Abnormal returns, risk, and options in large data sets

Multiples Valuation and Abnormal Returns

Chaotic Test and Non-Linearity of Abnormal Stock Returns: Selecting an Optimal Chaos Model in Explaining Abnormal Stock Returns around the Release Date of Annual Financial Statements

For many investors, it is important to predict the future trend of abnormal stock returns. Thus, in this research, the abnormal stock returns of the listed companies in Tehran Stock Exchange were tested since 2008- 2017 using three hypotheses. The first and second hypotheses examined the non-linearity and non-randomness of the abnormal stock returns ′ trend around the release date of annual fin...

PRICING STOCK OPTIONS USING FUZZY SETS

We use the basic binomial option pricing method but allow someor all the parameters in the model to be uncertain and model this uncertaintyusing fuzzy numbers. We show that with the fuzzy model we can, with areasonably small number of steps, consider almost all possible future stockprices; whereas the crisp model can consider only n + 1 prices after n steps.

Visualising large data sets

Large data sets are different and new methods of display are needed for dealing with them. This paper reviews the standard problems in displaying large numbers of cases and variables, both continuous and categorical, and emphasises the need for improving current software. Much could be achieved by adding interactive tools to standard displays to provide greater flexibility and to facilitate a m...

Reasoning with Large Data Sets

Efficient reasoning is a critical factor for successful Semantic Web applications. In this context, applications may require vast volumes of data to be processed in a short time. We develop novel reasoning techniques which will extend current reasoning methods as well as existing database technologies in order to enable large scale reasoning. We propose advances and key design principles primar...