scholarly journals PREDICTING FINANCIAL DISTRESS FOR ROMANIAN COMPANIES

2018 ◽  
Vol 24 (6) ◽  
pp. 2318-2337 ◽  
Author(s):  
Gheorghe Ruxanda ◽  
Cătălina Zamfir ◽  
Andreea Muraru
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Stock Exchange ◽  
Support Vector ◽  
Financial Indicators ◽  
New Approach ◽  
Linear Classifier ◽  
Vector Machines ◽  
Components Analysis ◽  
Bayesian Logistic Regression

Using a moderately large number of financial ratios, we tried to build models for classifying the companies listed on the Bucharest Stock Exchange into low and high risk classes of financial distress. We considered four classification techniques: Support Vector Machines, Decision Trees, Bayesian logistic regression and Fisher linear classifier, out of which the first two proved to have the highest prediction accuracy. Classifiers were trained and tested on randomly drown samples and on four different databases built starting from the initial financial indicators. As the literature related to the topic on Romanian data is very scarce, our study, by using a variety of methods and combining feature selection and principal components analysis, brings a new approach to predicting financial distress for Romanian companies.

Predicting corporate financial distress using data mining techniques

2016 ◽  
Vol 58 (2) ◽  
pp. 216-230 ◽  
Author(s):  
Mahdi Salehi ◽  
Mahmoud Mousavi Shiri ◽  
Mohammad Bolandraftar Pasikhani
Keyword(s):  
Financial Distress ◽  
Nearest Neighbor ◽  
Stock Exchange ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Content Type ◽  
Vector Machines ◽  
Research Population ◽  
Accumulated Losses ◽  
Using Data

Purpose – Financial distress is the most notable distress for companies. During the past four decades, predicting corporate bankruptcy and financial distress has become a significant concern for the various stakeholders in firms. This paper aims to predict financial distress of Iranian firms, with four techniques: support vector machines, artificial neural networks (ANN), k-nearest neighbor and na i ve bayesian classifier by using accounting information of the firms for two years prior to financial distress. Design/methodology/approach – The distressed companies in this study are chosen based on Article 141 of Iranian Commercial Codes, i.e. accumulated losses exceeds half of equity, based on which 117 companies qualified for the current study. The research population includes all the companies listed on Tehran Stock Exchange during the financial period from 2011-2012 to 2013-2014, that is, three consecutive periods. Findings – By making a comparison between performances of models, it is concluded that ANN outperforms other techniques. Originality/value – The current study is almost the first study in Iran which used such methods to analyzing the data. So, the results may be helpful in the Iranian condition as well for other developing nations.

Persian Handwritten Number Recognition Using Adapted Framing Feature and Support Vector Machines

2016 ◽  
Vol 15 (01) ◽  
pp. 1650004 ◽  
Author(s):  
Hedieh Sajedi ◽  
Mehran Bahador
Keyword(s):  
Support Vector Machines ◽  
Recognition Rate ◽  
Nearest Neighbors ◽  
Polynomial Kernel ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
New Approach ◽  
Number Recognition ◽  
Vector Machines

In this paper, a new approach for segmentation and recognition of Persian handwritten numbers is presented. This method utilizes the framing feature technique in combination with outer profile feature that we named this the adapted framing feature. In our proposed approach, segmentation of the numbers into digits has been carried out automatically. In the classification stage of the proposed method, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) are used. Experimentations are conducted on the IFHCDB database consisting 17,740 numeral images and HODA database consisting 102,352 numeral images. In isolated digit level on IFHCDB, the recognition rate of 99.27%, is achieved by using SVM with polynomial kernel. Furthermore, in isolated digit level on HODA, the recognition rate of 99.07% is achieved by using SVM with polynomial kernel. The experiments illustrate that applying our proposed method resulted higher accuracy compared to previous researches.

