International Journal of Financial Engineering

Many supply chain finance problems can be fundamentally solved by using blockchain. The current technical challenge of applying blockchain in supply chain finance is the lack of systematic privacy protection architecture. Based on the architecture of Julongchain, through the nonlinear principal component analysis in private data feature extraction, this paper proposed a new type of privacy protection architecture based on specific supply chain financial system. This privacy protection solution split privacy data by decision machine, then mixed data using mixed mechanism, finally determined the characteristic weight of privacy data by applying the decentralized decision institution.

We investigate the effect of using an optimized exit rule on pairs trading. For every asset pair, we optimize the positions so that resulting intraday portfolio value is best fitted to an Ornstein–Uhlenbeck (OU) process through maximum likelihood estimation. Using various asset pairs, we examine the risks and returns of pairs trading strategies with and without an optimize exit rule. We provide empirical evidence that using an optimized exit rule improves the profitability of the trades and reduces turnovers.

In this paper, we propose two hybrid models to release some limitations and enhancement of the results. In this regard, three popular GARCH-type models are utilized for more accurate estimating of volatility, as the most important parameter for option pricing. Furthermore, the two non-parametric models based on Artificial Neural Networks and Neuro-Fuzzy Networks tuned by Particle Swarm Optimization algorithm are proposed to price call options for the S&P 500 index. By comparing the results obtained using these models, we conclude that both Neural Network and Neuro-Fuzzy Network models outperform the Black–Scholes model.

Purpose: Our purpose to write this paper is to analyze how VRIO mediates the relationship between transformational leadership and project performance by examining the direct and mediated relationships on project performance outcomes. This study combined the two dynamic concepts: project management and strategic management with the support of both literatures.

Design/methodology/approach: The resource-based view is operationalized to analyze the effect of project management assets and characteristics to gain a competitive edge in the industry and market as whole. The parallel mediation has been analyzed applying VRIO framework between transformational leadership and project performance, moreover its impact on firm-based performance is supported by the literature.

Finding: Transformational leadership scientifically predicts project performance. Results demonstrated two mediations to be accepted (Rare resources and Valuable Resources) however, both got rejected in the Pakistani context.

Research Limitations: The sample size considered for our study was only 256 and data were gathered from comparatively small software companies due to the fact that software companies in Islamabad and Rawalpindi are small comparatively in India or USA.

Practical Implication: The research contributions are quite interesting; these would be followed by the transformational leaders in software industry to attain competitive edge in market using project assets characteristics.

Originality/value: A useful combination of transformational leadership and resource-based view theory is used to reach project potential and improvements to sustain in competition.

The corona virus outbreak, which originated in China, has infected lakhs of people. Its spread has left businesses around the world counting costs. The corona virus is going global, and it could bring the world economy to a standstill. COVID-2019 that began in the depths of China’s Hubei province is spreading rapidly, persuading the World Health Organization to declare it as a pandemic. There are now significant outbreaks from South Korea to Italy and Iran, from America to Britain. The ongoing spread of the new corona virus has become one of the biggest threats to the global economy and financial markets. The economic impact of the COVID-2019 pandemic has introduced extraordinary volatility in global financial markets, as participants are obliged to reassess their valuations of all investments and associated derivatives as the situation develops. In an environment where uncertainty makes it unusually hard to price assets and for market-makers to operate, exchanges are providing the only way to establish consensus on these valuations in real time. Volatility has reached levels comparable with the Global Financial Crisis of 2008, with one-day losses not seen since 1987. The situation is made more challenging by high levels of indebtedness and already low interest rates. The financial markets are all integrated into one as global markets in the current era of globalization. It is important that financial markets remain able to perform their role — providing investors with liquidity, facilitating price discovery, and allowing for risk transfer and the transmission of monetary policy. This study aims at examining the performance of the selected Asian stock markets amidst the times of COVID-2019. This study intends to examine the interlinkages of Asian stock markets selected and to observe the impact of COVID-2019 on these markets. The period of study is from 1st December, 2019 to 31st March, 2020. The tools adopted for the study are correlation, regression, ANOVA and paired sample $t$ test.

Recently, Ross proposed an idea, now known as the “Recovery Theorem,” that asserts that the real (physical) probability measure can be recovered from the market prices of derivatives. This work has generated a great deal of controversy in the finance literature. The purpose of this paper is to revisit the core idea of the recovery theorem and to examine its implications. In particular, issues concerning the so-called factorization of the pricing kernel will be examined from the viewpoint of the Flesaker–Hughston representation.

Internet of Everything (IoE) in the business is a multiple and distributed interconnection of strategies and devices over the Internet. It is a group of technology which requires a set of protocols, data server storage strategy, infrastructure mechanism, integration of all technology, and communication protocols with all the connecting devices. The Internet of Things (IoT) is likely to convey accelerative, innovative, encouraging elucidations in numerous domains including business and managements. It allows devices to communicate with us very efficiently and accomplish the decisions with applications with other devices. The IoT creates transforming businesses, financial prudence and business culture. IoT platforms with greater access 5G technology are evolving as the upcoming tools and substantial support in the global IoEs setup in business ecosystem.

