Search results for: coal price

Authors: Velly Anatasia

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The worldwide annual expenditure for cosmetics is estimated at U.S. $18 billion and many players in the field are competing aggressively to capture more and more markets. Players in the cosmetics industry strive to be the foremost by establish customer loyalty. Furthermore, customer loyalty is portrayed by brand loyalty. Therefore, brand loyalty is the key determine of winning the competition in tight market. This study examines the influence of brand loyalty on cosmetics buying behavior of female consumers in Jakarta as capital of Indonesia. The seven factors of brand loyalty are brand name, Product quality, price, design, promotion, servicesquality and store environment. The paper adopted descriptive analysis, factor loading and multiple regression approach to test the hypotheses. The data has been collected by using questionnaires which were distributed and self-administered to 125female respondents accustomed using cosmetics. The findings of this study indicated that promotion has shown strong correlation with brand loyalty. The research results showed that there is positive and significant relationship between factors of brand loyalty (brand name, product quality, price, design, promotion, services quality and store environment) with cosmetics brand loyalty.

Keywords: brand loyalty, brand name, product quality, service quality, promotion

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Authors: Pisit Potjanajaruwit

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The objectives of the study were to determine the marketing mix factors that influencing tourist’s destination decision making for cultural tourism in the Chonburi province. Both quantitative and qualitative data were applied in this study. The samples of 400 cases for quantitative analysis were tourists (both Thai and foreign) who were interested in cultural tourism in the Chonburi province, and traveled to cultural sites in Chonburi and 14 representatives from provincial tourism committee of Chonburi and local tourism experts. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. The study found that Thai and foreign tourists are influenced by different important marketing mix factors. The important factors for Thai respondents were physical evidence, price, people, and place at high importance level. For foreign respondents, physical evidence, price, people, and process were high importance level, whereas, product, place, and promotion were moderate importance level.

Keywords: Chonburi Province, decision making, cultural tourism, marketing mixed

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Authors: Simon Huston, Ebraheim Lahbash, Ali Parsa

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The development of the United Arab Emirates (UAE) into a regional trade, tourism, finance and logistics hub has transformed its real estate markets. However, speculative activity and price volatility remain concerns. UAE residential market values (MV) are exposed to fluctuations in capital flows and migration which in turn are affected by geopolitical uncertainty, oil price volatility, and global investment market sentiment. Internally, a complex interplay between administrative boundaries, land tenure, building quality and evolving location characteristics fragments UAE residential property markets. In short, the UAE Residential Valuation System (UAE-RVS) confronts multiple challenges to collect, filter and analyze relevant information in complex and dynamic spatial and capital markets. A robust (RVS) can mitigate the risk of unhelpful volatility, speculative excess or investment mistakes. The research outlines the institutional, ontological, dynamic, and epistemological issues at play. We highlight the importance of system capabilities, valuation standard salience and stakeholders trust.

Keywords: valuation, property rights, information, institutions, trust, salience

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Authors: Murad A. Bein

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This paper empirically examines the time-varying relationship and spillover effects between the international crude oil price and ten emerging equity markets, namely three oil-exporting countries (Brazil, Mexico, and Russia) and seven Central and Eastern European (CEE) countries (Bulgaria, Croatia, Czech Republic, Hungary, Poland, Romania, and Slovakia). The results revealed that there are spillover effects from oil markets into almost all emerging equity markets save Slovakia. Besides, the oil supply glut had a homogenous effect on the emerging markets, both net oil-exporting, and oil-importing countries (CEE). Further, the time variance drastically increased during financial turmoil. Indeed, the time variance remained high from 2009 to 2012 in response to aggregate demand shocks (global financial crisis and Eurozone debt crisis) and quantitative easing measures. Interestingly, the time variance was slightly higher for the oil-exporting countries than for some of the CEE countries. Decision-makers in emerging economies should therefore seek policy coordination when dealing with financial turmoil.

Keywords: crude oil, spillover effects, emerging equity, time-varying, aggregate demand shock

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Authors: Mark Daniel De Luna, Ma. Katreena Pillejera, Wei-Hsin Chen