scholarly journals Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018 ◽  
Vol 11 (1) ◽  
pp. 64 ◽  
Author(s):  
Kyoung-jae Kim ◽  
Kichun Lee ◽  
Hyunchul Ahn
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Support Vector ◽  
Model Parameters ◽  
Financial Sustainability ◽  
Business Analytics ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

scholarly journals A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6319
Author(s):  
Esam Mahdi ◽  
Víctor Leiva ◽  
Saed Mara’Beh ◽  
Carlos Martin-Barreiro
Keyword(s):  
Investment Decisions ◽  
Trading Strategies ◽  
Support Vector ◽  
Uncertain Environments ◽  
New Approach ◽  
Adverse Conditions ◽  
Related Data ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Real World Situation

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.

scholarly journals Predicting the Istanbul Stock Exchange Index Return using Technical Indicators

2013 ◽  
Vol 2 (3) ◽  
pp. 111-117
Author(s):  
Senol Emir
Keyword(s):  
Stock Exchange ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Model Parameters ◽  
Technical Indicators ◽  
Istanbul Stock Exchange ◽  
Vector Machines ◽  
Classical Linear Regression ◽  
Daily Returns

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

scholarly journals Firm's default — new methodological approach and preliminary evidence from Poland

Equilibrium ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. 753-773
Author(s):  
Tomasz Berent ◽  
Bogusław Bławat ◽  
Marek Dietl ◽  
Przemysław Krzyk ◽  
Radosław Rejman
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Predictive Power ◽  
Methodological Approach ◽  
Value Added ◽  
Research Literature ◽  
Support Vector ◽  
Accuracy Measurement ◽  
Accuracy Ratio ◽  
Sampling Procedures

Research background: Bankruptcy literature is populated with scores of (econometric) models ranging from Altman’s Z-score, Ohlson’s O-score, Zmijewski’s probit model to k-nearest neighbors, classification trees, support vector machines, mathematical programming, evolutionary algorithms or neural networks, all designed to predict financial distress with highest precision. We believe corporate default is also an important research topic to be identified with the prediction accuracy only. Despite the wealth of modelling effort, a unified theory of default is yet to be proposed. Purpose of the article: Due to the disagreement both on the definition and hence the timing of default, as well as on the measurement of prediction accuracy, the comparison (of predictive power) of various models can be seriously misleading. The purpose of the article is to argue for the shift in research focus from maximizing accuracy to the analysis of the information capacity of predictors. By doing this, we may yet come closer to understanding default itself. Methods: We critically appraise the bankruptcy research literature for its methodological variety and empirical findings. Default definitions, sampling procedures, in and out-of-sample testing and accuracy measurement are all scrutinized. In an empirical part, we use a double stochastic Poisson process with multi-period prediction horizon and a comprehensive database of some 15,000 Polish non-listed companies to illustrate the merits of our new approach to default modelling. Findings & Value added: In the theoretical part, we call for the construction of a single unified default forecasting platform estimated for the largest dataset of firms possible to allow testing the utility of various sources of micro, mezzo, and macro information. Our preliminary empirical evidence is encouraging. The accuracy ratio amounts to 0.92 for t = 0 and drops to 0.81 two years ahead of default. We point to the pivotal role played by the information on firm’s liquidity (alternatively in profitability) and — in contrast to Altman’s tradition — hardly any contribution to predictive power of other financial ratios. Macro data is shown to be critical. It adds, on average, more than 10 p.p. to accuracy ratio.  In the future, we hope to integrate listed and non-listed firms data into one model, ideally at higher frequency than annual, and include the information on firm's competitiveness position.

scholarly journals Classification of the Priority of Auditing XBRL Instance Documents with Fuzzy Support Vector Machines Algorithm

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Guang-Yih Sheu
Keyword(s):  
Support Vector Machines ◽  
Financial Distress ◽  
Fuzzy Variable ◽  
Financial Ratios ◽  
Misclassification Rate ◽  
Benford’S Law ◽  
Support Vector ◽  
Vector Machines ◽  
Benford's Law ◽  
Fuzzy Support Vector Machines

