Search results for: big data markets

Authors: Cinthia C. Costa, Heloisa L. Burnquist, Joaquim J. M. Guilhoto

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This study identified the impact of special agricultural safeguards (SSG) for the global market of meat and for the Brazilian economy. The tariff lines subject to SSG were selected and the period of analysis was 1995 (when the rules about the SSGs were established) to 2015 (more recent period for which there are notifications). The value of additional tariff was calculated for each of the most important tariff lines. The import volume and the price elasticities for imports were used to estimate the impacts of each additional tariff estimated on imports. Finally, the effect of Brazilian exports of meat without SSG taxes was calculated as well as its impact in the country’s economy by using an input-output matrix. The most important markets that applied SSGs were the U.S. for beef and European Union for poultry. However, the additional tariffs could be estimated in only two of the sixteen years that the U.S. applied SSGs on beef imports, suggesting that its use has been enforced when the average annual price has been higher than the trigger price level. The results indicated that the value of the bovine and poultry meat that could not be exported by Brazil due to SSGs to both markets (EU and the U.S.) was equivalent to BRL 804 million. The impact of this loss in trade was about: BRL 3.7 billion of the economy’s production value (at 2015 prices) and almost BRL 2 billion of the Brazilian Gross Domestic Product (GDP).

Keywords: beef, poultry meat, SSG tariff, input-output matrix, Brazil

Procedia PDF Downloads 92

Authors: Petra Hofmann, Dana Koniel, Jussi Luukkanen, Walter Nieminen, Lea Hannola, Ilkka Donoghue

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The Covid-19 pandemic forced people to isolate and become physically less connected. The pandemic has not only reshaped people’s behaviours and needs but also accelerated digital transformation (DT). DT of cities has become an imperative with the outlook of converting them into smart cities in the future. Embedding digital infrastructure and smart city initiatives as part of normal design, construction, and operation of cities provides a unique opportunity to improve the connection between people. The Internet of Things (IoT) is an emerging technology and one of the drivers in DT. It has disrupted many industries by introducing different services and business models, and IoT solutions are being applied in multiple fields, including smart cities. As IoT and data are fundamentally linked together, IoT solutions can only create value if the data generated by the IoT devices is analysed properly. Extracting relevant conclusions and actionable insights by using established techniques, data analytics contributes significantly to the growth and success of IoT applications and investments. Companies must grasp DT and be prepared to redesign their offerings and business models to remain competitive in today’s marketplace. As there are many IoT solutions available today, the amount of data is tremendous. The challenge for companies is to understand what solutions to focus on and how to prioritise and which data to differentiate from the competition. This paper explains how IoT and data analytics can impact competitive advantage and how companies should approach IoT and data analytics to translate them into concrete offerings and solutions in the smart city context. The study was carried out as a qualitative, literature-based research. A case study is provided to validate the preservation of company’s competitive advantage through smart city solutions. The results of the research contribution provide insights into the different factors and considerations related to creating competitive advantage through IoT and data analytics deployment in the smart city context. Furthermore, this paper proposes a framework that merges the factors and considerations with examples of offerings and solutions in smart cities. The data collected through IoT devices, and the intelligent use of it, can create competitive advantage to companies operating in smart city business. Companies should take into consideration the five forces of competition that shape industries and pay attention to the technological, organisational, and external contexts which define factors for consideration of competitive advantages in the field of IoT and data analytics. Companies that can utilise these key assets in their businesses will most likely conquer the markets and have a strong foothold in the smart city business.

Keywords: data analytics, smart cities, competitive advantage, internet of things

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Authors: Petra Hofmann, Dana Koniel, Jussi Luukkanen, Walter Nieminen, Lea Hannola, Ilkka Donoghue

Abstract:

