Search results for: market timing

Authors: Chanpen Meenakorn

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This research aims to study; (1) behavior of the tourists affecting the satisfaction level of tourism at the Amphawa floating market in Samut Songkhram province, (2) to study the satisfaction level of tourism at the Amphawa floating market. The research method will use quantitative research; data was collected by questionnaires distributed to the tourist who visits the Amphawa floating market for 480 samples. Data was analyzed by SPSS software to process descriptive statistic including frequency, percentage, mean, standard deviation and inferential statistic is t-test, F-test, and chi-square. The results showed that the behavior of tourists had known tourist attractions in the province comes from the mouth of relatives and friends suggested that he come here before and the reasons to visit is to want to pay homage to the various temples for the frequency to visit travel an average of 2-4 times and  the satisfaction of the tourists in the province found that the satisfaction level of tourists in the province at the significant level of the place, convenient  and services have a high level of satisfaction.

Keywords: amphawa floating market behavior of the tourists, satisfaction level, sustainable tourism, Samut Songkhram province

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Authors: Elbert S. Moyon, Edsel Matt O. Morales, Jaymer M. Jayoma, Kent C. Espejon, Jayson C. Dollete, Mark Phil B. Pacot

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This research paper focuses on the potential of using Web-GIS technology in prioritizing farm-to-market road projects by the Philippines’ Department of Agriculture (DA). The study aimed to explore the benefits of Web-GIS in addressing the limitations faced by the DA in terms of Farm to Market Road profiling and project prioritization, which include a lack of access to updated data, limited spatial analysis capabilities, and difficulties in sharing information between stakeholders. The research methodology involves a comprehensive literature review and a case study of a Web-GIS application developed for the DA, which was used to profile and prioritize farm-to-market road projects in the Philippines. The results showed that the Web-GIS technology provides the DA with an effective tool for analyzing and visualizing data, which can help in profiling and prioritizing road projects based on various criteria such as economic, social, and environmental impacts. The study also showed that Web-GIS technology could help in reducing the time and effort required for road project prioritization and improve communication between stakeholders.

Keywords: GIS, web application, farm-to-market road, FMR prioritization, Django, GeoServer

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Authors: Golamhassan Ghahramani Aghdam, Pedram Bavili Tabrizi

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The aim of this research is to investigate the relationship between accounting information and the value of the companies accepted in Tehran Exchange Market. The dependent variable in this research is the value of a company that is measured by price coefficients, and the independent variables are balance sheet information, profit and loss information, cash flow state information, and profit quality characteristics. The profit quality characteristic index is to be related and to be on-time. This research is an application research, and the research population includes all companies that are active in Tehran exchange market. The number of 194 companies was selected by the systematic method as the statistics sample in the period of 2018-2019. The multi-variable linear regression model was used for the hypotheses test. The results show that there is no relationship between accounting information and companies’ value (stock value) that can be due to the lack of efficiency of the investment market and the inability to use the accounting information by investment market activists.

Keywords: accounting information, company value, profit quality characteristics, price coefficient

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Authors: Zakir Hossain

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Nowadays reading e-books on laptops, smartphones, and tablets have become a new leisure activity for Vietnamese youth. Since 2011 the copyrighted e-book market began to develop in Vietnam with the participation of five local enterprises. Over the last five years, thousands of e-books were published including the first online early education book series for children from 0 to 6 years old. Research shows that 61% Vietnamese find reading e-books is comfortable, and 45% feel convenient buying books online. More than half of the Vietnamese consider reading online far better than buying printed books, and surprisingly people over age 30 desire reading online while those under 18 prefer reading printed books. Hence with a market of more than 40 million regular internet users including 22 million smartphone users, Vietnam has ample opportunities to develop the e-book market and contribute a great deal to the diversity of the local reading culture which is essential for Building a Lifelong Learning Society, a state ambition of Vietnam by 2020. However, the e-book market in Vietnam is still in its infancy and is growing far too slowly than e-book producers had expected. All five e-book enterprises are facing numerous challenges. While the big profit that e-book technology can bring has been clearly recognised in other countries, e-books in Vietnam only make up less than 1% share of the book market. The objective of the study is to identify the difficulties and barriers to the development of the e-book market in Vietnam through an extensive literature review available in English. The study revealed that illegal e-books due to copyright infringement and an inconvenient payment system to purchase e-books are the major obstacles. The great potential of e-books in Vietnam is a reality but requires government enforcement of copyright protection laws, a new area of focus for the e-book market. Furthermore, Vietnamese readers should change their habits from using free and illegal e-books to develop the e-publishing industry in Vietnam.