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The raw bamboo (Phyllostachys mankinoi), with a moisture content of 13.54 % and a higher heating value (HHV) of 17.657 MJ/kg, was subjected to torrefaction under a high gravity (higee) environment using a rotating packed bed. The performance of the higee torrefaction was explored in two parts: (1) effect of rotation and temperature and (2) effect of duration on the solid yield, HHV and energy yield. By statistical analyses, the results indicated that the rotation, temperature and their interaction has a significant effect on the three responses. Same remarks on the effect of duration where when the duration (temperature and rotation) increases, the HHV increases, while the solid yield and energy yield decreases. Graphical interpretations showed that at 300 °C, the rotating speed has no evident effect on the responses. At 30-min holding time, the highest HHV reached (28.389 MJ/kg) was obtained in the most severe torrefaction condition (the rotating speed at 1800 rpm and temperature at 300 °C) with an enhancement factor of HHV corresponding to 1.61 and an energy yield of 63.51%. Upon inspection, the recommended operating condition under a 30-min holding time is at 255 °C-1800 rpm since the enhancement factor of HHV (1.53), HHV (26.988 MJ/kg), and energy yield (65.21%) values are relatively close to that of the aforementioned torrefaction condition. The Van Krevelen diagram of the torrefied biomass showed that the ratios decrease as the torrefaction intensifies, hence improving the hydrophobicity of the product. The spreads of the results of the solid yield, enhancement factor (EF) of HHV, energy yield, and H/C and O/C ratios were in accordance with the trends of the responses. Overall, from the results presented, it can be concluded that the quality of the product from the process is at par to that of coal (i.e. HHV of coal is 21-35 MJ/kg). The Fourier transform infrared (FTIR) spectroscopy results indicated that cellulose and lignin may have been degraded at a lower temperature accompanied with a high rotating speed. The results suggested that torrefaction under higee environment indicates promising process for the utilization of bamboo.

Keywords: heat transfer, high gravity environment, FTIR, rotation, rotating speed, torrefaction

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Authors: Maria Evnevich

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In modern conditions, in most markets, price competition is becoming less effective. On the one hand, there is a gradual decrease in the level of marginality in main traditional sectors of the economy, so further price reduction becomes too ‘expensive’ for the company. On the other hand, the effect of price reduction is leveled, and the reason for this phenomenon is likely to be informational. As a result, it turns out that even if the company reduces prices, making its products more accessible to the buyer, there is a high probability that this will not lead to increase in sales unless additional large-scale advertising and information campaigns are conducted. Similarly, a large-scale information and advertising campaign have a much greater effect itself than price reductions. At the same time, the cost of mass informing is growing every year, especially when using the main information channels. The article presents generalization, systematization and development of theoretical approaches and best practices in the field of customer focus approach to business management and in the field of relationship marketing in the modern digital economy. The research methodology is based on the synthesis and content-analysis of sociological and marketing research and on the study of the systems of working with consumer appeals and loyalty programs in the 50 largest client-oriented companies in Russia. Also, the analysis of internal documentation on customers’ purchases in one of the largest retail companies in Russia allowed to identify if buyers prefer to buy goods for complex purchases in one retail store with the best price image for them. The cost of attracting a new client is now quite high and continues to grow, so it becomes more important to keep him and increase the involvement through marketing tools. A huge role is played by modern digital technologies used both in advertising (e-mailing, SEO, contextual advertising, banner advertising, SMM, etc.) and in service. To implement the above-described client-oriented omnichannel service, it is necessary to identify the client and work with personal data provided when filling in the loyalty program application form. The analysis of loyalty programs of 50 companies identified the following types of cards: discount cards, bonus cards, mixed cards, coalition loyalty cards, bank loyalty programs, aviation loyalty programs, hybrid loyalty cards, situational loyalty cards. The use of loyalty cards allows not only to stimulate the customer to purchase ‘untargeted’, but also to provide individualized offers, as well as to produce more targeted information. The development of digital technologies and modern means of communication has significantly changed not only the sphere of marketing and promotion, but also the economic landscape as a whole. Factors of competitiveness are the digital opportunities of companies in the field of customer orientation: personalization of service, customization of advertising offers, optimization of marketing activity and improvement of logistics.

Keywords: customer focus, digital economy, loyalty program, relationship marketing

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Authors: Tamar Trop, Michal Roffeh

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One of the emerging trends in construction is installing an exterior “green wall” (GW). GW is an overarching and most common term for various techniques of incorporating greenery into buildings’ vertical elements, mainly facades. This green infrastructure yields numerous benefits for the urban environment, the public, and the buildings’ tenants and users, such as enhancing air quality and biodiversity, managing stormwater runoff, mitigating urban heat island and climate change, improving urban aesthetics and mental wellbeing, improving indoor comfort conditions, and saving energy. Yet, the penetration rate of GWs into the construction market, especially into the housing sector, is still very slow. Furthermore, the research regarding prospective homebuyers’ willingness to purchase and pay a price premium for GW apartments is scarce and does not refer to newly built buildings and specific GW types. This research aims to narrow these knowledge gaps by exploring the willingness of prospective homebuyers in Israel to purchase a newly built apartment with a hydroponic living wall, the size of the PP that they would be willing to pay for it, and the various factors ̶ knowledge-related, concern, economic, and personal ̶ that influence these motivations. A nationwide online survey was conducted among a sample of 514 adults using a structured questionnaire. Findings show that despite low familiarity with GWs and strong concerns about various kinds of nuisance, technical issues, and maintenance costs, potential homebuyers express a relatively high willingness to purchase and pay a significant price premium for such an apartment. The main motivations behind this willingness were found to be potential energy savings and governmental incentives. Study findings can contribute to a better understanding of the maturity of the housing market in Israel to adopt GWs and to better tailor intervention tools for increasing GWs’ uptake among potential homebuyers.

