Review on Financial Forecasting using Neural Network and Data Mining Technique

The rise of economic globalization and evolution of information technology, financial data are being generated and accumulated at an extraordinary speed. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial data to support companies and individuals in strategic planning and investment decision-making. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. There has been a large body of research and practice focusing on exploring data mining techniques to solve financial problems. This paper describes data mining in the context of financial application from both technical and application perspective by comparing different data mining techniques

Comparing Decision Trees and Association Rules for Stock Market Expectations in BIST100 and BIST30

With the increased financial fragility, methods have been needed to predict financial data effectively. In this study, two leading data mining technologies, classification analysis and association rule mining, are implemented for modeling potentially successful and risky stocks on the BIST 30 index and BIST 100 Index based on the key variables of index name, index value, and stock price. Classification and Regression Tree (CART) is used for classification, and Apriori is applied for association analysis. The study data set covered monthly closing values during 2013-2019. The Apriori algorithm also obtained almost all of the classification rules generated with the CART algorithm. Validated by two promising data mining techniques, proposed rules guide decision-makers in their investment decisions. By providing early warning signals of risky stocks, these rules can be used to minimize risk levels and protect decision-makers from making risky decisions

Materiality Maps – Process Mining Data Visualization for Financial Audits

Financial audits are a safeguard to prevent the distribution of false information which could detrimentally influence stakeholder decisions. The increasing integration of computer technology for the processing of business transactions create new challenges for auditors who have to deal with increasingly large and complex data. Process mining can be used as a novel Big Data analysis technique to support auditors in this context. A challenge for using this type of technique is the representation of analyzed data. Process mining algorithms usually discover large sets of mined process variants. This study introduces a new approach to visualize process mining results specifically for financial audits in an aggregate manner as materiality maps. Such maps provide an overview about the processes identified in an organization and indicate which business processes should be considered for audit purposes. They reduce an auditor’s information overload and help to improve decision making in the audit process

Data Classification and Its Application in Credit Card Approval

We are all now living in the information age. The amount of data being collected by businesses, companies and agencies is large. Recent advances in technologies to automate and improve data collection have increased the volumes of data. Lying hidden in all this data is potentially useful information that is rarely made explicit or taken advantage of. In this context, data mining has arisen as an important research area that helps to reveal the hidden useful information from the raw data collected. Many intensive researches have been conducted to enhance the capability of data mining solution in providing the intelligence so that different types of businesses can make informed decisions. This project demonstrates how data mining can address the need of business intelligence in the process of decision-making. An analysis on the field of data mining is done to show how data mining, especially data classification, can help in businesses such as targeted marketing, credit card approval, fraud detection, medical diagnosis, and scientific work. This project is involved with identification of the available algorithms used in data classification and the implementation of C4.5 decision tree induction algorithm in solving the data classifying task. Sample credit card approval dataset is used to demonstrate the functionality of a data mining solution prototype, which includes the typical tasks of a decision tree induction process: data selection, data preprocessing, decision tree induction, tree pruning, rules generation and validation. The result of this application using the sample credit card approval dataset includes a decision tree, a set of rules derived from the decision tree and its accuracy. These outputs help to identify the pattern of applicants who are more likely to be accepted or rejected. The set of rules can be used as part of the knowledge base in expert system or decision support system for financial institutions

Stock Price Prediction using Neural Network with Hybridized Market Indicators

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. The hybridized approach was tested with published stock data and the results obtained showed remarkable improvement over the use of only technical analysis variables. Also, the prediction from hybridized approach was found satisfactorily adequate as a guide for traders and investors in making qualitative decisions

Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms

Machine Learning and Data Mining are two key components in decision making systems which can provide valuable in-sights quickly into huge data set. Turning raw data into meaningful information and converting it into actionable tasks makes organizations profitable and sustain immense competition. In the past decade we saw an increase in Data Mining algorithms and tools for financial market analysis, consumer products, manufacturing, insurance industry, social networks, scientific discoveries and warehousing. With vast amount of data available for analysis, the traditional tools and techniques are outdated for data analysis and decision support. Organizations are investing considerable amount of resources in the area of Data Mining Frameworks in order to emerge as market leaders. Machine Learning is a natural evolution of Data Mining. The existing Machine Learning techniques rely heavily on the underlying Data Mining techniques in which the Patterns Recognition is an essential component. Building an efficient Data Mining Framework is expensive and usually culminates in multi-year project for the organizations. The organization pay a heavy price for any delay or inefficient Data Mining foundation. In this research, we propose to build a cost effective and efficient Data Mining (DM) and Machine Learning (ML) Framework on cloud computing environment to solve the inherent limitations in the existing design methodologies. The elasticity of the cloud architecture solves the hardware constraint on businesses. Our research is focused on refining and enhancing the current Data Mining frameworks to build an enterprise data mining and machine learning framework. Our initial studies and techniques produced very promising results by reducing the existing build time considerably. Our technique of dividing the DM and ML Frameworks into several individual components (5 sub components) which can be reused at several phases of the final enterprise build is efficient and saves operational costs to the organization. Effective Aggregation using selective cuboids and parallel computations using Azure Cloud Services are few of many proposed techniques in our research. Our research produced a nimble, scalable portable architecture for enterprise wide implementation of DM and ML frameworks

Classification of Bank Customers by Data Mining: a Case Study of Mellat Bank branches in Shiraz

This research predicts through studying significant factors in customer relationship management and applying data mining in bank. Financial institutions and other firms in competitive market need to follow proper understanding of customer behavior. Customers’ data are analyzed to identify specific opportunities and investment, to classify and predict the behaviors; further, data are eventually used for decision-making. Therefore, data mining as knowledge exploring (discovery) approach plays a significant role through a variety of algorithms. This study classifies bank customers by using decision tree algorithm. Three decision tree models including ID3, C4.5, and CART were applied for classifying and finally for prediction. Results of simple sampling method and k-fold cross validation show that forecast accuracy of C4.5 decision tree using simple sampling was higher than other models. Thus, predicting customers’ behavior through C4.5 decision tree was considered the ideal prediction for bank

Web Application for Consultant Services

The emergence of internet has changed the system from the circulation of data that has shifted us from a world of paper documents to a world of online documents and databases systems.Consultancy services provide options for multiple different domains to be covered under one place. To be exact multiple services are provided under one company that acts as consultancy. Data mining plays an important role in many decision making application and research domains. Predictions of a things based on data available is one of the important features of data mining. Loan and insurance recommendation system is one of data mining and machine learning application where the system needs to recommend the banks that can provide loan to users and at the same time provide users with insurance providing companies that can provide proper scheme to users. We will use K-NN based approach for providing users with such recommendations. The K-NN algorithm performs analysis on that data. Based on the result of analysis, description of suitable financial services and insurance services will be displayed to the user.Finally it guides the user so that they can register themselves for those insurance policies which they find suitable