Understanding Coefficients in Money Management [stocksmarketarticles]

Understanding Coefficients in Money Management [stocksmarketarticles]

Using two simple approaches, the regression analysis and the excel formula (Covariance / Variance), this video demonstrates how to calculate the beta of a stock versus a benchmark be it an index, industry or commodity. You can download the spreadsheet here; 2003 Excel: www.dollarhane.com/beta.xls 2007 Excel: www.dollarhane.com/beta.xlsx

stocksmarketarticles.blogspot.com VsCap: How to calculate the Beta of a stock

In its share capital NSPH has 44.04 million outstanding shares among them 29.42 million shares have been floated in market exchange. Company's beta coefficient included 2.42. Beta factors measures the amount of market risk associated with market trade. Nanosphere rallies 16% on Jefferies upgrade - BAX, TMO, COV, ATRS, NSPH

 

Understanding Coefficients in Money Management

 

In the late 1960s and early 1970s, there was a general and enthusiastic endorsement of the value of beta coefficients as a measure of risk and an indicator of reasonable expectations of the returns on portfolios, given specified behavior of the market. Beta coefficients were embedded in an intricate, comprehensive, and plausible theory and had the further advantage of an esoteric name. Some leading brokerage firms and others began to manufacture and distribute betas on a large scale.

Controversies quickly arose. One of the least important was over the method of estimating betas for individual assets. The practical importance of the differences between the best and worst estimates was never large.

Of greater apparent importance, initially, was the instability of betas for individual stocks.

Even betas produced by the most sophisticated methods were quite unstable. Betas based on actual data for a previous period, say, two years, typically accounted for less than one third of the variation among betas of the same stocks in the future.

The seriousness of this fact is not great when one realizes that one is interested in betas for portfolios rather than for their component assets. The law of large numbers helps somewhat. Estimates of beta are sometimes too high and sometimes too low. These discrepancies are partially offsetting with the result that estimates for portfolios are often quite good predictors of future betas for portfolios.

The virtue of the historical beta as a predictor of the future beta is quite sensitive to the correlation between the actual portfolio and the market index for which the beta is relevant.

If the correlation were 1.0, the historical beta would be a perfect predictor. That is, if a portfolio always moved in perfect lockstep with the market index, the beta of the portfolio would always predict the portfolio's response to the market. A portfolio with a beta of 0.5 would move up and down half as fast as the market, a portfolio with a beta of 0.75, three fourths as fast, and so forth.

Skepticism about the value of betas became acute in some financial institutions when they calculated betas for their own portfolios and discovered that future reactions to the market were quite different from expectations created by the historical betas. A portfolio supposed to decline only half as much as the market sometimes declined much more and sometimes much less. This was true whether the historical beta for the portfolio was estimated by calculating its own average sensitivity to the market in the past or whether it was considered to be a weighted average of the betas of the component assets.

 

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