Keywords: green façade, green wall, living wall, willingness to pay

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Authors: Sara Sousa

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The environmental goods and services have often been neglected in crucial decisions affecting the environment mainly because the difficulty in estimating their economic value, since we are dealing with non-market goods and, thus, without a price associated. Nevertheless, the inexistence of prices does not necessarily mean these goods have no value. The environment is a key element in today's society that seeks to be as sustainable as possible, where the environmental assets have both use and non-use values. To estimate the use value, researchers may apply the revealed preference methods. This paper provides a theoretical review of the main concepts and methodologies on the economic valuation of the environment, with particular emphasis on the revealed preference techniques. Based on a detailed literature review, this study concludes that, despite some inherent limitations, the revealed preference methodologies – travel cost, hedonic price, and averting behaviour – represent essential tools for the researchers who accept the challenge to estimate the use value of environmental goods and services based on the actual individuals` behaviour. The main purpose of this study is to contribute to an increased theoretical information on the economic valuation of environmental assets, allowing researchers and policymakers to improve future decisions regarding the environment.

Keywords: economic valuation, environmental goods, revealed preference methods, total economic value

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Authors: Renzo Castillo, George Tsatsaronis

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High-temperature ceramic membranes for air separation represents an important option to reduce the significant efficiency drops incurred in state-of-the-art cryogenic air separation for high tonnage oxygen production required in oxyfuel power stations. This study is focused on the thermodynamic analysis of two power plant model designs: the state-of-the-art supercritical 600ᵒC hard coal plant (reference power plant Nordrhein-Westfalen) and the membrane-based oxyfuel concept implemented in this reference plant. In the latter case, the oxygen is separated through a mixed-conducting hollow fiber perovskite membrane unit in the three-end operation mode, which has been simulated under vacuum conditions on the permeate side and at high-pressure conditions on the feed side. The thermodynamic performance of each plant concept is assessed by conventional exergetic analysis, which determines location, magnitude and sources of efficiency losses, and advanced exergetic analysis, where endogenous/exogenous and avoidable/unavoidable parts of exergy destruction are calculated at the component and full process level. These calculations identify thermodynamic interdependencies among components and reveal the real potential for efficiency improvements. The endogenous and exogenous exergy destruction portions are calculated by the decomposition method, a recently developed straightforward methodology, which is suitable for complex power stations with a large number of process components. Lastly, an improvement priority ranking for relevant components, as well as suggested changes in process layouts are presented for both power stations.

Keywords: exergy, carbon capture and storage, ceramic membranes, perovskite, oxyfuel combustion

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Authors: Phutthiwat Waiyawuththanapoom, Pimploi Tirastittam, Manop Tirastittam

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Thailand is the agriculture country which mainly exports the agriculture product to the other countries in so many ways which are fresh vegetable, chilled vegetable or frozen vegetable. The gross export for Thailand’s vegetable is 30-40 billion baht per year, and the growth rate is about 15-20 percent per year. Ginger is one of the main vegetable product that Thailand export to Japan because Thailand’s Ginger has a good quality and be able to supply Japan’s demand with a reasonable price. This research paper is aimed to study the factors which affect the efficiency of the supply chain process of Thailand’s ginger to Japan. There are 5 factors which related to the exporting Thailand’s ginger to Japan which are quality, price, equipment and supply standard, custom process and distribution pattern. The result of the research showed that the factor which reached the 'very good' significant level is quality of Thailand’s ginger with the score of 4.86. The other 5 factors are in the 'good' significant level. So the most important factor for Thai ginger farmer to concern is the quality of the product.

Keywords: critical success factor, export, ginger, supply chain

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Authors: Waheed Akhtar

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Bears in Asia facing multiple threats and challenges such as hunting, illegal trading, habitat loss, and human conflicts. According to IUCN Red List, the Asiatic black bear (Ursus thibetanus) is listed as Vulnerable since 1990, population declining by 49% during the last 30 years. The present study was conducted in Manglawar (DwaSaro Mountain) from April-August 2021, to collect all the information on Asiatic black bear observation, illegal hunting, and cub poaching. According to the response of the local community, very intensive illegal hunting and cub poaching were observed. Hunters usually installed many traps in the routes of black bears and when they move in the winter season the cubs get trapped and they collect them and kept in a specialized wooden box that is mainly helpful for further transportation. These cubs are then brought to the concerned Market where they sell them to many dealers. One of the potential observers of the illegal trading responds towards the Market price of the cubs, “The average price of the black bear cub is ranging from 45000-50000 Pakistani Rupees”. Apart from cubs' poaching, the black bear is also hunted for its skin, claws, and teeth.