In this paper, we examine up-to-date landscape of IoT platforms, 5G technology scope for business and management to how they work and the main aspects to consider when choosing a platform provider for cutting-edge decisions. It involves a set of approaches for business behaviors of the IoT and IoE classification, which can be defined as IoE business outlines in the cutting edge 5G technology.

Financial distress and companies’ failure have always been a complicated and intriguing problem for businesses. Because of the unfavorable impacts of financial distress on companies and societies, accounting and finance researchers around the world are thinking of ways to anticipate corporate financial distress. Several models are provided in the literature for predicting financial distress. This research develops nonlinear decision tree and linear discriminant analysis models to predict financial distress of companies listed in Iranian Stock Exchange during 2010 to 2015. The drivers are firms’ financial ratios, intellectual capital and performance indicators. According to the results, intellectual capital and financial performance indices have no informational content in decision tree model. Comparing the result show that both models predict financial distress with 90.9% and 81.8% accuracy, respectively. Moreover, the difference between the accuracy of the models however is not meaningful. In other words, two models were very close to each other in terms of predictive power.

This study is investigated the impact of ownership structure on bank’s risk and efficiency in Bangladesh. We have used Z-Score as risk-taking variable. As ownership structure is the main variable here, we have taken managerial ownership, institutional ownership, and general public ownership as proxies for ownership structure. Here, another important main variable efficiency has been used as a stability. For our study, we have selected 32 banks randomly. Data have been collected from annual reports of these banks. The time frame of the data is 2000–2014. The study results suggested that using Z-score as proxy for risk taking and the proxy of managerial ownership has negative association and the institutional ownership has significant positive effect on Z-score of commercial banks in Bangladesh. The proxies we have used for ownership structure i.e., managerial ownership and general public ownership have significant negative association on efficiency. On the other hand, institutional ownership has positive effect on the overall efficiency of the banks in Bangladesh. At the end of the study, we have also suggested some policy implications regarding ownership structure, risk, and efficiency of the banks.

This paper investigates the relationship between e-banking and commercial bank performance in Bangladesh. A five-year aggregate quarterly commercial bank data as provided by Payment System Department of Central Bank of Bangladesh was used in this study. A multiple regression procedure was used to determine the significance of the relationship between the independent variables (electronic banking indicators) and the profitability measures of return on assets (ROA) and return on equity (ROE) as the dependent variables. The findings were that only IB was statistically significant at 0.01% level under both ROA and ROE models with p-values of 0.0000 and 0.0000, respectively. However, the predictive power of ROE was found to be higher than ROA using adjusted r-square and d-statistic. Some of the other variables (POS, CHQ) were also statistically significant ($p>0.01$) and the rest of variables had mixed result. The results show that at an aggregate level, commercial banks in Bangladesh widely use the traditional banking methods such as use of cheques. The results are expected to inform bank managers on the need to discover the optimal supply of electronic banking services such as MOB, EFT and ATM payment systems in order to improve bank profitability. This study will further inform policy makers on the need to improve on Information and Communication Technology (ICT) in order to cater for optimal use of electronic banking service in Bangladesh.

The nonlinear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week’s closing exchange rate. Though a good correlation was obtained during some period, but as a whole no such cointegration relation can be found out. Using the Discrete Wavelet Analysis, the data was decomposed and the presence of Granger Causal relations was tested. Unfortunately, no significant relationships are being found. We then studied the Wavelet Coherence of the two pairs, namely NSE-Nifty & Gold and NSE-Nifty & Crude. For different frequencies, the coherence between the pairs have been studied. At lower frequencies, some relatively good coherence have been found.

In this paper, we report for the first time the co-movements between Crude Oil, Gold and Indian Stock Market Index using Wavelet Analysis (both Discrete and Continuous), a technique which is most sophisticated and recent in market analysis. Thus, for long-term traders they can include gold and/or crude in their portfolio along with NSE-Nifty index in order to decrease the risk (volatility) of the portfolio for the Indian Market. But for short-term traders, it will not be effective, not to include all the three in their portfolio.

During the post demonetization and GST, Indian economy was struggling continuously to recover from the economic crisis. In the financial year 2019–2020, GDP rate fell from 7% to 5.4% which is about 18.20%. BSE Sensex index was 42273 as on January 20th of 2020 but on April 8, 2020, it was 29894. During the Financial year 2019–2020, a reduction of 26% in mid cap index was observed, but at the same time, sensitive index reduced by 22%. These things affect the share market and financial stability of people. The stock market over the last one-year became volatile and crashed. To handle the downwards economy, Government took the initiative and announced deep tax cuts for businesses in the month of August 2019. But in the beginning of the year 2020, there was another sluggish phase which stubborn the economy. This time, it was a virus, named as COVID-19(coronavirus), which created a pandemic situation and spread all over the world. Nation-wide lock down was announced to fight with COVID-19 as there was no vaccine introduced. Starting from agriculture to textile, apparel, automotive, aviation, hotels and restaurants, poultry, chemicals, consumer durables, entertainment, sports, FMCG (fast moving consumer goods), pharmaceutical, ecommerce, IT and moreover corporate sectors were adversely affected due to this pandemic and lock down rules. Therefore, this paper focuses on the impact of corona on the perception of Indian investors towards investment in equity fund.