedure, the data collection procedure, data analysis and presentation. The sources of the data were also provided for in this chapter with detailed analysis of explanatory variables forming the proxies of the pricing of foreign exchange risk and how the variables were constructed.
3.2 Research Philosophy
This study employed Post-positivism philosophy. Post-positivism philosophy is also known as scientific methods of quantitative research or of critical realism whose assumptions are built on claims about warrant knowledge meaning knowledge are objective. Since post– positivist believes in realistic independency from our thinking about what science can study, hence acknowledging objective reality that all observation has errors, and all theories are reversible (Lincoln and Guba, 2000).
Popper (as cited in Guba and Lincoln, 1994) explained the distinction between theory verification – falsification. Guba and Lincoln (1994) illustrated that while a million white swans can never ascertain with absolute confidence the proposition that all swans are white, one black swan can entirely falsify it. This makes critical realist important to our ability to
39
know reality with certainty. Therefore, making it more important to outline what knowledge
claims a researcher makes in his study, if the strategy of inquiry conforms to the procedure
adopted in that study, the methods of data collection and the analysis that the researcher
incorporates in his study.
3.3 Research Design
This study employed a descriptive, non – experimental research design and employed a
cross- sectional time series data where secondary data formed the basis of the data that was
used in the study. According to Kothari (2004) a study is non – experimental, if the
independent variables in the study are free from manipulations and therefore, non –
experimental research design is concerned with testing hypothesis of fundamental
relationship between variables while reducing biasness and increases reliability, as it consent
to depiction of conclusion about causality.
3.4 Empirical Research Model: Unconditional International Arbitrage Pricing Model.
This study adopted APT theoretical model which forms the backbone on which the empirical
model was found. Moreover, by borrowing from Ross (1986), Jorion (1991), Choi (1998)
and Himmel (2002), the study assumed a three factor model, in which the exchange rates
proxy components in the model was orthogonal to the abnormal return generating process on the market which is a linear function of the orthogonalised exchange rate proxy components on risk factors (F) as below;
= + , , + , , + , , + , (1)
In which coefficient , is an orthogonalised current account deficit, coefficient , is
the orthogonalised inflation rate differential, coefficient , is the orthogonalised interest
40 rate differential. - is the market abnormal return at period t as measured in Kenyan
shillings when the factor premiums is under consideration; , , , are the estimated market returns in respect to factors; , and , respectively and , are the zero – mean residual terms of the assets.
This is in line (Elton and Cruber, 1991) study which refers to Orthogonalized exchange risk factor as the difference between the actual percentage changes in exchange rate risk factor proxy and the estimated values. In that sense, the model must conform to the condition that the mean value to the Orthogonalized current account deficit, inflation rate differential and interest rate differential are zero conditional to the market factor.
3.4.1 Operationalization of the Variables in GMM model.
Adopting (Carrieri and Majerbi, 2006) the factors incorporated in the model are not directed by specified theoretical framework hence adopts the empirical traditions and determines the global risk factors based on implications of established asset pricing model. Where is the demeaned value of the risk factors ( mean and E ( ε = 0. Should regression be restricted by the assumption that the intercept is equivalent to zero, it then follows that the influential economic variables forming the proxies to exchange rate should be used, and therefore, the exchange rate components should be orthogonal to market and in line with the original assumption of APT. Hence, the effects of all other factors that affect exchange rate leading to exchange rate risk when omitted in the model should be near zero. The explanatory variables adopted by this study were constructed as below.
41
3.4.1.1 Constructing Stock returns and or Market Abnormal returns
This study employed the monthly closing prices for the entire period after which, the
− Pt Pt−1 monthly excess stock return ( R) was computed as follows; R = ( )X100 Where P t - P t−1
is the average current month’s ( t) rate, P t-1 is the average rate of the previous month and R is the excess stock return. In finance, market returns assumes that the mean of the stock performance is expected to be the same as that of the mean of the market performance, and therefore the study adopted the percentage change in NSE - 20 indexes as a measure in this
Nse − Nse circumstance. Hence normal market returns would be R = ( t t−1 )X100 nse Nse t−1 ,
It then followed that the supernormal return (capital gain\ loss) for asset A was computed as:
− − Pt Pt−1 Nse t Nse t−1 = { ( )X100 } – { ( )X100 } Pt−1 Nse t−1
Where is supernormal returns (capital gain/ loss) on asset A relative to market normal return as dictated by NSE – 20 index, is market gains above the previous NSE – 20 index at time t, Pt is the current month’s ( t) closing rate, P t-1 is the closing rate of the previous month, Nse t is the current month’s ( t’s ) NSE – 20 index, Nse t-1 is the previous NSE – 20
index time t -1.
To generate the average market abnormal return the total accumulative supernormal
average returns for entire sample of thirty six assets (firms) was computed as below;
42
R + R + R +...... + R ( A 1, A 2, A 3, ,nA ) Where is the accumulative average abnormal N
market return, RA 1, , RA 2, , RA 3, , R ,nA is the Supernormal return on assets; , , , …. . where ‘n’ is all the thirty sixth firm on NSE forming the sample under the study.
3.4.1.2 Constructing the Explanatory Variables
This study concentrated on Kenyan shillings against American dollar. It is from this point that this study made a comparison between Kenyan ninety one days (91 days) Treasury bill rates ( , , Kenyan 12 – month overall inflation rates( , , against ninety one days (91 day) American Treasury bill rates ( , ) and American monthly inflation rate
( , respectively. In order to generate an interest rate differential, the study generated the difference between Kenyan monthly interest rate and American monthly interest rate, while to generate inflation rate differential, the study also computed for the difference between Kenyan monthly inflation rate and American monthly inflation rates. Therefore, the formulae for deriving the inflation rate differential and interest rate differentials were;
, = , - , ; for inflation rate differential and
, = , - , ; for interest rate differential respectively.
Current account deficit , was extracted from the monthly economic review reports under the balance of payment raw.
In line with the above, this study incorporated the use of the Generalised Method of Moment
(GMM) in analysing the data. Smith and Wickens (as cited in Kodongo, 2011) posited that the parametric estimation of the study suits the use of GMM, due to its robustness to
43
heteroscedasticity. Going by Hall (1993), it is argued that GMM model excuses a study from
making explicit specification of the data generating process in the study, and hence, the
parameters estimation under GMM are computationally not involving and more efficient
unlike probability density function (PDF) and maximum likelihood procedure (ML). This
makes GMM a consistent, asymptotically normal and efficient estimator while maximising
the objective function of the model hence, it exhibit relatively small biasness even with as
small sample sizes as sixty. In this study, the Variables were operationalized as below:
Table 3.1: Operationalization of the Variables
Variables Type Operationalization Measurement Hypothesised
Direction
Current account deficit Independent , Percentage None
Inflation rate differentials Independent , Percentage None
Interest rate differential Independent , Interval None
Abnormal returns (Capital Dependent Percentage None gains/ loss)
Source: (Author, 2014)
3.5 Target Population
Target population can be defined as a process that researchers employs to generate the results of the study Mugenda and Mugenda (2003). The population for this study was all the sixty one listed firms on Nairobi Securities exchange as at December 2012. This was in respect to a definition by Ogula (2005), that a population is any group of people, objects or institutions
that share at least one character in common. Firms that were not in trade either because of
being delisted, suspended or dormant for the entire period of ten years were excluded from
44 the study. To ensure representativeness and utilisation of the research findings and by using inclusion exclusion criteria, firms that were studied included; Kakuzi, Rea Vipingo
Plantations, Sasin, Car and General (K) Ltd, Kenya Airways, National Media Group, TPS
Eastern Africa (Serena) Ltd, Barclays Bank, CFC Stanbic Holdings, Diamond Trust Bank
Kenya Ltd, Housing Finance Co. Ltd, Centum Investment Company ltd, Jubilee Holdings
Ltd, Kenya Commercial Bank Ltd, National Bank of Kenya, NIC Bank Ltd, Pan African
Insurance holdings Ltd, Standard Chartered Bank Ltd, Athi River Mining, Bamburi Cement
Ltd, British American Tobacco Kenya Ltd, Crown Berger Ltd, Olympia Capital holdings
Ltd, E.A. Cables Ltd, E.A Portland Cenment Ltd, East African Breweries Ltd, Sameer Africa
Ltd, Kenya Oil Co Ltd, Mumias Sugar Co. Ltd, Kenya Power & Lighting Ltd, Total Kenya
Ltd, Unga Group Ltd, City Trust Ltd, Express Ltd, Williamson Tea Kenya Ltd and Standard
Group Ltd. These firms formed a sample for the study that represented fifty nine point zero two percent (59.02%) of the total population.
