This post is a summary of the research I’ve undertaken for my thesis with regards to price multiples valuation models such as price-to-earnings (PER), price-to-book (PBV), price-to-sales, price-to-cash flow, EV/EBITDA, EV/Sales etc coupled with value drivers of share prices in the Malaysian context. The aim of this study is to give investors a better understanding of the appropriate valuation models and value drivers of stock returns in making investment decisions coupled with providing a faster way of analyzing the whole stock universe (though still nothing beats an in-depth analysis of individual stocks). This could be quite lengthy, so pardon me. If you don’t feel bored, read on :p Perhaps you could get a tip or two if you’re interested to do similar studies on other markets probably, and maybe share with me as well :)
Samples used are 373 firms listed in KLSE that cover most of the constituents of FBM EMAS spanning from year 2000 to 2010.
Identifying comparable firms:
Firstly before analyzing the appropriate price multiples to use, identification of comparable firms is needed. There are basically three industry classification systems available from Bloomberg terminal (The lifeline of most investment or finance professionals) for Malaysian firm such as Global Industry Classification Standard (GICS), Industrial Classification Benchmark (ICB) and Bloomberg Industry Classification System (BICS), though there are plenty of other classification systems such as SIC, Dow Jones, Fama and French Classification etc which are not available in Bloomberg though. To determine the most appropriate classification system, the system which has the highest explanatory power of industry’s averages of variables over the individual firms’ variables coupled with the lowest intra-industry variances is considered the most appropriate. The variables tested include PER, PBV, PS, ROE, operating margin, sales growth and stock returns, representing the valuations, profitability and growth of the firms. It was found that GICS clearly had the best results by having the highest explanatory powers and lowest intra-industry variances. The results were consistent with prior researches on EU and US markets as well where GICS clearly outperformed other industry classification systems. Therefore, next time when you want to extract comparable firms in Bloomberg terminal, GICS would likely give you the closest comparable firms for your analysis.
Appropriate valuation models:
Valuation models include market value multiples and enterprise value multiples based on 1-year forward and 1-year trailing net income, profit before tax, operating profit, EBITDA, sales, book value and cash flow. 1-year forward values are based on ex-post data, assuming perfect foresight by research analysts. Two empirical tests were done to determine the appropriate valuation models. Firstly, OLS cross-section regression and valuation errors were done to determine how best the fair values computed by different valuation models explain and fit onto the stock prices, a method commonly used by prior researches in the past. However, this could be of little contribution to investors as they would not be able to take advantage of the arbitrage opportunities if the fair values fit perfectly the stock prices. A more practical empirical model would be the convergence test where the convergence rates of the market values towards the fair values are measured. The winner among all the valuation models for the OLS regression as well as convergence test was price-to-earnings before tax (P/EBT), followed by PER and PBV. The worst valuation models appeared to be price-to-sales (P/S) and price-to-cashflow (P/CF). For convergence test, price multiples based on forward values outperformed trailing values, implying that investors would have greater arbitrage opportunities using forward values. Other observations included: (1) Market value multiples outperformed enterprise value multiples; (2) As we move the value drivers from bottomline to the topline of the income statement (i.e. net income to sales), results were poorer; (3) Convergence rates of market values towards the fair values improved as convergence duration increased, an indication of the inefficient market that Malaysia had and contrary to US findings where convergence results deteriorated as time went by.
Some of the industries in Malaysia and their appropriate value drivers for valuation models are shown below:
In summary, forward earnings and book values are appropriate models to use in equity valuation. EV/EBITDA and EV/Sales which were highly revered by some researchers in the past appeared to be poor valuation models to use. P/CF and P/S also might not contribute much to equity valuations in Malaysia. Hmmm…..The results are quite in line with the valuation models that are commonly used among research analysts. Perhaps the popularity of PER and PBV among research analysts could have caused the results to favor these two, as this might be a self-fulfilling prophesy as investors use these models to bring the market values towards the fair values computed by these models.
Firm-specific value drivers of stock returns:
This could be useful for deciding which firms to invest should the firms have similar upside based on their fair values. Several value drivers are tested, such as beta, book-to-market (inverse of PBV), earnings yield (inverse of PER), dividend yield, net gearing and market capitalization. Multivariate analysis using panel data regression tests is used to determine the explanatory powers and significance of these value drivers. All in all, book-to-market and market capitalization had the most significant impact on stock returns, followed by net gearing and beta. Earnings yield and dividend yield appeared insignificant in most of the industries. Book-to-market, market capitalization and dividend yield are negatively correlated to stock returns whereas net gearing, earnings yield and beta are positively correlated to stock returns. Surprisingly, beta appeared to be not so significant in affecting stock returns, rendering the application of CAPM in Malaysia rather pointless :P On the other hand, higher net gearing in fact favor stock prices, of course provided that the borrowings do not bring the firms close to default risks. This could be due to greater efficiency in the capital structure where higher borrowings could bring in tax savings and at the same time allow greater expansion of business operation. Most of the industries have more or less similar results as the overall market, except for agricultural products (Mainly oil palm companies) which had net gearing as the most significant driver, and construction firms of which dividend yield was significant in the negative direction to stock returns. Banks and industrial conglomerates (like Sime Darby) are not affected by net gearing at all.
In summary, lower market capitalization, lower book-to-market ratio and higher net gearing favor higher stock returns.
Summary:
GICS provides the best industry classification of firms
Most appropriate valuation models: Market value multiples based on forward values of earnings and book values performed the best. Enterprise value-based models coupled with sales, cash flow and EBITDA multiples performed poorly.
Lower book-to-market, lower market capitalization and higher net gearing significantly affect stock returns in the positive direction.
GICS provides the best industry classification of firms
Most appropriate valuation models: Market value multiples based on forward values of earnings and book values performed the best. Enterprise value-based models coupled with sales, cash flow and EBITDA multiples performed poorly.
Lower book-to-market, lower market capitalization and higher net gearing significantly affect stock returns in the positive direction.