Keywords: copyright, e-book, e-book reading, e-publishing, Vietnam

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Authors: Abderrazek Ben Maatoug, Ibrahim Fatnassi, Wassim Ben Ayed

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In this paper we investigate the issue of market risk quantification for Middle East and North Africa (MENA) Islamic market equity. We use Value-at-Risk (VaR) as a measure of potential risk in Islamic stock market, for long and short position, based on Riskmetrics model and the conditional parametric ARCH class model volatility with normal, student and skewed student distribution. The sample consist of daily data for the 2006-2014 of 11 Islamic stock markets indices. We conduct Kupiec and Engle and Manganelli tests to evaluate the performance for each model. The main finding of our empirical results show that (i) the superior performance of VaR models based on the Student and skewed Student distribution, for the significance level of α=1% , for all Islamic stock market indices, and for both long and short trading positions (ii) Risk Metrics model, and VaR model based on conditional volatility with normal distribution provides the best accurate VaR estimations for both long and short trading positions for a significance level of α=5%.

Keywords: value-at-risk, risk management, islamic finance, GARCH models

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Beata Wentura-Dudek

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In Sweden the needs of the labor market are regularly monitored. Test results and forecasts translate directly into the education system in this country, which is largely a state system. Sweden is one of the first countries in Europe that has used active labor market policies. It is realized that there is an active unemployment which includes a wide range of activities that can be divided into three groups: Active forms of influencing the creation of new jobs, active forms that affect the labor supply and active forms for people with disabilities. Most of the funding is allocated there for subsidized employment and training. Research conducted in Sweden shows that active forms of counteracting unemployment focused on the long-term unemployed can significantly raise the level of employment in this group.

Keywords: Sweden, research conducted in Sweden, labour market, labour market policies, unemployment, active forms of influencing the creation of new jobs, active forms of counteracting unemployment, employment, subsidized employment education

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Authors: Mahmoud Hassan

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Understanding the impact of market- and non-market-based environmental policy instruments on renewable energy consumption (REC) is crucial for the design and choice of policy packages. This study aims to empirically investigate the effect of environmental policy stringency index (EPS) and its components on REC in 27 OECD countries over the period from 1990 to 2015, and then use the results to identify what the appropriate environmental policy mix should look like. By relying on the two-step system GMM estimator, we provide evidence that increasing environmental policy stringency as a whole promotes renewable energy consumption in these 27 developed economies. Moreover, policymakers are able, through the market- and non-market-based environmental policy instruments, to increase the use of renewable energy. However, not all of these instruments are effective for achieving this goal. The results indicate that R&D subsidies and trading schemes have a positive and significant impact on REC, while taxes, feed-in tariff and emission standards have not a significant effect. Furthermore, R&D subsidies are more effective than trading schemes for stimulating the use of clean energy. These findings proved to be robust across the three alternative panel techniques used.