Keywords: first report, illegal hunting, cub poaching, parts trading, Ursus thibetanus

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Authors: Siri-orn Champatong

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The purpose of this research was to study factors that were related with travel behaviors of Thai tourists at the Ayothaya Floating Market, Phra Nakhon Sri Ayutthaya. The quantitative research was conducted with 400 samples of Thai tourists traveling to the Ayothaya Floating Market. The Questionnaire was a tool used to collect data, and the statistics used for data analysis were mean and Pearson product moment correlation coefficient. The results found that Thai tourists focused on attraction, easy access and facilities of the tourist spot at a high level. In addition, they gave priority to the marketing mix in the dimension of products, price, and distribution channels at a high level as well. For marketing promotion, it was at the moderate level. The results of hypothesis testing revealed that factors related to the attractions of the tourist destination, easy access to the tourist destination, the facilities of the tourist spot, and product and price of the marketing mix were associated with travel behaviors in the aspect of the number of visits used and the budget on tourism.

Keywords: floating market, marketing mix, tourism attractions, travelling behavior

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Authors: Aysen Caliskan, Hanzade Haykiri-Acma, Serdar Yaman

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Co-firing of coal and biomass blends is an effective method to reduce carbon dioxide emissions released by burning coals, thanks to the carbon-neutral nature of biomass. Besides, usage of biomass that is renewable and sustainable energy resource mitigates the dependency on fossil fuels for power generation. However, most of the biomass species has negative aspects such as low calorific value, high moisture and volatile matter contents compared to coal. Torrefaction is a promising technique in order to upgrade the fuel properties of biomass through thermal treatment. That is, this technique improves the calorific value of biomass along with serious reductions in the moisture and volatile matter contents. In this context, several woody biomass materials including Rhododendron, hybrid poplar, and ash-tree were subjected to torrefaction process in a horizontal tube furnace at 200°C under nitrogen flow. In this way, the solid residue obtained from torrefaction that is also called as 'biochar' was obtained and analyzed to monitor the variations taking place in biomass properties. On the other hand, some Turkish lignites from Elbistan, Adıyaman-Gölbaşı and Çorum-Dodurga deposits were chosen as coal samples since these lignites are of great importance in lignite-fired power stations in Turkey. These lignites were blended with the obtained biochars for which the blending ratio of biochars was kept at 10 wt% and the lignites were the dominant constituents in the fuel blends. Burning tests of the lignites, biomasses, biochars, and blends were performed using a thermogravimetric analyzer up to 900°C with a heating rate of 40°C/min under dry air atmosphere. Based on these burning tests, properties relevant to burning characteristics such as the burning reactivity and burnout yields etc. could be compared to justify the effects of torrefaction and blending. Besides, some characterization techniques including X-Ray Diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy and Scanning Electron Microscopy (SEM) were also conducted for the untreated biomass and torrefied biomass (biochar) samples, lignites and their blends to examine the co-combustion characteristics elaborately. Results of this study revealed the fact that blending of lignite with 10 wt% biochar created synergistic behaviors during co-combustion in comparison to the individual burning of the ingredient fuels in the blends. Burnout and ignition performances of each blend were compared by taking into account the lignite and biomass structures and characteristics. The blend that has the best co-combustion profile and ignition properties was selected. Even though final burnouts of the lignites were decreased due to the addition of biomass, co-combustion process acts as a reasonable and sustainable solution due to its environmentally friendly benefits such as reductions in net carbon dioxide (CO2), SOx and hazardous organic chemicals derived from volatiles.

Keywords: burnout performance, co-combustion, thermal analysis, torrefaction pretreatment

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Authors: Modupeoluwa Solawon, Folusho Oluwole

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The resource curse hypothesis is a widely discussed topic that suggests despite expectations of boosting economic development and improving the well-being of citizens, natural resource wealth in a country can lead to negative outcomes. The study focused on crude oil price, crude oil production, the pump price of petrol, agricultural production, and natural resources rent in Nigeria to determine the possible curse of these resources. The study also looked into the well-being of the citizens by employing gross domestic product per capita. The data used for the study were drawn from the World Bank Data Indicators in 2022, limited to annual data from 1981 to 2022, using the autoregressive distributed lag (ARDL) as the main estimation technique. The findings of the study revealed that natural resource rent influenced the GDP per capita detrimentally, indicating that natural resource rent has not led to better welfare for Nigerians. This effect could likely be a result of corruption in the system, causing the inability of the rents to promote better welfare in Nigeria. In conclusion, the study recommends reducing the cost of living in Nigeria and making productive use of revenues generated from its natural resources.

