Since the beta systematic risk coefficient and the standard deviation are both important statistics in the received capital market theory [22] and the received option theory [1], considerabe effort has been expended on obtaining empirical estimates of these statistics [30]. The ordinary least squares (OLS) technique is typically utilized to estimate beta as the regression coefficient of a simple linear regression. However, the OLS betas for common stocks were found to be disconcertingly unstable over time [5, 6, 13, 15, 25]. But, whether the OLS beta or an adjusted beta were used, the regression statistics could still only explain less than half of the variability of most New York Stock Exchange (NYSE) stocks' returns (more specifically, R2

It has been suggested by many [1, 2, 5, 6, 7, 10 and more] and denied by few that, ceteris paribus, a well-informed risk-averse investor should prefer investments which have positively skewed distributions of rates of return. Passing over the models which underlie such assertions, the question is addressed empirically here. Do (as opposed to “should”) investors prefer investments that are positively skewed, ceteris paribus?

The purpose of this paper is to examine the intertemporal relationship between variations in the prices of individual common stocks and variations in the rest of the stock market. Empirical data are analyzed to determine the frequency with which stock prices precede, occur simultaneously, and follow movements in the market average.

Part 1: The characteristics of securities: risk and return debt securities equity and asset-backed securities. Part 2: The market place: security markets security markets indexes regulation of the securities markets taxes. Part 3: Introduction to financial analysis: sources of financial information analysis of financial statements the interest-rate risk factor the default risk factor bond selection. Part 5: Investing in stocks: common stock analysis earnings analysis. Part 6: Other risk factors: the market risk factor the purchasing power risk factor and the industry risk factor the management risk factor and other risk factors. Part 7: Pulling things together and making decisions with APT: making buy-sell decisions arbitrage pricing theory (APT). Part 8 The behaviour of stock prices technical analysis choosing between technical analysis or fundamental analysis. Part 9: Other investments: options, warrants and convertibles futures contracts investing in real assets. Part 10: Portfolio management: capital market theory international diversification investments performance evaluation.

Includes traditional institutional material and newer, more abstract risk-return theories. The book contains examples, applications and appendices outlining the mathematics of the theories.

Since Markowitz [15, pp. 98–101] and Sharpe [19] developed the single-index market model (SIMM hereafter) it has received considerable research attention. Empirical tests have established the model's econometric significance [3, 12, 13] in partial equilibrim analysis. However, research into the relationship between the SIMM and its macroeconomic environment has been meager. It has been shown that the market factor changes intertemporally [13, 16, 18, 20]. However, whether these changes in the market factor and, more basically, changes in the macroeconomic situation affect the SIMM is unknown.

After Markowitz [14, p. 100] and Sharpe [19, 20] suggested estimating the beta systematic risk coefficient for market assets, finance professors, stock brokers, investment managers, and others began expending large quantities of resources each year on estimating betas. Unfortunately however, it appears that the ordinary least-squares (OLS) regressions used in nearly every instance may be inappropriate. This paper suggests that many stocks' beta coefficients move randomly through time rather than remain stable as the OLS model presumes.