Keywords: environmental policy stringency, renewable energy consumption, two-step system-GMM estimation, linear dynamic panel data model

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Authors: Marc S. Paolella, Pawel Polak

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The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and non-ellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio optimization with active risk monitoring. The former selects optimal portfolio weights. The latter, independently, initiates market exit in case of excessive risks. The strategy agrees with the stylized fact of stock market major sell-offs during the initial stage of market downturns. The advantages of the new framework are illustrated with an extensive empirical study. It leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk fear portfolio strategy outperforms various competing types of optimal portfolios, even in the presence of conservative transaction costs and frequent rebalancing. The risk monitoring of the optimal portfolio can serve as an early warning system against large market risks. In particular, the new strategy avoids all the losses during the 2008 financial crisis, and it profits from the subsequent market recovery.

Keywords: comfort, financial crises, portfolio optimization, risk monitoring

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Authors: Seyi Olalekan Olawuyi, Abbyssiania Mushunje

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The potential benefits of agricultural production to improve the welfare condition of smallholder farmers in developing countries is no more a news because it has been widely documented. Yet majority of these farming households suffer from shortfall in production output to meet both the consumption needs and market demand which adversely affects output market participation and by extension welfare condition. Therefore, this study investigated the impacts of output market participation on households’ welfare of cassava-based farmers in Oyo State, Nigeria. Multistage sampling technique was used to select 324 sample size used for this study. The findings from the data obtained and analyzed through composite score and crosstab analysis revealed that there is varying degree of output market participation among the farmers which also translate to the observed welfare profile differentials in the study area. The probit model analysis with respect to the selection equation identified gender of household head, household size, access to remittance, off-farm income and ownership of farmland as significant drivers of output market participation in the study area. Furthermore, the treatment effect model of the welfare equation and propensity score matching (PSM) technique were used as robust checks; and the findings attest to the fact that, complimentarily with other significant variables highlighted in this study, output market participation indeed has a significant impact on farming households’ welfare. As policy implication inferences, the study recommends female active inclusiveness and empowerment in farming activities, birth control strategies, secondary income smoothing activities and discouragement of land fragmentation habits, to boost productivity and output market participation, which by extension can significantly improve farming households’ welfare.

Keywords: Cassava market participation, households' welfare, propensity score matching, treatment effect model

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Authors: Ly Nguyen, Vidit Munshi

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During the Covid-19 recession, the United States government has implemented several instruments to mitigate the impacts and revitalize the economy. This paper explores the effects of the various government policy decisions on stock returns, housing prices, and investor psychology during the pandemic in the United States. A numerous previous literature studies on this subject, yet very few focus on the context similar to what we are currently experiencing. Our monthly data covering the period from January 2019 through July 2021 were collected from Datastream. Utilizing the VAR model, we document a dynamic relationship between the market and policy actions throughout the period. In particular, the movements of Unemployment, Stock returns, and Housing prices are strongly sensitive to changes in government policies. Our results also indicate that changes in production level, stock returns, and interest rates decisions influence how investors perceived future market risk and expectations. We do not find any significant nexus between monetary and fiscal policy. Our findings imply that information on government policy and stock market performance provide useful feedback to one another in order to make better decisions in the current and future pandemic. Understanding how the market responds to a shift in government practices has important implications for authorities in implementing policy to avoid assets bubbles and market overreactions. The paper also provides useful implications for investors in evaluating the effectiveness of different policies and diversifying portfolios to minimize systematic risk and maximize returns.

Keywords: Covid-19 recession, United States, government policies, investor psychology, housing prices, stock market returns

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Authors: Zafar Mahmood, Surayya Naz, Nazir Shah Khattak

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The degree of freedom relates to our capability to observe or model the energy paths within the system. Higher the number of energy paths being modeled leaves to us a higher degree of freedom, but increasing the time and modeling complexity rendering it useless for today’s world’s need for minimum time to market. Since the number of residuals that can be uniquely isolated are dependent on the number of independent outputs of the system, increasing the number of sensors required. The examples of discrete position sensors that may be used to form an array include limit switches, Hall effect sensors, optical sensors, magnetic sensors, etc. Their mechanical design can usually be tailored to fit in the transitional path of an STME in a variety of mechanical configurations. The case studies into multi-sensor system were carried out and actual data from sensors is used to test this generic framework. It is being investigated, how the proper modeling of limit switches as timing sensors, could lead to unified and neutral residual space while keeping the implementation cost reasonably low.

