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Date of Award
Restricted Thesis: Campus only access
Bachelor of Arts
Stock markets operate efficiently. Investors obtain new information which is then reflected in prices immediately. However, that does not mean people cannot anticipate this or use past information to make better investment decisions. To incorporate these expectations or prior information, investors use models when predicting an asset's future price. The goal of this paper is to assess the usefulness of some of these models. This paper will compare and contrast the effectiveness of three different macroeconomic forecasting models when determining future price movements in stock prices. These models will be evaluated on their accuracy when predicting direction and magnitude over different time spans. Running this analysis will give a result that should indicate which model is better.
Investors possessing the power to predict future prices of stocks or any asset with reasonable certainty would benefit tremendously. The models presented in this paper will attempt to not only correctly predict the direction of price movements, but also determine the best investments given an investment criteria. Under current market conditions, we have seen rapid growth in the stock market. The Dow Jones Industrial Average and the S&P 500 climb higher nearly every day. Finding stocks that follow the market seems to require less effort than usual. Where these models will benefit investors most is during economic downturns. During downturns, making positive returns can be a challenge. Leveraging past data may be able to help investors navigate uncertain times.
Lawrance, John, "Economic and other advanced forecasting methods applied to stock prices" (2018). Honors Theses. 1294.