An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations

The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm

Fuzzy relations as a prediction tool of business failure causes in the construction sector

Este artículo evalúa las técnicas utilizadas para la detección y predicción de las causas del fracaso empresarial. Se exponen las principales limitaciones de los modelos clásicos de predicción de insolvencia empresarial y se incorpora el análisis fuzzy como alternativa para identificar la relación entre las causas del fracaso y los síntomas visibles en las empresas. En forma complementaria se utiliza el Balanced Scorecard como herramienta de análisis global de la empresa y base para la detección de las causas del fracaso. La aplicación del Balanced Scorecard permite definir un listado de causas originarias de los problemas en las empresas. Estas son valoradas a través de etiquetas lingüísticas para detectar las enfermedades más frecuentes que pueden conducir al fracaso empresarial. Respecto a los modelos tradicionales, la metodología aplicada en este trabajo permite predecir el posible fracaso de una empresa e identificar las causas del mismo.This article evaluates the techniques used for the detection and prediction of business cause failures. The main limitations of the classical prediction models for business insolvency are exposed and a fuzzy analysis as an alternative to identify the relation between failure causes and the businesses´ visible symptoms. As complement, a Balanced Scorecard is used as global analysis tool for the base company in order to detect the cause of failure. The application of the balance scorecard allows defining a list of origination causes for the problems faced by the companies. These are valued using linguistic labels for detecting the most common diseases that can lead to business failure. Concerning traditional models, the applied methodology in the work allows to predict the possible failure of a company and identify the causes.Este artigo avalia as técnicas utilizadas para a detecção e predição das causas do fracasso empresarial. Se expõem as principais limitações dos modelos clássicos de predição de insolvência empresarial e se incorpora a análise fuzzy como alternativa para identificar a relação entre as causas do fracasso e os sintomas visíveis nas empresas. Em forma complementar se utiliza o Balanced Scorecard como ferramenta de análise global da empresa e base para a detecção das causas do fracasso. A aplicação do Balanced Scorecard permite definir um listado de causas originárias dos problemas nas empresas. Estas são valoradas através de etiquetas linguísticas para detectar as doenças mais frequentes que podem conduzir ao fracasso empresarial. Respeito aos modelos tradicionais, a metodologia aplicada neste trabalho permite prever o possível fracasso de uma empresa e identificar as causas do mesmo

Corporate Bankruptcy Prediction

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy

The Financial Performance of the Commercial Banks In Crisis Period: Evidence From Turkey As an Emerging Market

Emerging markets have been heavily affected by the global crisis due to integration with the global economy through trade and capital flows. For this reason, the findings in this paper are of great help and interest to international investors considering that Turkey is one of the major emerging markets in Europe with a linkage with international markets. The objective of this study is to identify the impacts of the financial crisis in the performances of the Turkish commercial banks by their ownership structures (private or public) over the years between 2005 and 2009 by using Grey Relation Analysis (GRA) method and to determine the financial ratios in their financial performances. The paper considers a five-year period encompassing the year of the crisis as well as two years before and after the financial turmoil. The banks, by their capital structures, are ranked based on their performances by use of the GRA method observing 14 financial ratios with respect to profitability, liquidity, active quality and capital sufficiency. Based on the findings in the paper, the performance ranking has been transformed from foreign-public-private banks before the crisis (2005-2006) to private-foreign-public banks during the crisis (2008-2009). Keywords: Financial Crisis, Turkish Banking Sector, Capital Structure, Financial Performance, Financial Ratios, Grey Relation Analysi