3.6 Sampling Design and Sample Size
Frankel and Klallan (2000) defined a sample as a group from which information is obtained and hence sampling is the process of choosing a number of individuals from a population.
Furthermore, Reddy (2007) argues that a sample should be chosen in a manner that represents the entire population under the study.
This entire population consisted of sixty one (61) firms listed on Kenyan Nairobi securities exchange for the entire period of ten years extending from January 2003 to December 2012.
The study applied census of thirty six (36) listed firms from the entire population for the entire period under the study. The sample was made up of only those firms who were in trade for the entire period of ten years without being delisted, suspended or dormant on NSE. The
45 number of firms under the study amounted to fifty nine points zero two percent (59.02%) of the total population as per December 2012, a sufficient number for ascertaining the insight of this study.
3.7 Data collection Procedure
The study employed the use of secondary data by the help of the inclusion exclusion criteria to select firms which provided the data for the past ten years, between; January 2003 to
December 2012. The data was retrieved from the following websites; Nairobi securities
Exchange (NSE),the Federal Reserve Bank for American 91 day treasury bills rates and inflation rates and from Central bank of Kenya both website and archives (CBK) for 91 days treasury bills and inflation rates respectively. The data was entered in the data collection forms attached at the appendix 1, form A and B respectively.
3.8 Data analysis and Presentation
The process of employing statistical or logical techniques systematically to describe and illustrate, condense and summarize, as well as evaluate data is basically referred to as data analysis. Smeenton and Goda (2003), state that various analytic methods provide a way of drawing inductive inferences from data and differentiating the phenomenon of interest (the signal) from the statistical fluctuations (noise) present in the data. This is a very important ingredient for ensuring data integrity and accuracy in the analysis of the research findings.
This study is in a form of time series data since the data and observation were compiled over a period of time (Kothari, 2004). The collected data were entered in the inbuilt computer E –
Views software to help in analysing and interpreting the data. The model of the study borrows from International Asset Pricing Model (IAPM), with the help of Iterated
46
Generalized Method of Moments (GMM) of Ferson and Foerster (1994) since, the use of iterated GMM estimates is critical as, it produces better finite sample properties and uses constant and simultaneous values of respective factors which posits a greater significance in
GMM regressions over OLS. It then follows that this study must comply with all the properties and assumptions of OLS.
To achieve more accurate results, the researcher employed descriptive statistics with emphasis on mean, median, Skewness and Kurtoisis . Normality test were achieved by use of
Bera- Jarque technique. By accepting the periodic correlation in error term as in SURM, then the study tested the model by imposing the requirement that the intercept term be zero
(error should be uncorrelated), fit to which the regression would then be free from
Autocorrelation problems. In that case, the study adopted Breusch – Godfrey Serial
Correlation LM Test to detect autocorrelation problem.
However a problem of multicollinearity prevails in a circumstance where the explanatory variables are highly correlated to each other. In this study, correlation matrix was adopted to test the independent variables against the presence of multicollinearity. In a scenario where there is no constant variance of error posits an indication that the variables under the study are suffering from the problem of heteroscedasticity. Using Eviews software the study employed the use white’s test to detect the presence of heteroscedasticity
The study also aimed to achieve stationarity of the variables in the model by incorporating the use of unit root test. Since stationary series are those with constant variance, constant mean and constant auto-covariance for every given lag term (Brooks, 2008), non stationary data results to spurious regressions where if standard regression techniques are applied the
47 resulting regression may be good under standard measure with a high but fails the standard asymptotic analysis assumption hence neither will the ‘t–ratios’ follows the t – distributions nor F – statistics follows the F - distribution. The study achieved this objective by using E – views software, with the help of an inbuilt Augmented Dickey – Fuller approach (ADF) to address the unit root test for stationarity discrepancies that would arise.
Fit to above diagnostics tests, the underlying hypothesis were tested by use of F – statistics since the data is made up of 480 usable observations and four parameters to estimate in the
GMM regression. F – Statistical values and p – Values were used to test the confidence of the hypothesis at 5% level. The findings of this study will be displayed and interpreted using a collection of tables and figures.
48
CHAPTER FOUR
RESULTS, INTERPRETATION AND DISCUSSION
4.1 Introduction
This chapter makes presentation of the statistical summary and results from empirical analysis and interpretations of the statistical inferences derived from the compiled data in a bid to accomplish the objective of the study. In this section data was constructed using the methodologies formulated in section 3.4.1.1, 3.4.1.2 and 3.4.1.3 in order to attain the precise values that were used in analysis as evidence in appendix three (iii). The constructed data was derived from the following sources; Monthly economic reviews sourced from (Central bank of Kenya [CBK], 2003:2012), Daily stock prices and Daily NSE- 20 indexes was sourced from (Nairobi Securities Exchange [NSE], 2003:2012), the American monthly 91 days treasury bill rates and inflation rates was sourced from Federal Reserve Bank website, with the American dollar forming the hard currency in comparison with the Kenya shilling.
4.2 Preliminary Analysis
4.2.1 Descriptive Analysis for the Risk factors
In this section, a set of tables and figures were used to present statistical summary of market stock returns in relation to the independent variables, using the NSE – 20 index as the bench mark for the excess returns while covering the entire period of ten years as from January
2003 to December 2012. The data was scrutinized as below;
To begin with, the study derived the normality and descriptive statistics for the explanatory variables and the risk factor as in the table below;
49
Table 4.1 Descriptive Statistic for the instrumental Variables
ABNORMAL CURRENT ACCOUNT INFLATION RATES INTEREST RATES RETURNS DEFICIT DIFFERENTIAL DIFFERENTIAL Mean 0.805677 -9.883182 8.065168 5.250667 Median 0.116845 -3.760307 6.635000 5.060000 Maximum 84.478123 95.212464 19.720000 20.530000 Minimum -40.611924 -97.424178 -0.410000 -0.110000 Std. Dev. 9.193502 44.244227 5.418445 4.237653 Skewness 5.593509 0.321655 0.480775 1.428179 Kurtosis 61.737019 2.919703 1.949543 5.393276 Jarque-Bera 17875.934 2.101478 10.140203 69.432761 Probability 0.000000 0.349679 0.006282 0.000000 Sum 96.681236 -1185.9818 967.820179 630.0800 Sum Sq. Dev. 10057.937 232948.641 3493.786 2136.966 Observations 120 120 120 120 Table 4.1 above presents the ten year descriptive statistics of the NSE – 20 index companies with four parameters under the study; abnormal returns, current account deficit, inflation rates differential and interest rates differential respectively. Under this study, it was observed that the abnormal returns realised a maximum level of 84.5 and a minimum level of -40.6 with a mean of 0.116845 and standard deviation of 9.193502, current account deficit had a maximum level of 95.212 and a minimum of -94.418 with a mean of -3.760307 and a standard deviation of 44.244227. On the other hand, inflation rates differential registered a maximum level of 19.72 with a minimum level of -0.410, a mean of 6.63500 and a standard deviation of 5.418445 while interest rates differential realised 20.53 maximum levels as well as minimum levels of -0.110 with a mean of 5.0600 and a standard deviation of 4.237653 was observed in the same period.
The findings in table 4.1 above clearly illustrated that the assumptions of normal distribution were not applicable on both the market returns and the explanatory variables in the study since the results were neither normally distributed nor were sufficiently explained by both the
50 mean and standard deviation. These evidences displayed the violation of the normal distribution assumption evident in all the instrumental variables in the model. The findings also conformed to Harvey and Siddique (2000) argument that the presents of huge positive skewness often lure investors to hold portfolios for a long time regardless of the negative expected returns. This makes researchers to concede that models with larger positive skewness have huge success in explaining the negative aspect of the expected risk premium.
