Search results for: stock predictions

Authors: Chiung-Yao Huang, Yu-Cheng Lin, Chiung-Hui Chen

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This study seeks to examine perceived environmental corporate social responsibility (ECSR) with a focus on negative environmental questions, related to intention of ethical investment intention after a environmental failure recovery. An empirical test was employed to test the hypotheses. We manipulated the information on negative ECSR activities of a hypothetical firm in a experimental design with a failure recovery treatment. The company’s negative ECSR recovery was depicted in a positive perspective (depicting a follow-up strong social action), whereas in the negative ECSR treatment it was described in a negative perspective (depicting a follow-up non social action). In both treatments, information about other key characteristics of the focal company were kept constant. Investors’ intentions to invest in the company’s stock were evaluated by multi-item scales. Results indicate that positive ECSR recovery information about a firm enhances investors’ intentions to invest in the company’s stock. In addition, perceived moral intensity has a significant impact on the intention of ethical investment and that perceived moral intensity also serves as a key moderating variable in the relationship between negative ECSR and the intention of ethical investment. Finally, theoretical and managerial implications of the findings are discussed. Practical implications: The results suggest that managers may need to be aware of perceived moral intensity as a key variable in restoring the intention of ethical investment. The results further suggest that perceived moral intensity has a direct, and it also has an moderating influence between ECSR and the intention of ethical investment. Originality/value: In an attempt to deepen the understanding of how investors perceptions of firm environmental CSR are connected with other investor‐related outcomes through ECSR recovery, the present research proposes a comprehensive model which encompasses ECSR and other key relationship constructs after a ECSR failure and recovery.

Keywords: ethical investment, Environmental Corporate Social Responsibility(ECSR), ECSR recovery, moral intensity

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Authors: Haouam Djemaa

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This study aimed to identify the possibility for applying fair value accounting by Algerian enterprises coted in capital maket (Algiers stock exchange). To achieve the objectives of this study, we made an interview with preparers of accounting information. The results document that enterprises are aware of fair value accounting in financial reporting because of its ability to provide useful accounting, but it depends on the availability of favorable circumstances for its application and this is what is missing in the Algerian environment.

Keywords: fair value, financial reporting, accounting information, valuation method

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Zeynep Şahin, Züleyha Yılmaz, Fikret Çankaya

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In today's business world, in which it is difficult to survive, the economic life of products, services or knowledge is considerably reduced. Competitors produce similar products or extra-featured ones instantly. In this environment, the contribution of companies to the social and economic environment is a preferred criterion by consumers alongside products or services. Therefore, consumers need to obtain more detailed information about companies. Besides, this drastic change in the market encourages companies to become sustainable. Sustainable business means the company puts consumed products back. Corporate sustainability, corresponds to sustainability at the level of the company, and gives equal importance to company growth and profitability together with environmental and social issues. The BIST Sustainability Index started to be calculated by the Istanbul Stock Exchange (BIST) in 2014 to evaluate the sustainability performance of companies in Turkey. The main objective of this study is to present the importance of sustainability reports in Turkey. To this aim, the performances of 15 companies in the BIST Sustainability Index were compared the periods before and after entering the index. On the other hand, sustainability reporting practices should be encouraged to increase studies on this issue. In this context, to remain on the agenda of the issue is a further objective of this study. To achieve these objectives, the financial data of the companies in the period before and after entering to the BIST Sustainability Index were analyzed using t-test in Statistical Package for the Social Sciences (SPSS) package. The results of the study showed that no significant difference between the performances of the companies in terms of the net profit margin, the return on assets and equity capital in these periods could be found. Therefore, it can be said that insufficient importance is given to sustainability issues in Turkey. The reasons for this situation might be considered as a lack of awareness due to the recent introduction and calculation of the index. It is expected that the awareness of firms and investors about sustainability will increase, and that they will demonstrate the necessary importance to this issue over time.

Keywords: sustainability reporting, sustainability index, firm performance, BIST sustainability index

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Authors: Pieter C. M. Cornelis

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European theme parks invest approximately 10 percent of their yearly turnover into new rides and park improvements. Without these investments these parks assume not to be a very competitive and appealing daytrip for their target audiences. However, the impact of investments in attracting new visitors is not well-known and seems to differ dramatically between parks. This paper presents a case study from the Netherlands in which a small amusement park applied a suggested, not yet proven, investment method. The results of the investment are discussed in (a) the form of return on investment and (b) the success of the predictions with regard to this investment. Suggestions for future research are presented.

Keywords: entertainment industry, innovation, investments, theme parks

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Authors: Nuri Onat, Omer Tatari, Gokhan Egilmez

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The U.S. buildings consume significant amount of energy and natural resources and they are responsible for approximately 40 % of the greenhouse gases emitted in the United States. Awareness of these environmental impacts paved the way for the adoption of green building movement. The green building movement is a rapidly increasing trend. Green Construction market has generated $173 billion dollars in GDP, supported over 2.4 million jobs, and provided $123 billion dollars in labor earnings. The number of LEED certified buildings is projected to be almost half of the all new, nonresidential buildings by 2015. National Science and Technology Council (NSTC) aims to increase number of net-zero energy buildings (NZB). The ultimate goal is to have all commercial NZB by 2050 in the US (NSTC 2008). Green Building Initiative (GBI) became the first green building organization that is accredited by American National Standards Institute (ANSI), which will also boost number of green buildings certified by Green Globes. However, there is much less focus on greening the residential buildings, although the environmental impacts of existing residential buildings are more than that of commercial buildings. In this regard, current research aims to model the residential green building movement with a dynamic model approach and assess the possible strategies to stabilize the carbon footprint of the U.S. residential building stock. Three aspects of sustainable development are considered in policy making, namely: high performance green building (HPGB) construction, NZB construction and building retrofitting. 19 different policy options are proposed and analyzed. Results of this study explored that increasing the construction rate of HPGBs or NZBs is not a sufficient policy to stabilize the carbon footprint of the residential buildings. Energy efficient building retrofitting options are found to be more effective strategies then increasing HPGBs and NZBs construction. Also, significance of shifting to renewable energy sources for electricity generation is stressed.

