МОДЕЛІ ПРОГНОЗУВАННЯ В МЕХАНІЗМІ РАННЬОГО ІНФОРМУВАННЯ І ПОПЕРЕДЖЕННЯ ФІНАНСОВИХ КРИЗ У КОРПОРАТИВНИХ СИСТЕМАХ

The paper is devoted to the problem of preventing financial crises in corporate systems, whose activities are becoming more and more complex in the context of globalization. The mechanism of early informing and crisis prevention in corporate systems is proposed, and includes five main modules: an analysis of the financial condition of the corporation, an analysis of the financial condition of subsidiaries, an evaluation of the impact of the financial crisis on a subsidiary on the threat of bankruptcy of the corporation as a whole, forecasting the financial condition of subsidiaries and corporation as a whole, anti-crisis management. The first four modules of the mechanism are the modules of implementation of proactive crisis management in the corporation, aimed at preventing the emergence of a crisis state, both in individual elements and in the corporate system as a whole. The fifth module is used in conditions of the current negative estimation of the financial condition of the corporation, and it is a "response" to existing crisis processes and phenomena in the corporation. After its implementation during the process of monitoring of the financial condition, proactive control modules are started to be used to allow early diagnosis and to prevent a crisis state. Particular attention is paid to such modules of proactive management as the evaluation of the impact of financial crises of subsidiaries on bankruptcy of the corporations as a whole and forecasting financial crises. A model basis for these two modules was developed. Neural networks, the mathematical apparatus of fuzzy logic, and the Caterpillar method were used for developing the models of estimation of the crisis threat in the corporate system. The developed set of models allowed to estimate the threat of financial crises in the parent enterprise and in the subsidiaries of the corporation, not only in the current but also in the perspective periods. The obtained results indicate that the financial condition of the investigated corporation is characterized by low level of the bankruptcy threat. Along with this, there is an increase in the threat of bankruptcy in a number of subsidiaries in the perspective period and the strong impact of local crises on the financial position of the corporation as a whole. The latter leads to the need of implementation of the anti-crisis measures in the corporate structure. An adequate tool for choosing anti-crisis measures and developing scenarios for the implementation of the anti-crisis management strategy is simulation modelling based on the concept of system dynamics.Рассматривается проблема предупреждения финансовых кризисов в корпоративных системах, деятельность которых становится все более сложной в контексте глобализации. Особое внимание уделяется оценке влияния финансовых кризисов дочерних компаний на банкротство корпораций в целом. Для оценки угрозы кризисов в корпоративной системе используются нейронные сети, математический аппарат нечеткой логики, метод «Caterpillar».Розглядається проблема запобігання фінансовим кризам у корпоративних системах, діяльність яких стає дедалі складнішою в контексті глобалізації. Запропоновано механізм раннього інформування і попередження криз у корпоративних системах, який включає п’ять основних модулів: аналіз фінансового стану корпорації, аналіз фінансового стану дочірніх підприємств, оцінка впливу фінансової кризи на дочірньому підприємстві на загрозу банкрутства корпорації в цілому, прогнозування фінансового стану дочірніх підприємств і корпорації в цілому, антикризове управління. Перші чотири модулі механізму є модулями реалізації проактивного антикризового управління в корпорації, спрямованого на недопущення появи кризового стану як в окремих елементах, так і корпоративної системі в цілому. П’ятий модуль використовується при поточній негативній оцінці стану корпорації і є «реакцією» на вже наявні кризові процеси і явища в корпорації. Після його реалізації у процесі моніторингу стану застосовуються модулі проактивного управління, що дозволяють здійснювати ранню діагностику і попереджати кризовий стан. Особливу увагу приділено таким модулям проактивного управління, як оцінка впливу фінансових криз дочірніх компаній на банкрутство корпорацій у цілому, прогнозування фінансових криз. Розроблено модельний базис цих двох модулів. Для побудови моделей оцінки загрози кризи корпоративної системи використовуються нейронні мережі, математичний апарат нечіткої логіки, метод «Caterpillar». Розроблений комплекс моделей дозволив оцінити загрозу формування фінансових криз на головному і дочірніх підприємствах корпорації не тільки в поточному періоді, а й у перспективному. Отримані результати свідчать, що фінансовий стан досліджуваної корпорації характеризується низьким рівнем загрози банкрутства. Поряд із цим спостерігається посилення загрози банкрутства на низці дочірніх підприємств у перспективному періоді і сильний вплив локальних криз на фінансовий стан корпорації в цілому. Останнє призводить до необхідності здійснення антикризових заходів у корпоративній структурі. Адекватним інструментом вибору антикризових заходів і формування сценаріїв реалізації стратегії антикризового управління є імітаційне моделювання, засноване на концепції системної динаміки