Moreover, on testing normality using the Jarque – Bera test, abnormal return registered a figure of 17875.934; current account deficit posited a figure of 2.101478, inflation rate differential registered 10.140203 and interest rate differential registered a figure of
69.432761 respectively. In respect to Jarque – Bera statistics, these figures signalled that the data in respect to the pricing of currency risk generating process could not be explained by the normal distribution. These findings were in concurrence with several past studies which realised that stocks and or market returns in respect to foreign exchange do not conform to normal distribution criterion. According to McFarland et al (as cited in Kodongo, 2011), the findings in the study signalled that the distribution for the priced currency risk were very complex to be potted by normal distribution, hence might have been created by the abnormal movements in the Kenyan exchange rate proxies against the American proxy variables
(Current Account deficit, stock returns, Inflation rate differential and the interest rate differential).
Furthermore the abnormal distribution criterion displayed above could also be due to the fluctuations in the movement of the explanatory variables and the dependent variable. The fluctuation might have been caused by the global financial depreciation that emanated from the United State of American which formed part of the fundamental concern in this study
51
since the contagion effect brought about by the financial depression badly affected the
inflation rate and the interest rates globally. This can further be explained by the outliers
effect recorded in the current account deficit where at a given time of the year, CAD reported
huge hiccups hence, the effect of those irregularities were evidence in the huge standard
deviation and large Jarque – Bera statistics corresponding to instrumental variables. Further elaborations of the descriptive analysis were represented in the figures below;
Figure 4.1 Normality Tests for Abnormal Return
60 Series: ABNORMAL_RETURNS Sample 2003M01 2012M12 50 Observations 120
40 Mean 0.805677 Median 0.116845 Maximum 84.47812 30 Minimum -40.61192 Std. Dev. 9.193502 20 Skewness 5.593509 Kurtosis 61.73702 10 Jarque-Bera 17875.93 Probability 0.000000 0 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80
It was observed that the abnormal returns for entire period didn’t follow normal distribution criteria as it was positively skewed at 5.593502, with a tail thickness of Kurtosis 61.73702.
Figure 4.2 Normality Tests for Current account deficit (CAD) 35 Series: CAD 30 Sample 2003M01 2012M12 Observations 120 25 Mean -9.883182 Median -3.760307 20 Maximum 95.21246 Minimum -97.42418 15 Std. Dev. 44.24423 Skewness 0.321655 10 Kurtosis 2.919703
5 Jarque-Bera 2.101478 Probability 0.349679 0 -100 -80 -60 -40 -20 0 20 40 60 80 100
52
Current account deficit in table 4.2 above failed to follow a normal distribution criterion,
where for normality test to hold, the Mean must equal the median and the mode. However, in
this study, the data was skewed to the right at 0.322 with mean -9.883, median -3.760 and
Kurtosis 2.919703 and hence contradicting the normality distribution criterion.
Figure 4.3 Normality Tests for Inflation rate differential (IFRD) 20 Series: IFRD Sample 2003M01 2012M12 16 Observations 120
Mean 8.065168 12 Median 6.635000 Maximum 19.72000 Minimum -0.410000 8 Std. Dev. 5.418445 Skewness 0.480775 Kurtosis 1.949543 4 Jarque-Bera 10.14020 Probability 0.006282 0 0 2 4 6 8 10 12 14 16 18 20
Moreover, Inflation rate differential failed to follow normal distribution criterion, as the data
was skewed to the right at 0.480775, with relatively long tail of Kurtosis 1.949543, a mean of
8.065168 and median of 6.635 respectively.
Figure 4.4 Normality Tests for Interest rate differential (ITRD)
20 Series: ITRD Sample 2003M01 2012M12 16 Observations 120
Mean 5.250667 12 Median 5.060000 Maximum 20.53000 Minimum -0.110000 8 Std. Dev. 4.237653 Skewness 1.428179 Kurtosis 5.393276 4 Jarque-Bera 69.43276 Probability 0.000000 0 0 2 4 6 8 10 12 14 16 18 20
53
In the figure 4.4 above, Interest rate differential didn’t follow a normal distribution criterion,
as the data was positively skewed at 1.428 with Kurtosis 5.393276, mean 5.251 and median
5.06 respectively. It then followed that figures (4.1, 4.2, 4.3 and figure 4.4) above; had
variations in their mean and median with the presents of excess skewness and excess
kurtosis. The results conforms to Carrieri and Majerbi, (2006) and (Tai, 2010) studies which
argued that the presents of excess skewness and excess kurtosis in all data levels strongly
rejected the normal distribution hypothesis. Autocorrelations was further tested as below;
Table 4.2: Breusch – Godfrey Serial Correlation LM Test
F-statistic 1.967139 Prob. F(10,105) 0.0442 Obs*R-squared 18.77653 Prob. Chi-Square(10) 0.0432
Test Equation: Dependent Variable: RESID Method: Least Squares Sample: 2003M02 2012M12 Included observations: 119
Variable Coefficient Std. Error t-Statistic Prob.
C 0.146852 1.569453 0.093569 0.9256 CAD -0.006241 0.186896 -0.033393 0.9734 IFRD -0.031128 0.276138 -0.112726 0.9105 ITRD -0.000242 0.007004 -0.034484 0.9726 RESID(-1) -0.397358 0.097652 -4.069132 0.0001 RESID(-2) -0.131533 0.104894 -1.253956 0.2126 RESID(-3) -0.011551 0.105289 -0.109705 0.9129 RESID(-4) 0.027412 0.104090 0.263352 0.7928 RESID(-5) 0.032873 0.103141 0.318720 0.7506 RESID(-6) -0.123512 0.103198 -1.196846 0.2341 RESID(-7) -0.145086 0.104069 -1.394125 0.1662 RESID(-8) -0.127563 0.105231 -1.212222 0.2281 RESID(-9) -0.102951 0.105743 -0.973596 0.3325 RESID(-10) -0.011570 0.098439 -0.117533 0.9067
R-squared 0.157786 Mean dependent var -4.81E-16 Adjusted R-squared 0.053512 S.D. dependent var 9.101017 S.E. of regression 8.854163 Akaike info criterion 7.309784 Sum squared resid 8231.602 Schwarz criterion 7.636739 Log likelihood -420.9321 Hannan-Quinn criter. 7.442550 F-statistic 1.513184 Durbin-Watson stat 2.000605 Prob(F-statistic) 0.124631
54
The above results showed the 5presents of no autocorrelation as Durbin–Watson statistic of 2.000605 were obtained making the model appropriate for statistical inference. This results agreed with (Brooks, 2008) study which posited DW - statistics of 2.000605 hence prompted the null hypothesis of no autocorrelation to be accepted.
The absence of autocorrelation justified the reliability of the estimators since their variance were not affected. The results further indicated that the absence of autocorrelation was attributed to time variation hence contradicting with Mech, (1993) that portfolio return autocorrelation was not caused by time – Varying expected returns.
The study further tested the problem of multicollinearity using correlation matrix as below;
Table 4.3: Correlation Structure for Instrumental Variables and Risk factors
Inflation Interest Rates Current Abnormal Rates Differential Account Returns Car Differential 91 T-bill (K- Deficit (K )- At NSE ( K - USA) USA) Us$ M Inflation Rates Pearson Correlation Differential ( K - USA) Sig. (2-tailed) (P) Interest Rates Pearson Correlation .575 ** Differential 91 T- Sig. (2-tailed) ( P) .000 bill (K-USA) Current Account Pearson Correlation -.342 ** -.584 ** Deficit (K )- Us$ M Sig. (2-tailed) (P) .000 .000 Pearson Correlation -.045 -.052 .159 Abnormal Returns Sig. (2-tailed) (P) .630 .572 .0085 Car At NSE
**. Correlation is significant at the 0.01 level (2-tailed) and (P) is the P - Value. Table 4.3 represented the correlation matrix for the risk factors and the explanatory variables with the level of significance being tested at 1% and 5% respectively. At 1% of significant levels, the correlation coefficient between the explanatory variables IFRD and ITRD was positively significant at (r =0.575, p value = 0.000); IFRD and CAD as well as ITRD and
CAD were negatively significant with Pearson correlation coefficient of r = -.342, and
55 r = -0.584 with both sharing p value of (0.0000) respectively. At 5% level of significant, the correlation coefficient between explanatory variable CAD and risk factor Abnormal Returns was positively significant Pearson correlation of 0.159 and p (0.0085) were realized. A negatively non significant correlation coefficient between IFRD and Abnormal returns and
ITRD and Abnormal returns were also realised Pearson correlation coefficient of r (-0.045) with P(0.630) and r (-0.052) and P (0.572) respectively. The magnitude of the results above justices that the data used is not suffering from multicollinearity problems. Furthermore, the study established the orthogonality conditions essential for setting the use of GMM estimation. Although, the study did not portray the presents of multicollinearity problems, could it had done so, the study could have further rallied behind (Brooks, 2008) study that proposed that a study can decide to ignore the problem of multicollinearity since; multicollinearity is a problem associated with data rather than the model employed and therefore a study can use a more adequate model when analysing the data to overcome it.
