Agnieszka Majewska

Proposed investment decision support system for stock exchange using text mining method

This work aims to design a proposed decision support system (DSS) for helping investors in making investment decision by using rule text-mining based algorithm to analyze news headline and implement analyzing pro-gram based on a manual analyzed headlines. The news analysis program (NAP) was used as an important stage in making investment decision on sample of the Gulf Cooperation Council (GCC) stock markets using Alarabia.net and Reuters.com which treated as a source of media noise that has an influence on the value of stock quoted stock market. The second kind of data that proposed to use in this system is the financial data of GCC stock market. The resulted data can be used in further steps to make better understanding of stock market companies behavior such as the statistical, data mining calculation for choosing the best period of time that give the best reaction of stock market ratios to the news indicators and using the vector measure construction method (VMCM) for classifying companies according to their response to the news.

A Comparison Of K-Means And Fuzzy C-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets

Abstract The main goal of this article is to compare data-mining clustering methods (k-means and fuzzy c-means) based on a sample of banking and energy companies on the Gulf Cooperation Council (GCC) stock markets. We examined these companies for a pattern that reflected the effect of news on the bank sector’s stocks throughout October, November, and December 2012. Correlation coefficients and t-statistics for the good news indicator (GNI) and the bad news indicator (BNI) and financial factors, such as PER, PBV, DY and rate of return, were used as diagnostic variables for the clustering methods.

Testing of Warrants Market Efficiency on the Warsaw Stock Exchange — Classical Approach

The efficiency of different markets was a subject of research by plenty of analysts. Most market research on the derivatives market was concentrated on valuation, but only a little part was concentrated on market efficiency. The goal of this article is to provide an empirical test of efficiency of the warrants quoted on the Warsaw Stock Exchange. One of the approaches of the derivatives’ market efficiency testing is researching a relationship between implied and historical volatility. The efficient market hypothesis assumes that volatility prediction, which is build on the sign from market, its named implied volatility, could be estimator of empirical volatility in the future, named historical volatility. Using standard procedures for estimating regression line by the OLS and for verification of econometric models, researchers could conclude about rejection or the lack of bases’ disallowable the hypotheses’ about market efficiency. The research includes testing weak and strong efficiency. There are two ways of testing the warrants market efficiency in the paper. The first approach includes empirical tests based on the following stages: 1. The calculation of underlying assets’ historical volatility. 2. The estimation of implied volatility from warrants prices. 3. Verification of hypothesis. The second approach consists in comparing the actual warrants prices and the estimated prices generated from the Black-Scholes pricing model.