Keywords: low-cost limit sensors, fault diagnostics, Single Throw Mechanical Equipment (STME), parameter estimation, parity-space

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Authors: Mehdi Seifbarghy, Zahra Nasiri

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Competitive location problem deals with locating new facilities to provide a service (or goods) to the customers of a given geographical area where other facilities (competitors) offering the same service are already present. The new facilities will have to compete with the existing facilities for capturing the market share. This paper proposes a new model to maximize the market share in which customers choose the facilities based on traveling time, waiting time and attractiveness. The attractiveness of a facility is considered as a parameter in the model. A heuristic is proposed to solve the problem.

Keywords: competitive location, market share, facility attractiveness, heuristic

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Authors: Olivia Rybak-Karkosz

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Art forgery is a serious problem present in the art market in every country despite its scale and experience. In the Polish art market, this problem has existed since its beginnings. The market expansion in recent years attracted new buyers, which led to growing prices of polish art. And that attracted deceitful sellers who supply the market with forgeries. Moreover, there are many new types of buyers, many of whom are art non-specialists. But even the most experienced collectors must be cautious when purchasing a piece of art. In this paper, the author would like to discuss legal acts in Polish law that criminalize the forgery of a piece of art and compare them with similar regulations from four European countries - the Italian Republic, Kingdom of the Netherlands, French Republic, and the Federal Republic of Germany. The author wants to verify if any solutions could inspire Polish legislators to implement them in domestic law to help reduce this crime and improve the criminal procedure of art forgery. The paper contains a concluding statement to implement a similar solution used in one of the presented countries.

Keywords: art forgery, comparative law, criminal law, criminal liability, protection of works of art

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Authors: Yong-Heng Shi, Peng Hao, Bai-Chen Xie

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Solar prosumers are playing increasingly prominent roles in the power system. However, its uncertainty affects the outcomes and functions of the power market, especially in the asymmetric information environment. Therefore, an important issue is how to take effective measures to reduce the impact of uncertainty on market equilibrium. We propose a two-level stochastic differential game model to explore the Cournot decision problem of prosumers. In particular, we study the impact of punishment and cooperation mechanisms on the efficiency of the Cournot game in which prosumers face uncertainty. The results show that under the penalty mechanism of fixed and variable rates, producers and consumers tend to take conservative actions to hedge risks, and the variable rates mechanism is more reasonable. Compared with non-cooperative situations, prosumers can improve the efficiency of the game through cooperation, which we attribute to the superposition of market power and uncertainty reduction. In addition, the market environment of asymmetric information intensifies the role of uncertainty. It reduces social welfare but increases the income of prosumers. For regulators, promoting alliances is an effective measure to realize the integration, optimization, and stable grid connection of producers and consumers.

Keywords: Cournot games, power market, uncertainty, prosumer cooperation

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Authors: Hung-Yi Chen, Shih-Jye Wu

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The existing literature provides little discussion on why a public monopolist gives up its market dominant position and allows private firms entering the market. We argue that the privatization of a public monopolist under a vertically related market may induce the entry of private firms. We develop a model of a mixed oligopoly with vertically related markets to explain the change in the market from a public monopolist to a mixed oligopoly and examine issues on privatizing the downstream public enterprise both in the short run and long run in the vertically related markets. We first show that the welfare-maximizing public monopoly firm is suboptimal in the vertically related markets. This is due to the fact that the privatization will reduce the input price charged by the upstream foreign monopolist. Further, the privatization will induce the entry of private firms since input price will decrease after privatization. Third, we demonstrate that the complete privatizing the public firm becomes a possible solution if the entry cost of private firm is low. Finally, we indicate that the public firm should partially privatize if the free-entry of private firms is allowed. JEL classification: F12, F14, L32, L33