Keywords: ARDL, corruption, natural resources, resource curse hypothesis

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Lidia Sanchis-Marco, Antonio Rubia, Pedro Serrano

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This paper studies the informational content of pricing errors in the term structure of sovereign CDS spreads. The residuals from a non-arbitrage model are employed to construct a Price discrepancy estimate, or noise measure. The noise estimate is understood as an indicator of market distress and reflects frictions such as illiquidity. Empirically, the noise measure is computed for an extensive panel of CDS spreads. Our results reveal an important fraction of systematic risk is not priced in default swap contracts. When projecting the noise measure onto a set of financial variables, the panel-data estimates show that greater price discrepancies are systematically related to a higher level of offsetting transactions of CDS contracts. This evidence suggests that arbitrage capital flows exit the marketplace during time of distress, and this consistent with a market segmentation among investors and arbitrageurs where professional arbitrageurs are particularly ineffective at bringing prices to their fundamental values during turbulent periods. Our empirical findings are robust for the most common CDS pricing models employed in the industry.

Keywords: credit default swaps, noise measure, illiquidity, capital arbitrage

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Authors: Krishnendu Mukhopadhyay, Subhashis Kundu, Mayank Tiwari, Sameeran Pani, Padmapal, Uttam Singh

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Blast Furnace is a counter current process where burden descends from top and hot gases ascend from bottom and chemically reduce iron oxides into liquid hot metal. One of the major problems of blast furnace operation is the erratic burden descent inside furnace. Sometimes this problem is so acute that burden descent stops resulting in Hanging and instability of the furnace. This problem is very frequent in blast furnaces worldwide and results in huge production losses. This situation becomes more adverse when blast furnaces are operated at low coke rate and high coal injection rate with adverse raw materials like high alumina ore and high coke ash. For last three years, H-Blast Furnace Tata Steel was able to reduce coke rate from 450 kg/thm to 350 kg/thm with an increase in coal injection to 200 kg/thm which are close to world benchmarks and expand profitability. To sustain this regime, elimination of irregularities of blast furnace like hanging, channeling, and scaffolding is very essential. In this paper, sustaining of zero hanging spell for consecutive three years with low coke rate operation by improvement in burden characteristics, burden distribution, changes in slag regime, casting practices and adequate automation of the furnace operation has been illustrated. Models have been created to comprehend and upgrade the blast furnace process understanding. A model has been developed to predict the process of maintaining slag viscosity in desired range to attain proper burden permeability. A channeling prediction model has also been developed to understand channeling symptoms so that early actions can be initiated. The models have helped to a great extent in standardizing the control decisions of operators at H-Blast Furnace of Tata Steel, Jamshedpur and thus achieving process stability for last three years.

Keywords: hanging, channelling, blast furnace, coke

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Authors: Ezgi Avci-Surucu, Doganbey Akgul

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Turkey’s energy reforms has achieved energy security through a variety of interlinked measures including electricity, gas, renewable energy and energy efficiency legislation; the establishment of an energy sector regulatory authority; energy price reform; the creation of a functional electricity market; restructuring of state-owned energy enterprises; and private sector participation through privatization and new investment. However, current strategies, namely; “Electricity Sector Reform and Privatization Strategy” and “Electricity Market and Supply Security Strategy” has been criticized for various aspects. The present paper analyzes the implementation of the aforementioned strategies in the framework of generation scheduling, transmission constraints, bidding structure and general aspects; and argues the deficiencies of current strategies which decelerates power investments and creates uncertainties. We conclude by policy suggestions to eliminate these deficiencies in terms of price and risk management, infrastructure, customer focused regulations and systematic market development.

Keywords: electricity markets, risk management, regulations, balancing and settlement, bilateral trading, generation scheduling, bidding structure

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Authors: Alexandra Papagianni, George Filis, Panagiotis Papadopoulos