Keywords: green building movement, residential buildings, carbon footprint, system dynamics

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Authors: Evelyn Castle, Adam Thompson

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Background: In 2013, the Kano State Primary Health Care Management Board redesigned its Routine immunization supply chain from diffused pull to direct delivery push. It addressed issues around stockouts and reduced time spent by health facility staff collecting, and reporting on vaccine usage. The health care board sought the help of a 3PL for twice-monthly deliveries from its cold store to 484 facilities across 44 local governments. eHA’s Health Delivery Systems group formed a 3PL to serve 326 of these new facilities in partnership with the State. We focused on designing and implementing a technology system throughout. Basic methodologies: GIS Mapping: - Planning the delivery of vaccines to hundreds of health facilities requires detailed route planning for delivery vehicles. Mapping the road networks across Kano and Bauchi with a custom routing tool provided information for the optimization of deliveries. Reducing the number of kilometers driven each round by 20%, - reducing cost and delivery time. Direct Delivery Information System: - Vaccine Direct Deliveries are facilitated through pre-round planning (driven by health facility database, extensive GIS, and inventory workflow rules), manager and driver control panel customizing delivery routines and reporting, progress dashboard, schedules/routes, packing lists, delivery reports, and driver data collection applications. Move: Last Mile Logistics Management System: - MOVE has improved vaccine supply information management to be timely, accurate and actionable. Provides stock management workflow support, alerts management for cold chain exceptions/stock outs, and on-device analytics for health and supply chain staff. Software was built to be offline-first with user-validated interface and experience. Deployed to hundreds of vaccine storage site the improved information tools helps facilitate the process of system redesign and change management. Findings: - Stock-outs reduced from 90% to 33% - Redesigned current health systems and managing vaccine supply for 68% of Kano’s wards. - Near real time reporting and data availability to track stock. - Paperwork burdens of health staff have been dramatically reduced. - Medicine available when the community needs it. - Consistent vaccination dates for children under one to prevent polio, yellow fever, tetanus. - Higher immunization rates = Lower infection rates. - Hundreds of millions of Naira worth of vaccines successfully transported. - Fortnightly service to 326 facilities in 326 wards across 30 Local Government areas. - 6,031 cumulative deliveries. - Over 3.44 million doses transported. - Minimum travel distance covered in a round of delivery is 2000 kms & maximum of 6297 kms. - 153,409 kms travelled by 6 drivers. - 500 facilities in 326 wards. - Data captured and synchronized for the first time. - Data driven decision making now possible. Conclusion: eHA’s Vaccine Direct delivery has met challenges in Kano and Bauchi State and provided a reliable delivery service of vaccinations that ensure t health facilities can run vaccination clinics for children under one. eHA uses innovative technology that delivers vaccines from Northern Nigerian zonal stores straight to healthcare facilities. Helped healthcare workers spend less time managing supplies and more time delivering care, and will be rolled out nationally across Nigeria.

Keywords: direct delivery information system, health delivery system, GIS mapping, Northern Nigeria, vaccines

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Authors: Mohammad Julhas Sujan, Md. Ferdous Alam

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Maternal, neonatal, and child health (MNCH) holds one of the prime focuses in Bangladesh’s national healthcare system. To save the lives of mothers and children, knowing the stock of MNCH medicines in different healthcare facilities and when to replenish them are essential. A robust information system not only facilitates efficient management of the essential MNCH medicines but also helps effective allocation of scarce resources. In Bangladesh, Supply chain management of the 25-essential life-saving medicines are currently tracked and monitored via an electronic logistics management information system (eLMIS). Our aim was to conduct a cross-sectional study with a year (2020) worth of data from 24 districts of Bangladesh to evaluate how eLMIS is helping the Government and other stakeholders in efficient supply chain management. Data were collected from 4711 healthcare facilities ranging from primary to secondary levels within a district. About 90% (4143) are community clinics which are considered primary health care facilities in Bangladesh. After eLMIS implementation, the average reporting rate across the districts has been increased (> 97%). The month of stock (MOS) of zinc is an average 6 months compared to Inj. Magnesium Sulphate which will take 2.5 years to consume according to the current average monthly consumption (AMC). Due to first approaching expiry, Tab. Misoprostol, 7.1% Chlorhexidine and Inj. Oxytocin may become unusable. Moreover, Inj. Oxytocin is temperature sensitive and may reduce its efficacy if it is stocked for a longer period. In contrast, Zinc should be sufficiently stocked to prevent sporadic stockouts. To understand how data are collected, transmitted, processed, and aggregated for MNCH medicines in a faster and timely manner, an electronic logistics management information system (eLMIS) is necessary. We recommend the use of such a system in developing countries like Bangladesh for efficient supply chain management of essential MNCH medicines.