The Covid-19 pandemic forced people to isolate and become physically less connected. The pandemic hasnot only reshaped people’s behaviours and needs but also accelerated digital transformation (DT). DT of cities has become an imperative with the outlook of converting them into smart cities in the future. Embedding digital infrastructure and smart city initiatives as part of the normal design, construction, and operation of cities provides a unique opportunity to improve connection between people. Internet of Things (IoT) is an emerging technology and one of the drivers in DT. It has disrupted many industries by introducing different services and business models, and IoT solutions are being applied in multiple fields, including smart cities. As IoT and data are fundamentally linked together, IoT solutions can only create value if the data generated by the IoT devices is analysed properly. Extracting relevant conclusions and actionable insights by using established techniques, data analytics contributes significantly to the growth and success of IoT applications and investments. Companies must grasp DT and be prepared to redesign their offerings and business models to remain competitive in today’s marketplace. As there are many IoT solutions available today, the amount of data is tremendous. The challenge for companies is to understand what solutions to focus on and how to prioritise and which data to differentiate from the competition. This paper explains how IoT and data analytics can impact competitive advantage and how companies should approach IoT and data analytics to translate them into concrete offerings and solutions in the smart city context. The study was carried out as a qualitative, literature-based research. A case study is provided to validate the preservation of company’s competitive advantage through smart city solutions. The results of the researchcontribution provide insights into the different factors and considerations related to creating competitive advantage through IoT and data analytics deployment in the smart city context. Furthermore, this paper proposes a framework that merges the factors and considerations with examples of offerings and solutions in smart cities. The data collected through IoT devices, and the intelligent use of it, can create a competitive advantage to companies operating in smart city business. Companies should take into consideration the five forces of competition that shape industries and pay attention to the technological, organisational, and external contexts which define factors for consideration of competitive advantages in the field of IoT and data analytics. Companies that can utilise these key assets in their businesses will most likely conquer the markets and have a strong foothold in the smart city business.

Keywords: internet of things, data analytics, smart cities, competitive advantage

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Authors: Yang Bao, Shi Wei Deng, WangQun Lin

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This paper introduces the concept and principle of data cleaning, analyzes the types and causes of dirty data, and proposes several key steps of typical cleaning process, puts forward a well scalability and versatility data cleaning framework, in view of data with attribute dependency relation, designs several of violation data discovery algorithms by formal formula, which can obtain inconsistent data to all target columns with condition attribute dependent no matter data is structured (SQL) or unstructured (NoSQL), and gives 6 data cleaning methods based on these algorithms.

Keywords: data cleaning, dependency rules, violation data discovery, data repair

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Authors: Raphael Odoh, Obida J. Oko, Mary S. Dauda

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The determination of Cd, Cr, Cu, Fe,Mn, Ni, Pb and Zn contents in edible oils (palm oil, ground-nut oil and soybean oil) bought from various markets of Benue and Taraba state were carried out with flame atomic absorption spectrophotometric technique. The method 3031 developed acid digestion of oils for metal analysis by atomic absorption or ICP spectrometry was used in the preparation of the edible oil samples for the determination of total metal content in this study. The overall results (µg/g) in palm oil sample ranged from 0.028-0.076, 0.035-0.092, 1.011-1.955, 2.101-4.892, 0.666-0.922, 0.054-0.095, 0.031-0.068 and 1.987-2.971 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn respectively, while in ground-nut oil the overall results ranged from 0.011-0.042, 0.011-0.052, 0.133-0.788, 1.789-2.511, 0.078-0.765, 0.045-0.092, 0.011-0.028 and 1.098-1.997 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn respectively. Of the heavy metals considered Cd and Ni showed the highest contamination in the soybean oil sample. The overall results in soybean oil samples ranged from 0.011-0.015, 0.017-0.032, 0.453-0.987, 1.789-2.511, 0.089-0.321, 0.011-0.016, 0.012-0.065 and 1.011-1.997 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn respectively. The concentration of Pb was the highest. The degree of contamination by each metal was estimated by the transfer factor. The transfer factors obtained for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in edible oils (palm oil, ground-nut oil and soybean oil) were 10.800, 16.500, 16.000, 18.813, 15.115, 14.230, 23.000 and 9.418 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in palm oil, and 7.000, 12.500, 8.880, 11.333, 7.708, 10.833, 15.00 and 6.608 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in ground-nut oil while for soybean oil the transfer factors were 13.000, 11.000, 7.642, 11.578, 4.486, 13.00, 12.333 and 4.412 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn respectively. The inter-element correlation was found among metals in edible oil samples using Pearson’s correlation co-efficient. There were positive and negative correlations among the metals determined. All Metals determined showed degree of contamination but concentrations lower than the USP specification.