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The liberalization of the energy market and the increasing penetration of fluctuating renewables (e.g., wind and solar power) have heightened the importance of the spot market for ensuring efficient electricity supply. This is further emphasized by the EU’s goal of achieving net-zero emissions by 2050. The day-ahead market (DAM) plays a key role in European energy trading, accounting for 80-90% of spot transactions and providing critical insights for next-day pricing. Therefore, short-term electricity price forecasting (EPF) within the DAM is crucial for market participants to make informed decisions and improve their market positioning. Existing literature highlights out-of-sample performance as a key factor in assessing EPF accuracy, with influencing factors such as predictors, forecast horizon, model selection, and strategy. Several studies indicate that electricity demand is a primary price determinant, while renewable energy sources (RES) like wind and solar significantly impact price dynamics, often lowering prices. Additionally, incorporating data from neighboring countries, due to market coupling, further improves forecast accuracy. Most studies predict up to 24 steps ahead using hourly data, while some extend forecasts using higher-frequency data (e.g., half-hourly or quarter-hourly). Short-term EPF methods fall into two main categories: statistical and computational intelligence (CI) methods, with hybrid models combining both. While many studies use advanced statistical methods, particularly through different versions of traditional AR-type models, others apply computational techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). Recent research combines multiple methods to enhance forecasting performance. Despite extensive research on EPF accuracy, a gap remains in understanding how forecasting strategy affects prediction outcomes. While iterated strategies are commonly used, they are often chosen without justification. This paper contributes by examining whether the choice of forecasting strategy impacts the quality of day-ahead price predictions, especially for multi-step forecasts. We evaluate both iterated and direct methods, exploring alternative ways of conducting iterated forecasts on benchmark and state-of-the-art forecasting frameworks. The goal is to assess whether these factors should be considered by end-users to improve forecast quality. We focus on the Greek DAM using data from July 1, 2021, to March 31, 2022. This period is chosen due to significant price volatility in Greece, driven by its dependence on natural gas and limited interconnection capacity with larger European grids. The analysis covers two phases: pre-conflict (January 1, 2022, to February 23, 2022) and post-conflict (February 24, 2022, to March 31, 2022), following the Russian-Ukraine conflict that initiated an energy crisis. We use the mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE) for evaluation, as well as the Direction of Change (DoC) measure to assess the accuracy of price movement predictions. Our findings suggest that forecasters need to apply all strategies across different horizons and models. Different strategies may be required for different horizons to optimize both accuracy and directional predictions, ensuring more reliable forecasts.

Keywords: short-term electricity price forecast, forecast strategies, forecast horizons, recursive strategy, direct strategy

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Authors: Bundit Pungnirund, Boonyada Pahasing

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The objective of this research is to study factors related to behaviors of Thai travelers traveling to Koh Kret Island, Nonthaburi Province. The subjects of this study included 400 Thai travelers coming to Koh Kred. Questionnaires were used to collect data which were analyzed by computer program to find mean and correlation coefficient by Pearson. The results showed that Thai travelers reported their opinions and attitudes in high level on the marketing service mix, product, price, place, promotion, personal, physical evidence, and process. They reported on travelling motivation factor, tourist attraction, and facility at high level. Moreover, marketing service mix, product, price, place, promotion, personal, physical, and process including travelling motivation factor, tourist attraction, and facility had positive relationship with the frequency in travelling at statistically significant level (0.01), though in a low relationship but in the same direction.

Keywords: factors, behaviors, Thai travelers, Koh Kled, Nonthaburi Province

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Authors: Ayanda Ndokwana, Stanley Fore

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Industrial prosperity and rapid expansion of human population in South Africa over the past two decades, have increased the use of conventional fossil fuels such as crude oil, coal and natural gas to meet the country’s energy demands. However, the inevitable depletion of fossil fuel reserves, global volatile oil price and large carbon footprint are some of the crucial reasons the South African Government needs to make a considerable investment in the development of the biofuel industry. In South Africa, this industry is still at the introductory stage with no large scale manufacturing plant that has been commissioned yet. Bioethanol is a potential replacement of gasoline which is a fossil fuel that is used in motor vehicles. Using bioethanol for the transport sector as a source of fuel will help Government to save heavy foreign exchange incurred during importation of oil and create many job opportunities in rural farming. In 2007, the South African Government developed the National Biofuels Industrial Strategy in an effort to make provision for support and attract investment in bioethanol production. However, capital investment in the production of bioethanol on a large scale, depends on the sound economic assessment of the available manufacturing technologies. The aim of this study is to evaluate the profitability of an advanced bioethanol manufacturing technology which uses maize as a feedstock in South Africa. The impact of fiber or bran fractionation in this technology causes it to possess a number of merits such as energy efficiency, low capital expenditure, and profitability compared to a conventional dry-mill bioethanol technology. Quantitative techniques will be used to collect and analyze numerical data from suitable organisations in South Africa. The dependence of three profitability indicators such as the Discounted Payback Period (DPP), Net Present Value (NPV) and Return On Investment (ROI) on plant capacity will be evaluated. Profitability analysis will be done on the following plant capacities: 100 000 ton/year, 150 000 ton/year and 200 000 ton/year. The plant capacity with the shortest Discounted Payback Period, positive Net Present Value and highest Return On Investment implies that a further consideration in terms of capital investment is warranted.

