ПРОГНОЗ ФІНАНСОВИХ ПРОБЛЕМ, ВИКОРИСТОВУЮЧИ МЕТАЕВРИСТИЧНІ МОДЕЛІ

Investors need to assess and analyze the financial statement, to make the logical decision. Using financial ratios is one of the most common methods. The main purpose of this research is to predict the financial crisis, using ratios of liquidity. Four models, Support vector machine, neural network back propagation, Decision trees and Adaptive Neuro–Fuzzy Inference System has been compared.Furthermore, the ratios of liquidity considered in a period of 89_93. The research method is qualitative and quantitative and type of casual comparative. The result indicates that the accuracy of the neural network, Decision tree, and Adaptive Neuro–Fuzzy Inference System showed that there is a significant differently 0/000 and 0/005 years this is more than support vector machine result. Therefore the result of support vector machine showed that there is a significant differently 0/001 in years. This has been shown that neural network in 2 years before the bankruptcy has the ability to predict a right thing. Therefore, the results have been shown that all four models were statistically significant. Consequently, there are no significant differences. All models have the precision to predict the financial crisis.Инвесторам необходимо оценить и проанализировать финансовую отчетность, принять логическое решение. Использование финансовых показателей является одним из самых распространенных методов. Основная цель этого исследования – прогнозировать финансовый кризис, используя соотношение ликвидности. Четыре модели: векторные машины поддержки, обратное распространение нейронных сетей, дерево решений и адаптивная нейро–нечеткая система вывода. Кроме того, коэффициенты ликвидности рассмотрены в период 2011–2015 гг. Метод исследования является качественным и количественным, а также тип случайной сравнительной. Результат показывает точность нейронной сети, дерево решений, и система Adaptive Neuro–Fuzzy Inference показала, что значительно отличается от 0/000 и 0/005 лет, это больше, чем поддержка векторной машины. Поэтому результат поддержки векторной машины показал, что существует значительно по–разному 0/001 лет. Это показало, что нейронная сеть за 2 года до банкротства имеет возможность прогнозировать правильно. Поэтому результаты показали, что все четыре модели были статистически значимыми. Итак, существенных различий нет. Все модели имеют точность прогнозирования финансового кризиса.Інвесторам необхідно оцінити та проаналізувати фінансову звітність, прийняти логічне рішення. Використання фінансових показників є одним з найпоширеніших методів. Основна мета цього дослідження – прогнозувати фінансову кризу, використовуючи співвідношення ліквідності. Чотири моделі: векторні машини підтримки, зворотне розповсюдження нейронних мереж, дерево рішень та адаптивна  система нейро–нечіткого висновку. Крім того, коефіцієнти ліквідності розглянуті в період 2011–2015 рр. Метод дослідження є якісним та кількісним, а також тип випадкової порівняльної. Результат показує точність нейронної мережі, дерево рішень, і система Adaptive Neuro–Fuzzy Inference показала, що значно відрізняється від 0/000 і 0/005 років, це більше, ніж підтримка векторної машини. Тому результат підтримки векторної машини показав, що існує значно по–різному 0/001 років. Це показало, що нейронна мережа за 2 роки до банкрутства має можливість прогнозувати правильну річ. Тому результати показали, що всі чотири моделі були статистично значущими. Отже, істотних відмінностей немає. Всі моделі мають точність прогнозування фінансової кризи

