Forthcoming and Online First Articles

International Journal of Financial Services Management

International Journal of Financial Services Management (IJFSM)

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International Journal of Financial Services Management (5 papers in press)

Regular Issues

  • Volatility clustering and persistence during Covid-19: evidence of asymmetric volatility in the Asia-Pacific stock markets   Order a copy of this article
    by Dharen Pandey, Vineeta Kumari 
    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 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.
    DOI: 10.1504/IJFSM.2022.10050200
     
  • Performance evaluation of Indian equity mutual funds: a bootstrap analysis   Order a copy of this article
    by Bushra Khalid, Nisar Ahmad Khan 
    Abstract: The majority of Indian studies on mutual funds have documented superior performance of equity funds vis-a-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.
    DOI: 10.1504/IJFSM.2022.10050408
     
  • Logistic regression versus artificial neural network model in prediction of financial inclusion: empirical evidence from the PMJDY program in India   Order a copy of this article
    by Magesh Kumar, Gnanadhas Delina, Senthil Kumar, Siamala Devi 
    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 slightly 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; Pradhan Mantri Jandhan Yojana; financial services; financial literacy; artificial neural network; logistic regression.
    DOI: 10.1504/IJFSM.2022.10050409
     
  • The optimisation of banking loan portfolio: a case of an Iranian commercial bank   Order a copy of this article
    by Mahdiyeh Rezaei Chayjan, Tina Bagheri, Ahmad Kianian, Niloufar Ghafari Someh 
    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 optimization 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 optimized portfolio is the most accurate technique.
    Keywords: loan portfolio; optimisation of banking loan; banking sector; semi-variance method; risky assets.
    DOI: 10.1504/IJFSM.2022.10050469
     
  • Credit risk detection based on machine learning algorithms   Order a copy of this article
    by Xin Wang, Kai Zong, Cuicui Luo 
    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 method to establish a credit default risk prediction framework. In this paper, the oversampling technique is fist applied to deal with imbalanced credit default data sets and then the credit risk detection performance of several machine learning algorithms is compared\label{key}. The empirical results show that the performance of the ensemble learning algorithms is the best.
    Keywords: machine learning; credit risk detection; ensemble learning.
    DOI: 10.1504/IJFSM.2022.10050887