PROPOSAL: Explore the accurate and timely prediction of common stock prices and returns

A procedure that permits an accurate and timely prediction of common stock prices and returns has been the focus of research by scores of academicians, economists, financial analysts, investors, and stockbrokers since the inception of equity stock markets. The development and perfection of such a procedure offers the potential of significant academic kudos, substantial financial rewards, and benefits for the general economy. Because of the potential rewards, the quest for such a procedure has endured over the decades.

The advent of high-capacity computer-aided analysis enabled investigators to use large financial databases with greater effectiveness than was possible before this development. The ability to use massive volumes of data with computer speed, efficiency, and effectiveness intensified the efforts devoted to the development of an accurate and timely predictive procedure.

Despite the effort, intellectual expertise, and the capacity to process information rapidly and efficiently, however, an accurate and timely procedure for the prediction of common stock prices and returns eluded all investigators. Studies to date neither explain nor describe the characteristics of price changes over any sampling interval at a satisfactory level of confidence. Instead, the most successful of the investigators, at best, (1) have been able to describe exceptions from a normal distribution of stock price changes and return di

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(log) normal distribution. If the real underlying distribution differs significantly from log normal, then the Black-Scholes option valuations may be biased relative to true values. As the investment community relies on Black-Scholes for the determination of option values, it is highly desirable to assess the log normal distribution as a descriptor of security returns. Contemporary research on stock volatility also applies autoregressive models. The autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) processes introduced by Engle (1982) and Bollerslev (1986) provide parsimonious models of the observed time-varying volatility for many financial time series [Bollerslev, et al., 1992). Other contemporary research into stock volatility applies stationarity models, such as SEMIFAR (Beran & Ocker, 2001). 4. Research questions The proposed study will investigate two research questions. These research questions are as follows: 1. With respect to stock price volatility, what are the advantages and disadvantages for investors associated with the Dow Jones Index of 30 Industrials, the S&P 500 Index, and ETF-Based Indices? 2. With respect to the volatility of stock
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Some common words found in the essay are:
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Approximate Word count = 1280
Approximate Pages = 5 (250 words per page)