Usage of control charts for time series analysis in financial management

We will deal with corporate financial proceeding using statistical process control, specifically time series control charts. The article outlines intersection of two disciplines, namely econometrics and statistical process control. Theoretical part discusses methodology of time series control charts, and in research part, the methodology is demonstrated on two case studies. The first focuses on analysis of Slovak currency from the perspective of its usefulness for generating profits through time series control charts. The second involves regulation of financial flows for a heteroskedastic financial process by EWMA and ARIMA control charts. We use Box-Jenkins methodology to find models of time series of annual Argentinian Gross Domestic Product available as a basic index from 1951-1998. We demonstrate the versatility of control charts not only in manufacturing but also in managing financial stability of cash flows. Specifically, we show their sensitivity in detecting even small shifts in mean which may indicate financial instability. This analytical approach is widely applicable and therefore of theoretical and practical interest

Gallium-free micromechanical sample preparation from ECAPed alluminium

Focused ion beam scanning electron microscopes (FIB-SEM) enable high precision site-specific material removal with practically no restriction on sample composition. Depending on the ion source (e.g. Ga+, Xe+), the rate of material removal differs significantly. In general, the design of Xe+ source allows using high ion beam currents that can be up to a few µA while maintaining beam quality and performance. However, the most relevant feature of Xe ions for this study is their non-metallic and inert nature which prevents any chemical interaction with the target material and formation of unwanted metallic compounds that alter the original properties of the sample that is being analyzed. Please click Additional Files below to see the full abstract

A comparative study of consumers’ readiness for internet shopping in two African emerging economies: Some preliminary findings

This research seeks to empirically investigate factors that could either inhibit or facilitate consumers’ readiness for Internet shopping in two highly influential countries in the African continent. In this study, a structured questionnaire-based crosssectional convenience sampling was used to elicit information from respondents in Ghana and Nigeria respectively. We have identified six cogent factors that are significantly influencing consumers’ readiness for Internet shopping in both countries, these six influential factors were all subjected to hypotheses testing using non-parametric statistical methods. Based on some of our findings, we found out that demographic variables, perceived level of distrust, Internet access availability, the proliferation of social media site usage amongst the younger population all have an important role to play in the uptake of Internet shopping in both countries. We also found out that the female gender compared to the male gender in Ghana would most likely have a higher perception level of distrust in Internet shopping. It is also interesting to note that perceived level of distrust is positively correlated with the demand for the promulgation (and implementation) of Internet transactions’ laws in Nigeria. By and large, we have equally pointed out some limitations of the present study and also provided some relevant future research directions given that this study is, arguably, the first of its kind in Africa to compare consumers’ readiness for Internet shopping in any two African emerging economies. We are optimistic that Internet shopping offers an emerging business opportunity for retail businesses to fully take advantage of the rising digitally literate African youth populace, who constantly crave for speed of service delivery, convenience and a mutually beneficial trust based relationship

Exploring roles of females in contemporary socio-politico-economic governance: An association rule approach

Undeniably, the engagement of females in socio-politico-economic activities of most countries in the world, especially in developing economies, is far less than males. Although females form the majority in most developing countries, they are rarely engaged in the discourse of cogent developmental issues. The rising interest of females in political and socio-economic discourse, especially in the western world, has sparked female interest in the governance structure of developing countries. Subsequently, with an increased penetration of the Internet and social media, the contribution of females to governance has even assumed a new level. Using primary data collected from six Sub-Saharan African (SSA) countries, the paper identifies relationships of females’ interest in socio-politico-economic governance on the countries surveyed. This paper equally digested a repertoire of data from relevant secondary sources on female involvement in the political landscape of SSA countries. To unravel some key relationships amongst the variables of interest in the study, we have used association rules (data mining technique). One of our key findings appears to indicate that the interest of females in political discourse is highly associated with the level of trust respondents have in the governance and leadership of the countries

Economic Applications of Statistics and Data Mining

Monografie moderních statistických metod a úloh data miningu v ekonomických aplikacíchA book of modern statistical methods and data mining algorithms for applications in economyZ(MSM 265300021