Keywords: Benue State, contamination, edible oils, heavy metals, markets, Taraba State

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Authors: Pavak Vyas, Hiral Vyas

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Spin-offs commonly known as demergers in India, represents dismantling of conglomerates which is a common phenomenon in financial markets across the world. Demergers are carried out with different motives. A demerger generally refers to a corporate restructuring where, a large company divests its stake in in its subsidiary and distributes the shares of the subsidiary - demerged entity to the existing shareholders without any consideration. Demergers in Indian companies are over a decade old phenomena, with many companies opting for the same. This study examines the demerger regulations in Indian capital markets and the announcement period price reaction of demergers during year 2010-2015. We study total 97 demerger announcements by companies listed in India and try to establish that demergers results into abnormal returns for the shareholders of the parent company. Using event study methodology we have analyzed the security price performance of the announcement day effect 10 days prior to announcement to 10 days post demerger announcement. We find significant out-performance of the security over the benchmark index post demerger announcements. The cumulative average abnormal returns range from 3.71% on the day of announcement of a private demerger to 2.08% following 10 days surrounding the announcement, and cumulative average abnormal returns range from 5.67% on the day of announcement of a public demerger to 4.15% following10 days surrounding the announcement.

Keywords: demergers, event study, spin offs, stock returns

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Authors: Zubier Arfan, Paul Johnson

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The electronification of financial markets and the rise of algorithmic trading has sparked a lot of interest from the mathematical community, for the market making-problem in particular. The research presented in this short paper solves the classic stochastic control problem in order to derive the strategy for a market-maker. It also shows how to calibrate and simulate the strategy with real limit order book data for back-testing. The ambiguity of limit-order priority in back-testing is dealt with by considering optimistic and pessimistic priority scenarios. The model, although it does outperform a naive strategy, assumes constant volatility, therefore, is not best suited to the LOB data. The Heston model is introduced to describe the price and variance process of the asset. The Trader's constant absolute risk aversion utility function is optimised by numerically solving a 3-dimensional Hamilton-Jacobi-Bellman partial differential equation to find the optimal limit order quotes. The results show that the stochastic volatility market-making model is more suitable for a risk-averse trader and is also less sensitive to calibration error than the constant volatility model.

Keywords: market-making, market-microsctrucure, stochastic volatility, quantitative trading

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Authors: Koryukaeva Ksenia, Jinfeng Sun

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Europe has been a traditional importer of Russian natural gas for more than 50 years. In 2021, Russian state-owned company Gazprom supplied about a third of all gas consumed in Europe. The Russia-Europe mutual dependence in terms of natural gas supplies has been causing many concerns about the energy security of the two sides for a long period of time. These days the issue has become more urgent than ever considering recent Russian invasion in Ukraine followed by increased large-scale geopolitical conflicts, making the future of Russian natural gas supplies and global gas markets as well highly uncertain. Hence, the main purpose of this study is to get insight into the possible futures of Russian pipeline natural gas exports by a scenario planning method based on Monte-Carlo simulation within LUSS model framework, and propose Russian pipeline natural gas export strategies based on the obtained scenario planning results. The scenario analysis revealed that recent geopolitical disputes disturbed the traditional, longstanding model of Russian pipeline gas exports, and, as a result, the prospects and the pathways for Russian pipeline gas on the world markets will differ significantly from those before 2022. Specifically, our main findings show, that (i) the events of 2022 generated many uncertainties for the long-term future of Russian pipeline gas export perspectives on both western and eastern supply directions, including geopolitical, regulatory, economic, infrastructure and other uncertainties; (ii) according to scenario modelling results, Russian pipeline exports will face many challenges in the future, both on western and eastern directions. A decrease in pipeline gas exports will inevitably affect country’s natural gas production and significantly reduce fossil fuel export revenues, jeopardizing the energy security of the country; (iii) according to proposed strategies, in order to ensure the long-term stable export supplies in the changing environment, Russia may need to adjust its traditional export strategy by performing export flows and product diversification, entering new markets, adapting its contracting mechanism, increasing competitiveness and gaining a reputation of a reliable gas supplier.

Keywords: Russian natural gas, Pipeline natural gas, Uncertainty, Scenario simulation, Export strategy

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Authors: William Castillo, Hugo Viza, Arturo Vargas