Keywords: adaption, eLMIS, MNCH, live-saving medicines

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Authors: Pi Hsia Liang

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The rise of Facebook, Twitter, and other social networks significantly changes in interconnections between people, enhancing the process of information dissemination and amplify the influence of that information. Therefore, to develop informational efficiency or signaling equilibrium type of information environment among social networks, without adverse selection effects, becomes an important issue. Thus, someone may post a piece of intentional information in relation to personal interest for trying to create marginal influence. Therefore, economists are seeking to establish theories of informational efficiency under social network environment in order to resolve adverse selection issues. Reputation could be one of the important factors in the process of creating informational efficiency. Additionally, investors how to process green information, or information of corporate social responsibility is a very important study. This study essentially employs experimental study for examining how investors use stock relevant green information in Facebook and various Taiwan local networks. Facebook, and blogs of Money DJ, Technews and cnYES, respectively, are the primary sites for this examination that also allow to differentiate effects between Facebook and other local social networks. Questionnaire is developed for such an experimental testing. Note that questionnaire allows this study to group, for example, decision frequency and length of time duration focusing on social networks that are used for discriminating investor type and competence of informed investor. This study selects 500 investors that can be separated into two respective 250 samples as the control group and 250 samples in such an experimental. The quantity of sample investor sufficiently results in statistic significance of this experimental study. The empirical results of this study can be used for explaining how financial information in relation to corporate social responsibility would be disseminated in social websites. Therefore, we can lead to better interpretation of price/earnings relationship type of study and empirical studies of green information usefulness or informational efficiency Note that the above mentioned empirical studies did not exist any social network and annual report of corporate social responsibility. This study expects to find the results that both network degree and network cluster significantly affected green information dissemination frequency. In other words, investors with more connections and with high clustered connections might exert a greater influence on their green information dissemination process. The preferred users of financial social networks could make better stock decision that could amplify effects of green information. In addition, Facebook would be more influential than other local Taiwan financial social networks, although Facebook is not a specialized financial social network. In other words, the popularity and reputation effects of Facebook significantly contribute to usefulness of green information and influence of green information. Third, it has a better chance to find rumor or cheating information in local Taiwan financial social networks than Facebook. In other words, Facebook possesses reputation effect, or a better informational efficiency. Or, even though Taiwan local financial social networks have marginal informational effects on stock price, because of shortage of informational efficiency or monitoring system, information could be a tool for those whom owning superior information.

Keywords: network effect on financial services, informational efficiency theory, social networks, social websites

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Authors: Menuka Maharjan

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In the name of food security, agriculture intensification through conventional farming is being implemented in Nepal. Government focus on increasing agriculture production completely ignores soil as well human health. This leads to create serious soil degradation, i.e., reduction of soil fertility and microbial activity and health hazard in the country. On this note, organic farming is sustainable agriculture approach which can address challenge of sustaining food security while protecting the environment. This creates a win-win situation both for people and the environment. However, people have limited knowledge on significance of organic farming for environment conservation and food security especially developing countries like Nepal. Thus, the objective of the study was to assess the impacts of organic farming on soil fertility and microbial activity compared to conventional farming and forest in Chitwan, Nepal. Total soil organic carbon (C) was highest in organic farming (24 mg C g⁻¹ soil) followed by conventional farming (15 mg C g⁻¹ soil) and forest (9 mg C g⁻¹ soil) in the topsoil layer (0-10 cm depth). A similar trend was found for total nitrogen (N) content in all three land uses with organic farming soil possessing the highest total N content in both 0-10 cm and 10-20 cm depth. Microbial biomass C and N were also highest under organic farming, especially in the topsoil layer (350 and 46 mg g⁻¹ soil, respectively). Similarly, microbial biomass phosphorus (P) was higher (3.6 and 1.0 mg P kg⁻¹ at 0-10 and 10-20 cm depth, respectively) in organic farming compared to conventional farming and forest at both depths. However, conventional farming and forest soils had similar microbial biomass (C, N, and P) content. After conversion of forest, the P stock significantly increased by 373% and 170% in soil under organic farming at 0-10 and 10-20 cm depth, respectively. In conventional farming, the P stock increased by 64% and 36% at 0-10 cm and 10-20 cm depth, respectively, compared to forest. Overall, organic farming practices, i.e., crop rotation, residue input and farmyard manure application, significantly alters soil fertility and microbial activity. Organic farming system is emerging as a sustainable land use system which can address the issues of food security and environment conservation by increasing sustainable agriculture production and carbon sequestration, respectively, supporting to achieve goals of sustainable development.

Keywords: organic farming, soil fertility, micobial biomas, food security

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Authors: Nabeel M. Alikunhi, Zenon B. Batang, Aymen Charef, Abdulaziz M. Al-Suwailem

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Saudi Arabia has a long history of using traps as a traditional fishing gear for catching commercially important demersal, mainly coral reef-associated fish species. Fish traps constitute the dominant small-scale fisheries in Saudi waters of Arabian Gulf (eastern seaboard of Saudi Arabia). Recently, however, traps have been increasingly used along the Saudi Red Sea coast (western seaboard), with a coastline of 1800 km (71%) compared to only 720 km (29%) in the Saudi Gulf region. The production trend for traps indicates a recent increase in catches and percent contribution to traditional fishery landings, thus ascertaining the rapid proliferation of trap fishing along the Saudi Red Sea coast. Reef-associated fish species, mainly groupers (Serranidae), emperors (Lethrinidae), parrotfishes (Scaridae), scads and trevallies (Carangidae), and snappers (Lutjanidae), dominate the trap catches, reflecting the reef-dominated shelf zone in the Red Sea. This ongoing investigation covers following major objectives (i) Baseline studies to characterize trap fishery through landing site visit and interview surveys (ii) Stock assessment by fisheries and biological data obtained through monthly landing site monitoring using fishery operational model by FLBEIA, (iii) Operational impacts, derelict traps assessment and by-catch analysis through bottom-mounted video camera and onboard monitoring (iv) Elucidation of fishing grounds and derelict traps impacts by onboard monitoring, Remotely Operated underwater Vehicle and Autonomous Underwater Vehicle surveys; and (v) Analysis of gear design and operations which covers colonization and deterioration experiments. The progress of this investigation on the impacts of the trap fishery on fish stocks and the marine environment in the Saudi Red Sea region is presented.

