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Developing a prediction model for customer churn from electronic banking services using data mining

Keramati, Abbas; Ghaneei, Hajar; Mirmohammadi, Seyed … - In: Financial innovation : FIN 2 (2016) 10, pp. 1-13

Background: Given the importance of customers as the most valuable assets of organizations, customer retention seems to be an essential, basic requirement for any organization. Banks are no exception to this rule. The competitive atmosphere within which electronic banking services are provided...

Persistent link: https://www.econbiz.de/10011542335

Comprehensive review of text‑mining applications in finance

Gupta, Aaryan; Dengre, Vinya; Kheruwala, Hamza Abubakar; … - In: Financial innovation : FIN 6 (2020) 39, pp. 1-25

Text-mining technologies have substantially affected financial industries. As the data in every sector of finance have grown immensely, text mining has emerged as an important field of research in the domain of finance. Therefore, reviewing the recent literature on text-mining applications in...

Persistent link: https://www.econbiz.de/10012317572

Predicting abnormal trading behavior from internet rumor propagation : a machine learning approach

Cheng, Li-Chen; Lu, Wei-Ting; Yeo, Benjamin - In: Financial innovation : FIN 9 (2023) 1, pp. 1-23

In 2021, the abnormal short-term price fluctuations of GameStop, which were triggered by internet stock discussions, drew the attention of academics, financial analysts, and stock trading commissions alike, prompting calls to address such events and maintain market stability. However, the impact...

Persistent link: https://www.econbiz.de/10014288898

A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance

Zhang, Lang; Hu, Haiqing; Zhang, Dan - In: Financial innovation : FIN 1 (2015) 14, pp. 1-21

model. This new assessment model based on SVM can raise the accuracy of classification between good credit and bad credit …

Persistent link: https://www.econbiz.de/10011541786

Evaluation of the robusticity of mutual fund performance in Ghana using enhanced resilient backpropagation neural network (ERBPNN) and fast adaptive neural network classifier (FANN...

Kong, Yusheng; Owusu-Akomeah, Micheal; Antwi, Henry Asante - In: Financial innovation : FIN 5 (2019) 10, pp. 1-12

Mutual fund investment continues to play a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors. The total mutual fund asset globally as at the end of 2016 was in excess of $40.4...

Persistent link: https://www.econbiz.de/10012265890

Indian stock market prediction using artificial neural networks on tick data

Selvamuthu, Dharmaraja; Kumar, Vineet; Mishra, Abhishek - In: Financial innovation : FIN 5 (2019) 16, pp. 1-12

Introduction: Nowadays, the most significant challenges in the stock market is to predict the stock prices. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature. Case description: Support Vector...

Persistent link: https://www.econbiz.de/10012266638

Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Zhong, Xiao; Enke, David - In: Financial innovation : FIN 5 (2019) 24, pp. 1-20

pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases … gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the … simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than …

Persistent link: https://www.econbiz.de/10012266935

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Nayak, Sarat; Mishra, Bijan Bihari - In: Financial innovation : FIN 5 (2019) 38, pp. 1-34

networks, such as multilayer perceptron have been established as better approximation and classification models for this domain …

Persistent link: https://www.econbiz.de/10012268496

Extreme learning with chemical reaction optimization for stock volatility prediction

Nayak, Sarat Chandra; Mishra, Bijan Bihari - In: Financial innovation : FIN 6 (2020) 16, pp. 1-23

gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need …). ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural …

Persistent link: https://www.econbiz.de/10012268745

Estimating stock closing indices using a GA-weighted condensed polynomial neural network

Nayak, Sarat Chandra; Misra, Bijan Bihari - In: Financial innovation : FIN 4 (2018) 21, pp. 1-22

features are fed to a single output neuron, and the synaptic weight values and biases of the CPNN are optimized by a genetic …

Persistent link: https://www.econbiz.de/10011921960