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The Pacific Alliance is a trade bloc that is composed of four emerging economies: Chile, Colombia, Peru, and Mexico. These economies have gained macroeconomic stability in the past decade and as a consequence present future economic progress. Under this positive scenario, international business firms have flourished. However, the literature in this region has been widely unexamined. Therefore, it is critical to fill this theoretical gap, especially considering that Latin America is starting to become a global player and it possesses a different institutional context than developed markets. This paper analyzes the effect of international entrepreneurial orientation and institutionalism on international performance, for the Pacific Alliance small-to-medium enterprises (SMEs). The literature considers international entrepreneurial orientation to be a powerful managerial capability – along the resource based view- that firms can leverage to obtain a satisfactory international performance. Thereby, obtaining a competitive advantage through the correct allocation of key resources to exploit the capabilities here involved. Entrepreneurial Orientation is defined around five factors: innovation, proactiveness, risk-taking, competitive aggressiveness, and autonomy. Nevertheless, the institutional environment – both local and foreign, adversely affects International Performance; this is especially the case for emerging markets with uncertain scenarios. In this way, the study analyzes an Entrepreneurial Orientation, key endogenous variable of international performance, and Institutionalism, an exogenous variable. The survey data consists of Pacific Alliance SMEs that have foreign operations in at least another country in the trade bloc. Findings are still in an ongoing research process. Later, the study will undertake a structural equation modeling (SEM) using the variance-based partial least square estimation procedure. The software that is going to be used is the SmartPLS. This research contributes to the theoretical discussion of a largely postponed topic: SMEs in Latin America, that has had limited academic research. Also, it has practical implication for decision-makers and policy-makers, providing insights into what is behind international performance.

Keywords: institutional theory, international entrepreneurial orientation, international performance, SMEs, Pacific Alliance

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Authors: Benjamin Blat Belmonte, Stephan Rinderknecht

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The transition to electric vehicles (EVs) stands at the forefront of innovative strategies designed to address environmental concerns and reduce fossil fuel dependency. As the number of EVs on the roads increases, so too does the potential for their integration into energy markets. This research dives deep into the transformative possibilities of using electric vehicle fleets, specifically electric bus fleets, not just as consumers but as active participants in the energy market. This paper investigates the feasibility and grid effects of electric vehicle fleet operators in the energy market. Our objective centers around a comprehensive exploration of the sector coupling domain, with an emphasis on the economic potential in both electricity and balancing markets. Methodologically, our approach combines data mining techniques with thorough pre-processing, pulling from a rich repository of electricity and balancing market data. Our findings are grounded in the actual operational realities of the bus fleet operator in Darmstadt, Germany. We employ a Mixed Integer Linear Programming (MILP) approach, with the bulk of the computations being processed on the High-Performance Computing (HPC) platform ‘Lichtenbergcluster’. Our findings underscore the compelling economic potential of EV fleets in the energy market. With electric buses becoming more prevalent, the considerable size of these fleets, paired with their substantial battery capacity, opens up new horizons for energy market participation. Notably, our research reveals that economic viability is not the sole advantage. Participating actively in the energy market also translates into pronounced positive effects on grid stabilization. Essentially, EV fleet operators can serve a dual purpose: facilitating transport while simultaneously playing an instrumental role in enhancing grid reliability and resilience. This research highlights the symbiotic relationship between the growth of EV fleets and the stabilization of the energy grid. Such systems could lead to both commercial and ecological advantages, reinforcing the value of electric bus fleets in the broader landscape of sustainable energy solutions. In conclusion, the electrification of transport offers more than just a means to reduce local greenhouse gas emissions. By positioning electric vehicle fleet operators as active participants in the energy market, there lies a powerful opportunity to drive forward the energy transition. This study serves as a testament to the synergistic potential of EV fleets in bolstering both economic viability and grid stabilization, signaling a promising trajectory for future sector coupling endeavors.

Keywords: electric vehicle fleet, sector coupling, optimization, electricity market, balancing market

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Authors: Qura-tul-aain Khair

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Purpose- The purpose of this paper is to empirically examining the strength of association of responsive market orientation and proactive market orientation with new product performance and exploring the possible moderating role of organizational learning based on contingency theory. Design/methodology/approach- Data for this study was collected from FMCG manufacturing industry and services industry, where customers are in contact frequently and responses are recorded on continuous basis. Sample was collected through convenience sampling. The data collected from different marketing department and sales personnel were analysed using SPSS 16 version. Findings- The paper finds that responsive market orientation is more strongly associated with new product performance. The moderator, organizational learning, plays it significant role on the relationship between responsive market orientation and new product performance. Research limitations/implications- this paper has taken sample from just FMCG industry and service industry, more work can be done regarding how different-markets require different market orientation behaviours. Originality/value- This paper will be useful for foreign business looking for investing and expanding in Pakistan, they can find opportunity to get sustained competitive advantage through exploring the proactive side of market orientation and importance of organizational learning.

