Impact of Fintech’s Development on Bank Performance: An Empirical Study from Vietnam

In recent years, fintech has exploded in popularity and importance in the finan- cial industry. Its impacts have spread widely throughout the world, including Vietnam. This study aims to investigate the effect of fintech’s development on bank performance in Vietnam. Based on the unstructured data about fintech on the financial expert web- sites from Vietnam, the word frequency statistic technique of the text mining approach is applied for measuring fintech’s development under the support of Python-based solu- tions. The bank-level data of 15 Vietnamese banks for the period from the first quarter of 2019 to the second quarter of 2021 are collected from the quarterly financial statements in the Vietstock organization. Python programming and text mining techniques are used to compile this dataset by gathering information from popular and relevant websites. The generalized least squares method is used for estimating the panel models. The estimation result shows the significant impact of fintech’s development on bank profitability, but the net interest margin does not associate with the fintech variable. Besides, some interesting findings are revealed: The slow banking transformation to adapt to the rise of fintech and the COVID-19 pandemic increased bank profitability. Furthermore, suggestions for the banks and fintech companies are recommended, and the limitations and directions for further research are also proposed

AdamOptimizer for the optimisation of Use Case Points estimation

Use Case Points is considered to be one of the most popular methods to estimate the size of a developed software project. Many approaches have been proposed to optimise Use Case Points. The Algorithmic Optimisation Method uses the Multiple Least Squares method to improve the accuracy of Use Case Points by finding optimal coefficient regressions, based on the historical data. This paper aims to propose a new approach to optimise the Use Case Points method based on Gradient Descent with the support of the TensorFlow package. The significance of its purpose is to conduct a new approach that might lead to more accurate prediction than that of the Use Case Points and the Algorithmic Optimisation Method. As a result, this new approach outweighs both the Use Case Points and the Algorithmic Optimisation Methods. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

An evaluation of technical and environmental complexity factors for improving use case points estimation

This paper presents a proposed method for improving the prediction ability of the Use Case Points method. Our main goal is to use the Least Absolute Shrinkage and Selection Operator Regression methods to find out which of the technical and environmental complexity factors significantly affect the accuracy of the Use Case Points method. Two regression models were used to calculate the selected significant variables. The results of several evaluation measures show that the proposed estimation method ability is better than the original Use Case Points method. The Sum of Squared Error of the proposed method is better than the results obtained by the original one. The study also enables project managers to understand how to assess the technical and environmental complexity factors better - since they do have an important impact on effort estimation. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

Propose-specific information related to prediction level at x and mean magnitude of relative error: A case study of software effort estimation

The prediction level at x (PRED(x)) and mean magnitude of relative error (MMRE) are measured based on the magnitude of relative error between real and predicted values. They are the standard metrics that evaluate accurate effort estimates. However, these values might not reveal the magnitude of over-/under-estimation. This study aims to define additional information associated with the PRED(x) and MMRE to help practitioners better interpret those values. We propose the formulas associated with the PRED(x) and MMRE to express the level of scatters of predictive values versus actual values on the left (sig(Left)), on the right (sig(Right)), and on the mean of the scatters (sig). We depict the benefit of the formulas with three use case points datasets. The proposed formulas might contribute to enriching the value of the PRED(x) and MMRE in validating the effort estimation.RVO/FAI/2021/002Faculty of Applied Informatics, Tomas Bata University in Zlin; [RVO/FAI/2021/002

Outage and bit error probability analysis in energy harvesting wireless cooperative networks

This study focuses on a wireless powered cooperative communication network (WPCCN), which includes a hybrid access point (HAP), a source and a relay. The considered source and relay are installed without embedded energy supply (EES), thus are dependent on energy harvested from signals from the HAP to power their cooperative information transmission (IT). Taking inspiration from this, the author group investigates into a harvest-then-cooperate (HTC) protocol, whereas the source and the relay first harvest the energy from the AP in a downlink (DL) and then collaboratively work in uplink (UL) for IT of the source. For careful evaluation of the system performance, derivations of the approximate closed-form expression of the outage probability (OP) and an average bit error probability ( ABER) for the HTC protocol over Rayleigh fading channels are done. Lastly, the author group performs Monte-Carlo simulations to reassure the numerical results they obtained.Web of Science255746

Outage performance analysis and SWIPT optimization in energy-harvesting wireless sensor network deploying NOMA

Thanks to the benefits of non-orthogonal multiple access (NOMA) in wireless communications, we evaluate a wireless sensor network deploying NOMA (WSN-NOMA), where the destination can receive two data symbols in a whole transmission process with two time slots. In this work, two relaying protocols, so-called time-switching-based relaying WSN-NOMA (TSR WSN-NOMA) and power-splitting-based relaying WSN-NOMA (PSR WSN-NOMA) are deployed to study energy-harvesting (EH). Regarding the system performance analysis, we obtain the closed-form expressions for the exact and approximate outage probability (OP) in both protocols, and the delay-limited throughput is also evaluated. We then compare the two protocols theoretically, and two optimization problems are formulated to reduce the impact of OP and optimize the data rate. Our numerical and simulation results are provided to prove the theoretical and analytical analysis. Thanks to these results, a great performance gain can be achieved for both TSR WSN-NOMA and PSR WSN-NOMA if optimal values of TS and PS ratios are found. In addition, the optimized TSR WSN-NOMA outperforms that of PSR WSN-NOMA in terms of OP.Web of Science193art. no. 61

Relationship between fintech by Google search and bank stock return: a case study of Vietnam