Aplicaciones en Economía del Aprendizaje Automático

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 06-05-2022This Thesis examines problems in economics from a Machine Learning perspective. Emphasisis given on the interpretability of Machine Learning algorithms as opposed to blackbox predictions models. Chapter 1 provides an overview of the terminology and Machine Learning methods used throughout this Thesis. This chapter aims to build a roadmap from simple decision tree models to more advanced ensemble boosted algorithms. Other Machine Learning models are also explained. A discussion of the advances in Machine Learning in economics is also provided along with some of the pitfalls that Machine Learning faces. Moreover, an example of how Shapley values from coalition game theory are used to help infer inference from the Machine Learning models' predictions. Chapter 2 analyses the problem of bankruptcy prediction in the Spanish economy and how Machine Learning, not only provides more predictive accuracy, but can also provide adierent interpretation of the results that traditional econometric models cannot. Several financial ratios are constructed and passed to a series of Machine Learning algorithms. Case studies are provided which may aid in better decision-making from financial institutions. A section containing supplementary material based on further analysis is also provided...Este Tesis examina problemas en economía desde la perspectiva de Aprendizaje Mecánico. Se hace hincapié en la interpretabilidad de los algoritmos de Aprendizaje Mecánico en lugar de modelos de predicción de black-box. Capítulo 1 Proporciona el resumen de la terminología y los métodos de Aprendizaje Mecánico utilizados a lo largo de esta tesis. El objetivo de este capítulo es construir la trayectoria desde un simple árbol de decisión hasta algoritmos impulsados por conjuntos más avanzados. También se explican otros modelos de Machine Learning. Asimismo, se proporciona una discusión de los avances en el Aprendizaje Mecánico en economía junto con algunos de los escollos que enfrenta el aprendizaje automático. Además, un ejemplo sobre cómo se utilizan los valores de Shapley de coalición de teoría de juegos y muestran cómo se puede tomar inferencia de los modelos de predicción. Capítulo 2 Analiza el problema de la predicción de quiebra en la economía española y cómo Aprendizaje Mecánico, no sólo proporciona una mayor precisión predictiva, sino que también puede proporcionar una interpretación diferente de los resultados en la que los modelos econométricos tradicionales no pueden. Se construyen una serie de ratios financieros y se pasan a una serie de algoritmos de Aprendizaje Mecánico. Se proporcionan estudios de casos que pueden ayudar a mejorar la toma de decisiones por parte de las instituciones financieras. También se proporciona una sección que contiene material complementario basado en un análisis más detallado...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu

Integrating Big Data Analytics with U.S. SEC Financial Statement Datasets and the Critical Examination of the Altman Z’-Score Model

The main aim of this thesis is to document the process of developing Big Data analytical applications and their integration with financial statement datasets. These datasets are publicly available on the U.S. SEC (Security and Exchange Commission) website which contains the annual and quarterly reports of approximately 8000 companies. Through its Electronic Data Gathering, Analysis and Retrieval (EDGAR) system, the SEC receives several terabytes of data in the mandatory filings from its registrants. This vast amount of data can potentially provide a valuable resource for those parties (such as investors, analysts, regulators and researchers) who are interested in assessing the financial performance and position of companies. Traditionally, the quarterly and annual reports were submitted in standard PDF, HTML and Text files. The data from these files could be manually extracted and analysed, but this process (still used by some analysts and researchers) is costly and time-consuming. In 2009, the SEC mandated all listed companies to use a digital reporting format known as XBRL (eXtensible Business Reporting Language). The intention of this was to improve financial reporting in terms of transparency and efficiency. In order to take advantage of structured data contained in the XBRL format, a variety of methods such as novel extraction algorithms and data mining techniques have been developed. However, several limitations and issues have emerged. These include a lack of automated connectivity between the EDGAR web interface and the terms used in structured taxonomies, and the inability to provide access to multiple files in a single query. Given the challenging and complex nature of these issues, this research project used the financial statement datasets available on the SEC website to extract relevant financial information from the company’s annual reports. The novel aspect of this research is providing big data analytical applications using cloud technologies that can efficiently perform datasets integration and transformation into a format suitable for further analysis. The result of this is that the extracted financial data can be analysed to assess the performance of companies, and this facilitates the critical examination of widely used credit assessment models such as the Altman Z’-Score

Estimating financial failure in businesses using artificial neural networks : Turkish manufacturing industry model study

Businesses need to be financially successful to achieve sustainable growth and maximise firm value. The financial failure of businesses is a situation that is carefully monitored by business managers, shareholders of the business, financial institutions that lend to the business, and the government. For this reason, in this study, the financial failure of 153 manufacturing companies operating in Turkey and traded on Borsa Istanbul has been tried to be estimated. In the research, the annual financial statements between the years 2009-2021 were used and artificial neural networks were preferred as the estimation method. Altman's Z score was used to define financial failure. In the artificial neural network model, 13 financial ratios were used as input variables. As the output variable, the firms that were below the value of 1.81 calculated as the Z score by Altman were considered unsuccessful, and the unsuccessful firms were assigned a value of 1 and the others a value of 0. This dummy variable consisting of 0 and 1 values is accepted as the output variable. According to the findings of the study, 1427 of 1631 observations that were initially considered to be financial failures were correctly estimated and a very high success rate of 87.49% was achieved. The findings will provide an important advantage to businesses and all stakeholders in terms of determining the causes of financial failure in advance.peer-reviewe

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level