Regardless of whether it is the exception or the rule, the favorable market conditions of the late 1990s for technology and Internet-based stocks illustrate the stock market’s critical role in resource allocation. A firm whose stock has appreciated rapidly finds it easier to raise additional funds through a secondary offering because higher prices mean a smaller percentage ownership of the firm needs to be offered to raise a given amount of capital. Favorable conditions also make it easier for privately held firms to raise funds through an initial public offering (IPO) of stock. Furthermore, a so-called hot IPO market entices venture capital firms to invest funds in hot industries and sectors in hopes of taking their firms public in such a favorable market. Many view these favorable market conditions as consistent with the market’s valuation of growth options and the motivating incentive necessary to make the fundraising portion of venture growth and creation possible. But while favorable market conditions can attract the investment capital necessary to grow a fledgling new industry, the market for technology and Internet-based stocks in the late 1990s appears to have overheated and, in hindsight, directed too much investment capital toward this sector. Thus, by the late 1990s, the return an investor in this sector could have rationally expected had fallen below what economic conditions could justify, as well as below what most investors actually anticipated.
Hypothesis testing in statistics is a way for you to test the results of a survey or experiment to see if you have meaningful results. You’re basically testing whether your results are valid by figuring out the odds that your results have happened by chance. If your results may have happened by chance, the experiment won’t be repeatable and so has little use.
"The Behavior of Stock Market Prices".
Steven L. Jones is an associate professor of finance at Indiana University’s Kelley School of Business, Indianapolis. Jeffry M. Netter is the C. Herman and Mary Virginia Terry Chair of Business Administration in the University of Georgia’s Terry College of Business. From 1986 to 1988, he was a senior research scholar at the U.S. Securities and Exchange Commission.
failing to reject the null hypothesis when it is false.
Indeed, the Nobel Laureate co-founder of the programme - Daniel Kahneman - announced his skepticism of resultant inefficiencies: "They're [investors] just not going to do it [beat the market].
rejecting the null hypothesis when it is true.
Conducting of customer segment analysis is essential to evaluate and prioritize the customer segments. Once the necessary data have been collected, it is required to be analyzed for validating each of the hypotheses. This helps in identifying whether a segmentation idea is right or wrong. Having done so, it is also important to analyze the relationships between validated hypotheses. The synthesis of these segmentation schemes is an overall segmentation of the customers which incorporates each of the validated segmentation hypotheses. This results in segments that are not only analytically proven to be attractive, but also intuitive and targetable for the purpose of developing and executing a segment-focused strategy for them.
rejecting the null hypothesis when it is false.
A growing field of research called behavioral finance studies how cognitive or emotional biases, which are individual or collective, create anomalies in market prices and returns that may be inexplicable via EMH alone.
If Z(critical) = 2.04, what is the p-value for your test?
The data collection work plan and the best practices described above are still relevant even if the data collection teams do not have access to any additional resources for data collection. When setting up the plan, it is necessary to identify potential weaknesses in the data set and special attention is paid to them as the data is being collected. These weaknesses might include (i) incomplete or hard-to-reach data, (ii) outdated data, (iii) data which is not easily standardized or has multiple definitions, and (iv) data which need qualitative judgment. For ensuring the quality of the data, it is necessary to conduct quality assurance before, during, and after the data collection process since the problematic data creates many issues during the segmentation analysis exercise.