Keywords: free entry, mixed oligopoly, public monopoly, the initiation of privatization, vertically related markets, mixed oligopoly

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: Vilma Deltuvaitė

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Comparisons of financial development across countries are central to answering many of the questions on factors leading to economic development. For this reason this study analyzes the implications of financial system’s development on country’s economic development. The aim of the article: to analyze the impact of financial system’s development on economic development. The following research methods were used: systemic, logical and comparative analysis of scientific literature, analysis of statistical data, time series model (Autoregressive Distributed Lag (ARDL) Model). The empirical results suggest about positive short and long term effect of stock market development on GDP per capita.

Keywords: banking sector, economic development, financial system’s development, stock market, private bond market

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Authors: M. G. Tanga, P. B. Telefo, D. N. Tarla

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Even though the WHO, the EPA and other regulatory bodies have recognized the effects of acute pesticide poisoning little data exists on health effects after long-term low-dose exposures especially in Africa and Cameroon. The aim of this study was to evaluate the impact of pesticides on the hepatic functions of market gardeners in the Western Region of Cameroon by studying some biochemical parameters. Sixty six male market gardeners in Foumbot, Massangam, and Bantoum were interviewed on their health status, habits and pesticide use in agriculture, including the spray frequency, application method, and pesticide dosage. Thirty men with no history of pesticide exposure were recruited as control group. Thereafter, their blood samples were collected for assessment of hepatic function biomarkers (ALT, AST, and albumin). The results showed that 56 pesticides containing 25 active ingredients were currently used by market gardeners enrolled in our study and most of their symptoms (headache, fatigue, skin rashes, eye irritation, and nausea) were related to the use of these chemicals. Compared to the control subjects market gardeners’ ALT levels (32.9 ± 7.19 UL-1 vs. 82.11 ± 35.40 UL-1; P < 0.001) and, AST levels (40.63 ± 6.52 UL-1 vs. 112.11 UL-1 ± 47.15 UL-1; P < 0.001) were significantly increased. These results suggest that liver function tests can be used as biomarkers to indicate toxicity before overt clinical signs occur. The market gardeners’ chronic exposure to pesticides due to poor application measures could lead to hepatic function impairment. Further research on larger scale is needed to confirm these findings and to establish a mechanism of toxicity.

Keywords: biomarkers, liver, pesticides, occupational exposure

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Authors: Ali Bhayani

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The article analyses determinants of the market entry modes used by corporate firms to expand internationally and explore whether higher education institutions uses the same determinants to decide on mode adopted to enter the market. Determinants like transaction costs, location advantage, idiosyncratic capabilities, isomorphic pressure to mimic, psychic distance, uncertainty, risks, the control over academic process, previous internationalisation experience and entry to homogenous markets are considered with regards to universities. A sample consisting of 40+ branch campuses from United Arab Emirates (UAE), host to highest number of branch campuses, is selected to study the determinants of the entry modes adopted. The aim of this article is not to prescribe or offer a solution for the best-available model of market entry that can be adopted by universities but rather to act as a trigger for a critical check up on universities planning to internationalize their offering. Determinants like idiosyncratic capabilities, isomorphic pressure and control over the academic process were found to be most prevalent. However, determinants like transaction cost efficiency, internationalisation experience, psychic distance, uncertainty and risks are not significant factors.