4.2.2 Simultaneous Co – movement within the Variables
This analysis aimed at establishing the relationship between the movements of the risk factors (abnormal returns) and the explanatory variables (current account deficit, inflation rate differential and the interest rate differential) respectively in the study as in the figures here under;
56
Figure: 4.5 Movements between abnormal returns and the Instrumental Variables
AVERAGE_ABNORMAL_RETURNS CURRENT_ACCOUNT_DEFICIT_
100 200
75 0 50
25 -200
0 -400 -25
-50 -600 03 04 05 06 07 08 09 10 11 12 03 04 05 06 07 08 09 10 11 12
INFLA TION__RATES_DIFFERE INTEREST_RA TES__DIFFEREN
20 25
20 15
15 10 10 5 5
0 0
-5 -5 03 04 05 06 07 08 09 10 11 12 03 04 05 06 07 08 09 10 11 12 The study constructed in the figure 4.5 above, used the absolute volatility movement series on each individual variable as in Ang, Hodrick, Xing, and Zhang, (2006). It was observed that abnormal returns exhibited a relatively stable movement with a few Sharpe variations in its movements at a given time. Current account deficit experienced a volatile movement that increased with time (moving away from zero). Inflation rate differential also registered volatile movements, although the turbulence was relatively gentle and stable. Interest rate differential on the other hand, realised unstable movements with some kind of hiccups at a given point. However, most of the entire period reported gentle turbulence in its movement.
This study went further to combine all the parameters under the study as below;
57
Figure: 4.6 The Simultaneous co-movement between Variables
200
100
0
-100
-200
-300
-400
-500
-600 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
AVERAGE ABNORMAL RETURNS CAR AT NSE CURRENT ACCOUNT DEFICIT (K )- US$ M INFLATION RATES DIFFERENTIAL ( K - USA) INTEREST RATES DIFFERENTIAL 91 TBILL (K-USA)
The study further incorporated the use of factor – mimicking portfolio along similar lines as
in Fama and French, (1993). It was observed that while current account deficit experienced
huge turbulence of movements, the abnormal returns, inflation rate differential and the
interest rate differential registered a relatively similar volatility patterns.
The volatility patterns represented in figure 4.6 above was presumed to be a linear function
of all instrumental variables, there was some kind of an implication that the data exhibited
time variation leading in the movement of both the explanatory variables and the risk factor.
The variation includes the change in the abnormal return movement as well as a change in
CAD, IFRD and ITRD movement too. These findings were in tandem with the results in
studies done by Duma, (1995) and Choi et al , (1998) which registered a similar result.
58
4.2.3 White’s Specification test for Heteroscedasticity
Heteroscedasticity occurs when the assumption of constant variance of disturbances is violated. When the variances of disturbances increases or decreases with increasing values of the regressors, then the data is said to be heteroscedastic . This study therefore, adopted the
White test method to correct estimates of the coefficient covariance in a case of the presence of heteroscedasticity of unknown form for the entire period under the study as below.
Table 4.4: White’s Specification test for Heteroscedasticity
Heteroscedasticity Test: White
F-statistic 0.354344 Prob. F(9,109) 0.9539 Obs*R-squared 3.382700 Prob. Chi-Square(9) 0.9472 Scaled explained SS 94.54821 Prob. Chi-Square(9) 0.0000
Test Equation: Dependent Variable: RESID Included observations: 119
Variable Coefficient Std. Error t-Statistic Prob.
C 397.1593 206.3754 1.924451 0.0569 Current Account De icit -0.000475 0.003181 -0.149202 0.8817 CAD*Inflation rate differential -0.015525 0.120636 -0.128691 0.8978 CAD*Interest rate differential -0.047535 0.171225 -0.277618 0.7818 Current Account Deficit 0.412613 1.216134 0.339283 0.7350 In lation rate differential 1.753673 3.237683 0.541645 0.5892 IFRD * Interest rate differential 1.861681 6.308915 0.295087 0.7685 Inflation rate differential -56.99966 50.13254 -1.136979 0.2580 Interest rate differential -1.792773 4.633790 -0.386891 0.6996 Interest rate differential -6.511367 49.02691 -0.132812 0.8946
R-squared 0.028426 Mean dependent var 82.13248 Adjusted R-squared -0.051796 S.D. dependent var 638.1260 S.E. of regression 654.4435 Akaike info criterion 15.88574 Sum squared resid 46684290 Schwarz criterion 16.11928 Log likelihood -935.2015 Hannan-Quinn criter. 15.98057 F-statistic 0.354344 Durbin-Watson stat 1.599456 Prob(F-statistic) 0.953867
Table 4.4 above reported the presents of heteroscedasticity in data compiled data. This was since the F-statistic obtained in the White’s Specification test were not significant
(F=0.354344, p value=0.9539). The findings clearly portrayed the presents of huge standard
59 error hence the need to test the stability must be emphasised to attain the hypothesis of constant coefficient. Jorion, (1990) observed the importance of correcting the standard error for heteroscedasticity among the variables as the correction tends to increase the standard error or else the regression should well be specified in accordance to be serially unrelated.
However, Hall (1993) observed that by incorporating GMM approach, heteroscedasticity can be overcome since the study needs not to make explicit specification of the data generation process.
4.2.4 The Unit Root test Results on the Monthly Data
The prevalence of stationarity in the time series data can only be achieved by carrying out a unit root test. It has been argued that in circumstances where there is an existence of structural breaks may be a clear indication that unit root test may give unreliable results. To the latter, the study adopted the Augmented Dickey-Fuller test (ADF) of (1979) for unit root tests as in table below;
Table 4.5: Unit root test results
Null Hypothesis: Unit root (individual unit root process) Series: Average Abnormal Returns, C A D, IFRD, ITRD Sample: 2003 2122
Method Statistic Prob.** ADF - Fisher Chi-square 175.676 0.0000 ADF - Choi Z-stat -9.97905 0.0000
** Probabilities for Fisher tests are computed using an asymptotic Chi -square distribution. All other tests assume asymptotic normality. Intermediate ADF test results
Series Prob. Lag Max Lag Obs ABNORMAL__RETURNS 0.0000 0 12 119 CAD 0.0000 0 12 119 IFRD 0.2814 12 12 107 ITRD 0.0329 3 12 116
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In the table 4.5 above, the Augmented Dickey-Fuller (ADF) (1979) unit root tests upholds the robustness of the test results as it ensures the inferences regarding stationarity are unlikely influenced by the choice of testing the procedure used. The tests were applied to each variable over the entire period of ten years at both the variable levels and the first difference respectively and the results were presented in the table above. The computed t - statistic for average abnormal returns was -9.97905 in the ADF series hence, the results implied the presence of a unit root on the abnormal returns on stocks at NSE series was stationary in their level series as a p- value of (0.0000) was realized. A key implication of these findings was the existence of a long run relationship between the abnormal returns to
Current account deficit (CAD), interest rates differentials (ITRD) and the inflation rates differentials (IFRD) at their lagged levels. This meant that in the long run, the dependent variable could be well predicted using the specified independent variables.
The results above are in line with Gujarati, (2004) observation that economic variables
(CAD, IFRD and ITRD) and stock return (abnormal returns) data realised fluctuations, therefore exhibited a random walk principle leading to realisation of a null hypothesis of non stationary on time series data being rejected.
4.2.5 The Co - integration test Results on the Monthly Data
The co- integration approach is coined on the econometric notion that two or more economic variables can be said to be co- integrated only if the residuals from the regression of two or more of the variables experiences stationarity i.e. the residuals registers a zero order of integration. This study employed a Granger causality test which is an adequate approach of establishing the relationship between variables irrespective of the presents of the structural breaks, (Granger et al, 2000). If two or more variables have a linear combination suggesting
61
the existence of long run equilibrium relationship between or among them, then the variables
are said to co- integrated between or among the variables. The results of the co-integration
test were as here under;
Table 4.6: Pairwise Granger Causality test Results
Lags: 12
Null Hypothesis: Obs F-Statistic Prob.
