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Open Access
Article
Publication date: 10 December 2019

Jessica Paule-Vianez, Milagros Gutiérrez-Fernández and José Luis Coca-Pérez

The purpose of this study is to construct the first short-term financial distress prediction model for the Spanish banking sector.

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Abstract

Purpose

The purpose of this study is to construct the first short-term financial distress prediction model for the Spanish banking sector.

Design/methodology/approach

The concept of financial distress covers a range of different types of financial problems, in addition to bankruptcy, which is not common in the sector. The methodology used to predict financial problems was artificial neural networks using traditional financial variables according to the capital, assets, management, earnings, liquidity and sensibility system, as well as a series of macroeconomic variables, the impact of which has been proven in a number of studies.

Findings

The results obtained show that artificial neural networks are a highly suitable method for studying financial distress in Spanish credit institutions and for predicting all cases in which an entity has short-term financial problems.

Originality/value

This is the first work that tries to build a model of artificial neural networks to predict the financial distress in the Spanish banking system, grouping under the concept of financial distress, apart from bankruptcy, other financial problems that affect the viability of these entities.

Details

Applied Economic Analysis, vol. 28 no. 82
Type: Research Article
ISSN: 2632-7627

Keywords

Open Access
Article
Publication date: 6 May 2022

Mohammed Ayoub Ledhem

The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric…

1332

Abstract

Purpose

The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.

Design/methodology/approach

This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).

Findings

The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.

Practical implications

This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.

Originality/value

This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

Details

Journal of Capital Markets Studies, vol. 6 no. 2
Type: Research Article
ISSN: 2514-4774

Keywords

Open Access
Article
Publication date: 1 July 2021

Xiaochun Guan, Sheng Lou, Han Li and Tinglong Tang

Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper…

2539

Abstract

Purpose

Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope.

Design/methodology/approach

In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system.

Findings

This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft.

Originality/value

This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.

Details

Industrial Robot: the international journal of robotics research and application, vol. 48 no. 5
Type: Research Article
ISSN: 0143-991X

Keywords

Open Access
Article
Publication date: 4 April 2023

Xiaojie Xu and Yun Zhang

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present…

Abstract

Purpose

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

Design/methodology/approach

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

Findings

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Originality/value

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

Details

EconomiA, vol. 24 no. 1
Type: Research Article
ISSN: 1517-7580

Keywords

Open Access
Article
Publication date: 3 August 2020

Zhao-Peng Li, Li Yang, Si-Rui Li and Xiaoling Yuan

China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various…

1274

Abstract

Purpose

China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market.

Design/methodology/approach

To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends.

Findings

Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios.

Originality/value

On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

Details

International Journal of Climate Change Strategies and Management, vol. 12 no. 5
Type: Research Article
ISSN: 1756-8692

Keywords

Open Access
Article
Publication date: 12 April 2019

Darlington A. Akogo and Xavier-Lewis Palmer

Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine…

1074

Abstract

Purpose

Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.

Design/methodology/approach

The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and tested their 6-layer CNN on 1,241 images of MDA-MB-468 and MCF7 breast cancer cell line in an end-to-end fashion, allowing the system to distinguish between the two different cancer cell types.

Findings

They obtained a 99% accuracy, providing a foundation for more comprehensive systems.

Originality/value

Value can be found in that systems based on this design can be used to assist cell identification in a variety of contexts, whereas a practical implication can be found that these systems can be deployed to assist biomedical workflows quickly and at low cost. In conclusion, this system demonstrates the potentials of end-to-end learning systems for faster and more accurate automated cell analysis.

Details

Journal of Industry-University Collaboration, vol. 1 no. 1
Type: Research Article
ISSN: 2631-357X

Keywords

Open Access
Article
Publication date: 21 April 2022

Myeongjin Kim and Joo Hyun Moon

This study aims to introduce a deep neural network (DNN) to estimate the effective thermal conductivity of the flat heat pipe with spreading thermal resistance.

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Abstract

Purpose

This study aims to introduce a deep neural network (DNN) to estimate the effective thermal conductivity of the flat heat pipe with spreading thermal resistance.

Design/methodology/approach

A total of 2,160 computational fluid dynamics simulation cases over up to 2,000 W/mK are conducted to regress big data and predict a wider range of effective thermal conductivity up to 10,000 W/mK. The deep neural networking is trained with reinforcement learning from 10–12 steps minimizing errors in each step. Another 8,640 CFD cases are used to validate.

Findings

Experimental, simulational and theoretical approaches are used to validate the DNN estimation for the same independent variables. The results from the two approaches show a good agreement with each other. In addition, the DNN method required less time when compared to the CFD.

Originality/value

The DNN method opens a new way to secure data while predicting in a wide range without experiments or simulations. If these technologies can be applied to thermal and materials engineering, they will be the key to solve thermal obstacles that many longing to overcome.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 33 no. 2
Type: Research Article
ISSN: 0961-5539

Keywords

Open Access
Article
Publication date: 31 May 2023

Xiaojie Xu and Yun Zhang

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction…

Abstract

Purpose

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

Design/methodology/approach

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

Findings

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

Originality/value

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

Details

Asian Journal of Economics and Banking, vol. 8 no. 1
Type: Research Article
ISSN: 2615-9821

Keywords

Open Access
Article
Publication date: 17 December 2021

Alexandre Cappellozza, Gustavo Hermínio Salati Marcondes de Moraes, Gilberto Perez and Alessandra Lourenço Simões

This paper aims to investigate the influence of moral disengagement, perceived penalty, negative experiences and turnover intention on the intention to violate the established…

Abstract

Purpose

This paper aims to investigate the influence of moral disengagement, perceived penalty, negative experiences and turnover intention on the intention to violate the established security rules.

Design/methodology/approach

The method used involves two stages of analysis, using techniques of structural equation modeling and artificial intelligence with neural networks, based on information collected from 318 workers of organizational information systems.

Findings

The model provides a reasonable prediction regarding the intention to violate information security policies (ISP). The results revealed that the relationships of moral disengagement and perceived penalty significantly influence such an intention.

Research limitations/implications

This research presents a multi-analytical approach that expands the robustness of the results by the complementarity of each analysis technique. In addition, it offers scientific evidence of the factors that reinforce the cognitive processes that involve workers’ decision-making in security breaches.

Practical implications

The practical recommendation is to improve organizational communication to mitigate information security vulnerabilities in several ways, namely, training actions that simulate daily work routines; exposing the consequences of policy violations; disseminating internal newsletters with examples of inappropriate behavior.

Social implications

Results indicate that information security does not depend on the employees’ commitment to the organization; system vulnerabilities can be explored even by employees committed to the companies.

Originality/value

The study expands the knowledge about the individual factors that make information security in companies vulnerable, one of the few in the literature which aims to offer an in-depth perspective on which individual antecedent factors affect the violation of ISP.

Open Access
Article
Publication date: 23 August 2022

Armin Mahmoodi, Leila Hashemi, Milad Jasemi, Jeremy Laliberté, Richard C. Millar and Hamed Noshadi

In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the…

958

Abstract

Purpose

In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study.

Design/methodology/approach

It can be seen that support vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the neural network performance.

Findings

Based on the result, the authors can say that both new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural networks (basic research) is 74.2.

Originality/value

In this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model: raw-based and signal-based. For comparison, the hit rate is considered the percentage of correct predictions for 16 days.

Details

Asian Journal of Economics and Banking, vol. 7 no. 1
Type: Research Article
ISSN: 2615-9821

Keywords

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