Machine Learning-Based Analysis of the Association Between Online Texts and Stock Price Movements

The paper presents the result of experiments that were designed with the goal of revealing the association between texts published in online environments (Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies at a micro level. The association between lexicon detected sentiment and stock price movements was not confirmed. It was, however, possible to reveal and quantify such association with the application of machine learning-based classification. From the experiments it was obvious that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, lags between the release of documents and related stock price changes, five levels of a minimal stock price change, three different weighting schemes for structured document representation, and six classifiers were studied. It has been shown that at least part of the movement of stock prices is associated with the textual content if a proper combination of processing parameters is selected

Automatizované získávání dat z veřejných rejstříků a generování dokumentů

The thesis deals with public registers, options of acquiring data from them and document generation. Firstly the system of basic registers is described and the overview of registers in Czech Republic is created. Then the module for data acquiring from chosen registers is created (in PHP). Finally the web application for generating standard types of documents (document model in XML, generating to PDF) is created. It is focused on agreements, but it can be extended on other document types. It consists of two modules: 1. administrative for user and document type management, 2. user for document management. Used technologies: FPDF, Nette, curl, PHP, MySQL, Apache