CAD does not Granger Cause ABNORMAL_RETURNS 107 1.22456 0.2808 ABNORMAL_RETURNS does not Granger Cause CAD 0.94098 0.5111
IFRD does not Granger Cause ABNORMAL_RETURNS 107 0.33094 0.9814 ABNORMAL_RETURNS does not Granger Cause IFRD 0.65740 0.7866
ITRD does not Granger Cause ABNORMAL_RETURNS 107 1.98656 0.0358 ABNORMAL_RETURNS does not Granger Cause ITRD 0.46203 0.9311
IFRD does not Granger Cause CAD 108 0.76599 0.68 31 CAD does not Granger Cause IFRD 1.69373 0.0829
ITRD does not Granger Cause CAD 108 0.64151 0.80 09 CAD does not Granger Cause ITRD 1.86324 0.0511
ITRD does not Granger Cause IFRD 108 2.41268 0.0 098 IFRD does not Granger Cause ITRD 1.02600 0.4333
The test results showed evidence of unidirectional causal relations between ITRD and abnormal return with p value of (0.0358), between CAD and ITRD with p value of (0.0511) and between ITRD and IFRD, with p value of (0.0098) respectively. The unidirectional causality experienced between ITRD and abnormal returns, CAD and ITRD and between
ITRD and IFRD respectively might have been caused by changes in policy management in regard to above variables. The relevancy of this argument is levelled on the expectation theory which incorporates the relevancy of economic exposure i.e. the change in the value of a firm due to the unexpected change in the exchange rate (operational exposure/ long term cash flow exposure), leads to current account deficit hence exchange rate risk, the purchasing power parity and the interest rate parity in respect to the economic expectations.
62
There were no evidence of casual relationship between; IFRD and abnormal return with p value of (0.9814), abnormal return and IFRD with p value of (0.7866), CAD and abnormal return where p value was (0.2808), abnormal return and CAD where p value was (0.5111),
IFRD and CAD with p value of (0.6831), abnormal return and IFRD with p value of
(0.9311), ITRD and CAD with p value of (0.8009) and finally between IFRD and ITRD where p value was (0.4333) respectively. The non-stationarity exhibited among variables was a likely indication that the variables were likely to provide limited information that would not be useful to all parties interested in decision making across the lagged period of time and that there were no expectation of the existence of a long run equilibrium relationship between the variables and that no presents of co-integration. Since OLS is not fit for this study, then it follows that the robustness of the findings in 4.3 below, heavily depends on the suitability of the model to be employed. Therefore since the study adopts the GMM model, it of great concern to outline that GMM assumes finite sample size when test are asymptotic, the
GMM assumption for stationarity of the instrumental variables and the serial dependence in the sample moments should fit for the model to hold. The above assumptions makes GMM model fit for this study.
4.3 Empirical results for Unconditional Multi – factor Asset Pricing Model
The pre - analysis test for Ordinary least square methods, posits the violation of one or more of its assumptions. According to (Brooks, 2008), if a model encounters a mixture of one or more violation of OLS assumptions, then the latter can be treated either by correcting to make sure that the assumptions are not violated or by ignoring the detected OLS problems and adopt an alternative techniques which should still be valid. In this case, this study adopted the use of GMM model in analysing its data. Going by Smith and Wickens (as cited
63
in Kodongo, 2011), the parametric estimations of this study suits the use of GMM, due to its
robustness to heteroscedasticity. Moreover, according to Hall (1993), GMM model excuses a
researcher from making explicit specification of the data generating process in the study, and
hence, the parametric estimations under GMM are never computationally involving and are
more efficient hence makes GMM to be consistent, asymptotically normal and an efficient
estimator although, it exhibits relatively small biasness even with small sample sizes as sixty.
4.3.1 Test Results for the three factor Model at Nairobi Securities Exchange
The Multi – factor asset pricing model in 3.4 was estimated as relatively unrelated regression
model using GMM procedure and the result were as in table 4.7 below:
Table 4.7: GMM Regression Results for Three factor Asset Pricing model on NSE.
Dependent Variable: ABNORMAL__RETURNS Method: Generalized Method of Moments Sample: 2003 2122 Instrument specification: ABNORMAL__RETURNS CAD IFRD ITRD C
Variable Coefficient Std. Error t-Statistic Prob.
CAD -0.009809 0.010166 -0.964897 0.3366 IFRD 0.066291 0.089992 0.736626 0.4628 ITRD -0.079378 0.097748 -0.812066 0.4184 C 0.150655 0.982144 0.153394 0.8784
R-squared 0.012097 Mean dependent var 0.805677 Adjusted R-squared -0.013452 S.D. dependent var 9.193502 S.E. of regression 9.255131 Sum squared resid 9936.265 Durbin-Watson stat 2.668074 J-statistic 1.478719 Instrument rank 5 Prob(J-statistic) 0.223975
GMM (INSTWGT=WHITE, GMMITER=1, COV =WHITE) ABNORMAL_RETURNS C CAD IFRD ITRD @ ABNORMAL_RETURNS C CAD IFRD ITRD ABNORMAL_RETURNS = C (1) + C (2)*CAD + C (3)*IFRD + C (4)*ITRD ABNORMAL_RETURNS ( ) = , + , , + , , + , , + ε , ,
64
Substituted Coefficients: ABNORMAL_RETURNS = -0.00980877304049*CAD + 0.0662907828596*IFRD - 0.0793776043177*ITRD + 0.150655060667 According to the regression equation in the table above, the coefficients obtained in the
GMM model holding all other factors into account (current account deficit, inflation rate differential and interest rate differential) constant at zero, abnormal returns on stocks at NSE-
20 index was 0.150655060667, approximately ( 0.151) 3decimal place.
The results further indicated that by holding all other independent variables (inflation rate differential and interest rate differential) at zero, there was a unit decrease in abnormal returns of -0.00980877304049 (-0.010) 3d.p in respect to current account deficit; by holding
(current account deficit and interest rate differential) constant at zero, led to a unit increase in abnormal returns of 0.0662907828596 (0.066) 3d.p in respect to inflation rates differential and lastly, by keeping all other factors constant at zero (current account deficit and inflation rate differential), there was a unit decrease in abnormal returns of -0.0793776043177 approximately (-0.079) 3d.p in respect to interest rates differential respectively.
Since the study incorporated Hansen (1982) of GMM which is preferred under weak statistical assumptions in order to test the over identification restrictions in both the model and data. It then followed that J – statistical result of (1.478719) failed to reject the validity of variables used in the model at all levels. It further displayed right tailed P - value of
(0.223975) for the significance levels hence, the study failed to reject a non significance hypothesis. This was contributed by the fact that the unconditional estimated monthly beta coefficient for foreign exchange rate proxy factors; CAD registered -0.010 with P – value
0.3366, beta 0.066 with P – value 0.4628 for IFRD and - 0.079 with P – value 0.4184 for
ITRD respectively, signalled lack of statistical significant to the risk premium. Furthermore,
65
the inverse relationship exhibited between the abnormal returns and the independent
variables (CAD, -0.010 and ITRD, -0.079 respectively) simply implied that any increase in
abnormal return would result to a decrease in the independent variables (CAD and ITRD
respectively).
The results above further seemed to have improved the model specifications in that by
incorporating all the three variables, the J – statistic revealed a relatively long tail of
1.478719 (1.479) 3d.p and a p – value of 0.223975 (0.224) 3d.p. This indicated that the
hypothetical notion that, all the three factors does not have a significant impact on the return
generating process failed to be rejected. This study results from the GMM model above
partly agreed with the following studies; (Tai, 2010) which observed that currency risk did
not appear to be priced in unconditional two factor model, (Kodongo, 2011) study which
realized that currency risk was not priced unconditionally when returns were measured in US
dollar, (Kodongo and Kalu, 2012) study which partly posited a similar results that currency
risk was neither priced nor integrated to the world market in Nigerian stock exchange and
finally it is fully in tandem with Choi et al., (1998) study which stated that by employing the unconditional multi – factor asset pricing model, in general currency risk was not priced in
Japanese and in U.S stock markets. However this results goes contra to studies done by Choi and Prasad ( 1995) that posited a significant exchange rate exposure, Dumas and Solnik
(1995) study results that under conditional International asset pricing model, currency risk priced in major world stock and currency markets and Carrieri and Majerbi (2006) study which ruled that exchange rate risk was globally priced and commanded a significant unconditional risk premium in emerging stock markets worldwide. These findings can be attributed to unfavourable political uncertainties, poor policy implementations, institutional
66 inadequacies, information unevenness and poor investments patterns of international investors.
