William sharpe propounded the capital asset pricing model in 1962, for which he was honoured with the Nobel Prize in Economic Sciences in 1990. The reason why the most prestigious award was bestowed upon him was that with this invention, the subjectivity revolving around stock prices boiled down to one formula. The cornerstone of that model was ‘beta’ , which compares the sensitivity of a stock’s price movement to that of the broader index.
This can be easily explained with the help of an example. Suppose the beta of Infosys Technologies is 1.2; this implies that with every 1% change in the index, the stock price of Infosys should move by 1.2% in the same direction. Higher the beta, higher will be the volatility in the stock price, and hence, riskier the investments . Lesser the beta, lesser will be the volatility in the stock price, and hence, it will be safer to invest in that stock. Also, if beta is positive, then the stock will move in the same direction as the index, which means that the stock price will go up if the index goes up and viceversa . If the beta is negative, the stock will move in the opposite direction visà-vis the index, but in reality, beta is rarely negative. The beta of the index or market is pegged at 1. Perhaps, all equity analysts use beta to estimate the cost of equity. In this edition, ET Intelligence Group tries to find out how powerful is the forecasting ability of beta in the context of the Indian stock market. Our sample comprises stocks of 44 companies, which have performed most consistently over the past decade. It includes companies like Reliance Industries, Tata Steel, HDFC, Hindustan Unilever and Colgate-Palmolive from the fast-moving consumer goods (FMCG) sector; Ambuja Cements and ACC from the cement sector; auto majors like Mahindra & Mahindra and Tata Motors; engineering giants like ABB, L&T and Siemens; besides leading stocks from pharma, financial services, hospitality and information technology sectors. This has been done to ensure that all sectors in the economy are duly represented in the sample. To check its effectiveness, we have taken the beta at the start of a year and then observed how the stock fared visà-vis the Nifty in that year. For instance, the beta of ABB was 0.76 on December 31, 1998. This indicates that ABB’s stock was expected to rise less than the Nifty and fall less than the Nifty. In 1999, ABB’s stock price fell by 49.7%, while the Nifty was up by 66.2%. The beta logic did not hold in this case because if the beta is positive, both the stock and Nifty should move in the same direction, instead of the opposite direction. The exercise was repeated for 44 stocks over 10 years from the start of calendar year 1999 to ’08 till date. We ended up with 440 observations, as there are 44 stocks over the course of 10 years. The logic of beta holds for only 172 of such observations, which implies a success rate of 39%. This shows that the odds are against the investor if he takes a call based on beta. In some cases, the success rate can be even worse. The chart clearly shows that for ABB, the basic rule of beta holds good only for one year out of 10. It must be observed that beta is positive for all 10 years. The beta logic does not hold in years ’01 to ’08 because though the beta is less than 1, ABB’s stock has shown higher volatility. This shows that estimating one-year returns considering the beta at the start of the year can be a self-defeating exercise. At this point, we must inform our readers that the purpose of our exercise is not to belittle the work done by William Sharpe. We only want to highlight the fact that the theory does not yield results, at least in the Indian context. However, the story does not end here. Upon further analysing data, we found that stocks with a beta higher than 1 — i.e. stocks which rise more with every rise in the market and fall more with every fall in the market — have given close to three times the returns of stocks with beta less than 1. To establish this, we made two portfolios of stocks: Portfolio 1 includes stocks which had a beta higher than 1 for a major part of the past 10 years. It includes stocks like Reliance Industries, Larsen & Toubro, Tata Steel, ACC, Tata Motors, Wipro et al. Portfolio 2 includes stocks which had a beta of less than 1 during most years in the past decade. It includes stocks like Asian Paints, Hindustan Unilever, Hero Honda, Colgate-Palmolive and Indian Hotels. While the high beta portfolio has yielded a return of 19.7% per annum on an average, the low beta portfolio has given only 7.1% return per annum in the past 10 years. Though in hindsight, this pattern appears to be very obvious, it must be noted that the outperformance is after including ’08, which has seen the worst crash of all times. Also, the extent of outperformance is beyond the estimate of market intellectuals. This shows that investors are better off investing in companies having beta higher than 1, rather than investing in companies with beta less than 1. In simple words, investors are better off betting money on seemingly risky stocks. However, the investment must be for a very long term, since we have already seen in the case of ABB that investing for one, two or three years based on beta can be risky. Another reason why keeping an eye on beta is important is that it tells investors what the market thinks of a particular stock. Suppose there is a company which is performing better than average, but the stock market does not give due consideration to this fact, and therefore, the stock does not move. In such cases, the beta tends to be lower. An astute investor should always be wary of investing in such stocks, as it indicates that the stock market is not discounting the fundamentals in stock price. Though it is clear that beta is one of the important factors to be considered while making investments, what is more important is how one uses it. Source : Economic Times --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "Kences1" group. 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