Keywords: red sea, Saudi Arabia, fish trap, stock assessment, environmental impacts

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Authors: Mindaye Teshome, Nesibu Yahya, Carlos Moreira Miquelino Eleto Torres, Pedro Manuel Villaa, Mehari Alebachew

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Arbagugu forest is one of the remnant dry Afromontane forests under severe anthropogenic disturbances in central Ethiopia. Despite this fact, up-to-date information is lacking about the status of the forest and its role in climate change mitigation. In this study, we evaluated the woody species composition, structure, biomass, and carbon stock in this forest. We employed a systematic random sampling design and established fifty-three sample plots (20 × 100 m) to collect the vegetation data. A total of 37 woody species belonging to 25 families were recorded. The density of seedlings, saplings, and matured trees were 1174, 101, and 84 stems ha-1, respectively. The total basal area of trees with DBH (diameter at breast height) ≥ 2 cm was 21.3 m2 ha-1. The characteristic trees of dry Afromontane Forest such as Podocarpus falcatus, Juniperus procera, and Olea europaea subsp. cuspidata exhibited a fair regeneration status. On the contrary, the least abundant species Lepidotrichilia volkensii, Canthium oligocarpum, Dovyalis verrucosa, Calpurnia aurea, and Maesa lanceolata exhibited good regeneration status. Some tree species such as Polyscias fulva, Schefflera abyssinica, Erythrina brucei, and Apodytes dimidiata lack regeneration. The total carbon stored in the forest ranged between 6.3 Mg C ha-1 and 835.6 Mg C ha-1. This value is equivalent to 639.6 Mg C ha-1. The forest had a very low number of woody species composition and diversity. The regeneration study also revealed that a significant number of tree species had unsatisfactory regeneration status. Besides, the forest had a lower carbon stock density compared with other dry Afromontane forests. This implies the urgent need for forest conservation and restoration activities by the local government, conservation practitioners, and other concerned bodies to maintain the forest and sustain the various ecosystem goods and services provided by the Arbagugu forest.

Keywords: aboveground biomass, forest regeneration, climate change, biodiversity conservation, restoration

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Authors: Mohammad R. Jalali, Mohammad M. Jalali

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The earliest theories of sloshing waves and solitary waves based on potential theory idealisations and irrotational flow have been extended to be applicable to more realistic domains. To this end, the computational fluid dynamics (CFD) methods are widely used. Three-dimensional CFD methods such as Navier-Stokes solvers with volume of fluid treatment of the free surface and Navier-Stokes solvers with mappings of the free surface inherently impose high computational expense; therefore, considerable effort has gone into developing depth-averaged approaches. Examples of such approaches include Green–Naghdi (GN) equations. In Cartesian system, GN velocity profile depends on horizontal directions, x-direction and y-direction. The effect of vertical direction (z-direction) is also taken into consideration by applying weighting function in approximation. GN theory considers the effect of vertical acceleration and the consequent non-hydrostatic pressure. Moreover, in GN theory, the flow is rotational. The present study illustrates the application of GN equations to propagation of sloshing waves and solitary waves. For this purpose, GN equations solver is verified for the benchmark tests of Gaussian hump sloshing and solitary wave propagation in shallow basins. Analysis of the free surface sloshing of even harmonic components of an initial Gaussian hump demonstrates that the GN model gives predictions in satisfactory agreement with the linear analytical solutions. Discrepancies between the GN predictions and the linear analytical solutions arise from the effect of wave nonlinearities arising from the wave amplitude itself and wave-wave interactions. Numerically predicted solitary wave propagation indicates that the GN model produces simulations in good agreement with the analytical solution of the linearised wave theory. Comparison between the GN model numerical prediction and the result from perturbation analysis confirms that nonlinear interaction between solitary wave and a solid wall is satisfactorilly modelled. Moreover, solitary wave propagation at an angle to the x-axis and the interaction of solitary waves with each other are conducted to validate the developed model.

Keywords: Green–Naghdi equations, nonlinearity, numerical prediction, sloshing waves, solitary waves

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Authors: Rebecca A. Deek

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There is evidence that erratic and extreme weather is becoming a common occurrence, and even predictions that this will become even more frequent and more severe. It also appears that the severity of earthquakes is intensifying. Some observers believe that human conduct has given reasons for such change; others attribute this to environmental and geological cycles. However, as some physicists, environmental scientists, politicians, and others continue to debate the connection between weather events, seismic activities, and climate change, other scientists, engineers, and urban planners are exploring how can our habitat become more responsive and resilient to such phenomena. There are a number of recent instances of nature’s destructive events that provide basis for the development of defensive measures.

