Search results for: price elasticity

Authors: Jie Zhang, Chaomin Zhang, Jihua Zhang, Haitong Li

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Using the theory of optimal distinctiveness, this study examines the effects of conformity and differentiation within corporate social responsibility (CSR) practices on capital market performance. Analysing data from Chinese A-share listed firms from 2007 to 2022, this paper demonstrates that when firms conform to the expected scope of CSR, such behaviour enhances investor attention and market acceptance, thereby boosting stock liquidity. Conversely, emphasising differentiation in CSR practices more effectively mitigates stock price crash risk by addressing principal–agent problems and decreasing information asymmetry. This paper also investigates how organisational and environmental factors moderate the relationship between conformity and differentiation in CSR practices and their impact on capital market performance. The results also show that the influence of conformity on stock liquidity is accentuated in smaller firms and environments with stringent legal oversight. By contrast, the benefits of differentiation in reducing stock price crash risk are amplified in firms with robust corporate governance and markets characterised by high uncertainty.

Keywords: corporate social responsibility, social responsibility practices, capital market performance, optimal distinctiveness

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Authors: Anyapak Prapannetivuth

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The purpose of this research is to investigate the marketing mix that is perceived to be important for the small sized hotels in Pattaya. Unlike previous studies, this research provides insights through a review of the marketing activities performed by the small sized hotels. Nine owners and marketing manager of small sized hotels and resorts, all local Chonburi people, were selected for an in-depth interview. A snowball sampling process was employed. The research suggests that seven marketing mixes (e.g. Product, Price, Place, Promotion, People, Physical Evidence and Process) were commonly used by these hotels, however, three types – People, price and physical evidence were considered most important by the owners.

Keywords: marketing mix, marketing tools, small sized hotels, pattaya

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Authors: Hanen Khanchel, Karim Ben Kahla

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Pharmaceutical companies are facing competition. Indeed, the price differences between competing products can be such that it becomes difficult to compensate them by differences in value added. The conditions of competition are no longer homogeneous for the players involved. The price of a product is a given that puts a company and its customer face to face. However, price fixing obliges the company to consider internal factors relating to production costs and external factors such as customer attitudes, the existence of regulations and the structure of the market on which the firm evolved. In setting the selling price, the company must first take into account internal factors relating to its costs: costs of production fall into two categories, fixed costs and variable costs that depend on the quantities produced. The company cannot consider selling below what it costs the product. It, therefore, calculates the unit cost of production to which it adds the unit cost of distribution, enabling it to know the unit cost of production of the product. The company adds its margin and thus determines its selling price. The margin is used to remunerate the capital providers and to finance the activity of the company and its investments. Production costs are related to the quantities produced: large-scale production generally reduces the unit cost of production, which is an asset for companies with mass production markets. This shows that small and medium-sized companies with limited market segments need to make greater efforts to ensure their profit margins. As a result, and faced with high and low market prices for raw materials and increasing staff costs, the company must seek to optimize its production time in order to reduce loads and eliminate waste. Then, the customer pays only value added. Thus, and based on this principle we decided to create a project that deals with the problem of waste in our company, and having as objectives the reduction of production costs and improvement of performance indicators. This paper presents the implementation of the Value Stream Mapping (VSM) project in a pharmaceutical company. It is structured as follows: 1) determination of the family of products, 2) drawing of the current state, 3) drawing of the future state, 4) action plan and implementation.

Keywords: VSM, waste, production time, kaizen, cartography, improvement

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Authors: Sonali Agarwal

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The investor perception about investment avenues is affected to a great degree by the current happenings, within the country, and on the global stage. The influencing events can range from government policies, bilateral trade agreements, election agendas, to changing exchange rates, appreciation and depreciation of currency, recessions, meltdowns, bankruptcies etc. The current research attempts to discover and unravel the effect of various macroeconomic variables (crude oil price, gold price, silver price and USD exchange rate) on the Indian mutual fund industry in general and the chosen funds (Axis Gold Fund, BSL Gold Fund, Kotak Gold Fund & SBI gold fund) in particular. Cointegration tests and Vector error correction equations prove that the chosen variables have strong effect on the NAVs (net asset values) of the mutual funds. However, the greatest influence is felt from the fund’s own past and current information and it is found that when an innovation of fund’s own lagged NAVs is given, variance caused is high that changes the current NAVs markedly. The study helps to highlight the interplay of macroeconomic variables and their repercussion on mutual fund industry.

