29,858 research outputs found
Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making
This paper is aimed at examining the role of annual reports’ sentiment in forecasting financial performance. The sentiment (tone, opinion) is assessed using several categorization schemes in order to explore various aspects of language used in the annual reports of U.S. companies. Further, we employ machine learning methods and neural networks to predict financial performance expressed in terms of the Z-score bankruptcy model. Eleven categories of sentiment (ranging from negative and positive to active and common) are used as the inputs of the prediction models. Support vector machines provide the highest forecasting accuracy. This evidence suggests that there exist non-linear relationships between the sentiment and financial performance. The results indicate that the sentiment information is an important forecasting determinant of financial performance and, thus, can be used to support decision-making process of corporate stakeholders
A Preliminary Investigation Of Decision Tree Models For Classification Accuracy Rates And Extracting Interpretable Rules In The Credit Scoring Task: A Case Of The German Data Set
For many years lenders have been using traditional statistical techniques such as logistic regression and discriminant analysis to more precisely distinguish between creditworthy customers who are granted loans and non-creditworthy customers who are denied loans. More recently new machine learning techniques such as neural networks, decision trees, and support vector machines have been successfully employed to classify loan applicants into those who are likely to pay a loan off or default upon a loan. Accurate classification is beneficial to lenders in terms of increased financial profits or reduced losses and to loan applicants who can avoid overcommitment. This paper examines a historical data set from consumer loans issued by a German bank to individuals whom the bank considered to be qualified customers. The data set consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off or defaulted upon. The paper examines and compares the classification accuracy rates of three decision tree techniques as well as analyzes their ability to generate easy to understand rules
Short-term water demand forecasting using machine learning techniques
Nowadays, a large number of water utilities still manage their operation on the instant water demand of
the network, meaning that the use of the equipment is conditioned by the immediate water necessity.
The water reservoirs of the networks are filled using pumps that start working when the water level
reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water
management based on future demand allows use of the equipment when energy is cheaper, taking
advantage of the electricity tariff in action, thus bringing significant financial savings over time. Shortterm water demand forecasting is a crucial step to support decision making regarding the equipment
operation management. For this purpose, forecasting methodologies are analyzed and implemented.
Several machine learning methods, such as neural networks, random forests, support vector machines
and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover,
the influence of factors such as weather, seasonality, amount of data used in training and forecast
window is also analysed. A weighted parallel strategy that gathers the advantages of the different
machine learning techniques is suggested. The results are validated and compared with those achieved
by autoregressive integrated moving average (ARIMA) also using benchmarks.publishe
Economic regimes identification using machine learning technics
43 páginas.Trabajo de Máster en EconomĂa, Finanzas y ComputaciĂłn. Director: Dr. JosĂ© Manuel Bravo Caro. Economic conditions over long time periods can be distinguished by regimes. Regime identification has been object of numerous investigations in economics and financial modeling for years. Recently, new machine learning technics such as decision trees, support vector machines and neural networks, among others, followed by alternative datasets and cheap computational processing power became available, allowing for alternative ways to model complex economic relationships. In the present work, we develop a supervised machine learning classifier using Random Forest technic to identify economic regimes using the S&P 500 stock market index series.Las condiciones econĂłmicas durante largos perĂodos de tiempo pueden distinguirse por regĂmenes. La identificaciĂłn del rĂ©gimen ha sido objeto de numerosas investigaciones en economĂa y modelos financieros durante años. Recientemente, se pusieron a disposiciĂłn nuevas tĂ©cnicas de aprendizaje automático, como árboles de decisiĂłn, máquinas de suporte vectorial y redes neuronales, entre otras, seguidas de conjuntos de datos alternativos y una capacidad de procesamiento computacional barata, que permite formas alternativas de modelar relaciones econĂłmicas complejas. En el presente trabajo, desarrollamos un clasificador de aprendizaje automático supervisado utilizando la tĂ©cnica de Random Forest para identificar regĂmenes econĂłmicos utilizando la serie del Ăndices de mercado S&P 500
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
Intelligent Financial Fraud Detection Practices: An Investigation
Financial fraud is an issue with far reaching consequences in the finance
industry, government, corporate sectors, and for ordinary consumers. Increasing
dependence on new technologies such as cloud and mobile computing in recent
years has compounded the problem. Traditional methods of detection involve
extensive use of auditing, where a trained individual manually observes reports
or transactions in an attempt to discover fraudulent behaviour. This method is
not only time consuming, expensive and inaccurate, but in the age of big data
it is also impractical. Not surprisingly, financial institutions have turned to
automated processes using statistical and computational methods. This paper
presents a comprehensive investigation on financial fraud detection practices
using such data mining methods, with a particular focus on computational
intelligence-based techniques. Classification of the practices based on key
aspects such as detection algorithm used, fraud type investigated, and success
rate have been covered. Issues and challenges associated with the current
practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and
Privacy in Communication Networks (SecureComm 2014
Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress
Recently, applying the novel data mining techniques for evaluating enterprise
financial distress has received much research alternation. Support Vector
Machine (SVM) and back propagation neural (BPN) network has been applied
successfully in many areas with excellent generalization results, such as rule
extraction, classification and evaluation. In this paper, a model based on SVM
with Gaussian RBF kernel is proposed here for enterprise financial distress
evaluation. BPN network is considered one of the simplest and are most general
methods used for supervised training of multilayered neural network. The
comparative results show that through the difference between the performance
measures is marginal; SVM gives higher precision and lower error rates.Comment: 13 pages, 1 figur
Application of support vector machines on the basis of the first Hungarian bankruptcy model
In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks
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