4.4 Hypothesis Testing
This study was led by three hypotheses as below:-
, Current - account deficit has no significant effect on stock returns at NSE.
, Inflation rate differential has no significant effect on stock returns at NSE.
, Interest rate differential has no significant effect on stock returns at NSE. The tables presented below displays individual null hypothesis significant tests as follows; Table 4.8: T and F Test statistics for hypothesis testing
Test Statistic Value Df Probability (p) ABNORMAL_RETURNS C CAD t-statistic -0.964897 116 0.3366 F-statistic 0.931027 (1, 116) 0.3366 ABNORMAL_RETURNS C IFRD t-statistic 0.736626 116 0.4628 F-statistic 0.542618 (1, 116) 0.4628 ABNORMAL_RETURNS C ITRD t-statistic -0.812066 116 0.4184 F-statistic 0.659451 (1, 116) 0.4184 The t tests were carried out at 5% level of significance. All the three parameters were not significant as t –statistic and F – statistics posited a p value of (CAD p=0.337, IFRD p=0.463 and ITRD p=0.418) respectively. The study results therefore failed to reject all the three null- hypotheses. Contrary to the GMM results for beta coefficients where CAD, IFRD and ITRD affected stock returns at NSE both negatively (CAD = -0.010 and ITRD = -0.079) and positively (IFRD =0.066) at different levels. However the levels at which exchange rate proxy variables affected the abnormal returns were insignificant as the GMM results identified a p value of 0.224. The significant test level for independence relationships between variables were determined by the use of the chi- square as; CAD p=0.335, IFRD p=0.461 and ITRD p=0.417. It meant that CAD, IFRD and ITRD were related to abnormal return at 33.5%, 46.1% and 41.7% respectively.
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CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
This chapter presents a summary and conclusions arrived at in the study, the recommendations and suggestions for further study on what the current research was unable to cover hence opening up avenues for future studies.
5.2 Summary of the Findings and Discussion
The main objective of this study was to ascertain if the pricing of foreign exchange risk impacted stock returns at the Nairobi Securities Exchange. The findings on the monthly data revealed a unidirectional causal relationship between ITRD and abnormal return, between
CAD and ITRD and finally between ITRD and IFRD at Kenyan Nairobi Securities exchange in relation to time variation. On contrary, there was no evidence of bidirectional causality between IFRD and abnormal return, between CAD and abnormal return and between IFRD and CAD respectively. Moreover, no unidirectional relationship was established between abnormal return and IFRD, ITRD and CAD, between IFRD and CAD and finally between
IFRD and ITRD respectively across the entire period while considering the time variation aspect. Lack of stable position for co-integration was not an indication of long run causality as these findings were in tandem with Ndung’u and Ngugi (1999) study which portrayed a weak feedback for real exchange movement in Kenyan capital flow. To add on, the results for unit root test in this study accepted the null hypothesis of stationarity. This further confirmed the robustness of GMM model for data analysis adopted by this study. These
68 results were in tandem with (Tai, 2010) study, which observed that for GMM to be used, the data must conform to stationarity condition of all variance and covariance processes.
These results further revealed that the data was positively skewed with excessive indices for
Kurtosis in respect to abnormal returns, CAD, IFRD, and ITRD respectively. In line with
(Tai, 2010), the presents of large values for kurtosis in this study strongly rejected the hypothesis of normal distributions for abnormal returns. This findings were similar to
Carrieri and Majerbi (2006) study, which revealed that the presences of excess skewness and kurtosis on entire data level substantiated the presents of non- normal distribution on asset returns in Emerging markets. However these findings were in contrast with Kodongo and
Kalu (2012), whose study revealed that Nigerian’s portfolio displayed excess Leptokurtosis with considerable negative skewness leading to large Jarque- Bera statistics hence, violation of normality distribution assumption. It should be outlined that the prevalence of substantial levels of positive skewness in distributions of asset returns may prompt investors to hold portfolios even though they have negative expectations Harvey and Siddique (2000).
According to the summary statistics for GMM regression model, the average absolute coefficients values obtained from 120 time series regression in respect to foreign exchange risk factors (CAD, IFRD and ITRD) were -0.010, 0.066 and -0.079 respectively, while the average adjusted s was -0.0135 in the model. Moreover, foreign exchange risk factors were not significant meaning that foreign exchange risk was unable to explain on time series what contributed to stock returns at NSE. This findings were in line with Du, (2012) study, which argued that lack of significant levels in foreign exchange risk factors, evidently displayed that foreign exchange risk lacked power to elucidate either the time series or cross section of stock returns for a wide range of test assets.
69
Lastly, the t and F tests were carried out at 5% level of significance. As presented in the
above Table 4.8 above. The study found that the entire three hypothesis were not significant
with p-values being greater than 0.05. The results presented the p values as; p=0.337,
p=0.463 and p=0.418 in respect to; , , , and , respectively. Therefore the study failed to reject all the three null hypotheses. Furthermore, the results obtained from the GMM model were not significant since the p value was 0.224. Hence, the findings concurred with the hypothesis testing results. This meant that although the coefficients in respect to CAD.
IFRD and ITRD affected stock returns at Nairobi Securities Exchange; their effects were not significant to the study.
5.3 Conclusions
To establish the conditions at which foreign exchange risk is related to stock returns or the level at which currency risk is priced on stock returns at NSE, are two closely inter- related issues that are crucial to both investors and corporate risk managers, therefore this raises the needs to be addressed amicably. However past studies have addressed the two issues separately hence their findings have failed to resolve the arguments being raised conclusively. This has escalated to further greater debates on the same. Empirical evidence presented by the findings of this study established that there was a strong positive significant correlation between the abnormal returns at NSE and CAD. Moreover, a negative non- significant correlation was observed among other two variables i.e. IFRD and ITRD respectively.
This study further found that, the White’s Specification tests were not significant. Thus the heteroscedasticity among the variables did not result to any variation in the results of the
70 explaining variables. By using the Augmented Dickey – Fuller (ADF), the result signified the presence of a unit root or that the abnormal returns on stock at NSE series were stationary.
In conclusion, the study result showed that foreign exchange risk was weakly priced and there existed a causal relationship between the abnormal returns to CAD, between CAD and
IFRD and between ITRD and CAD. However there was no evidence of bidirectional causality among the rest of the variables. The findings of this study correspond to (Tai, 2010) study, which discovered that foreign exchange risk did not appear to be priced in unconditional two factor model, it also partly posited a similar results to (Kodongo and Kalu,
2012) study which displayed that foreign exchange risk was not priced in Nigerian stock exchange and that, Nigerian stocks markets was not integrated to the world market.
Moreover, going by the coefficient results found from running the GMM model, this study partly agree with (Kodongo, 2011), that foreign exchange risk was weakly price with time as the study registered weak beta values of -0.010 for current account deficit, 0.066 for inflation rate differential and -0.079 for interest rate differential in (3.d.p) respectively. This implied that, though the beta coefficient for the foreign exchange risk factors portrayed the presents of a weak priced model, the effect of the pricing were insignificant hence prompted the study to reject all the three null hypothesis.
5.4 Policy Recommendation
The policy regulators such as CBK who are in charge of monetary policy and trading of T-
Bills as well as managing the current account deficits have a reason to be concerned with volatility in exchange rates and the resulting impact on the stock market. The CBK should therefore institute monetary policies that will guarantee interest rates to be determined by the
71 market as well as try to cap the inflationary pressures since they affect the purchasing power between Kenyan market and American market negatively.
Market authorities of Kenya which govern the NSE are likely to contend with a wide range of factors that can greatly influence the operational and informational efficiency of the financial markets. This also implies that regulators must be in a position to assess the nature of activity in the markets and the environment outside their traditional jurisdictions with an aim of integrating NSE to the world developed securities markets. Since there is a colossal need to develop NSE locally and compete globally simultaneously both for funds and provision of financial services, then the regulators should therefore oversee and facilitate development and growth of the securities market. In line with this, the Market authorities must consider introducing policies that will reduce the disparities in inflation rates, interest rate and imbalance of trade leading to current account deficit.