Keywords: higher education, UAE, internationalisation, market entry, international branch campuses

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Authors: Yan-Shen Yang, Bai-Chen Xie

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Background and significance of the study: Both green power trading schemes and interregional power transmission are effective ways to increase green power absorption and achieve renewable power development goals. China accelerates the construction of interregional power transmission lines and the green power market. A critical issue focusing on the close interaction between these two approaches arises, which can heavily affect the green power quota allocation and renewable power development. Existing studies have not discussed this issue adequately, so it is urgent to figure out their relationship to achieve a suitable power market design and a more reasonable power grid construction.Basic methodologies: We develop an equilibrium model of the power market in China to analyze the coupling effect of these two approaches as well as their influence on power generation and interregional transmission in China. Our model considers both the Tradable green certificate (TGC) and green power market, which consists of producers, consumers in the market, and an independent system operator (ISO) minimizing the total system cost. Our equilibrium model includes the decision optimization process of each participant. To reformulate the models presented as a single-level one, we replace the producer, consumer, ISO, and market equilibrium problems with their Karush-Kuhn-Tucker (KKT) conditions, which is further reformulated as a mixed-integer linear programming (MILP) and solved in Gurobi solver. Major findings: The result shows that: (1) the green power market can significantly promote renewable power absorption while the TGC market provides a more flexible way for green power trading. (2) The phenomena of inefficient occupation and no available transmission lines appear simultaneously. The existing interregional transmission lines cannot fully meet the demand for wind and solar PV power trading in some areas while the situation is vice versa in other areas. (3) Synchronous implementation of green power and TGC trading mechanism can benefit the development of green power as well as interregional power transmission. (4) The green power transaction exacerbates the unfair distribution of carbon emissions. The Carbon Gini Coefficient is up to 0.323 under the green power market which shows a high Carbon inequality. The eastern coastal region will benefit the most due to its huge demand for external power.

Keywords: green power market, tradable green certificate, interregional power transmission, power market equilibrium model

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Authors: K. Matušínská, H. Starzyczná, M. Stoklasa

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The paper deals with behaviour of the segment 50+ in the financial market in the Czech Republic. This segment could be said as the strong market power and it can be a crucial business potential for financial business units. The main defined objective of this paper is analysis of the customers´ behaviour of the segment 50-60 years in the financial market in the Czech Republic and proposal making of the suitable marketing approach to satisfy their demands in the area of product, price, distribution and marketing communication policy. This paper is based on data from one part of primary marketing research. Paper determinates the basic problem areas as well as definition of financial services marketing, defining the primary research problem, hypothesis and primary research methodology. Finally suitable marketing approach to selected sub-segment at age of 50-60 years is proposed according to marketing research findings.

Keywords: population aging in the Czech Republic, segment 50-60 years, financial services marketing, marketing research, marketing approach

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Authors: Martin Pellon Consunji

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Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

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Authors: Ahmed Kiani

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The United Arab Emirates is clearly facing a multitude of challenges in curbing its greenhouse gas emissions to meet its pre-allotted framework of Kyoto protocol and COP21 targets due to its hunger for modernization, industrialization, infrastructure growth, soaring population and oil and gas activity. In this work, we focus on the bonafide zero emission electric vehicles market penetration in the country’s transport industry for emission reduction. We study the global electric vehicle market trends, the complementary battery technologies and the trends by manufacturers, emission standards across borders and prioritized advancements which will ultimately dictate the terms of future conditions for the United Arab Emirate transport industry. Based on our findings and analysis at every stage of current viability and state-of-transport-affairs, we postulate policy recommendations to local governmental entities from a supply and demand perspective covering aspects of technology, infrastructure requirements, change in power dynamics, end user incentives program, market regulators behavior and communications amongst key stakeholders. 

Keywords: electric vehicles, greenhouse gas emission reductions, market analysis, policy recommendations

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Authors: Ruixin Zhang, Joel Carboni, Songchenchen Gong