Keywords: organizational learning, proactive market orientation, responsive market orientation, new product performance

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Authors: Periklis Brakatsoulas

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Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns.

Keywords: forecasting, long memory, momentum, returns

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: Fatimh Alshahri, Muna Alqahtani

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The specific activities of 238U, 226Ra, 40K and 222Rn in chemical fertilizers were measured using gamma ray spectrometer and Cr-39 detector. In this study 21 chemical fertilizers were collected from Eastern Saudi Arabia markets. The specific activities of 238U ranged from 23 ± 0.5 to 3900 ± 195 Bq kgˉ¹, 226Ra ranged from 5.6 ± 2.8 to 392 ± 18 Bq kgˉ¹ and 40K ranged from 18.4 ± 3 to 16476 ± 820 Bq kgˉ¹. The radon concentrations and the radon exhalation rates were found to vary from 3.2 ± 1.2 to 1531.6 ± 160 Bq mˉ³ and from 1.6 to 773.7 mBq mˉ² hˉ¹, respectively. Radium equivalent activities (Raeq) were calculated for the analyzed samples to assess the radiation hazards arising due to the use of these chemical fertilizers in the agriculture soil. The Raeq for Six local samples (NPK and SSP) and one imported sample (SOP) were greater than the acceptable value 370 Bq kgˉ¹. The total air absorbed doses rates in air 1 m above the ground (D) were calculated for all samples. All samples, except one imported granule sample (DAP), were higher than the estimated average global terrestrial radiation of 55 nGy hˉ¹. The highest annual effective dose was in TSP fertilizers (2.1 mSvyˉ¹). The results show that the local TSP, imported SOP and local NPK (sample 13) fertilizers were unacceptable for use as fertilizers in agricultural soil. Furthermore, the toxic elements and trace metals (Pb, Cd, Cr, Co, Ni, Hg and As) were determined using atomic absorption spectrometer. The concentrations of chromium in chemical fertilizers were higher than the global values.

Keywords: chemical fertilizers, 238U, 222Rn, trace metals, Saudi Arabia

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Authors: Alexis Naranjo

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In the current global stage, a new sense and mix feelings against the globalization has started to take shape thanks to events such as Brexit and the 2016 US election. The perceptions towards the globalization have started to focus in a resistance movement called the 'anti-globalization movement'. This paper examines the current global stage vs. leadership decisions in a time when market integrations are not longer seeing as an opportunity for an economic growth buster. The biggest economy in the world the United States of America has started to face a new beginning of something called 'anti-globalization', in the current global stage starting with the United Kingdom to the United States a new strategy to help local economies has started to emerge. A new nationalist movement has started to focus on their local economies which now represents a direct threat to the globalization, trade agreements, wages and free markets. Business leaders of multinationals now in our days face a new dilemma, how to address the feeling that globalization and outsourcing destroy and take away jobs from local economies. The initial perception of the literature and data rebels that companies in Western countries like the US sees many risks associate with outsourcing, however, saving cost associated with outsourcing is greater than the firm’s local reputation. Starting with India as a good example of a supplier of IT developers, analysts and call centers we can start saying that India is an industrialized nation which has not yet secured its spot and title. India has emerged as a powerhouse in the outsource industry, which makes India hold the number one spot in the world to outsource IT services. Thanks to the globalization of economies and markets around the globe that new ideas to increase productivity at a lower cost has been existing for years and has started to offer new ideas and options to businesses in different industries. The economic growth of the information technology (IT) industry in India is an example of the power of the globalization which in the case of India has been tremendous and significant especially in the economic arena. This research paper concentrates in understand the behavior of business leaders: First, how multinational’s leaders will face the new challenges and what actions help them to lead in turbulent times. Second, if outsourcing or withdraw from a market is an option what are the consequences and how you communicate and negotiate from the business leader perspective. Finally, is the perception of leaders focusing on financial results or they have a different goal? To answer these questions, this study focuses on the most recent data available to outline and present the findings of the reason why outsourcing is and option and second, how and why those decisions are made. This research also explores the perception of the phenomenon of outsourcing in many ways and explores how the globalization has contributed to its own questioning.

