Creating an Intelligent System for Bankruptcy Detection: Semantic data Analysis Integrating Graph Database and Financial Ontology

In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company’s financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers the Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database

Computational Modelling for Bankruptcy Prediction: Semantic data Analysis Integrating Graph Database and Financial Ontology

In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company's financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database system. The Ontological layer defines the basic concepts of the financial risk management as well as the objects that serve as sources of knowledge for predicting a company's bankruptcy. The Graph Database layer utilises a powerful semantic data technology, which serves as a semantic data repository for our model. The article provides a detailed description of the construction of the Ontology and its informal conceptual representation. We also present a working prototype of the Graph Database system, constructed using the Neo4j application, and show the connection between well-known financial ratios. We argue that this methodology which utilises state of the art semantic data management mechanisms enables data processing and relevant computations in a more efficient way than approaches using the traditional relational database. These give us solid grounds to build a system that is capable of tackling the data of any complexity level

Developing a Generic Predictive Computational Model using Semantic data Pre-Processing with Machine Learning Techniques and its application for Stock Market Prediction Purposes

In this paper, we present a Generic Predictive Computational Model (GPCM) and apply it by building a Use Case for the FTSE 100 index forecasting. This involves the mining of heterogeneous data based on semantic methods (ontology), graph-based methods (knowledge graphs, graph databases) and advanced Machine Learning methods. The main focus of our research is data pre-processing aimed at a more efficient selection of input features. The GPCM model pipeline’s cycles involve the propagation of the (initially raw) data to the Graph Database structured by an ontology and regular updates of the features’ weights in the Graph Database by the feedback loop from the Machine Learning Engine. The Graph Database queries output the most valuable features that, in turn, serve as the input for the Machine Learning-based prediction. The end-product of this process is fed back to the Graph Database to update the weights. We report on practical experiments evaluating the effectiveness of the GPCM application in forecasting the FTSE 100 index. The underlying dataset contains multiple parameters related to predicting time-series data, where Long Short-Term Memory (LSTM) is known to be one of the most efficient machine learning methods. The most challenging task here has been to overcome the known restrictions of LSTM, which is capable of analysing one input parameter only. We solved this problem by combining several parallel LSTMs, a Concatenation unit, which merges the LSTMs’ outputs (into a time-series matrix), and a Linear Regression Unit, which produces the final resul

Semantic Data Pre-Processing for Machine Learning Based Bankruptcy Prediction Computational Model

This paper studies a Bankruptcy Prediction Computational Model (BPCM model) – a comprehensive methodology of evaluating companies’ bankruptcy level, which combines storing, structuring and pre-processing of raw financial data using semantic methods with machine learning analysis techniques. Raw financial data are interconnected, diverse, often potentially inconsistent, and open to duplication. The main goal of our research is to develop data pre-processing techniques, where ontologies play a central role. We show how ontologies are used to extract and integrate information from different sources, prepare data for further processing, and enable communication in natural language. Using ontology, we give meaning to the disparate and raw business data, build logical relationships between data in various formats and sources and establish relevant context. Our Ontology of Bankruptcy Prediction (OBP Ontology) which provides a conceptual framework for companies’ financial analysis, is built in the widely established Prote ́ge ́ environment. An OBP Ontology can be effectively described with a graph database. Graph database expands the capabilities of traditional databases tackling the interconnected nature of economic data and providing graph-based structures to store information allowing the effective selection of the most relevant input features for the machine learning algorithm. To create and manage the BPCM Graph Database (Graph DB), we use the Neo4j environment and Neo4j query language, Cypher, to perform feature selection of the structured data. Selected key features are used for the Machine Learning Engine – supervised MLP Neural Network with Sigmoid activation function. The programming of this component is performed in Python. We illustrate the approach and advantages of semantic data pre-processing applying it to a representative use case

Developing of a bankruptcy prediction model for the uk construction companies

The paper provides a comparative analysis and classification of existing methods of diagnosing and forecasting the financial condition of companies. Furthermore, the features of carrying out multiple discriminant analysis (MDA) were discussed in details. Besides, the relevance of Altman’s and Taffler’s models was proved by testing these models using the financial data of current construction companies. Finally, a new model of diagnostic the financial condition of British construction companies based on MDA has been developed

Developing of a bankruptcy prediction model for the uk construction companies

The paper provides a comparative analysis and classification of existing methods of diagnosing and forecasting the financial condition of companies. Furthermore, the features of carrying out multiple discriminant analysis (MDA) were discussed in details. Besides, the relevance of Altman’s and Taffler’s models was proved by testing these models using the financial data of current construction companies. Finally, a new model of diagnostic the financial condition of British construction companies based on MDA has been developed

Study of Machine Learning Models for IoT based Efficient Classroom Usage

This paper presents performance analysis and comparison of machine learning algorithms for future use in a smart campus framework. The following error rates, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and R squared error are considered for models such as Random Forest (RF), Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Support Vector Regression (SVR), Polynomial Regression (PR), Generic Predictive Computation Model (GPCM). The investigation how to reduce the processing time for the algorithms is presented. The following error rates such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) are considered for Random Forest, Multiple Linear Regression, Decision Tree Regression, Support Vector Regression, Polynomial Regression models and Machine Learning tools taken from Use Cases of Generic Predictive Computation Model (GPCM) are partially applied. Testing with our arbitrary data will be conducted. A lower error rate for selected algorithms with reduced number of parameters (5 parameters) as opposed to 11 parameters is achieved

SMARTEST - knowledge and learning repository

SMARTEST is a knowledge repository that assists and facilitates learning. It represents knowledge and learning activities as graphs, which present information in a clear, visual format that is easy to follow and understand. Nodes (coloured circles) contain content such as instructions or concepts relevant to the subject it is being used for; the lines connecting together two nodes (edges), show the relationship between them. Students can follow instructions using these graphs and visually see the links between the concepts or entities represented by the nodes. The nodes can then be colour-coded by students depending on their understanding of what that node represents. If a student has had difficulty understanding a particular concept, they can simply choose the colour that best represents their situation and level of understanding. This allows teachers to get clear feedback from students on specific parts of a subject and enable them to then help that student better understand the topic at hand. Furthermore, if several students are having a problem with a certain topic, this is communicated to the teacher and as a group the teacher can tackle the problem. There are two types of graphs: learning paths and ontologies. A learning path sets out steps for students to go through to acquire knowledge for their subject and build on the already acquired knowledge to complete the next steps. It allows students to see what steps they will need to take to achieve the final goal and creates a visual sense of accomplishment as students get closer and closer to the end of the learning path. An ontology is a set of concepts and categories in a subject area or domain that shows their properties and the relations between them. SMARTEST has been developed within a project undertaken at the University of Westminster and is sponsored by Quintin Hogg Trust