Finding kernel function for stock market prediction with support vector regression

Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. Time series forcasting, Neural Network (NN) and Support Vector Machine (SVM) are once commonly used for prediction on stock price. In this study, the data mining operation called time series forecasting is implemented. The large amount of stock data collected from Kuala Lumpur Stock Exchange is used for the experiment to test the validity of SVMs regression. SVM is a new machine learning technique with principle of structural minimization risk, which have greater generalization ability and proved success in time series prediction. Two kernel functions namely Radial Basis Function and polynomial are compared for finding the accurate prediction values. Besides that, backpropagation neural network are also used to compare the predictions performance. Several experiments are conducted and some analyses on the experimental results are done. The results show that SVM with polynomial kernels provide a promising alternative tool in KLSE stock market prediction

Price Barriers in the Stock Market and Their Effect on the Black-Scholes Option Pricing Model

The predicted price of an American option by the Black-Scholes (B-S) Option Pricing Model is known to differ from the market price of that option systematically with respect to time to expiration, distance in- or out-of-the-money, and liquidity of the option. We examine the possibility of price barriers in the stock market causing further systemic pricing differences between the market price and B-S predicted price. These differences occur when an option’s strike price is near a price barrier and differ in effect and significance depending on the position of the barrier relative to the underlying stocks’ price. We find round number price barriers in the stock market are beginning to be internalized into the option market. Additionally, Bollinger bands and Gann levels appear to receive special attention from investors, but do not act as price barriers