Keywords: anti-globalization, globalization, leadership, outsourcing

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Authors: Jasmin Cowin

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Augmented reality digital technologies, big data, and the need for a teacher workforce able to meet the demands of a knowledge-based society are poised to lead to major changes in the field of education. This paper explores applications and educational use cases of augmented reality digital technologies for educational organizations during the Fourth Industrial Revolution. The Fourth Industrial Revolution requires vision, flexibility, and innovative educational conduits by governments and educational institutions to remain competitive in a global economy. Educational organizations will need to focus on teaching in and for a digital age to continue offering academic knowledge relevant to 21st-century markets and changing labor force needs. Implementation of contemporary disciplines will need to be embodied through learners’ active knowledge-making experiences while embracing ubiquitous accessibility. The power of distributed ledger technology promises major streamlining for educational record-keeping, degree conferrals, and authenticity guarantees. Augmented reality digital technologies hold the potential to restructure educational philosophies and their underpinning pedagogies thereby transforming modes of delivery. Structural changes in education and governmental planning are already increasing through intelligent systems and big data. Reimagining and redesigning education on a broad scale is required to plan and implement governmental and institutional changes to harness innovative technologies while moving away from the big schooling machine.

Keywords: fourth industrial revolution, artificial intelligence, big data, education, augmented reality digital technologies, distributed ledger technology

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Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Hoda A. Abdel Hafez

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Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: mining big data, big data, machine learning, telecommunication

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Authors: Manfred F. Maute, Olga Naumenko, Raymond T. Kong

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Using data from a nationally representative financial panel of Canadian households, this study develops a psychographic segmentation of the customers of a value-expressive credit card service and tests for effects on relational response differences. The variety of segments elicited by agglomerative and k means clustering and the familiar profiles of individual clusters suggest that the face validity of the psychographic segmentation was quite high. Segmentation had a significant effect on customer satisfaction and relationship depth. However, when socio-demographic characteristics like household size and income were accounted for in the psychographic segmentation, the effect on relational response differences was magnified threefold. Implications for the segmentation of financial services markets are considered.

Keywords: customer satisfaction, financial services, psychographics, response differences, segmentation

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Authors: Mahdi Karazmodeh, Pooyan Jafari

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One of the most interesting topics in finance is the relation between stock prices and exchange rates. During the past decades different stock markets in different countries have been the subject of study for researches. The volatilities of exchange rates and its effect on stock prices during the past 10 years have continued to be an attractive research topic. The subject of this study is one of the most important indices, FTSE 100. 20 firms with the highest market capitalization in 5 different industries are chosen. Firms are included in oil and gas, mining, pharmaceuticals, banking and food related industries. 5 different criteria have been introduced to evaluate the relationship between stock markets and exchange rates. Return of market portfolio, returns on broad index of Sterling are also introduced. The results state that not all firms are sensitive to changes in exchange rates. Furthermore, a Granger Causality test has been run to observe the route of changes between stock prices and foreign exchange rates. The results are consistent, to some level, with the previous studies. However, since the number of firms is not large, it is suggested that a larger number of firms being used to achieve the best results. However results showed that not all firms are affected by foreign exchange rates changes. After testing Granger Causality, this study found out that in some industries (oil and gas, pharmaceuticals), changes in foreign exchange rate will not cause any changes in stock prices (or vice versa), however, in banking sector the situation was different. This industry showed more reaction to these changes. The results are similar to the ones with Richards and Noel, where a variety of firms in different industries were evaluated.

Keywords: stock prices, foreign exchange rate, exchange rate exposure, Granger Causality

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Authors: Sumaira Ashraf, Elisabete G.S. Félix, Zélia Serrasqueiro

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Traditional financial distress prediction models performed well to predict bankrupt and insolvent firms of the developed markets. Previous studies particularly focused on the predictability of financial distress, financial failure, and bankruptcy of firms. This paper contributes to the literature by extending the definition of financial distress with the inclusion of early warning signs related to quotation of face value, dividend/bonus declaration, annual general meeting, and listing fee. The study used five well-known distress prediction models to see if they have the ability to predict early warning signs of financial distress. Results showed that the predictive ability of the models varies over time and decreases specifically for the sample with early warning signs of financial distress. Furthermore, the study checked the differences in the predictive ability of the models with respect to the financial crisis. The results conclude that the predictive ability of the traditional financial distress prediction models decreases for the firms with early warning signs of financial distress and during the time of financial crisis. The study developed a new model comprising significant variables from the five models and one new variable earning response. This new model outperforms the old distress prediction models before, during and after the financial crisis. Thus, it can be used by researchers, organizations and all other concerned parties to indicate early warning signs for the emerging markets.

