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Stock Stats
Statistics in the World of Stocks
Many students at various levels in mathematics at times find themselves asking the question, why do I need to learn this? It is admittedly true that many people will never really use algebra in their daily lives, and the complex world of statistics and probability also goes unutilized by many. Algebra, probability, and statistics can all be useful to everyone, in actuality, and more importantly there are specific instances where these areas of mathematics prove absolutely invaluable. When it comes to the stock market, many people try to use statistical models to predict when certain stocks are good values, or when they are poised to skyrocket investors to new fortunes, but there are also more sensible applications of certain probability and statistics concepts that are reasonable means of influencing stock-buying decisions. Applying a few basic methods of analysis to some elements of various business and stock performance can yield probabilities for future success.
Specifically interested in buying the stock of an automotive company, I first assessed what relevant information I had available that probability and statistics concepts could be applied to. After identifying this information as current financial data on several automotive companies, past performance of these...
It already became clear how statistics could be applied in a more extensive manner, by finding the degree of correlation between sales record and stock performance in the automotive industry in order to determine if the former is a reliable predictor of the latter.
This would be quite an extensive application of statistics, however, and wile the results could prove highly useful this is somewhat beyond the scope of what is possible here. The probability and statistics concepts that were chosen for application are highly relevant, however, if somewhat simpler to apply. The relative strength of the several automotive companies in each of the past ten years was determined, in terms of market share, profit, and sales revenue, and from this the probability of each company potentially becoming the leader in each of these areas in the next several years was calculated as a simple probability. The common graphs created by charting each stock's daily price were also examined for any relationship to standard statistical distributions, in shorter as ell as longer segments. Any similarity would have suggested a definite statistical relationship of stock prices.
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