Keywords: biomimicry, natural disasters, protection of human lives, resilient infrastructures

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Authors: Rachid Belhachemi, Pierre Rostan, Alexandra Rostan

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This paper develops a discrete-time option pricing model for index options. The model consists of two key ingredients. First, daily stock return innovations are driven by a continuous hidden threshold mixed skew-normal (HTSN) distribution which generates conditional non-normality that is needed to fit daily index return. The most important feature of the HTSN is the inclusion of a latent state variable with a continuum of states, unlike the traditional mixture distributions where the state variable is discrete with little number of states. The HTSN distribution belongs to the class of univariate probability distributions where parameters of the distribution capture the dependence between the variable of interest and the continuous latent state variable (the regime). The distribution has an interpretation in terms of a mixture distribution with time-varying mixing probabilities. It has been shown empirically that this distribution outperforms its main competitor, the mixed normal (MN) distribution, in terms of capturing the stylized facts known for stock returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence. Second, heteroscedasticity in the model is captured by a threeexogenous-factor GARCH model (GARCHX), where the factors are taken from the principal components analysis of various world indices and presents an application to option pricing. The factors of the GARCHX model are extracted from a matrix of world indices applying principal component analysis (PCA). The empirically determined factors are uncorrelated and represent truly different common components driving the returns. Both factors and the eight parameters inherent to the HTSN distribution aim at capturing the impact of the state of the economy on price levels since distribution parameters have economic interpretations in terms of conditional volatilities and correlations of the returns with the hidden continuous state. The PCA identifies statistically independent factors affecting the random evolution of a given pool of assets -in our paper a pool of international stock indices- and sorting them by order of relative importance. The PCA computes a historical cross asset covariance matrix and identifies principal components representing independent factors. In our paper, factors are used to calibrate the HTSN-GARCHX model and are ultimately responsible for the nature of the distribution of random variables being generated. We benchmark our model to the MN-GARCHX model following the same PCA methodology and the standard Black-Scholes model. We show that our model outperforms the benchmark in terms of RMSE in dollar losses for put and call options, which in turn outperforms the analytical Black-Scholes by capturing the stylized facts known for index returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence.

Keywords: continuous hidden threshold, factor models, GARCHX models, option pricing, risk-premium

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Authors: A. Archibong-Eso, J. Shi, Y Baba, S. Alagbe, W. Yan, H. Yeung

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This study presents over 100 experiments conducted in a 25.4 mm internal diameter (ID) horizontal pipeline. Oil viscosity ranging from 3.5 Pa.s–5.0 Pa.s are used with superficial velocities of oil and water ranging from 0.06 to 0.55 m/s and 0.01 m/s to 1.0 m/s, respectively. Pressure gradient measurements and flow pattern observations are discussed. Numerical simulation of some flow conditions is performed using the commercial CFD code ANSYS Fluent® and the simulation results are compared with experimental results. Results indicate that CFD numerical simulation performed moderately well in predicting the flow configurations observed in this study while discrepancies were observed in the pressure gradient predictions.

Keywords: flow patterns, plug, pressure gradient, rivulet

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Authors: Maffo Maffo Nicole Liliane, Mounmemi Kpoumie Hubert, Libalah Moses, Ouandji Angele, Zapfack Louis

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Forest degradation causes biodiversity and carbon loss and thus indirectly contributes to climate change. In order to assess the contribution of forests to climate change mitigation, the present study was conducted in the Ngambe-Ndom-Nyanon Communal Forest with the main objective of assessing the floristic diversity and estimating the carbon stock in the different reservoirs of the said forest. Nine plots of 2000 m² each were installed in 3 TOSs of the forest (young secondary forests, gallery forests and fallow lands) with a total area of 18,000 m² or 1,8 ha. All trees with a Diameter at Breast Height (DBH) ≥ 5 cm were inventoried at 1.30 m from the ground in each plot. Species richness, floristic diversity indices, and structural parameters were studied. 1542 trees divided into 162 species, 122 genera and 44 families were identified. The most important families were listed: Myristicaceae (30.22%), Apocynaceae (25.20%), Fabaceae (24.41%), Euphorbiaceae (22.91%) and Phyllanthaceae (20.23%). The richest genera are: Cola, Macaranga, Oncoba (4 species each); the genera Diospyros, Trichilia, Vitex and Zanthoxylum (3 species each). The ecologically important species within the forest studied are: Funtumia africana (26.14%), Coelocaryon preussii (18.46%), Pycnanthus angolensis (15.57%), Tabernaemontana crassa (14.85%) and Olax subscorpioidea (13.04%). Assessment of carbon stocks in the six forest reservoirs studied (living trees and roots, understorey, dead wood, litter and rootlets) shows that they vary according to the land-use types. It is 119.41 t.C.ha-¹ in gallery forest, 115.2 t.C.ha-¹ in young secondary forest and 90.56 t.C.ha-¹ in fallow. The Wilcoxon statistical test shows that the carbon in the young secondary forest is identical to that in the fallow, which is identical to the carbon in the gallery forest. At the individual species level, the largest diameter class [25-35[ sequesters the most carbon (232.94 tC/ha). This work shows that the quantity of carbon sequestered by a biotope is a function of the age of the stand.

