Modeling and Forecasting Stock Market Prices with Sigmoidal Curves

Download or read book Modeling and Forecasting Stock Market Prices with Sigmoidal Curves written by Daniel Tran and published by . This book was released on 2017 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pricing stock market data is difficult because it is inherently noisy and prone to unexpected events. However, stock market data generally exhibits trends in the medium and long term. A typical successful stock index exhibits an initiation phase, rapid growth, and then saturation whereby the price plateaus. Sigmoidal curves can effectively model and forecast stock market data because it can represent nonlinear stock behavior within confidence interval bounds. This thesis surveys various members of the sigmoidal family of curves and determines which curves best fit stock market data. We explore several techniques to filter our data, such as the moving average, single exponential smoothing, and the Hodrick-Prescott filter. We fit the sigmoidal curves to raw data using the Levenberg-Marquardt algorithm. This thesis aggregates these analysis techniques and apply them towards gauging the opportune time point to sell stocks.