Data mining with clustering

Data mining is a new discipline lying at the interface of statistics, database technology, pattern recognition, machine learning, and other areas. It is concerned with the secondary analysis of lar- ge databases in order to find previously unsuspected relationships which are of interest or value to the database owners. There are two keys to success in data mining. First is coming up with a precise formulation of the problem you are trying to solve. A focused statement usually results in the best payoff. The second key is using the right data. After choosing from the data available to you, or perhaps buying external data, you may need to transform and combine it in significant ways. New problems arise, partly as a consequence of the sheer size of the data sets involved, and partly because of issues of pattern matching. H owever, since statistics provides the intellectual glue underlying the effort, it is important for statisticians to become involved. There are very real opportunities for statisticians to make significant contributions. The main definition of data mining and the special data mining tasks are mentioned in the first part of this paper. The data mining problem was also discussed in previous issues of E+M. One method (clustering) was chosen to be a subject of this article. One of the opportunities to gain knowledge from data is a use of clustering analysis. Clustering analysis belongs to unsupervised methods of data mining. We put here a focus on this method. Some basic principles are described in the second part of this paper. This method is examined on two examples from the marketing field. In the first example is used software Statgraphics 5.0Plus (www.statgraphics.com) to solve clustering problem (nearest neighbour algorithm and Eucleidi- an distance), and in the second example is used Statistica 6.0Cz software (from Statoft, Inc., www.statsoft.com or www.statsoft.cz). But the building models is only one step in knowledge discovery. It is vital to properly collect and prepare the data, and to check the models against the real world. The „best“ model is often found after building models of several different types, or by trying different technologies or algorithms

Economic Applications of Statistics and Data Mining

Monografie moderních statistických metod a úloh data miningu v ekonomických aplikacíchA book of modern statistical methods and data mining algorithms for applications in economyZ(MSM 265300021

Data mining and its use

Příspěvek prezentuje pozadí a metodologii provádění rozsáhlého empirického výzkumu, který je v současnosti v kompetenci mezinárodní a mezikontinetální spolupráce mladých vědeckých pracovníků Českého vysokého učení technického v Praze, Univerzity Tomáše Bati ve Zlíně a jiných mezinárodních partnerů. Autoři staví na základní platformě, kterou je hypotéza, že projektové řízení představuje primární roli v kreativním pojetí a podání konkurenceschopnosti v rámci globalizované a znalostně orientované ekonomiky. Autoři provádí empirický výzkum, jehož hlavním cílem a přínosem je určení relevantního stavu a role aplikace projektového managementu v České republice s tím, že je zde kladen neméně důležitý důraz na testování zjištěného stavu v závislosti na reálných podmínkách současné ekonomické situace a podnikatelského prostředí České republiky. V předkládaném příspěvku se autoři věnují kritické analýze literárních pramenů vztahující se k problematice projektového řízení v konsekvenci na metodologii daného mezinárodního výzkumu. Výzkum prezentovaný v rámci daného příspěvku prezentuje námi prováděnou první fázi nadřazeného výzkumu „PM Capabilities, Dynamic and Associated Critical Success Factors (CSFs)“. Výsledky této hrubé fáze budou dále použity jako základní premisa rozsáhlých analytických prací prováděných v rámci České republiky stejně tak jako výchozí základna pro komparaci výsledků ostatních ve výzkumu participujících zemí. Hlavním cílem je využití našich zjištění týkajících se současného stavu aplikace projektového řízení na národní i mezinárodní úrovni směrem k vytvoření metodologie pro klasifikaci firem a organizací ve vztahu k jejich stavu aplikace a vytvoření strategické páky projektového řízení. Účelem je tedy vytvoření rámce umožňujícího hodnocení a klasifikaci firem ve vztahu k relevantním faktorům.Databases today can range in size into the terabytes. Within these masses of data lies hidden information of strategic importance. But when there are so many trees, how do you draw meaningful conclusions about the forest? The newest answer is data mining, which is being used both to increase revenues and to reduce costs. There are two keys to success in data mining. First is coming up with a precise formulation of the problem you are trying to solve. A focused statement usually results in the best payoff. The second key is using the right data. After choosing from the data available to you, or perhaps buying external data, you may need to transform and combine it in significant ways. Data mining offers great promise in helping organizations uncover patterns hidden in their data that can be used to predict the behavior of customers, products and processes. However, data mining software tools need to be guided by users who understand the business, the data, and the general nature of the analytical methods involved. Realistic expectations can yield rewarding results across a wide range of applications, from improving revenues to reducing costs. Building models is only one step in knowledge discovery. It's vital to properly collect and prepare the data, and to check the models against the real world. The "best" model is often found after building models of several different types, or by trying different technologies or algorithms. Choosing the right data mining products means finding a tool with good basic capabilities, an interface that matches the skill level of the people who'll be using it, and features relevant to your specific business problems