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Since the Sustainable Development Goals (SDGs) officially became the 17 development goals set by the United Nations in 2015, it has become an inevitable trend in project management development globally. Since Sustainability and glob-alization are the main focus and trends in the 21st century, project management contains system-based optimization, and or-ganizational humanities, environmental protection, and economic development. As a populous country globally, with the advanced development of economy and technology, China becomes one of the biggest markets in the wine industry. However, the develop-ment of society also brings specific environmental issues. Climate changes have already brought severe impacts on the Chinese wine market, including consumer behavior, wine production activities, and organizational humanities. Therefore, the implementation of sustainable project management in Chinese wine market is essential. Surveys based analysis is the primary method to interpret how the climate variation effect the Chinese wine market and the importance of sustainable project management implementation for green market growth in China. This paper proposes the CWW Conceptual model that can be used in the wine industry, the new 7 Drivers Model, and SPM Framework to interpret the main drivers that impact project management implementation in the wine industry and to offer the directions to wine companies in China which would help them to achieve the green growth.

Keywords: project management, sustainability, green growth, climate changes, Chinese wine market

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Authors: Chinmay Shah

Abstract:

The project is all about development of a close loop traffic light control system using PLC (Programmable Logic Controller). This project is divided into two parts which are hardware and software. The hardware part for this project is a model of four way junction of a traffic light. Three indicator lamps (Red, Yellow and Green) are installed at each lane for represents as traffic light signal. This traffic control model is a replica of actuated traffic control. Actuated traffic control system is a close loop traffic control system which controls the timing of the indicator lamps depending on the fluidity of traffic for a particular lane. To make it autonomous, in each lane three IR sensors are placed which helps to sense the percentage of traffic present on any particular lane. The IR Sensors and Indicator lamps are connected to LG PLC XGB series. The PLC controls every signal which is coming from the inputs (IR Sensors) to software and display to the outputs (Indicator lamps). Default timing for the indicator lamps is 30 seconds for each lane. But depending on the percentage of traffic present, if the traffic is nearly 30-35%, green lamp will be on for 10 seconds, for 65-70% traffic it will be 20 seconds, for full 100% traffic it will be on for full 30 seconds. The software part that operates with LG PLC is “XG 5000” Programmer. Using this software, the ladder logic diagram is programmed to control the traffic light base on the flow chart. At the end of this project, the traffic light system is actuated successfully by PLC.

Keywords: close loop, IR sensor, PLC, light control system

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Authors: Fatemeh Torabi, Jamal El-Den, Narumon Sriratanviriyakul

Abstract:

Perpetual changes in the workplace and daily business activities bring a need for imbedding organizational knowledge sharing within the organizations’ culture, routines and processes. Organizations should adapt to the changing in the environment in order to survive. Accordingly, the management should promote a knowledge sharing culture which might result in knowledge accumulation, hence better response to these changing environmental conditions. Researchers in the field of strategy and marketing stressed that employees’, as well as the overall performance of the organization, would improve as a result of implementing a knowledge-oriented culture. The research investigated the significant impact of knowledge sharing on market response and the competitiveness of organizations. A knowledge sharing framework was developed based on current literary frameworks with additional constructs such as employees’ learning commitments, experiences and prior knowledge. Linear regression was used to analyze the relationships among dependent and independent variables. The research’s results indicated strong positive correlation between the dependent and independent variables, especially in organizational market sharing. We anticipate that this correlation would improve organizational knowledge sharing related practices and the associated knowledge entities. The research posits the introduced framework could be a solid ground for further investigations on how some organizational factors would influence the organization’s response to the market as well as on competitiveness. Final results support all hypotheses. Finding of this research show that knowledge sharing intention had the significant and positive effect on market response and competitiveness of organizations.