5.5 Recommendation for Practice
Macroeconomic principles in an open economy entails that capital mobility greatly affects the choice of exchange rate policy. Therefore, if a government is faced with financial integrated economy, then the government faces two choices of; monetary policy autonomy and or a fixed exchange rate (Mundell, 1963). It then follows that, the government have an option to either fix the exchange rates or adopt a sustainable independent monetary policy. In line with the above, this study posse a lot of lessons that should be adopted in order to achieve a level playing ground between the two markets under the study.
The monetary policies across the two market boundaries in question (Kenyan Securities
Market and the American Securities market) should be concerned with the effect of
72 international capital flow on the macroeconomic stability between the two shared markets and the challenges brought about by the capital flow disparities as they entails low inflation rate, low interest rates and strong currency from the source markets to attract investors hence, leading to current account deficit. The effect of cash flow disparity is evidence in this study due to the findings that foreign exchange risk was weakly priced and did not command a significant effect when abnormal market returns was measured against CAD, IFRD and
ITRD jointly. As a result, abnormal return was affected by; CAD at -0.010, IFRD at 0.067 and ITRD at -0.079. This is evidently an important aspect of concern. Therefore, CBK should consider fixing the price floor and price seal to control the effects of inflation rate and cost of borrowing (interest rate). It should also consider setting favourable trading environment aimed at controlling (narrowing the gap) in imbalance in trade between Kenya and America hence reducing the negative effect of current account deficit on abnormal market return.
5.6 Recommendations for Further Research
This study experienced several limitations where the Kenya Beural of statistics, the CBK and the NSE were neither consistence in compilation and archiving of information nor had impressed digitalization to document relevant information relating to all the proxy variables
(current account deficit, inflation rate differential, interest rate differential, and terms of trade, public debt, political stability and economic performance) fully. Therefore, future studies should be directed towards adding more or all foreign exchange proxy variables to ascertain more comprehensive results relating to this study, or same study should be conducted on a bigger time span rather than the current ten years and confirm if same effects of CAD, IFRD and ITRD would be realized.
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APPENDICES
Appendix i: Data Collection Form A
Enter the monthly data under the variables provided in the form below and calculate the
inflation rate differential and interest rate differentials between Kenya and USA.
TIME Inflation Inflation Inflation rate Interest Interest Interest Current IN rate rate Differential rate rate rate account MONTH (KEN) (USA) (KEN Less (KEN) (USA) Differential deficit USA) rates (KEN Less (KEN) USA) Jan‘03
Feb‘03
March‘03
April‘03
May‘03
June ‘03
July ‘03
Aug ‘03
Sept ‘03
Nov ‘12
Dec ‘12
Source: (Author, 2014)
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Appendix ii: Data Collection Form B
Enter monthly data under the variables provided in the form below. The table contains the monthly NSE – 20 index and each company’s monthly stock prices as from January 2003 to
December 2012.
TIME IN Monthly MONTHLY STOCK RETURNS OF ALL LISTED COMPANIES (MONTH) NSE - 20 AT NAIROBI SECURITIES EXCHANGE FROM JANUARY Indices 2003 TO DECEMBER 2012 NAME OF THE STOCKS LISTED AT NSE A CO. (1) CO. (2) CO. (3) CO. (4) CO. (-----) CO. (N)
Jan 2003
Feb 2003 March 2003 April 2003 May 2003 June 2003 July 2003 August 2003 Sept 2003
Nov 2012 Dec 2012
Source: (Author, 2014)
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Appendix iii: Constructed Data for Analysis
CURRENT INFLATION RATES INTEREST RATES Date- ABNORMAL ACCOUNT DEFICIT DIFFERENTIAL DIFFERENTIAL Month RETURNS (CAD) Ksh – USA$ (IFRD) K - USA 91DAY T. BILL (ITRD) Jan-03 0 -18.00 4.0818 7.21 Feb-03 4.645 -51.00 5.4545 6.6 Mar-03 -0.744 -14.00 8.1824 5.11 Apr-03 0.8635 -2.00 9.4551 5.12 May-03 12.883 -12.00 13.0052 4.77 Jun-03 0.879 -41.00 12.0196 2.08 Jul-03 1.3531 -66.80 8.8022 0.64 Aug-03 0.4608 -39.00 5.9491 0.23 Sep-03 4.112 -20.00 5.7004 -0.11 Oct-03 2.9963 -61.67 7.0002 0.08 Nov-03 0.1199 -9.55 7.0145 0.35 Dec-03 0.5959 -27.99 6.3696 0.56 Jan-04 5.233 -84.80 7.2524 0.7 Feb-04 7.3107 -37.71 8.0915 0.64 Mar-04 4.5434 -1.01 6.8528 0.65 Apr-04 0.5791 -38.73 5.6733 1.17 May-04 -0.8691 -31.65 2.5606 1.85 Jun-04 0.51 -74.37 3.7937 0.74 Jul-04 -4.0473 -57.01 6.5189 0.38 Aug-04 -2.9096 -125.43 13.9444 0.79 Sep-04 -1.169 -110.36 17.1626 1.1 Oct-04 0.3964 -35.87 16.5642 2.19 Nov-04 -0.855 -45.74 15.7185 2.99 Dec-04 -2.7509 -86.37 15.4126 5.85 Jan-05 1.0363 -119.96 13.15 5.93 Feb-05 0.021 -59.96 12.31 6.05 Mar-05 -0.0697 -69.44 12.36 5.89 Apr-05 0.4631 -289.61 14.31 5.9 May-05 -0.4698 -65.65 13.13 5.82 Jun-05 5.0997 -160.85 10.25 5.53 Jul-05 7.3546 -47.90 9.88 5.37 Aug-05 -0.0489 -251.10 4.98 5.22 Sep-05 3.7747 105.48 2.57 5.16 Oct-05 0.3259 143.76 1.78 4.48 Nov-05 -0.0035 -94.04 2.34 3.96 Dec-05 0.4374 -101.05 2.58 4.18 Jan-06 1.1039 -52.88 6.38 3.99 Feb-06 0.8876 -90.27 7.34 3.59 Mar-06 0.8402 -29.91 6.65 3.09
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Apr-06 -40.6119 -205.83 3.03 2.42 May-06 84.4781 -41.77 2.02 2.29 Jun-06 1.0977 -5.65 1.75 1.81 Jul-06 2.8958 -15.29 1.65 0.94 Aug-06 3.9703 -126.70 2.63 1 Sep-06 4.2168 -84.99 3.61 1.64 Oct-06 8.6626 -141.09 4.14 1.91 Nov-06 -6.6594 -117.60 4.35 1.47 Dec-06 -0.3859 -27.71 5.73 0.88 Jan-07 -4.0649 -117.84 2.19 1.02 Feb-07 -5.7064 -176.07 0.66 1.19 Mar-07 -0.8134 -21.15 0.01 1.38 Apr-07 -5.6708 -27.79 -0.41 1.78 May-07 1.8817 -14.32 -0.41 2.04 Jun-07 4.429 -92.08 1.38 1.92 Jul-07 2.0096 -142.38 2.84 1.7 Aug-07 0.6662 -105.68 2.86 3.1 Sep-07 -8.2723 -98.13 3.27 3.46 Oct-07 5.7818 -215.10 3.18 3.65 Nov-07 -1.9547 -120.84 4.31 4.25 Dec-07 -0.7474 -29.02 3.91 3.87 Jan-08 -1.2879 -234.10 7.93 4.2 Feb-08 -0.6479 -6.03 9.17 5.16 Mar-08 -0.1236 -98.58 10.81 5.64 Apr-08 -0.4016 -24.73 14.76 6.06 May-08 -0.9642 -156.63 17.15 6.03 Jun-08 -0.2992 -49.08 16.24 5.87 Jul-08 0.1138 -217.80 15.55 6.4 Aug-08 -0.0799 -405.50 16.65 6.3 Sep-08 0.81 -313.56 16.88 6.56 Oct-08 3.1641 -301.57 15.99 7.08 Nov-08 1.2758 -178.01 16.65 8.2 Dec-08 1.9304 -148.04 15.66 8.56 Jan-09 -2.4767 -239.79 11.31 8.33 Feb-09 2.9335 -189.22 12.94 7.25 Mar-09 -0.4824 -78.66 12.89 7.1 Apr-09 -1.6764 -233.86 10.85 7.18 May-09 3.1514 -179.62 7.89 7.27 Jun-09 -0.1516 -188.77 6.74 7.15 Jul-09 -1.1601 -185.20 6.62 7.06 Aug-09 1.1203 -157.20 5.59 7.08 Sep-09 -0.0854 -270.86 5.1 7.17 Oct-09 -0.2225 -239.49 5.14 7.19