Keywords: floristic diversity, carbon stocks, evergreen forests, communal forest, Ngambé-Ndom-Nyanon

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Authors: Aditya Sharma

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Profits earned from relative strength strategy of zero-cost portfolio i.e. taking long position in winner stocks and short position in loser stocks from recent past are termed as momentum profits. In recent times, there has been lot of controversy and concern about sources of momentum profits, since the existence of these profits acts as an evidence of earning non-normal returns from publicly available information directly contradicting Efficient Market Hypothesis. Literature review reveals conflicting theories and differing evidences on sources of momentum profits. This paper aims at re-examining the sources of momentum profits in Indian capital markets. The study focuses on assessing the effect of fundamental as well as behavioral sources in order to understand the role of investor behavior in stock returns and suggest (if any) improvements to existing behavioral asset pricing models. This Paper adopts calendar time methodology to calculate momentum profits for 6 different strategies with and without skipping a month between ranking and holding period. For each J/K strategy, under this methodology, at the beginning of each month t stocks are ranked on past j month’s average returns and sorted in descending order. Stocks in upper decile are termed winners and bottom decile as losers. After ranking long and short positions are taken in winner and loser stocks respectively and both portfolios are held for next k months, in such manner that at any given point of time we have K overlapping long and short portfolios each, ranked from t-1 month to t-K month. At the end of period, returns of both long and short portfolios are calculated by taking equally weighted average across all months. Long minus short returns (LMS) are momentum profits for each strategy. Post testing for momentum profits, to study the role market risk plays in momentum profits, CAPM and Fama French three factor model adjusted LMS returns are calculated. In the final phase of studying sources, decomposing methodology has been used for breaking up the profits into unconditional means, serial correlations, and cross-serial correlations. This methodology is unbiased, can be used with the decile-based methodology and helps to test the effect of behavioral and fundamental sources altogether. From all the analysis, it was found that momentum profits do exist in Indian capital markets with market risk playing little role in defining them. Also, it was observed that though momentum profits have multiple sources (risk, serial correlations, and cross-serial correlations), cross-serial correlations plays a major role in defining these profits. The study revealed that momentum profits do have multiple sources however, cross-serial correlations i.e. the effect of returns of other stocks play a major role. This means that in addition to studying the investors` reactions to the information of the same firm it is also important to study how they react to the information of other firms. The analysis confirms that investor behavior does play an important role in stock returns and incorporating both the aspects of investors’ reactions in behavioral asset pricing models help make then better.

Keywords: investor behavior, momentum effect, sources of momentum, stock returns

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Authors: Fatemeh Rouhi, Hadi Nassiri

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Earning forecast is a key element in economic decisions but there are some situations, such as conflicts of interest in financial reporting, complexity and lack of direct access to information has led to the phenomenon of information asymmetry among individuals within the organization and external investors and creditors that appear. The adverse selection and moral hazard in the investor's decision and allows direct assessment of the difficulties associated with data by users makes. In this regard, the role of trustees in corporate governance disclosure is crystallized that includes controls and procedures to ensure the lack of movement in the interests of the company's management and move in the direction of maximizing shareholder and company value. Therefore, the earning forecast of companies in the capital market and the need to identify factors influencing this study was an attempt to make relationship between moral hazard and corporate governance with earning forecast quality companies operating in the capital market and its impact on Earnings Forecasts quality by the company to be established. Getting inspiring from the theoretical basis of research, two main hypotheses and sub-hypotheses are presented in this study, which have been examined on the basis of available models, and with the use of Panel-Data method, and at the end, the conclusion has been made at the assurance level of 95% according to the meaningfulness of the model and each independent variable. In examining the models, firstly, Chow Test was used to specify either Panel Data method should be used or Pooled method. Following that Housman Test was applied to make use of Random Effects or Fixed Effects. Findings of the study show because most of the variables are positively associated with moral hazard with earnings forecasts quality, with increasing moral hazard, earning forecast quality companies listed on the Tehran Stock Exchange is increasing. Among the variables related to corporate governance, board independence variables have a significant relationship with earnings forecast accuracy and earnings forecast bias but the relationship between board size and earnings forecast quality is not statistically significant.

Keywords: corporate governance, earning forecast quality, moral hazard, financial sciences

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Authors: Medani Prasad Rijal

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Forest itself is a factory as well as product. It supplies tangible and intangible goods and services. It supplies timber, fuel wood, fodder, grass leaf litter as well as non timber edible goods and medicinal and aromatic products additionally provides environmental services. These environmental services are of local, national or even global importance. In Nepal, more than 19 thousands community forests are providing environmental service in less economic benefit than actual efficiency. There is a risk of cost of management of those forest exceeds benefits and forests get converted to open access resources in future. Most of the environmental goods and services do not have markets which mean no prices at which they are available to the consumers, therefore the valuation of these services goods and services establishment of paying mechanism for such services and insure the benefit to community is more relevant in local as well as global scale. There are few examples of carbon trading in domestic level to meet the country wide emission goal. In this contest, the study aims to explore the public attitude towards carbon offsetting and their responsibility over service providers. This study helps in promotion of environment service awareness among general people, service provider and community forest. The research helps to unveil the carbon pool scenario in community forest and willingness to pay for carbon offsetting of people who are consuming more energy than general people and emitting relatively more carbon in atmosphere. The study has assessed the carbon pool status in two community forest and valuated carbon service from community forest through willingness to pay in Dharan municipality situated in eastern. In the study, in two community forests carbon pools were assessed following the guideline “Forest Carbon Inventory Guideline 2010” prescribed by Ministry of Forest and soil Conservation, Nepal. Final outcomes of analysis in intensively managed area of Hokse CF recorded as 103.58 tons C /ha with 6173.30 tons carbon stock. Similarly in Hariyali CF carbon density was recorded 251.72 mg C /ha. The total carbon stock of intensively managed blocks in Hariyali CF is 35839.62 tons carbon.

