Testing the Power of the Multivariate Asset Pricing Models:
Kuwait Stock Exchange
The behavior of asset prices is important for different types of decision makers and all professional investors. Asset pricing models are essential for explaining stocks prices, forecasting stocks returns and planning to invest in securities. CAPM stands for (Capital Asset Pricing Model) which has been proposed firstly by Sharpe (1964), is considered as one of the first established models in asset pricing, the model has been widely practiced in capital markets. Sharpe (1964) in his model explains assets prices and returns based on the relation between return and systematic risk.
In our research we aims to find more about the validity of the CAPM extended models. Wher we will test the validity of three different extended factor models in explaining asset prices for the frontier stock markets, the case Kuwait Stock Exchange (KSE). Testing the power and validity of alternative widely used asset pricing models will be considered as a filling for a key gap for the Kuwaiti capital and stock market. This research study can be considered as the first attempt to tests the power and validity of the four and five factor asset pricing models for KSE. These models are created by augmenting the factor of momentum for Carhart to the three-variable model and liquidity premium as a fifth asset pricing persistent factor that captures the liquidity level and liquidity risk. In addition, the study will show whether small companies can outperform large companies (SML), companies with high book to market ratio can outperform the companies with low book to market ratio (BTM), momentum (WML) is fairly priced and liquidity level and risk (LIQ) are persistent factors.
This research aims to answer the following questions:
· Can asset pricing models predict stock prices in frontier stock markets?
· What is the best model to predict the stock prices in frontier stock markets?
· Can stock market factors predict the stock prices in frontier stock markets?
The research objectives are:
· To test whether the three different asset pricing models explain the stocks real prices in the KSE.
· To compare the validity for three different asset pricing models to explain and predict asset prices in the KSE.
· To investigate whether companies are fairly priced in Kuwait Stock Exchange based on size (SML), book over market ratio (HML), momentum factor (WML) and level of liquidity and liquidity risk (LIQ).
The classical Capital Asset Pricing Model was insufficient in explaining security prices in many of the stock markets, various studies considered establishment of other extended models to price stocks more sufficiently and many researcher recommended extended models that avoid the gaps of CAPM.
Fama and French (1993, 1996), propose an extended (three-factor) asset pricing model based on market capitalization and value premium to explain stock prices. The factors of the model are the systematic risk, market capitalization and the book to market value. Fama and French method has been widely used as a practical guide for professional investors and the validity of the model was highly tested and supported by academic research. The three-factor model has been tested for three different global stock markets by Griffin (2003) and he found that the model explain stock market returns. Lam (2002) finds an evidence for the favourability of the three-factor model. Iqbal and Brooks (2007) compared CAPM and the three-factor model for the Stock Market of Karachi, they used statistical GARCH and EGARCH methods for testing the model, they recommended the three-factor model to understand assets prices and predict prices. O’Brien et al. (2008), compared CAPM with the three-factor model in the Australian market and they recommended the three-factor model to explain stock prices, where the model explained nearly 70% of the stock market prices.
The three-factor model most serious drawback and gap is the ignorance of momentum effect. Jegadeesh and Titman (1993), argue that momentum effect can capture the movement of the stock prices, securities that do good comparative to the market in the last yearly period have a tendency to continue to do well for the next year period, and securities that do bad continue to do bad for the next year period. The three-factor model and the classical CAPM do not consider the momentum factor. Carhart (1997) established the four-factor model as an extension to the three-factor model considering the momentum factor, where he recommended the new model after his study based on his test. He created and tested the power and validity of their model by using the momentum of Jegadeesh and Titman (1993). Carhart (1997) found that the new model with the momentum factor can explain stock prices and returns. L’Her et al. (2004) tested the power and validity of the Carhart model in explaining stock market returns for the (CSM) Canadian stock market, he found that Carhart model is valid for explaining stock prices and returns in the CSM. Naceur and Chaibi (2007) searched for the most valid model in the Tunisian Stock market, the findings of their study recommended Carhart to evaluate and forecast the required rate of return for the stocks. The validity of Carhart model has been tested for Hong Kong market by Lam et al. (2010) and they found that Carhart model can explain asset prices and returns in Hong Kong market. Carhart model also can explain Istanbul Stock Exchange (ISE) asset prices and returns based on Unlu (2012) findings.
Chan and Faff (2005) tested a new extended liquidity asset pricing model in the framework of Fama and French model with Australian data. They find supporting evidence for the liquidity augmented three-factor model with share turnover as a proxy for liquidity.
The study sample period will be from 1995 to 2013 and we will use monthly stock returns and monthly KSE market index. Financial reports will be collected from the KSE and discount rates will be achieved from the Central Bank of Kuwait.
The returns will be calculated on a monthly basis for the 1995-2013 period. The portfolios will be constructed using the framework of Fama and French (1993) for portfolio construction. The factors of size and book to market value ratio will be used as portfolio factors. Market capitalization will be used as the proxy of the firm size and the book to market ratio will be used as the proxy of the company value.
We will use the previous constructed portfolios to establish the three-factor model:
𝐸𝑅: expected return
𝑅𝑓: the rate of risk free
𝑀𝑟: market return
𝑆𝑀𝐵: small companies return minus big companies return
BTM: high minus low B/M ratio
To calculate the momentum factor, we will divide the stocks into two equally weighted groups based on their ratio of market capitalization. The first group is the S (small) companies group and the second group is the B (big) companies group. Then we will construct six equally weighted portfolios for the momentum factor, based on stocks returns we will sort the stocks in one of these three portfolios W (winner), N (Neutral) and the L (loser) portfolio, and we will use these portfolios for the building of the “WML” winners minus losers risk factor. The four-factor model; will be:
𝑊𝑀𝐿: winners minus losers return
The stock turnover ratio will be used as the liquidity proxy as similar to the liquidity proxy of Chan and Faff (2005). We will calculate the stock turnover by dividing the stocks monthly volume by the number of outstanding shares. Then we will construct an equally-weighted six portfolios, were stocks will be sorted based on their level of liquidity. We will construct six equally-weighted portfolios in order to capture the liquidity risk “LIQ”, the five factor mode is:
In our study we will test five different asset pricing factors and anomalies that are widely used to predict the stock market prices. These factors are the systematic risk, small companies risk (small companies minus big companies returns), book to market value premium and WML (winner return minus losers return as a proxy of momentum) and LMH (the return of liquid minus illiquid stocks).
We will run the generalized method of moments to augment the factors in the previous models. Later we will analyse the results of the GMM regression to explain the power of the previous models in KSE. We will also repeat the test using Fama Mac-Beth framework which will give different results from GMM, because GMM takes into account pre-estimated beta (Acharya & Pedersen 2005).
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Corporate Governance And Performance Of Banking Sector In Kuwait
Length: 4 pages (2200 Words)
Corporate governance has been gaining popularity and attention in the emerging economies, while in the developed economies it is in great demand, in light of the many financial scandals that have been in large proportions due to failure of regulation and corporate Governance. Poor corporate governance always leads to the poor performance reports by companies, which results in stakeholder dissatisfaction, which is not good for any company (O’Regan et al 2005).
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