LIQUIDITY PREMIUM DEVELOPMENT

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Liquidity Premium

An investor who is considering purchasing an illiquid stock needs to, not only consider the firm specific risk, but also the risk of illiquidity. This means that the investor should require a premium for taking on the risk of illiquidity, the risk of having to sell at an unfavourable price due to a lack of buyers. The required compensation is called the liquidity risk premium (Amihud & Mendelson, 1986b). There are two types of liquidity risks, systematic liquidity risk and characteristic liquidity risk (Bradrania & Peat, 2014). Markets can experience fluctuations in the level of liquidity and investors that purchase stocks which returns are more sensitive to changes in liquidity should require higher expected returns (Pástor & Stambaugh, 2003). These stocks are more susceptible to liquidity market shocks which implies stock prices are more volatile in times of dire liquidity.

ETF Sampling

The tracking error is often used as a measure of the performance of index mutual funds and ETFs. It measures how well a fund has been able to track its benchmark. It is reasonable to assume that ETF issuers would try to minimise this error as much as possible in order to attract capital. Less liquid securities in the benchmark have a negative impact on the tracking error due to increases in the expense ratio of the fund (Buetow & Henderson, 2012). Keim (1999) found that a complete replication strategy can induce more costs than a simple sample replication of an index due to trading in the benchmark’s most illiquid stocks. Frino and Gallagher (2001) acknowledges that the best technique to use, whether it is full replication or partial replication depends on the liquidity of the underlying index.

Fama-MacBeth Approach

The Fama-MacBeth regression approach was developed by Fama and MacBeth (1973) and was originally developed to test the efficiency of the CAPM model. The model has been extended to estimate different kinds of risk factors that can determine assets’ prices and are frequently used as a tool to empirically test asset pricing models. Furthermore, the model works well in cross-sectional regressions with panel data (Fama & MacBeth, 1973). The approach involves performing two steps. The first step is to regress each asset’s individual return against a proposed risk factor in a time-series procedure to determine that asset’s level of exposure to the risk factors, retrieving the asset’s factor exposure.

Models

To recap, two research questions were stated at the beginning of the study: 1) How the liquidity premium has developed between 1997 and 2016 and 2) How possible alterations in the liquidity premium can be linked to the steep capital inflow to the ETF market. We hypothesize that the characteristic risk premium has decreased between 1997 and 2016 but have no hypothesis on how the systematic risk premium has developed. Moreover, we hypothesize that the capital inflow to the ETF market has been a factor which has influenced the decrease in the characteristic risk premium. In Chapter 3.2.1 and Chapter 3.2.2 below, two different models are presented. In Chapter 3.2.1, the model to test the development of the liquidity premium is presented and is hence the model which is used to test the first research question.

Liquidity Premium Model

In order to make the foundation of the model to test the liquidity premium, the three risk factors used in the Fama-French Three-Factor Model were included. The risk factors that have been found to impact the returns of stocks are: the market risk, the market capitalisation of a company and the book-to-market value of a company. The reason for including these variables is to risk-adjust the returns of securities. The factors are commonly used by researchers when investigating the liquidity premium such as by Bradrania and Peat (2014), Liu (2006) and Pástor and Stambaugh (2003). In this study, portfolios are used instead of stocks since there can be large fluctuations in the risk factors between individual stocks. Creating portfolios is done in order to avoid errors in the estimations (Amihud & Mendelson, 1986b).

Data Collection

Equations (6), (8) and (9) were used in regressions while equations (2), (3), (4), (5) and (7) were used in order to acquire the variables needed for those equations. The data needed in the equations was collected for the years between 1997 and 2016. There were two reasons for choosing this time-period. Firstly, there is limited recent research about the liquidity premium, providing an opportunity to contribute to the research field. Secondly, these years made it possible to incorporate capital inflow to the ETF market due to their inception in the early 1990s.

Table of Contents :

• INTRODUCTION
• BACKGROUND
• PROBLEM
• PURPOSE
• OUTLINE
• FRAME OF REFERENCE
  • LITERATURE REVIEW
    • 2.1.1 Market Liquidity
    • 2.1.2 Liquidity Premium
    • 2.1.3 Liquidity Measures
    • 2.1.4 Indexation
    • 2.1.5 Exchange Traded Funds
    • 2.1.6 ETF Spillovers
    • 2.1.7 ETF Sampling
  • FINANCIAL THEORIES
    • 2.2.1 Efficient Market Hypothesis
    • 2.2.2 Capital Asset Pricing Model
    • 2.2.3 Fama-French Three-Factor Model
    • 2.2.4 Fama-MacBeth Approach

• METHOD
  • METHODOLOGY
  • MODELS
    • 3.2.1 Liquidity Premium Model
    • 3.2.2 ETF Model
  • DATA COLLECTION
• PORTFOLIO CONSTRUCTION
  • REGRESSION METHOD
  • QUALITY OF THE METHOD
  • EMPIRICAL RESULTS
  • ROBUSTNESS TESTS
  • LIQUIDITY PREMIUM RESULTS
    • 4.2.1 20-Year Results
    • 4.2.2 5-Year Results
    • 4.2.3 1-Year Results
  • ETF RESULTS
• ANALYSIS
  • QUALITY OF THE RESULTS
  • LIQUIDITY PREMIUM DEVELOPMENT
  • ETFS’ EFFECT ON THE LIQUIDITY PREMIUM
  • LIQUIDITY BASED TRADING STRATEGIES
  • CONCLUSIONS
  • DISCUSSION

GET THE COMPLETE PROJECT
ALTERATIONS IN THE LIQUIDITY PREMIUM AS AN EFFECT OF EXCHANGE TRADED FUNDS A STUDY PERFORMED ON NASDAQ COMPOSITE BETWEEN 1997 AND 2016