Keywords: bioethanol, economic evaluation, maize, profitability indicators

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Authors: Yosra Mefteh Rekik, Younes Boujelbene

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We introduce in this paper a new conceptual model representing the stock market dynamics. This model is essentially based on cognitive behavior of the intelligence investors. In order to validate our model, we build an artificial stock market simulation based on agent-oriented methodologies. The proposed simulator is composed of market supervisor agent essentially responsible for executing transactions via an order book and various kinds of investor agents depending to their profile. The purpose of this simulation is to understand the influence of psychological character of an investor and its neighborhood on its decision-making and their impact on the market in terms of price fluctuations. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Keywords: artificial intelligence methods, artificial stock market, behavioral modeling, multi-agent based simulation

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Authors: M. F. Wu, F. T. Cheng

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Leather industry is the most important traditional industry to provide the leather products in the world for thousand years. The fierce global competitive environment and common awareness of global carbon reduction make livestock supply quantities falling, salt and wet blue leather material reduces and the price skyrockets significantly. Exchange rate fluctuation led sales revenue decreasing which due to the differences of export exchanges and compresses the overall profitability of leather industry. This paper applies activity-based benefit assessment approach to build up fitness fur input mixed model, fur is Wet Blue, which concerned with four key factors: the output rate of wet blue, unit cost of wet blue, yield rate and grade level of Wet Blue to achieve the low cost strategy under given unit price of leather product condition of the company. The research findings indicate that applying this model may improve the input cost structure, decrease numbers of leather product inventories and to raise the competitive advantages of the enterprise in the future.

Keywords: activity-based benefit assessment approach, input mixed, output rate, wet blue

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Authors: M. G. Cattaneo

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The non-effectiveness of certain codified human rights is particularly apparent with reference to the lack of access to essential drugs in developing countries, which represents a breach of the human right to receive adequate health assistance. This paper underlines the conflict and the legal contradictions between human rights, namely health rights, international Intellectual Property Rights, in particular patent law, as well as international trade law. The paper discusses the crucial links between R&D costs for innovation, patents and new medical drugs, with the goal of reformulating the hierarchies of priorities and of interests at stake in the international intellectual property (IP) law system. Different from what happens today, International patent law should be a legal instrument apt at rebalancing an axiological asymmetry between the (conflicting) needs at stake The core argument in the paper is the proposal of an alternative pathway, namely a feasible proposal for a patent law reform. IP laws tend to balance the benefits deriving from innovation with the costs of the provided monopoly, but since developing countries and industrialized countries are in completely different political and economic situations, it is necessary to (re)modulate such exchange according to the different needs. Based on this critical analysis, the paper puts forward a proposal, called Trading Time for Space (TTS), whereby a longer time for patent exclusive life in western countries (Time) is offered to the patent holder company, in exchange for the latter selling the medical drug at cost price in developing countries (Space). Accordingly, pharmaceutical companies should sell drugs in developing countries at the cost price, or alternatively grant a free license for the sale in such countries, without any royalties or fees. However, such social service shall be duly compensated. Therefore, the consideration for such a service shall be an extension of the temporal duration of the patent’s exclusive in the country of origin that will compensate the reduced profits caused by the supply at the price cost in developing countries.

Keywords: global health, global justice, patent law reform, access to drugs

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Adeola Adenikinju, Olusanya E. Olubusoye, Lateef O. Akinpelu, Dilinna L. Nwobi

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Since the enactment of the Fiscal Responsibility Act (2007), the Federal Government of Nigeria (FGN) has made public its fiscal budget and the subsequent implementation report. A critical review of these documents shows significant variations in the five macroeconomic variables which are inputs in each Presidential budget; oil Production target (mbpd), oil price ($), Foreign exchange rate(N/$), and Gross Domestic Product growth rate (%) and inflation rate (%). This results in underperformance of the Federal budget expected output in terms of non-oil and oil revenue aggregates. This paper evaluates first the existing variance between budgeted and actuals, then the relationship and causality between the determinants of Federal fiscal budget assumptions, and finally the determinants of FGN’s Gross Oil Revenue. The paper employed the use of descriptive statistics, the Autoregressive distributed lag (ARDL) model, and a Profit oil probabilistic model to achieve these objectives. This model permits for both the static and dynamic effect(s) of the independent variable(s) on the dependent variable, unlike a static model that accounts for static or fixed effect(s) only. It offers a technique for checking the existence of a long-run relationship between variables, unlike other tests of cointegration, such as the Engle-Granger and Johansen tests, which consider only non-stationary series that are integrated of the same order. Finally, even with small sample size, the ARDL model is known to generate a valid result, for it is the dependent variable and is the explanatory variable. The results showed that there is a long-run relationship between oil revenue as a proxy for budget performance and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a short-run relationship between oil revenue and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a long-run relationship between non-oil revenue and its determinants; inflation rate, GDP growth rate, and foreign exchange rate. The grangers’ causality test results show that there is a mono-directional causality between oil revenue and its determinants. The Federal budget assumptions only explain 68% of oil revenue and 62% of non-oil revenue. There is a mono-directional causality between non-oil revenue and its determinants. The Profit oil Model describes production sharing contracts, joint ventures, and modified carrying arrangements as the greatest contributors to FGN’s gross oil revenue. This provides empirical justification for the selected macroeconomic variables used in the Federal budget design and performance evaluation. The research recommends other variables, debt and money supply, be included in the Federal budget design to explain the Federal budget revenue performance further.

