Examining the dynamics of macroeconomic indicators and banking stock returns with bayesian networks

According to the modern portfolio theory, the direction of the relationship between the securities in the portfolio is stated to be effective in reducing the risk. Moreover, securities in high correlation are avoided by taking place in the same portfolio. The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship. Also, portfolio returns could be refreshed simultaneously when new information has arrived. The study aims to provide dynamic information through Bayesian networks and to investigate the relationship between macroeconomic indicators and stock returns of Turkish major bank stocks based on the Arbitrage Pricing Model. The dataset includes stock returns of four banks listed in the Borsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables such as BIST-100 Index, oil prices, inflation, exchange, and interest rate & money supply are gathered for the same period. The results suggest that the Bayesian network models allow dynamics among stock returns could be investigated in more detail. Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods

Ideal Self-Congruence: Neobanking by Traditional Banks and the Impact on Market Share - A Case of Uae Banks

Purpose: The aim of this study is to examine the effect of adopting neobanking on the market share of traditional banks in the UAE and test the influence of financial performance indicators on the banks’ market share after the digital transformation.   Theoretical framework: The financial service sector has been undergoing major transformation due to technological developments and innovations in terms of operating efficiency, client acquisition and organizational structure. Banks are accelerating digital transformation in an attempt to enhance digital presence, lower costs and gain market share.  Neobanking is a recent innovation in the Fintech space that has disrupted the financial services sector.   Design/methodology/approach: This study employs published data of quarterly financial statements from 2012- 2021. Chow Test was applied, with known structural breaks in the data, based on the implementation of neobanking and our results are based on pooled regression.   Findings: The results reveal that neobanking has influenced the bank specific factors and those factors have affected the market share. NPL, ROE and NIM are critical for the market share with each variable affecting all banks contrarily. This paper further identifies that NPL and NIM has a favourable impact on the market share of only one bank. Cost efficiency has no effect on the market share of the banks in the period after launching neobanking.   Research, Practical & Social implications: The study has important implications for the management of banks as the results affirm that structural changes made to adopt digital transformation by firms is the key to derive the favorable effects in terms of increased revenue, profitability and lower credit risk.   Originality/value: Neobanking is the most recent disruptor in the financial services sector and effect of digitalization in banking sector is becoming the focus of literature of commercial banks. This paper provides insights into bank specific variables that impact financial performance after its digital transformation

Architecture and Design of Medical Processor Units for Medical Networks

This paper introduces analogical and deductive methodologies for the design medical processor units (MPUs). From the study of evolution of numerous earlier processors, we derive the basis for the architecture of MPUs. These specialized processors perform unique medical functions encoded as medical operational codes (mopcs). From a pragmatic perspective, MPUs function very close to CPUs. Both processors have unique operation codes that command the hardware to perform a distinct chain of subprocesses upon operands and generate a specific result unique to the opcode and the operand(s). In medical environments, MPU decodes the mopcs and executes a series of medical sub-processes and sends out secondary commands to the medical machine. Whereas operands in a typical computer system are numerical and logical entities, the operands in medical machine are objects such as such as patients, blood samples, tissues, operating rooms, medical staff, medical bills, patient payments, etc. We follow the functional overlap between the two processes and evolve the design of medical computer systems and networks.Comment: 17 page

Strategic Unification of Artificial Intelligence in Foreign Direct Investment Application Forms

A foreign direct investment (FDI) is a very popular method of investing overseas but different from a stock investment in a foreign company. It could be purchasing of an interest in a company by an investor located outside its borders and in most cases, governments pay special interest on them. This is a business decision to acquire a substantial stake in a foreign business or to buy it outright as to expand its operations to a new region. Embedding artificial intelligence (AI) across the business requires significant investment and a change in overall approach. It is highly constructive and productive transformation that should be planned professionally, applied systematically, and managed strategically. AI drives meaningful value to business through better decision-making and consumer-facing applications. The general perception about filling a FDI application is a cumbersome job. Some countries manage this stage very methodically and investors always give priority for them as they can commence the production/business activities within a short period. Those countries who fail to gain this competitive advantage tend to lose the FDI opportunities even if they own various other advantages of resources to attract investors. This paper attempts to evaluate the potential of embedding a strategic unification of artificial intelligence in the application forms used to fill by investors at the time of starting foreign direct investment projects

Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs

The purpose of the paper is to identify the factors of financial development that have the greatest impact on open innovation in 7 emerging countries. The analysis was performed featuring the MF-X-DMA method, as well as its further verification for autocorrelation and heteroscedasticity. The time period covers years from 2002 to 2020. The article states that the main indicators to improve financial development should enhance the process of bank lending and equity market development. An important area is the development of competition by providing equal access to information to all market participants in a continuously refining technical infrastructure. Regression analysis with the MF-X-DMA method confirms the statistical significance of this influence. The article fills the knowledge gap into the link between open innovations and the relatively low capitalization of the modern emerging countries’ financial market, low liquidity in small cap stocks at the financial market and concentration of the banking sector, as well as risks arising in the process of globalization. Another analysis has also been conducted by generating a novel fuzzy decision-making model. In the first stage, the determinants of open innovation-based fintech potential are weighted for the emerging economies. For this purpose, M-SWARA methodology is taken into consideration based on bipolar q-ROFSs and golden cut. The second stage of the analysis includes evaluating the emerging economies with the determinants of open innovation-based fintech potential. In this context, emerging seven countries are examined with ELECTRE methodology. It found the most significant factor is the open innovation-based fintech potential

Применение метода отбора признаков для долгосрочного прогноза индекса Амманской фондовой биржи

Фондовые биржи — неотъемлемая часть мировой экономики; благодаря отслеживанию ежедневных операций, фондовые индексы отражают изменения показателей деятельности представленных на финансовом рынке фирм. Для построения модели прогнозирования фондового индекса Иордании в данной статье исследованы факторы, напрямую влияющие на индекс фондовой биржи. Чтобы выявить, какие секторы экономики оказывают наибольшее влияние на модель прогнозирования, авторы применили четыре метода отбора признаков для изучения связи между 23 секторами и индексом Амманской фондовой биржи (ASEI100) за период 2008–2018 гг. В каждой модели были выделены 10 наиболее значимых факторов, которые затем они были объединены и внесены в таблицу частот. Для проверки достоверности основных факторов, которые наиболее часто встречались в четы- рех моделях, а также для оценки их влияния на ASEI использовались методы линейной регрессии и обычных наименьших квадратов. Результаты исследования показали, что существует шесть основных секторов, непосредственно влияющих на общий фондовый индекс в Иордании: здравоохранение, горнодобывающая промышленность, производство одежды, текстиля и изделий из кожи, недвижимость, финансовые услуги, транспорт. Показатели этих секторов можно использовать для прогнозирования изменений индекса Амманской фондовой биржи в Иордании. Кроме того, линейная регрессия выявила статистически значимую взаимосвязь между шестью секторами (независимые переменные) и ASEI (зависимая переменная). Полученные результаты, описывающие наиболее важные секторы экономики Иордании, могут быть использованы инвесторами для принятия инвестиционных решений

Применение метода отбора признаков для долгосрочного прогноза индекса Амманской фондовой биржи

Фондовые биржи — неотъемлемая часть мировой экономики; благодаря отслеживанию ежедневных операций, фондовые индексы отражают изменения показателей деятельности представленных на финансовом рынке фирм. Для построения модели прогнозирования фондового индекса Иордании в данной статье исследованы факторы, напрямую влияющие на индекс фондовой биржи. Чтобы выявить, какие секторы экономики оказывают наибольшее влияние на модель прогнозирования, авторы применили четыре метода отбора признаков для изучения связи между 23 секторами и индексом Амманской фондовой биржи (ASEI100) за период 2008–2018 гг. В каждой модели были выделены 10 наиболее значимых факторов, которые затем они были объединены и внесены в таблицу частот. Для проверки достоверности основных факторов, которые наиболее часто встречались в четы- рех моделях, а также для оценки их влияния на ASEI использовались методы линейной регрессии и обычных наименьших квадратов. Результаты исследования показали, что существует шесть основных секторов, непосредственно влияющих на общий фондовый индекс в Иордании: здравоохранение, горнодобывающая промышленность, производство одежды, текстиля и изделий из кожи, недвижимость, финансовые услуги, транспорт. Показатели этих секторов можно использовать для прогнозирования изменений индекса Амманской фондовой биржи в Иордании. Кроме того, линейная регрессия выявила статистически значимую взаимосвязь между шестью секторами (независимые переменные) и ASEI (зависимая переменная). Полученные результаты, описывающие наиболее важные секторы экономики Иордании, могут быть использованы инвесторами для принятия инвестиционных решений