JournalTOCs

International Journal of Financial Services Management
Number of Followers: 1

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1460-6712 - ISSN (Online) 1741-8062
Published by Inderscience Publishers

[439 journals]

Credit risk detection based on machine learning algorithms
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  Authors: Xin Wang, Kai Zong, Cuicui Luo
  Pages: 183 - 189
  Abstract: As the global economic environment has become more complicated in recent years, more and more credit bonds have defaulted. The credit risk early warning model plays a very effective role in preventing and controlling financial risk and debt default. This paper uses machine learning methods to establish a credit default risk prediction framework. In this paper, the oversampling technique is first applied to deal with imbalanced credit default data sets and then the credit risk detection performance of several machine learning algorithms is compared. The empirical results show that the performance of the ensemble learning algorithms is the best.
  Keywords: machine learning; credit risk detection; ensemble learning
  Citation: International Journal of Financial Services Management, Vol. 11, No. 3 (2022) pp. 183 - 189
  PubDate: 2022-11-09T23:20:50-05:00
  DOI: 10.1504/IJFSM.2022.126871
  Issue No: Vol. 11, No. 3 (2022)
The optimisation of banking loan portfolio: a case of an Iranian
commercial bank
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  Authors: Mahdiyeh Rezaei Chayjan, Tina Bagheri, Ahmad Kianian, Niloufar Ghafari Someh
  Pages: 190 - 215
  Abstract: As loans are the main earning assets for banks, Iranian banking sector has always been active in financing businesses through this means. Loans are considered the dominant means of finance in Iran but the evaluation process on allocation of loans is mostly retrospective. This has caused a considerable amount of default during decades. In this paper, we have focused on introducing a method of optimisation for commercial banks to create their optimum loan portfolios. To achieve this goal, we have examined different methods and we have used data on 21 industries, loan data from one of the large scale Iranian commercial banks and proposed a new method with a fresh approach to risk and return. The findings of the present study shows that the semi-variance or undesired variance method and the adjusted standard deviation to estimate the banking optimised portfolio is the most accurate technique.
  Keywords: loan portfolio; optimisation; banking sector; semi variance method; risky assets
  Citation: International Journal of Financial Services Management, Vol. 11, No. 3 (2022) pp. 190 - 215
  PubDate: 2022-11-09T23:20:50-05:00
  DOI: 10.1504/IJFSM.2022.126867
  Issue No: Vol. 11, No. 3 (2022)
Performance evaluation of Indian equity mutual funds: a bootstrap
analysis
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  Authors: Bushra Khalid, Nisar Ahmad Khan
  Pages: 216 - 231
  Abstract: The majority of Indian studies on mutual funds have documented superior performance of equity funds vis-à-vis the market benchmark. The question is whether this superior performance, demonstrated by fund managers, is the result of genuine stock selection abilities or merely the outcome of luck. We apply a bootstrap statistical technique as suggested by Kosowski et al. (2006) to evaluate the performance of the Indian open-end, domestic growth-oriented equity mutual fund schemes over the 2000 to 2019 period. The bootstrap analysis helps to segregate genuine stock-selection skills from luck. Our findings signify that superior performance demonstrated by the top mutual funds cannot be attributed to the sampling variation (luck) and these fund managers do possess genuine stock-picking skills.
  Keywords: mutual funds; performance evaluation; Carhart four-factor model; bootstrap analysis
  Citation: International Journal of Financial Services Management, Vol. 11, No. 3 (2022) pp. 216 - 231
  PubDate: 2022-11-09T23:20:50-05:00
  DOI: 10.1504/IJFSM.2022.126864
  Issue No: Vol. 11, No. 3 (2022)
Volatility clustering and persistence during COVID-19: evidence of
asymmetric volatility in the Asia-Pacific stock markets
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  Authors: Dharen Kumar Pandey, Vineeta Kumari
  Pages: 232 - 244
  Abstract: We analyse 17 stock market indices in the Asia-Pacific region to examine the impacts of the COVID-19 on the Asia-Pacific stock markets by interpreting the generalised autoregressive conditional heteroskedasticity (GARCH) coefficients. While evidencing the absence of ARCH and GARCH effects during the pre-COVID period, we also evidence volatility clustering and persistence during the COVID period. It is evidenced that negative impacts result in higher volatility than positive impacts. The presence of time-varying volatility in the Asia-Pacific region has not been previously studied. The available literature has focused either on a single market or on the developed markets. Hence, the findings of this study are expected to contribute significantly to the finance literature.
  Keywords: volatility; COVID-19; GARCH; EGARCH; stock market; Asia-Pacific; asymmetry
  Citation: International Journal of Financial Services Management, Vol. 11, No. 3 (2022) pp. 232 - 244
  PubDate: 2022-11-09T23:20:50-05:00
  DOI: 10.1504/IJFSM.2022.126863
  Issue No: Vol. 11, No. 3 (2022)
Logistic regression vs. artificial neural network model in prediction of
financial inclusion: empirical evidence from PMJDY program in India
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  Authors: R. Magesh Kumar, G. Delina, R. Senthil Kumar, S. Siamala Devi
  Pages: 245 - 267
  Abstract: Pradhan Mantri Jandhan Yojana (PMJDY) is a financial inclusion program launched by the Government of India in 2014 to deliver various banking services through a basic bank account feature to the vulnerable population. The primary objective of this study is to find if there is a significant difference between the two predictive models - Logistic Regression (LR) and Artificial Neural Network (ANN) in terms of classification accuracy on forecasting the account usage among the two groups of customers i.e. regular users and non-regular users. The study also uncovers the significant predictors that are important in forecasting the account usage. The results suggest both the LR and ANN models have shown good prediction accuracy. However, the findings indicate the Multilayer Perceptron Neural Network (MLPNN) using the standardised rescaling approach of a covariate has a slight better prediction than the LR model with a correct classification rate of 82.8% in the testing and validating stage of the sample cases. The practical implications of the study will provide meaningful results to the banking authorities, bureaucrats and policymakers for enriching the financial services to the underprivileged segment of the population.
  Keywords: financial inclusion; financial services; financial literacy; PMJY; Pradhan Mantri Jandhan Yojana; artificial neural network; logistic regression
  Citation: International Journal of Financial Services Management, Vol. 11, No. 3 (2022) pp. 245 - 267
  PubDate: 2022-11-09T23:20:50-05:00
  DOI: 10.1504/IJFSM.2022.126865
  Issue No: Vol. 11, No. 3 (2022)