Keywords: carbon, offsetting, sequestration, valuation, willingness to pay

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: P. W. Tsai, J. W. Chen, C. W. Chen, C. Y. Chen

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In recent years, many researchers are involved in the field of fuzzy theory. However, there are still a lot of issues to be resolved. Especially on topics related to controller design such as the field of robot, artificial intelligence, and nonlinear systems etc. Besides fuzzy theory, algorithms in swarm intelligence are also a popular field for the researchers. In this paper, a concept of utilizing one of the swarm intelligence method, which is called Bacterial-GA Foraging, to find the stabilized common P matrix for the fuzzy controller system is proposed. An example is given in in the paper, as well.

Keywords: half-circle fuzzy numbers, predictions, swarm intelligence, Lyapunov method

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Authors: Ari Prasetyo, Taufik Faturohman

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The COVID-19 pandemic in Indonesia is part of the ongoing worldwide pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was confirmed to have spread to Indonesia on 2 March 2020. Moreover, the government of Indonesia has been conducting a local lockdown to limit people's movement from one city to another city. Therefore, this situation has impact on business operation, especially on highway companies listed on the Indonesia stock exchange. This study evaluates and measures three companies’ financial performance and health conditions before and during the COVID-19 pandemic from 2016 – 2020. The measurement is conducted by using financial ratio analysis and the Altman Z-score method. The ratio used to measure the financial ratio analysis is taken from the decree of the Ministry of SOE’s KEP-100/MBU/2002 about the company’s health level condition. From the decree, there are eight financial ratios used such as return on equity (ROE), return on investment (ROI), current ratio, cash ratio, collection period, inventory turnover, total asset turnover, and total equity to total asset. Altman Z-score is used to calculate the financial distress condition. The result shows that the highway companies for the period 2016 – 2020 are as follows: PT Jasa Marga (Persero) Tbk (AA, BB, BB, BB, C), PT Citra Marga Nusaphala Persada Tbk (BB, AA, BB, BBB, C), and PT Nusantara Infrastructure Tbk (BB, BB, AA, BBB, C). In addition, the Altman Z-score assessment performed in 2016-2020 shows that PT Jasa Marga (Persero) Tbk was in the grey zone area for 2015-2018 and in the distress zone for 2019-2020. PT Citra Marga Nusaphala Persada Tbk was in the grey zone area for 2015-2019 and in the distress zone for 2020. PT Nusantara Infrastructure Tbk was in the grey zone area for 2015-2018 and in the distress zone for 2019-2020.

Keywords: financial performance, financial ratio, Altman Z-score, financial distress, highway company

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Authors: Shpetim Rezniqi

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Financial Policies in the process of global crisis the current crisis has swept the world with special emphasis, most developed countries, those countries which have most gross -product world and you have a high level of living.Even those who are not experts can describe the consequences of the crisis to see the reality that is seen, but how far will it go this crisis is impossible to predict. Even the biggest experts have conjecture and large divergence, but agree on one thing: - The devastating effects of this crisis will be more severe than ever before and can not be predicted.Long time, the world was dominated economic theory of free market laws. With the belief that the market is the regulator of all economic problems. The market, as river water will flow to find the best and will find the necessary solution best. Therefore much less state market barriers, less state intervention and market itself is an economic self-regulation. Free market economy became the model of global economic development and progress, it transcends national barriers and became the law of the development of the entire world economy. Globalization and global market freedom were principles of development and international cooperation. All international organizations like the World Bank, states powerful economic, development and cooperation principles laid free market economy and the elimination of state intervention. The less state intervention much more freedom of action was this market- leading international principle. We live in an era of financial tragic. Financial markets and banking in particular economies are in a state of thy good, US stock markets fell about 40%, in other words, this time, was one of the darkest moments 5 since 1920. Prior to her rank can only "collapse" of the stock of Wall Street in 1929, technological collapse of 2000, the crisis of 1973 after the Yom Kippur war, while the price of oil quadrupled and famous collapse of 1937 / '38, when Europe was beginning World war II In 2000, even though it seems like the end of the world was the corner, the world economy survived almost intact. Of course, that was small recessions in the United States, Europe, or Japan. Much more difficult the situation was at crisis 30s, or 70s, however, succeeded the world. Regarding the recent financial crisis, it has all the signs to be much sharper and with more consequences. The decline in stock prices is more a byproduct of what is really happening. Financial markets began dance of death with the credit crisis, which came as a result of the large increase in real estate prices and household debt. It is these last two phenomena can be matched very well with the gains of the '20s, a period during which people spent fists as if there was no tomorrow. All is not away from the mouth of the word recession, that fact no longer a sudden and abrupt. But as much as the financial markets melt, the greater is the risk of a problematic economy for years to come. Thus, for example, the banking crisis in Japan proved to be much more severe than initially expected, partly because the assets which were based more loans had, especially the land that falling in value. The price of land in Japan is about 15 years that continues to fall. (ADRI Nurellari-Published in the newspaper "Classifieds"). At this moment, it is still difficult to çmosh to what extent the crisis has affected the economy and what would be the consequences of the crisis. What we know is that many banks will need more time to reduce the award of credit, but banks have this primary function, this means huge loss.

Keywords: globalisation, finance, crisis, recomandation, bank, credits

Procedia PDF Downloads 389

Authors: Liang-Chien Lee, Chih-Hsiang Chang, Ying-Shu Tseng

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Comparing with prior studies mainly focused on the effect of a certain event (it may be the initial announcement of bad news or the repeated announcements of identical bad news) on stock price, the aim of this study is to explore how investors react to subsequent bad news with identical content. Empirical results show that as a result of behavioral pitfalls, investors underreact to the initial announcement of the bad news (i.e., unknown bad news) and overreact to the repeated announcements of the identical bad news (i.e., known bad news).

