The chapter begins the objectives of the research and clearly expound on the way they are going to be achieved. It also comes up with a clear research design that assists in addressing the research questions thus achieving the desired objectives. Without any doubt, every aspect of research work must be accompanied with through and extensive data collection methods to prove the research conclusions that will be made later in the topic. There is also need to distinguish and define variables in this study that include the dependent and independent variables. Analysis of data is also expounded on listing the major methods of statistical analysis to be used and later the methods to be used in presenting the data are also listed. The analytical and empirical methods used are also expounded on with the validity and reliability of the research being addressed. Throughout, the ethical considerations to be considered are identified and prioritized.
FPI flows have fuelled many EM crisis such as the 1997 AFC as mass portfolio inflows often turn out to be disastrous following the reversal of such flows. The overall objective of this study is to quantify the relationship between the dependent variables (FPI and domestic economic indicators) and financial assets using descriptive approach without any intervention on the outcome. The study will enable to determine which independent variables between the FPI flows and domestic economic indicators have greater implication on Malaysian financial assets.
Research Problems to be addressed
The heightened volatility of foreign portfolio flows in EMs in recent years has destabilize the domestic financial markets of many EM countries such as Malaysia and significantly impacted the returns of its financial assets. Meanwhile, the domestic investors and regulators always been at the mercy of FPI flows and exposed to unnecessary market volatility.
This research employed the quantitative research approach using the exploratory research design. According to Collis and Hussey (2003), exploratory research is typically led with a specific end goal to give a good comprehension of a circumstance, which in this specific study is the impact of the FPI flows and domestic economic indicators on financial assets in Malaysia. The quantitative research method involves the observation and the description of a subject without influencing it which include the formulation of objectives of the study, the designing of the various methods of data collection and finally the analysis of the results
Independent and Dependent variables
This study undertake a test to assess the impact of the FPI flows and domestic economic indicators on Malaysias financial asset prices spanning from 2007 to end 2015. The period being research is sufficient to cover the FPI flow developments since the 2008 GFC that has witnessed huge volatility as a result of loose monetary policies adopted by major economies that magnified global liquidity.
As depicted in Figure 13, independent variables include both the FPI flows and domestic economic indicators comprises of GDP, Industrial Production Index and Inflation. Meanwhile, the dependent variables are the financial assets consist of the equity market index, exchange rate, and bond yield (interest rate).
Figure 13: Linkages between independent and dependent variables
The research model in this study was developed based on the hypotheses developed in chapter 2. The purpose of this model is to test the hypothesized direct relationship between the Independent variables (IVs) and the Dependent Variables (DVs) as depicted below.
Figure 1, Research Model
As this scholarly research was underway, a lot of materials were used as sources of information. This involved collecting of past journals, articles, books in libraries that had detailed information about the economy of Malaysia. Publications on the effects of FPI on financial assets in other countries were also referred to in order to help outline the expectations of the research. Also websites that are official and widely recognized such as the IMF, World Bank, BNM, Bursa Malaysia Berhad and Department of Statistics, Malaysia websites were used especially for the purposes of data collection.
Historical statistical data were obtained from various sources that includes official institutions such as BNM; Bursa Malaysia, IMF; World Bank; Department of Statistics, Malaysia (DoSM); Maybank Investment Berhad and Bloomberg.
Independent variable data on FPI flows and domestic economic indicators such as IPI Index and CPI are officially being released on monthly basis while GDP data are released on quarterly and yearly basis. Meanwhile for financial assets, the monthly closing price were obtain, despite the availability of daily closing prices. To ensure consistency in data analysis, this research opted to use monthly data to conduct various statistical analysis.
The study consist of almost 684 monthly time series for all the variables spanning from January 2007 to December 2015 period (Appendix xx). A total of 108 monthly time series data for each of the variables were collected except for the GDP data which had 36 quarterly time series data for the period as monthly GDP data is not being published officially.
FPI flows data refers to the monthly net foreign portfolio flows measured by the debt flows into the Malaysian Government debt securities and equity flows into the Bursa Malaysia Berhad in Malaysian Ringgit terms. Due to unavailability of FPI flows data from a single source, the researcher obtained it from two sources by combining the monthly debt and equity net FPI flows. The monthly debt flow data was obtained from BNM website while the monthly equity flow data was obtained from Maybank Investment Bank Berhad, one of the member of Bursa Malaysia Berhad as the equity flow data was not made available in public domains but only provided to members via subscription.