Due to the ongoing global debate regarding the relationship between fintech and banks, including developing countries, this study aims to investigate this relationship in the case of Vietnam, an emerging nation. The study analyzes the relationship between fintech search and bank stock returns, which are measures of fintech and banks, respectively. The time series data for fintech and bank stock returns were obtained from Google Trends and Vietstock, respectively. Exploratory factor analysis was utilized to derive the fintech variables, while the bank stock return variable was calculated using a basket of eight listed banks from 2017w46 to 2021w46. The results were estimated using the vector autoregression and Granger causality method and validated with the copula method. A key finding of this study is the presence of a simultaneous negative change and bidirectional causality between bank stock returns and fintech lending. Furthermore, several other interesting findings were discovered: (1) the causal relationship from fintech to bank stock returns is weaker compared with the opposite direction; (2) unidirectional causality exists between different types of fintech, such as influence from FinFintech to FinLending, from FinPayment to FinLending and FinWallet, from FinMoney to FinFintech, from FinWallet to FinLending, and from FinProduct to FinFintech; and (3) there is an equal occurrence of simultaneous increase or decrease between bank stock returns and certain types of fintech, specifically between BankReturn and FinPayment, BankReturn and FinLending, as well as BankReturn and FinWallet. These findings shed light on the complex relationship between fintech and banks, offering insights that contribute to our understanding of this dynamic interplay in the context of Vietnam’s emerging fintech landscape

Analyzing public opinions regarding virtual tourism in the context of COVID-19: Unidirectional vs. 360-degree videos

Over the last few years, more and more people have been using YouTube videos to experience virtual reality travel. Many individuals utilize comments to voice their ideas or criticize a subject on YouTube. The number of replies to 360-degree and unidirectional videos is enormous and might differ between the two kinds of videos. This presents the problem of efficiently evaluating user opinions with respect to which type of video will be more appealing to viewers, positive comments, or interest. This paper aims to study SentiStrength-SE and SenticNet7 techniques for sentiment analysis. The findings demonstrate that the sentiment analysis obtained from SenticNet7 outperforms that from SentiStrength-SE. It is revealed through the sentiment analysis that sentiment disparity among the viewers of 360-degree and unidirectional videos is low and insignificant. Furthermore, the study shows that unidirectional videos garnered the most traffic during COVID-19 induced global travel bans. The study elaborates on the capacity of unidirectional videos on travel and the implications for industry and academia. The second aim of this paper also employs a Convolutional Neural Network and Random Forest for sentiment analysis of YouTube viewers' comments, where the sentiment analysis output by SenticNet7 is used as actual values. Cross-validation with 10-folds is employed in the proposed models. The findings demonstrate that the max-voting technique outperforms compared with an individual fold.IGA/CebiaTech/2022/001TBU in Zlin [CZ.02.2.69/0.0/19_073/0016941]; Faculty of Applied Informatics, Tomas Bata University in Zlin [IGA/CebiaTech/2022/001

Percepção e influência turística no turismo virtual usando modelo de análise sentimental bayesiana no Vietnã baseado na eWOM para o desenvolvimento sustentável

Objective: The advancement of Internet technology brought up the tourism industry towards new development and opportunities. With application of the Internet technology tourism industry comprises a vast range of virtual communities such as Trip Advisor, Agoda, Travelocity and so on. Existing research concentrated on evaluating the factors influencing virtual communities' behaviour with limited evaluation of tourist perception. This paper focused on examining the tourists' perception of a virtual tour through the sentimental analysis model based on eWOM for sustainable development. Method: The developed model comprises the group average Bayesian network with the computation of the polarity of the tourist perception. A Bayesian network is a data-driven method involved in estimating dependence among the variable with probabilistic computation. Results: The analysis is based on data collected from sample population in Vietnam with consideration of the 11 variables. Participation intensity, social identity, functional value, emotional value, sharing, interaction, and user satisfaction are among eleven primary variables that have been chosen. Conclusions: The analysis of the results expressed that the user satisfaction level is based on the user's experience and functional value. Additionally, the analysis stated that social value comprises the intermediary role in virtual tourism. This research adds to research methodologies of user engagement methods as well as serves as a reference for theoretical research and management practise in the virtual tourist community. © 2023 The Author(s)

Comparing multiple linear regression, deep learning and multiple perceptron for functional points estimation

This study compares the performance of Pytorch-based Deep Learning, Multiple Perceptron Neural Networks with Multiple Linear Regression in terms of software effort estimations based on function point analysis. This study investigates Adjusted Function Points, Function Point Categories, Industry Sector, and Relative Size. The ISBSG dataset (version 2020/R1) is used as the historical dataset. The effort estimation performance is compared among multiple models by evaluating a prediction level of 0.30 and standardized accuracy. According to the findings, the Multiple Perceptron Neural Network based on Adjusted Function Points combined with Industry Sector predictors yielded 53% and 61% in terms of standardized accuracy and a prediction level of 0.30, respectively. The findings of Pytorch-based Deep Learning are similar to Multiple Perceptron Neural Networks, with even better results than that, with standardized accuracy and a prediction level of 0.30, 72% and 72%, respectively. The results reveal that both the Pytorch-based Deep Learning and Multiple Perceptron model outperformed Multiple Linear Regression and baseline models using the experimental dataset. Furthermore, in the studied dataset, Adjusted Function Points may not contribute to higher accuracy than Function Point Categories.Faculty of Applied Informatics, Tomas Bata University in Zlin [IGA/CebiaTech/2022/001, RVO/FAI/2021/002