Keywords: financial distress, emerging market, prediction models, Z-Score, logit analysis, probit model

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Authors: Piyush Hajela

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Public spaces within Indian markets are vibrant, colorful and contain dimensions that make them attractive and therefore act as popular gathering spaces. Most of these public spaces emerge as squares, plazas of varied shapes and sizes spread at different locations within the market. These public spaces grow organically and are discovered by the people themselves as they respond positively to the collective human senses. On the other hand, there are the planned and designed public spaces as well that are less active. This research evaluates both the planned and the organically transformed public spaces in Indian markets from an Urban Design point of view. The purpose of such research is to provide a basis for design solutions to ensure the success of designed public spaces. The evaluation is done for identified Attributes, namely Comfort, Protection, Familiarity, Activities, Form, Legibility, Engagement, Safety, Accessibility, Environment and Transformations by which a Public Space attains its recognition. The evaluation is based on a rating done for forty-four parameters falling under eleven attributes of public space. An opinion survey of professionals is conducted for their priorities of attributes while designing Public spaces. A comparison is made to rank these attributes between Planned and Organically transformed Public spaces and, opinion of the professionals. After dues analysis, the research suggests the learning from the organically transformed Public spaces for ensuring the success of designed public spaces. The suggestions may be in the form of Design decisions or administrative regulations, or both for achieving the desirables.

Keywords: assessment, attributes, engagement, interaction

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Authors: Jacky Liu

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This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

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Authors: M. Maleki Sadabad, A. Pointing, N. Marashi

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With respect to the role and increasing importance of technology for countries development, in recent decades technology development has paid attention in a systematic form. Nowadays the markets face with highly complicated and competitive conditions in foreign markets, therefore, evaluation and selection of technology effectiveness and also formulating technology strategy have changed into a vital subject for some organizations. The study introduces the standards of empowerment evaluation and technology attractiveness especially strategic technologies which explain the way of technology evaluation, selection and finally formulating suitable technology strategy in the field of drilling in South Azadegan oil field. The study firstly identifies the key challenges of oil fields in order to evaluate the technologies in field of drilling in South Azadegan oil field through an interview with the experts of industry and then they have been prioritised. In the following, the existing and new technologies were identified to solve the challenges of South Azadegan oil field. In order to explore the ability, availability, and attractiveness of every technology, a questionnaire based on Julie indices has been designed and distributed among the industry elites. After determining the score of ability, availability and attractiveness, every technology which has been obtained by the average of expert’s ideas, the technology package has been introduced by Morin’s model. The matrix includes four areas which will follow the especial strategy. Finally, by analysing the above matrix, the technology options have been suggested in order to select and invest.

Keywords: technology, technology identification, drilling technologies, technology capability

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Authors: Nidhal Mgadmi, Abdelhafidh Othmani

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This article examines the causal relationship between stock indices and cryptocurrencies during the current war between Russia and Ukraine. The econometric investigation runs from February 24, 2022, to April 12, 2023, focusing on seven stock market indices (S&P500, DAX, CAC40, Nikkei, TSX, MOEX, and PFTS) and seven cryptocurrencies (Bitcoin, Ethereum, Litcoin, Dash, Ripple, DigiByte and XEM). In this article, we try to understand how investors react to fluctuations in financial assets to seek safe havens in cryptocurrencies. We used dynamic causality to detect a possible causal relationship in the short term and seven models to estimate the long-term relationship between cryptocurrencies and financial assets. The causal relationship between financial market indexes and cryptocurrency coins in the short run indicates that three famous cryptocurrencies (BITCOIN, ETHEREUM, RIPPLE) and the two digital assets with minor popularity (XEM, Digibyte) are impacted by the German, Russian, and Ukrainian stock markets. In the long run, we found a positive and significate effect of the American, Canadian, French, and Ukrainian stock market indexes on Bitcoin. Thus, the stability of the traditional financial markets during the current war period can be explained on the one hand by investors’ fears of an unstable business climate, and on the other hand, by speculators’ sentiment towards new electronic products, which are perceived as hedging instruments and a safe haven in the face of the conflict between Ukraine and Russia.

Keywords: causality, stock indices, cryptocurrency, war, Russia, Ukraine

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Authors: Theertha Chandroth

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This paper presents a comparative study on how to check JSON (JavaScript Object Notation) data against XML (eXtensible Markup Language) data from a software testing point of view. JSON and XML are widely used data interchange formats, each with its unique syntax and structure. The objective is to explore various techniques and methodologies for validating comparison and integration between JSON data to XML and vice versa. By understanding the process of checking JSON data against XML data, testers, developers and data practitioners can ensure accurate data representation, seamless data interchange, and effective data validation.