Concluding the conformity of XBRL (eXtensible Business Reporting Language) instance documents law to the Benford's law yields apparently different results before and after a company's financial distress. These results bring an idea of finding fraudulent documents from the inspection of financial ratios since the unacceptable conformity implies a large likelihood of a fraudulent document. Fuzzy support vector machines models are developed to implement such an idea. The dependent variable is a fuzzy variable quantifying the conformity of an XBRL instance document to the Benford's law; whereas, independent variables are financial ratios. Nevertheless, insufficient data are available to define any membership function for describing the fuzziness in independent and dependent variables, but the interval factor method is introduced to express that fuzziness. Using the resulting fuzzy support vector machines model, it is suggested that the price-to-book ratio versus equity ratio may be used to classify the priority of auditing XBRL instance documents. The misclassification rate is less than 30 \%. In conclusion, a new and promising application of fuzzy support vector machines algorithm has been found in this study.

scholarly journals Prediction of Radiation Frost Using Support Vector Machines Based on Micrometeorological Data

2019 ◽  
Vol 10 (1) ◽  
pp. 283
Author(s):  
Yongzong Lu ◽  
Yongguang Hu ◽  
Pingping Li ◽  
Kyaw Tha Paw U ◽  
Richard L. Snyder
Keyword(s):  
Yangtze River ◽  
Prediction Accuracy ◽  
Short Wave ◽  
Yangtze River Delta ◽  
Wave Radiation ◽  
Support Vector ◽  
Short Wave Radiation ◽  
Long Wave ◽  
Vector Machines ◽  
Long Wave Radiation

Radiation frost happens frequently in the Yangtze River Delta region, which causes high economic loss in agriculture industry. It occurs because of heat losses from the atmosphere, plant and soil in the form of radiant energy, which is strongly associated with the micrometeorological characteristics. Multidimensional and nonlinear micrometeorological data enhances the difficulty in predicting the radiation frost. Support vector machines (SVMs), a type of algorithms, can be supervised learning which widely be employed for classification or regression problems in research of precision agriculture. This paper is the first attempt of using SVMs to build prediction models for radiation frost. Thirty-two kinds of micrometeorological parameters, such as daily mean temperature at six heights (Tmean0.5, Tmean1.5, Tmean2.0, Tmean3.0, Tmean4.5 and Tmean6.0), daily maximum and minimum temperatures at six heights (Tmax0.5, Tmax1.5, Tmax2.0, Tmax3.0, Tmax4.5 and Tmax6.0, and Tmin0.5, Tmin1.5, Tmin2.0, Tmin3.0, Tmin4.5 and Tmin6.0), daily mean relative humidity at six heights (RH0.5, RH1.5, RH2.0, RH3.0, RH4.5 and RH6.0), net radiation (Rn), downward short-wave radiation (Rsd), downward long-wave radiation (Rld), upward long-wave radiation (Rlu), upward short-wave radiation (Rsu), soil temperature (Tsoil) and soil heat flux (G) and daily average wind speed (u) were collected from November 2016 to July 2018. Six combinations inputs were used as the basis dataset for testing and training. Three types of kernel functions, such as linear kernel, radial basis function kernel and polynomial kernel function were used to develop the SVMs models. Five-fold cross validation was conducted for model fitting on training dataset to alleviate over-fitting and make prediction results more reliable. The results showed that an SVM with the radial basis function kernel (SVM-BRF) model with all the 32 micrometeorological data obtained high prediction accuracy in training and testing sets. When the single type of data (temperature, humidity and radiation data) was used for the SVM without any functions, prediction accuracy was better than that with functions. The SVM-BRF model had the best prediction accuracy when using the multidimensional and nonlinear micrometeorological data. Considering the complexity level of the model and the accuracy of prediction, micrometeorological data at the canopy height with the SVM-BRF model has been recommended for radiation frost prediction in Yangtze River Delta and probably could be applied in elsewhere with the similar terrains and micro-climates.

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