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

The prediction of stock prices has become an exciting area for researchers as well as academicians due to its economic impact and potential business profits. This study proposes a novel multiclass classification ensemble learning approach for predicting stock prices based on historical data using feature engineering. The proposed approach comprises four main steps, which are pre-processing, feature selection, feature engineering, and ensemble methods. We use 11 datasets from Nasdaq and S&P 500 to ensure the accuracy of the proposed approach. Furthermore, eight feature selection algorithms are studied and implemented. More importantly, a feature engineering concept is applied to construct two new features, which are appears to be very auspicious in terms of improving classification accuracy, and this is considered the first study to use feature engineering for multiclass classification using ensemble methods. Finally, seven ensemble machine learning (ML) algorithms are used and compared to discover the ultimate collaboration prediction model. Besides, the best feature selection algorithm is proposed. This study proposes a novel multiclass classification approach called Gradient Boosting Machine with Feature Engineering (GBM-wFE) and Principal Component Analysis (PCA) as the feature selection. We find that GBM-wFE outperforms the previous studies and the overall prediction results are auspicious, as MAPE of 0.0406% is achieved, which is considered the best result compared to the available studies in the literature

Handling Concept Drifts in Regression Problems -- the Error Intersection Approach

Machine learning models are omnipresent for predictions on big data. One challenge of deployed models is the change of the data over time, a phenomenon called concept drift. If not handled correctly, a concept drift can lead to significant mispredictions. We explore a novel approach for concept drift handling, which depicts a strategy to switch between the application of simple and complex machine learning models for regression tasks. We assume that the approach plays out the individual strengths of each model, switching to the simpler model if a drift occurs and switching back to the complex model for typical situations. We instantiate the approach on a real-world data set of taxi demand in New York City, which is prone to multiple drifts, e.g. the weather phenomena of blizzards, resulting in a sudden decrease of taxi demand. We are able to show that our suggested approach outperforms all regarded baselines significantly

A novel bearing multi-fault diagnosis approach based on weighted permutation entropy and an improved SVM ensemble classifier

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available

Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

YesAlthough many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid crossbenchmarking- cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress

Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic

Malware detection : a framework for reverse engineered android applications through machine learning algorithms

Today, Android is one of the most used operating systems in smartphone technology. This is the main reason, Android has become the favorite target for hackers and attackers. Malicious codes are being embedded in Android applications in such a sophisticated manner that detecting and identifying an application as a malware has become the toughest job for security providers. In terms of ingenuity and cognition, Android malware has progressed to the point where they’re more impervious to conventional detection techniques. Approaches based on machine learning have emerged as a much more effective way to tackle the intricacy and originality of developing Android threats. They function by first identifying current patterns of malware activity and then using this information to distinguish between identified threats and unidentified threats with unknown behavior. This research paper uses Reverse Engineered Android applications’ features and Machine Learning algorithms to find vulnerabilities present in Smartphone applications. Our contribution is twofold. Firstly, we propose a model that incorporates more innovative static feature sets with the largest current datasets of malware samples than conventional methods. Secondly, we have used ensemble learning with machine learning algorithms such as AdaBoost, SVM, etc. to improve our model’s performance. Our experimental results and findings exhibit 96.24% accuracy to detect extracted malware from Android applications, with a 0.3 False Positive Rate (FPR). The proposed model incorporates ignored detrimental features such as permissions, intents, API calls, and so on, trained by feeding a solitary arbitrary feature, extracted by reverse engineering as an input to the machine

Simulation Applications in Company Default Prediction

This study applies a simulation methodology, Monte Carlo, to the field of corporate default prediction, where its presence is only superficial. It attempts to augment a famous model from a methodology already highly supported in traditional literature – the Z”-score of Altman (1983), created through multiple discriminant analysis – and transform it stochastically without the use of the highly complex intelligent models already available in literature. A sample of 20 000 Portuguese companies from the Agriculture, Forestry, Fishing, Mining and Construction sectors is analyzed, yielding results that support the Monte Carlo method as a strong competitor for simple approaches like the logit transformation. This helps to build the foundation for what may possibly be a path towards models easier to apply in practice for the average Micro, Small and Medium enterprise (MSME) and beyond. The evaluation to the model on this study also takes inspiration off the innovative points of view of Mitton (2021) and Zhang (2022) to scrutinize results through empirical tests of the model under a high number of parameter conditions, instead of relying heavily on statistical significance, which is often overrepresented and overvalued in literature, and easily manipulatable

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