Keywords: knowledge management, knowledge sharing, market response, organizational competitiveness

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Authors: Cheryl Marie Cordeiro

Abstract:

Although fisheries and aquaculture studies might seem marginal to international business (IB) studies in general, fisheries and aquaculture IB (FAIB) management is currently facing increasing pressure to meet global demand and consumption for fish in the next coming decades. In part address to this challenge, the purpose of this systematic review of literature (SLR) study is to investigate the use of the term ‘market access’ in its context of use in the generic literature and business sector discourse, in comparison to the more specific literature and discourse in fisheries, aquaculture and seafood. This SLR aims to uncover the knowledge/interest gaps between the academic subject discourses and business sector practices. Corpus driven in methodology and using a triangulation method of three different text analysis software including AntConc, VOSviewer and Web of Science (WoS) analytics, the SLR results indicate a gap in conceptual knowledge and business practices in how ‘market access’ is conceived and used in the context of the pharmaceutical healthcare industry and FAIB research and practice. While it is acknowledged that the product orientation of different business sectors might differ, this SLR study works with the assumption that both business sectors are global in orientation. These business sectors are complex in their operations from product to market. This SLR suggests a conceptual model in understanding the challenges, the potential barriers as well as avenues for solutions to developing market access for FAIB.

Keywords: market access, fisheries and aquaculture, international business, systematic literature review

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Authors: Ryan B. Simpson, Margaret A. Waskow, Aishwarya Venkat, Elena N. Naumova

Abstract:

The Centers for Disease Control and Prevention (CDC) estimate that 31 known foodborne pathogens cause 9.4 million cases of these illnesses annually in US. Over 90% of these illnesses are associated with exposure to Campylobacter, Cryptosporidium, Cyclospora, Listeria, Salmonella, Shigella, Shiga-Toxin Producing E.Coli (STEC), Vibrio, and Yersinia. Contaminated products contain parasites typically causing an intestinal illness manifested by diarrhea, stomach cramping, nausea, weight loss, fatigue and may result in deaths in fragile populations. Since 1998, the National Outbreak Reporting System (NORS) has allowed for routine collection of suspected and laboratory-confirmed cases of food poisoning. While retrospective analyses have revealed common pathogen-specific seasonal patterns, little is known concerning the stability of those patterns over time and whether they can be used for preventative forecasting. The objective of this study is to construct a calendar of foodborne outbreaks of nine infections based on the peak timing of outbreak incidence in the US from 1996 to 2017. Reported cases were abstracted from FoodNet for Salmonella (135115), Campylobacter (121099), Shigella (48520), Cryptosporidium (21701), STEC (18022), Yersinia (3602), Vibrio (3000), Listeria (2543), and Cyclospora (758). Monthly counts were compiled for each agent, seasonal peak timing and peak intensity were estimated, and the stability of seasonal peaks and synchronization of infections was examined. Negative Binomial harmonic regression models with the delta-method were applied to derive confidence intervals for the peak timing for each year and overall study period estimates. Preliminary results indicate that five infections continue to lead as major causes of outbreaks, exhibiting steady upward trends with annual increases in cases ranging from 2.71% (95%CI: [2.38, 3.05]) in Campylobacter, 4.78% (95%CI: [4.14, 5.41]) in Salmonella, 7.09% (95%CI: [6.38, 7.82]) in E.Coli, 7.71% (95%CI: [6.94, 8.49]) in Cryptosporidium, and 8.67% (95%CI: [7.55, 9.80]) in Vibrio. Strong synchronization of summer outbreaks were observed, caused by Campylobacter, Vibrio, E.Coli and Salmonella, peaking at 7.57 ± 0.33, 7.84 ± 0.47, 7.85 ± 0.37, and 7.82 ± 0.14 calendar months, respectively, with the serial cross-correlation ranging 0.81-0.88 (p < 0.001). Over 21 years, Listeria and Cryptosporidium peaks (8.43 ± 0.77 and 8.52 ± 0.45 months, respectively) have a tendency to arrive 1-2 weeks earlier, while Vibrio peaks (7.8 ± 0.47) delay by 2-3 weeks. These findings will be incorporated in the forecast models to predict common paths of the spread, long-term trends, and the synchronization of outbreaks across etiological agents. The predictive modeling of foodborne outbreaks should consider long-term changes in seasonal timing, spatiotemporal trends, and sources of contamination.

Keywords: foodborne outbreak, national outbreak reporting system, predictive modeling, seasonality

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