Keywords: XML, JSON, data comparison, integration testing, Python, SQL

Procedia PDF Downloads 92

Authors: Kwang Yeon Kim

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This paper will explore the directly proportional relationship between the influence of Hispanophone media in U.S. markets and Hispanic population growth. Though this growth has origins across south and central America, in U.S. media markets Mexican and Cuban immigrants, have traditionally been considered the most influential. Having endured significant historical discrimination, disparagement, and ethnic framing from conventional Anglophone media, such groups have sought to form their own identities as media consuming and producing Americans of Latin American origin. Although immigrants to the U.S. have traditionally faced obstacles in access to education, children of Mexican-Americans (Chicanos) and Cuban-Americans have made significant progress in overcoming these obstacles, partly explaining their media dominance. This is particularly true in the case of Cuban-Americans, for whom such media presence is not predicted by share of population. By conducting comparative studies of Chicano media and Cuban-Americans media, common ground was found in strategies of reliance on media-driven identity formation. In contrast to the mainstream media portrayal of Latino/as with limiting, negative stereotypes, Spanish-language media’s goal is to form the identity of being Latino for those living in the United States. Providing both news from countries of origin and local news within the United States, Chicano and Cuban-American media performs rituals of recollection while rooting such populations in more proximate media paradigms.

Keywords: Chicano identity, Cuban-Americans, Hispanophone media, Latino/a community

Procedia PDF Downloads 183

Authors: Elizabeth Snyman-Van Deventer

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Numerous corporate scandals, which included investment scams, corporate malfeasance, unethical conduct and conflicts of interest, contributed to the collapse of WorldCom, Global Crossing, Xerox, Tyco, Enron, Sprint, AbbVie and Imclone and led to alarmed investors abandoning public securities markets and the tumbling of U.S stock markets. These companies suffered significant financial losses due to substantial and fraudulent misstatements and other illegal, corrupt or unethical practices. Executives were convicted of fraud and sentenced to prison. The corporate financial scandals, governance failures, and the ensuing public outcries led to mandatory legislation, e.g. the Sarbanes-Oxley Act in the USA. In European corporate scandals such as Parmalat, Royal Dutch Ahold, Vivendi, Adecco and Elan, the boards missed financial misrepresentations. In South Africa, Steinhoff is the most well-known example of corporate collapse, but now we can also add Tongaat Hulett. It seems as if fraud and corruption may be the major sources of these corporate collapses. In most instances, there is either the active involvement of the directors and managers in these fraudulent or corrupt practices, or there is a negligent or even intentional failure to act by directors to prevent these activities. However, besides directors and managers, auditors and lawyers failed in most of these companies to fulfil their professional duties. In most of these major collapses, the ethics of especially auditors and directors could be questioned. This paper will first provide a brief overview of corporate collapses. Secondly, the reasons for these collapses, with a focus on unethical conduct, will be discussed.

Keywords: professional duties, corporate collapses, ethical conduct, legal ethics, directors, auditors

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Authors: Bolin Hua

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In city governance, various data are involved, including city component data, demographic data, housing data and all kinds of business data. These data reflects different aspects of people, events and activities. Data generated from various systems are different in form and data source are different because they may come from different sectors. In order to reflect one or several facets of an event or rule, data from multiple sources need fusion together. Data from different sources using different ways of collection raised several issues which need to be resolved. Problem of data fusion include data update and synchronization, data exchange and sharing, file parsing and entry, duplicate data and its comparison, resource catalogue construction. Governments adopt statistical analysis, time series analysis, extrapolation, monitoring analysis, value mining, scenario prediction in order to achieve pattern discovery, law verification, root cause analysis and public opinion monitoring. The result of Multi-source data fusion is to form a uniform central database, which includes people data, location data, object data, and institution data, business data and space data. We need to use meta data to be referred to and read when application needs to access, manipulate and display the data. A uniform meta data management ensures effectiveness and consistency of data in the process of data exchange, data modeling, data cleansing, data loading, data storing, data analysis, data search and data delivery.

Keywords: multi-source data fusion, urban comprehensive management, information fusion, government data

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Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

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Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined.

Keywords: data mining, distributed data mining, privacy protection, privacy preserving

Procedia PDF Downloads 492