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China

© 2018 Elsevier B.V. With atmospheric environmental pollution becoming increasingly serious, developing an early warning system for air quality forecasting is vital to monitoring and controlling air quality. However, considering the large fluctuations in the concentration of pollutants, most previous studies have focused on enhancing accuracy, while few have addressed the stability and uncertainty analysis, which may lead to insufficient results. Therefore, a novel early warning system based on fuzzy time series was successfully developed that includes three modules: deterministic prediction module, uncertainty analysis module, and assessment module. In this system, a hybrid model combining the fuzzy time series forecasting technique and data reprocessing approaches was constructed to forecast the major air pollutants. Moreover, an uncertainty analysis was generated to further analyze and explore the uncertainties involved in future air quality forecasting. Finally, an assessment module proved the effectiveness of the developed model. The experimental results reveal that the proposed model outperforms the comparison models and baselines, and both the accuracy and the stability of the developed system are remarkable. Therefore, fuzzy logic is a better option in air quality forecasting and the developed system will be a useful tool for analyzing and monitoring air pollution

A Hybrid Approach of Traffic Flow Prediction Using Wavelet Transform and Fuzzy Logic

The rapid development of urban areas and the increasing size of vehicle fleets are causing severe traffic congestions. According to traffic index data (Tom Tom Traffic Index 2016), most of the larger cities in Canada placed between 30th and 100th most traffic congested cities in the world. A recent research study by CAA (Canadian Automotive Association) concludes traffic congestions cost drivers 11.5 million hours and 22 million liters of fuel each year that causes billions of dollars in lost revenues. Although for four decades’ active research has been going on to improve transportation management, statistical data shows the demand for new methods to predict traffic flow with improved accuracy. This research presents a hybrid approach that applies a wavelet transform on a time-frequency (traffic count/hour) signal to determine sharp variation points of traffic flow. Datasets in between sharp variation points reveal segments of data with similar trends. These sets of data, construct fuzzy membership sets by categorizing the processed data together with other recorded information such as time, season, and weather. When real-time data is compared with the historical data using fuzzy IF-THEN rules, a matched dataset represents a reliable source of information for traffic prediction. In addition to the proposed new method, this research work also includes experiment results to demonstrate the improvement of accuracy for long-term traffic flow prediction

A disaster response model driven by spatial-temporal forecasts

In this research, we propose a disaster response model combining preparedness and responsiveness strategies. The selective response depends on the level of accuracy that our forecasting models can achieve. In order to decide the right geographical space and time window of response, forecasts are prepared and assessed through a spatial–temporal aggregation framework, until we find the optimum level of aggregation. The research considers major earthquake data for the period 1985–2014. Building on the produced forecasts, we develop accordingly a disaster response model. The model is dynamic in nature, as it is updated every time a new event is added in the database. Any forecasting model can be optimized though the proposed spatial–temporal forecasting framework, and as such our results can be easily generalized. This is true for other forecasting methods and in other disaster response contexts

Baltic Dry Index Estimation With NARX Neural Network Model

BDI is a global trade indicator followed by those interested in maritime trade. But it has volatility, seasonality, and uncertain cyclicality. For this reason, in this study, the BDI has been estimated to provide preliminary information to those interested in maritime trade. NARX Neural Network which performs successfully in complex and nonlinear real-life problems is used. In addition, the NARX neural network model has not been found in a previous study used for BDI estimation. Eleven independent variables are used in this study, what increases the predictive power. Independent variables are Bloomberg Commodities Index (BCOM), Twitter-Based Economic Uncertainty Index (TEU), Twitter-Based Market Uncertainty Index (TMU), S&P 500 Index, MSCI World Index, €/$ Parity, VIX (CBOE), US 10-Year Bond Yield (%), Brent Oil (USD/Barrel), Economic Uncertainty Index and World Trade Volume (USD Billion). The Twitter-Based Economic Uncertainty Index (TEU) and Twitter-Based Market Uncertainty Index (TMU), which were not used before in BDI estimation studies, were included in the analysis and contributed to the literature. The data set contains daily data for the period 9.07.2012–31.08.2020. 11-day estimate values covering 1.09.2020–15.09.2020 are calculated. MAPE, MAE and RMSE performance criteria were calculated for the estimation values. Value of MAPE (2.96%), value of MAE (36.6%) and value of RMSE (46.68) were obtained. As a result, the estimate values were compared with the actual values