Data for the domestic economic indicators consist of GDP, IPI and CPI were obtained from Bloomberg and Department of Statistics, Malaysia website. As monthly GDP data not officially being published, the quarterly GDP data was used in this study. This limitation resulted in missing values for GDP data as it will only corresponds with the respective dependent variables data once in 3 months while the high number of missing values may make it difficult to conduct statistical tests such as linear regression analysis.
As GDP data is critical for this study, this research paper resorted to use the method in SPSS called the mean series of near points to replace the missing values. The second economic indicator, the Industrial Production Index (IPI), was included as part of this analysis as an insurance against any margin of errors resulting from the missing values for the GDP as it has been widely used to assess current condition and short-term expectation of GDP (Sedilot and Pain, 2003; Runstler and Sedilot, 2003; Mitchell et al, 2005) as Industrial production constitute among the largest component of GDP. Despite being volatile the month-on-month IPI growth data was used as it captures the short-run dynamics and seasonal developments.
The IPI is a good proxy for GDP by measuring the change in output in Malaysian manufacturing, mining, construction, and electricity sectors. However, it excludes the services sector which has seen significant growth in contribution towards Malaysias GDP. Hence, retaining the GDP data with some adjustment to replace the missing values avoid any distortion in the outcome of the statistical tests.
Meanwhile, the inflation as measured by the Consumer Price Index on year-on-year basis was used opposed to the month-on-month basis as it is a good indicator of long-term inflation expectation that will be used as yardstick for monetary policy prescription that will have huge implication on the performance of financial assets (Cristadoro et al, 2005).
Data for the monthly closing prices of the dependent variables that include KLCI Index, USD/MYR and yield of 3 Years MGS were obtained from the Bloomberg database. The equity index is measured by the FTSE Bursa Malaysia KLCI Index that tracking the performance of 30 companies with largest market capitalization listed on the Main Board of Bursa Malaysia. The KLCI Index, which is part of the MSCI Emerging Market Index, a benchmark used by global portfolio managers in their asset allocation strategy investing in Emerging Markets (MSCI, 2016).For currency, the Malaysian Ringgit against the US Dollar (USD/MYR) was used in the analysis instead of Malaysian Ringgit crosses against other currencies as the USD has been the dominant currency for FPI flows in Malaysia, consisting of almost 68.5% of total portfolio investment in Malaysia (IMF, 2015). Meanwhile the second closest currency is the Euro Dollar (EUR) that stood at 1.46% (Appendix xxx).The monthly closing prices of the USD/MYR that traded onshore were used for this research instead of the non-deliverable forward (NDF) Malaysian Ringgit. This is in view that NDF currencies are generally priced and traded outside the borders of a currencys home territory to circumvent trading controls in the onshore market without involving the delivery of home currency (McCauley, Shu and Ma, 2013).
Meanwhile, the researcher opted to use the 3 year benchmark Malaysian Government Securities (MGS) as a proxy for interest rate as the yield of this short-term government bond is highly sensitive to movement in the benchmark interest rate termed as Overnight Policy Rate (OPR). As the OPR is not being traded, bond investors expectation about the future changes in OPR will be reflected through the changes in the yield of 3 year MGS.
Problems faced in data collectionAs readily made total FPI flow data wasnt available, researcher resorted to obtain the data using the following method:
Current month FPI Flow = (Current month closing of Foreign Holdings of Bonds - Previous month closing of Foreign Holdings of Bonds) + (Current month closing of Foreign Holdings of Equity - Previous month closing of Foreign Holdings of Equity)
There were limited sources to obtain data for FPI flows into equities that is made available to Bursa Malaysia members via subscription. Researcher managed to obtain the data by writing to Maybank Investment Berhad, a member of Bursa Malaysia Berhad.
Data Analysis. The aim of data analysis in this section is to examine and investigate impact of FPI flows and domestic economic indicators on asset prices in the context of Malaysia. The following statistical analysis were conducted for this research using the SPSS Statistics program:
Descriptive analysis that provides information about the variables that include the mean of each variable, its standard deviation and the minimum and maximum values.
Normality test to assess if the data set of each variables are well modelled by a normal distribution which is the main assumption to undertake parametric tests such as Pearson Correlation.
Correlation analysis between variables to assess the strength and direction of the relationship between two variables.
Linear Regression Analysis, a forecasting technique that examine the relationship of causal effect among the variable. The value of each dependent variables will be forecasted using the...
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