An Introduction to Conceptual Graphs

Cointegration approach to the passive portfolio management enables to replicate the selected stock index and to construct a portfolio with profitability and risk similar to market. This paper analyzes several options for improving this method. It focuses on one of the key tasks, which is an estimate of long-run equilibrium relationship. Five different methods were proposed and compared. The results confirmed the relevance of using the Engle-Granger methodology in all previous surveys, but it also suggested some interesting properties related to the estimate of regression coefficients based on different variants of the Minkowski metric or to estimate regression equation without intercept. KeywordsCointegration cointegrating vector long-run equilibrium relationship Minkowski metric Engle-Granger methodology Johansen's cointegration method JEL Classification G11

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“…The super-metric that includes both of these metrics is the Minkowski metric. According to Polovina and Hill (2007) it is calculated as follows:…”

Section: Engle-granger Methodology Without Intercept In the Regressiomentioning

confidence: 99%

Some ideas for improving quality of the index tracking based on cointegration

Pastor

Sabol

Glova

2016

Business: Theory and Practice

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“…To demonstrate CS, Sowa devised Conceptual Graphs (CGs) (Sowa, 1984;Polovina, 2007;Sowa, 2008). Essentially, CGs are a system of logic that express meaning in a form that is logically precise, humanly readable, and computationally tractable.…”

Section: Figure 3 Eima Overviewmentioning

confidence: 99%

Modularising the Complex Meta-Models in Enterprise Systems Using Conceptual Structures

Polovina

Scheruhn

Rosing³

2018

Developments and Trends in Intelligent Technologies and Smart Systems

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The development of meta-models in Enterprise Modelling, Enterprise Engineering, and Enterprise Architecture enables an enterprise to add value and meet its obligations to its stakeholders. This value is however undermined by the complexity in the meta-models which have become difficult to visualise thus deterring the human-driven process. These experiences have driven the development of layers and levels in the modular meta-model. Conceptual Structures (CS), described as “Information Processing in Mind and Machine”, align the way computers work with how humans think. Using the Enterprise Information Meta-model Architecture (EIMA) as an exemplar, two forms of CS known as Conceptual Graphs (CGs) and Formal Concept Analysis (FCA) are brought together through the CGtoFCA algorithm, thereby mathematically evaluating the effectiveness of the layers and levels in these meta-models. The work reveals the useful contribution that this approach brings in actualising the modularising of complex meta-models in enterprise systems using conceptual structures.

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“…Conceptual graphs provide one such environment for ontology driven knowledge representation, providing a means to represent knowledge that is both understandable by software and people [36]. Ontology driven knowledge representation, and specifically conceptual graphs deal with data acquisition and KM though the use of an ontological vocabulary, and the subsequent relationships that are defined between the concepts in that vocabulary, providing a means to explore and model the semantics of domain knowledge [37].…”

Section: Ontology Driven Knowledge Representationmentioning

confidence: 99%