Dalia Kriksciuniene

Evaluation framework of hierarchical clustering methods for binary data

The article aims to evaluate hierarchical clustering methods according to their performance for binary data type. We explore the accuracy of ten hierarchical clustering methods by experimenting with ten different distance measures. The three types of well, poorly and very poorly separated clusters of binary data sets are generated by selecting the appropriate parameters for binomial distribution and Monte Carlo method. In order to evaluate the precision of clustering methods the binary data sets are transformed to distance matrices. The error level each method is explored in relationship to distance measures, cluster types and data distributions. The Complete linkage, Flexible-beta and Wards methods have best clustering performance for the case of two well separated clusters of binary data.

Intelligent Systems for Retail Banking Optimization - Optimization and Management of ATM Network System.

The article analyzes the problems of optimization and management of ATM (Automated Teller Machine) network system, related to minimization of operating expenses, such as cash replenishment, costs of funds, logistics and back office processes. The suggested solution is based on merging up two different artificial intelligence methodologies – neural networks and multi-agent technologies. The practical implementation of this approach enabled to achieve better effectiveness of the researched ATMs network. During the first stage, the system performs analysis, based on the artificial neural networks (ANN). The second stage is aimed to produce the alternatives for the ATM cash management decisions. The performed simulation and experimental tests of method in the distributed ATM networks reveal good forecasting capacities of ANN.

Analysis of the day-of-the-week anomaly for the case of emerging stock market

The aim of the article is to explore the day-of-the-week effect in emerging stock markets. This effect relates to the attempts to find statistically significant dependences of stock trading anomalies, which occur in particular days of the week (usually the first or the last trading day), and which could be important for creating profitable investment strategies. The main question of the research is to define, if this anomalies affects the entire market, or it is applicable only for the specific groups of stocks, which could be recognized by identifying particular features. The investigation of the day-of-the-week effect is performed by applying two methods: traditional statistical analysis and artificial neural networks. The experimental analysis is based on financial data of the Vilnius Stock Exchange, as of the case of emerging stock market with relatively low turnover and small number of players. Application of numerous tests and methods reveals better effectiveness of the artificial neural networks for indicating significance of day-of-the-week effect.

Short-Term investment risk measurement using var and CVaR

The article studies the short-term investment risk in currency market. We present the econometric model for measuring the market risk using Value at Risk (VaR) and conditional VaR (CVaR). Our main goals are to examine the risk of hourly time intervals and propose to use seasonal decomposition for calculation of the corresponding VaR and CVaR values. The suggested method is tested using empirical data with long position EUR/USD exchange hourly rate.

Entropy-Based Indicator for Predicting Stock Price Trend Reversal

Predicting changes of stock price long term trend is an important problem for validating strategies of investment to the financial instruments. In this article we applied the approach of analysis of information efficiency and long term correlation memory in order to distinguish short term changes in trend, which can be evaluated as informational ‘nervousness’, from the reversal point of long term trend of the financial time series. By integrating two econometrical measures of information efficiency - Shannon’s entropy (SH) and local Hurst exponent (HE) - we designed aggregated entropy-based (EB) indicator and explored its ability to forecast the turning point of trend of the financial time series and to calibrate the stock market trading strategy.

The Impact of Taxes on Intra-week Stock Return Seasonality

In this paper we explore the impact of trading taxes (commissions) on day-of-the-week effect in the Lithuanian Stock market. We applied the computational model for processing trading activities only on the particular days of the week. The suggested algorithm of trading shares not only reveals presence of the day-of-the-week anomaly, but allows comparing it to the influence of the trading taxes by estimating the final return of the selected shares. As the taxes of each transaction depend on the investment sum, therefore the suggested algorithm had to optimize the number of operations for ensuring the biggest gain. The research revealed significance of intra-week stock return seasonality for majority of shares (17 out of total 24). The advantages of the suggested method include its ability to better specify the shares for performing intra-week seasonality-based transactions, even though embracing of the trading commissions reduces visibility of the effect.

E-Collaboration: A Problem-Based Virtual Classroom Approach to International E-Business Qualification

We present e-collaboration as an innovative e-learning concept, which provides three main potentials for e-business qualification. Collaboration in the virtual classroom develops the soft skills necessary for working in global virtual teams. Project experiences, based on authentic case studies, help students to transfer their academic knowledge to the e-business application level. Our problem- based collaborative framework invites for integrating university learners and company experts, thus forming a lifelong e-collaboration society. Our conclusions are based on empirical case study results of a tri-national virtual collaborative learning project, carried on in May 2004 by Dresden university of Technology (Germany), Szczecin University (Poland) and Vilnius University (Lithuania).

Research of Conventional Data Mining Tools for Big Data Handling in Finance Institutions

The article investigates the usability of conventional data mining tools for handling data sets generated in financial institutions. It discloses the characteristics of “big data” which reveal limitations and new requirements for analytical software to deal with huge data flows related to financial transactions. The performance characteristics of four different conventional data mining tools, their visualization and clustering models are tested for experimental set of big data extracted from bank local data warehouse. The ranking of critical characteristics is provided for each stage of analysis of big data set.

Multi-level Fuzzy Rules-Based Analysis of Virtual Team Performance

The article presents model for evaluation of virtual team performance based on the intelligent methods of Multi-level fuzzy rules and Fuzzy Signature. The hierarchical system of parameters for virtual team performance evaluation is elaborated by applying expert survey. The aggregated measure of performance of virtual project team is derived from twelve parameters assigned to three categories (team, task and interaction). The experimental research is based on fuzzy analysis of interaction data of virtual teams which worked on implementation of three software solution projects. The research results provide evidence for the feasibility of using the proposed method as the tool for virtual project managers seeking to improve their leadership techniques, and to derive parameters for performance evaluation based on intelligent computing methods.

Evolution of Information Efficiency in Emerging Markets

In the article we investigate the informational efficiency of Nasdaq OMX Baltic stock exchange by applying Shannon’s entropy measure for symbolized time series. The complexity of the problem of market efficiency evaluation has lead to application of various soft computing methods and to even contradictory outcomes confirming or denying the efficient market hypothesis. The goal of the article is to explore the possibilities of quantitative evaluation of market effectiveness, by presenting the computational method and its experimental research for the financial data of the emerging Baltic market. The computations were performed for different time spans and symbolic word lengths. The research results allowed to conclude that the efficiency of Baltic market strongly falls behind the developed countries, and it raises expectations for modelling profitable trading strategies. Application of the entropy measure allows to explore the evolution of the market efficiency and to apply the algorithm for predicting the forthcoming crises of financial markets.