Keywords: ARDL, budget performance, oil price, oil quantity, oil revenue

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Authors: Peter Shi

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Algorithmic trading is a rapidly expanding domain within quantitative finance, constituting a substantial portion of trading volumes in the US financial market. The demand for rigorous and robust mathematical theories underpinning these trading algorithms is ever-growing. In this study, the author establishes a new stock market model that integrates the Efficient Market Hypothesis and the statistical arbitrage. The model, for the first time, finds probabilistic relations between the rational price and the market price in terms of the conditional expectation. The theory consequently leads to a mathematical justification of the old market adage: buy-low and sell-high. The thresholds for “low” and “high” are precisely derived using a max-min operation on Bayes’s error. This explicit connection harmonizes the Efficient Market Hypothesis and Statistical Arbitrage, demonstrating their compatibility in explaining market dynamics. The amalgamation represents a pioneering contribution to quantitative finance. The study culminates in comprehensive numerical tests using historical market data, affirming that the “buy-low” and “sell-high” algorithm derived from this theory significantly outperforms the general market over the long term in four out of six distinct market environments.

Keywords: efficient market hypothesis, behavioral finance, Bayes' decision, algorithmic trading, risk control, stock market

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Authors: Majid Lotfi Ghahroud, Seyed Jalal Tabatabaei, Ebrahim Karami, AmirArsalan Ghergherechi, Amir Ali Saeidi

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In this study, the relationship between investment growth and stock returns of companies listed in Tehran Stock Exchange and whether their relationship -behavioral or risk factors- are discussed. Generally, there are two perspectives; risk-based approach and behavioral approach. According to the risk-based approach due to increase investment, systemic risk and consequently the stock returns are reduced. But due to the second approach, an excessive optimism or pessimism leads to assuming stock price with high investment growth in the past, higher than its intrinsic value and the price of stocks with lower investment growth, less than its intrinsic value. The investigation period is eight years from 2007 to 2014. The sample consisted of all companies listed on the Tehran Stock Exchange. The method is a portfolio test, and the analysis is based on the t-student test (t-test). The results indicate that there is a negative relationship between investment growth and stock returns of companies and this negative correlation is stronger for firms with higher cash flow. Also, the negative relationship between asset growth and stock returns is due to behavioral factors.

Keywords: behavioral theory, investment growth, risk-based theory, stock returns

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Authors: Reza Tirsadi Librawan, Tara Vergita Rakhma

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The increasing demand for energy and concern of global warming are intertwined issues of critical importance. With the pressing needs of clean, efficient and cost-effective energy conversion processes, an alternative clean energy source is needed. Biomass is one of the preferable options because it is clean and renewable. The efficiency for biomass conversion is constrained by the relatively low energy density and high moisture content from biomass. This study based on bio-based resources presents the Biomass Direct Chemical Looping Combustion Process (BDCLC), an alternative process that has a potential to convert biomass in thermal cracking to produce electricity and CO2. The BDCLC process using iron-based oxygen carriers has been developed as a biomass conversion process with in-situ CO2 capture. The BDCLC system cycles oxygen carriers between two reactor, a reducer reactor and combustor reactor in order to convert coal for electric power generation. The reducer reactor features a unique design: a gas-solid counter-current moving bed configuration to achieve the reduction of Fe2O3 particles to a mixture of Fe and FeO while converting the coal into CO2 and steam. The combustor reactor is a fluidized bed that oxidizes the reduced particles back to Fe2O3 with air. The oxidation of iron is an exothermic reaction and the heat can be recovered for electricity generation. The plant design’s objective is to obtain 5 MW of electricity with the design of the reactor in 900 °C, 2 ATM for the reducer and 1200 °C, 16 ATM for the combustor. We conduct process simulation and analysis to illustrate the individual reactor performance and the overall mass and energy management scheme of BDCLC process that developed by Aspen Plus software. Process simulation is then performed based on the reactor performance data obtained in multistage model.

Keywords: biomass, CO2 capture, direct chemical looping combustion, power generation

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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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