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Nov-09 -2.7563 -64.89 3.72 7.17 Dec-09 2.0136 -133.58 3.96 6.77 Jan-10 -3.6662 -244.39 4.58 6.5 Feb-10 0.2349 -149.08 3.76 6.1 Mar-10 5.3945 -97.68 2.46 5.83 Apr-10 0.5025 -95.26 2.16 5.01 May-10 2.8574 -85.56 2.57 4.05 Jun-10 -3.6078 -175.16 2.23 2.86 Jul-10 -0.0722 -183.82 2.33 1.44 Aug-10 1.1218 -146.92 2.2 1.67 Sep-10 0.8523 -251.57 2.3 1.89 Oct-10 -0.29 -242.27 2.65 1.99 Nov-10 -0.6697 -354.83 3.17 2.07 Dec-10 -2.5567 -232.30 3.47 2.14 Jan-11 0.6304 -314.62 4.36 2.31 Feb-11 3.6144 -203.65 5.3 2.46 Mar-11 1.74 -312.59 8.23 2.67 Apr-11 -1.3371 -203.22 11.19 3.2 May-11 -1.7816 -529.21 12.17 5.31 Jun-11 -4.7502 -357.57 13.72 8.91 Jul-11 -0.4981 -299.11 14.91 8.95 Aug-11 -11.5135 -583.90 16.53 9.21 Sep-11 11.7391 -385.11 17.24 11.92 Oct-11 5.8279 -271.41 18.72 14.78 Nov-11 0.1821 -528.14 19.72 16.13 Dec-11 -0.0836 -470.90 18.96 18.29 Jan-12 -0.3138 -293.21 18.42 20.53 Feb-12 0.9562 -208.14 16.94 19.61 Mar-12 -1.809 -463.88 15.75 17.72 Apr-12 -3.573 -230.30 13.27 15.93 May-12 -0.0795 -394.14 12.56 11.09 Jun-12 1.7814 -315.25 10.55 10 Jul-12 -1.6608 -505.97 8.34 11.85 Aug-12 -2.0578 -461.67 6.68 10.83 Sep-12 -1.6014 -422.36 6.03 7.66 Oct-12 -1.152 -250.40 4.89 8.88 Nov-12 -1.7162 -530.12 4.02 9.71 Dec-12 0.2482 -454.40 3.96 8.23 Source: (Constructed data from Central Bank of Kenya websites, Nairobi Securities Market,
Federal Reserve Bank websites).
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Appendix iv: Hypothesis testing
0,1 Current - account deficit has no effect on stock returns at NSE
Wald Test: Equation: ABNORMAL_RETURNS C CAD
Test Statistic Value Df Probability
t-statistic -0.964897 116 0.3366 F-statistic 0.931027 (1, 116) 0.3366 Chi-square 0.931027 1 0.3346
Null Hypothesis: C(1)=0 Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(1) -0.009809 0.010166
Restrictions are linear in coefficients. 0,2 Inflation rate differential has no effect on stock returns at NSE.
Wald Test: Equation: ABNORMAL_RETURNS C IFRD
Test Statistic Value Df Probability
t-statistic 0.736626 116 0.4628 F-statistic 0.542618 (1, 116) 0.4628 Chi-square 0.542618 1 0.4613
Null Hypothesis: C(2)=0 Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(2) 0.066291 0.089992
Restrictions are linear in coefficients.
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0,3 Interest rate differential has no effect on stock returns at NSE.
Wald Test: Equation: ABNORMAL_RETURNS C ITRD
Test Statistic Value Df Probability
t-statistic -0.812066 116 0.4184 F-statistic 0.659451 (1, 116) 0.4184 Chi-square 0.659451 1 0.4168
Null Hypothesis: C(3)=0 Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(3) -0.079378 0.097748
Restrictions are linear in coefficients.
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Appendix v: Research Permit
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Appendix vi: Listed Companies at NSE
Agricultural SYMBOL LISTING NOTES EGAD Eaagads Limited Coffee growing and sales KAZU Coffee, tea, passionfruits, Kakuzi Limited avocados, citrus , pineapples others KAPC Kapchorua Tea Company Tea growing, processing and Limited marketing LIMR Limuru Tea Company Tea growing Limited RUP Rea Vipingo Sisal Estate Sisal STC Sasini Tea and Coffee Tea, coffee GWKL Williamson Tea Kenya Tea growing, processing and Limited distribution Automobiles and Accessories Automobiles, engineering, CARG Car & General Kenya agriculture CMC CMC Holdings Automobile distribution MSHA Marshalls East Africa Automobile assembly FEAL Sameer Africa Limited Tires Banking BARC Barclays Bank (Kenya) Banking, finance CFCO CFC Stanbic Holdings Banking, finance Diamond Trust Bank DTK Banking, finance Group Banking, finance; crosslisted EQTY Equity Bank Group at the Uganda Securities Exchange Housing Finance HOUS Mortgage financing Company of Kenya IMHL I&M Holdings Limited Banking, Financial services Banking & finance . Crosslisted on the Uganda Kenya Commercial Bank Securities Exchange, the Dar KCBK Group es Salaam Stock Exchange and the Rwanda Over The Counter Exchange NABK National Bank of Kenya Banking, finance National Industrial Credit NINC Banking, finance Bank SCBK Standard Chartered Kenya Banking, finance Cooperative Bank of COOP Banking, finance Kenya
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Commercial and Services EXPK Express Kenya Limited Logistics HBL Hutchings Biemer Limited Furniture Kenya's flagship airline; crosslisted at Uganda KAL Kenya Airways Securities Exchange and Dar es Salaam Stock Exchange LKL Longhorn Kenya Limited Publishing Newspapers, magazines, NMG Nation Media Group radio stations, television stations SCAN Scangroup Advertising and marketing STDN Standard Group Limited Publishing TPS TPS Serena Hotels & resorts UCHU Uchumi Supermarkets Supermarkets Construction and Allied Cement, fertilizers, ARM Athi River Mining Limited minerals; mining and manufacturing BAMB Bamburi Cement Limited Cement CRWN Crown-Berger (Kenya) Paint manufacturing CABL East African Cables Limited Cable manufacture East African Portland Cement manufacture and EAPC Cement Company marketing Energy and Petroleum KGEN Kengen Electricity generation Petroleum importation, KOCL KenolKobil refining, storage & distribution Kenya Power and Lighting Electricity transmission, KPLA Company distribution and retail sale Petroleum importation and TOPL Total Kenya Limited distribution Electric power distribution . UMEME Umeme Cross listing from Uganda Securities Exchange [1] Insurance [British-American BAI Insurance Investments Co.(Kenya)] Liberty Kenya Holdings CIHL Limited (formally CFC Insurance Insurance) Insurance, investments; JHL Jubilee Holdings Limited cross listed at the Uganda Securities Exchange
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Kenya Re-Insurance KRIN Reinsurance Corporation Pan Africa Insurance PAIC Insurance Holdings Fixed income security market segment Kenya Power & Lighting -- -- Ltd 4% Pref 20.00 Kenya Power & Lighting ------Ltd 7% Pref 20.00 Investment Centum Investment CENTUM Investments Company Construction and building OLYM Olympia Capital Holdings materials TRCY TransCentury Investments Investments
Manufacturing and Allied Machinery distribution and BAUM A Baumann and Company marketing, investments Industrial gases, welding BOC BOC Kenya products British American Tobacco BAT Tobacco products Limited Carbacid Investments Carbon dioxide CARB Limited manufacturing Beer, spirits; crosslisted at Uganda Securities EABL East African Breweries Exchange and Dar es Salaam Stock Exchange
EVRD Eveready East Africa Batteries
Fruit growing, preservation KOL Kenya Orchards Limited and distribution, fruit-juice manufacture and marketing
Mumias Sugar Company Sugar cane growing, sugar MSCL Limited manufacture & marketing UNGA Unga Group Flour milling Telecommunication and Technology
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ACES Access Kenya Group Internet service provider SCOM Safaricom Mobile telephony
Source: (NSE, 2014)
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