Keywords: subsequent bad news, behavioral finance, Investors’ misreaction, behavioral pitfalls

Procedia PDF Downloads 331

Authors: A. Shaygani, R. Saifi, M. S. Saidi, M. Sani

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Particle size distribution, the most important characteristics of aerosols, is obtained through electrical characterization techniques. The dynamics of charged nano-particles under the influence of electric field in electrical mobility spectrometer (EMS) reveals the size distribution of these particles. The accuracy of this measurement is influenced by flow conditions, geometry, electric field and particle charging process, therefore by the transfer function (transfer matrix) of the instrument. In this work, a wire-cylinder corona charger was designed and the combined field-diffusion charging process of injected poly-disperse aerosol particles was numerically simulated as a prerequisite for the study of a multi-channel EMS. The result, a cloud of particles with non-uniform charge distribution, was introduced to the EMS. The flow pattern and electric field in the EMS were simulated using computational fluid dynamics (CFD) to obtain particle trajectories in the device and therefore to calculate the reported signal by each electrometer. According to the output signals (resulted from bombardment of particles and transferring their charges as currents), we proposed a modification to the size of detecting rings (which are connected to electrometers) in order to evaluate particle size distributions more accurately. Based on the capability of the system to transfer information contents about size distribution of the injected particles, we proposed a benchmark for the assessment of optimality of the design. This method applies the concept of Von Neumann entropy and borrows the definition of entropy from information theory (Shannon entropy) to measure optimality. Entropy, according to the Shannon entropy, is the ''average amount of information contained in an event, sample or character extracted from a data stream''. Evaluating the responses (signals) which were obtained via various configurations of detecting rings, the best configuration which gave the best predictions about the size distributions of injected particles, was the modified configuration. It was also the one that had the maximum amount of entropy. A reasonable consistency was also observed between the accuracy of the predictions and the entropy content of each configuration. In this method, entropy is extracted from the transfer matrix of the instrument for each configuration. Ultimately, various clouds of particles were introduced to the simulations and predicted size distributions were compared to the exact size distributions.

Keywords: aerosol nano-particle, CFD, electrical mobility spectrometer, von neumann entropy

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Authors: A. B. Bolkhir, A. Elshafie, T. K. Yousif

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This paper highlights the methods of error estimation in finite element analysis (FEA) results. It indicates that the modeling error could be eliminated by performing finite element analysis with successively finer meshes or by extrapolating response predictions from an orderly sequence of relatively low degree of freedom analysis results. In addition, the paper eliminates the round-off error by running the code at a higher precision. The paper provides application in finite element analysis results. It draws a conclusion based on results of application of methods of error estimation.

Keywords: finite element analysis (FEA), discretization error, round-off error, mesh refinement, richardson extrapolation, monotonic convergence

Procedia PDF Downloads 493

Authors: C. Recommandé, J. C. Blind, A. Clavel, R. Gourichon, V. Le Gal

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Simulations are developed in this paper with usual DSGE model equations. The model is based on simplified version of Smets-Wouters equations in use at European Central Bank which implies 10 macro-economic variables: consumption, investment, wages, inflation, capital stock, interest rates, production, capital accumulation, labour and credit rate, and allows take into consideration the banking system. Throughout the simulations, this model will be used to evaluate the impact of rate shocks recounting the actions of the European Central Bank during 2008.

Keywords: CC-LM, Central Bank, DSGE, liquidity shock, non-standard intervention

Procedia PDF Downloads 457

Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Ceylan Sozer, Ece Ceylan Baba

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The reforms and changes that began with industrialization in cities and continued with globalization in 1980’s, created many changes in urban environments. City centers which are desolated due to industrialization, began to get crowded with globalization and became the heart of technology, commerce and social activities. While the immediate and intense alterations are planned around rigorous visions in developed countries, several urban areas where the processes were underestimated and not taken precaution faced with irrevocable situations. When the effects of the globalization in the cities are examined, it is seen that there are some anticipatory system plans in the cities about the future problems. Several cities such as New York, London and Tokyo have planned to resolve probable future problems in a systematic scheme to decrease possible side effects during globalization. The decisions in urban planning and their applications are the main points in terms of sustainability and livability in such mega-cities. This article examines the effects of globalization on urban planning through 3 mega cities and the applications. When the applications of urban plannings of the three mega-cities are investigated, it is seen that the city plans are generated under light of past experiences and predictions of a certain future. In urban planning, past and present experiences of a city should have been examined and then future projections could be predicted together with current world dynamics by a systematic way. In this study, methods used in urban planning will be discussed and ‘Anticipatory System’ model will be explained and relations with global-urban planning will be discussed. The concept of ‘anticipation’ is a phenomenon that means creating foresights and predictions about the future by combining past, present and future within an action plan. The main distinctive feature that separates anticipatory systems from other systems is the combination of past, present and future and concluding with an act. Urban plans that consist of various parameters and interactions together are identified as ‘live’ and they have systematic integrities. Urban planning with an anticipatory system might be alive and can foresight some ‘side effects’ in design processes. After globalization, cities became more complex and should be designed within an anticipatory system model. These cities can be more livable and can have sustainable urban conditions for today and future.In this study, urban planning of Istanbul city is going to be analyzed with comparisons of New York, Tokyo and London city plans in terms of anticipatory system models. The lack of a system in İstanbul and its side effects will be discussed. When past and present actions in urban planning are approached through an anticipatory system, it can give more accurate and sustainable results in the future.

Keywords: globalization, urban planning, anticipatory system, New York, London, Tokyo, Istanbul

Procedia PDF Downloads 142