Keywords: cointegration, Granger causality, impulse response, macroeconomic variables, mutual funds, stationarity, unit root test, variance decomposition, VECM

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Authors: Amit Das

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Background and significance of the study: Generally investors and market forecasters use financial statement for investigation while it awakens contribute to investing. The main vicinity of financial accounting and reporting practices recommends a few basic financial performance indicators, namely, return on capital employed, return on assets and earnings per share, which is associated considerably with share prices. It is principally true in case of Indian pharmaceutical companies also. Share investing is intriguing a financial risk in addition to investors look for those financial evaluations which have noteworthy shock on share price. A crucial intention of financial statement analysis and reporting is to offer information which is helpful predominantly to exterior clients in creating credit as well as investment choices. Sound financial performance attracts the investors automatically and it will increase the share price of the respective companies. Keeping in view of this, this research work investigates the impact of financial performance indicators on share price of pharmaceutical companies in India which is listed in the Bombay Stock Exchange. Methodology: This research work is based on secondary data collected from moneycontrol database on September 28, 2015 of top 101 pharmaceutical companies in India. Since this study selects four financial performance indicators purposively and availability in the database, that is, earnings per share, return on capital employed, return on assets and net profits as independent variables and one dependent variable, share price of 101 pharmaceutical companies. While analysing the data, correlation statistics, multiple regression technique and appropriate test of significance have been used. Major findings: Correlation statistics show that four financial performance indicators of 101 pharmaceutical companies are associated positively and negatively with its share price and it is very much significant that more than 80 companies’ financial performances are related positively. Multiple correlation test results indicate that financial performance indicators are highly related with share prices of the selected pharmaceutical companies. Furthermore, multiple regression test results illustrate that when financial performances are good, share prices have been increased steadily in the Bombay stock exchange and all results are statistically significant. It is more important to note that sensitivity indices were changed slightly through financial performance indicators of selected pharmaceutical companies in India. Concluding statements: The share prices of pharmaceutical companies depend on the sound financial performances. It is very clear that share prices are changed with the movement of two important financial performance indicators, that is, earnings per share and return on assets. Since 101 pharmaceutical companies are listed in the Bombay stock exchange and Sensex are changed with this, it is obvious that Government of India has to take important decisions regarding production and exports of pharmaceutical products so that financial performance of all the pharmaceutical companies are improved and its share price are increased positively.

Keywords: financial performance indicators, share prices, pharmaceutical companies, India

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Authors: Periklis Brakatsoulas

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

Keywords: forecasting, long memory, momentum, returns

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Authors: Prama Mukhopadhyay

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Starting from transport to food, today’s world platform economy and digital markets have taken over almost every sphere of consumers’ lives. Sellers and buyers are getting connected through platforms, which is acting as an intermediary. It has made consumer’s life easier in terms of time, price, choice and other factors. Having said that, there are several concerns regarding platforms. There are competition law concerns like unfair pricing, deep discounting by the platforms which affect the consumer welfare. Apart from that, the biggest problem is lack of transparency with respect to the business models, how it operates, price calculation, etc. In most of the cases, consumers are unaware of how their personal data are being used. In most of the cases, they are unaware of how algorithm uses their personal data to determine the price of the product or even to show the relevant products using their previous searches. Using personal or non-personal data without consumer’s consent is a huge legal concern. In addition to this, another major issue lies with the question of liability. If a dispute arises, who will be responsible? The seller or the platform? For example, if someone ordered food through a food delivery app and the food was bad, in this situation who will be liable: the restaurant or the food delivery platform? In this paper, the researcher tries to examine the legal concern related to platform economy from the consumer protection and consumer welfare perspectives. The paper analyses the cases from different jurisdictions and approach taken by the judiciaries. The author compares the existing legislation of EU, US and other Asian Countries and tries to highlight the best practices.

Keywords: competition, consumer, data, platform

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Authors: Christine P. Gibson, Ramona Alex, Michael Farney, Johana Vallejo-Elias, Mitra Esfandiarei

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Aortic aneurysm is the leading cause of death in Marfan syndrome (MFS), a connective tissue disorder caused by mutations in fibrillin-1 gene (FBN1). MFS aneurysm is characterized by weakening of the aortic wall due to elastin fibers fragmentation and disorganization. The above-average height and distinct physical features make young adults with MFS desirable candidates for competitive sports; but little is known about the exercise limit at which they will be at risk for aortic rupture. On the other hand, aerobic cardiovascular exercise has been shown to have protective effects on the heart and aorta. We have previously reported that mild aerobic exercise can delay the formation of aortic aneurysm in a mouse model of MFS. In this study, we aimed to investigate the effects of various levels of exercise intensity on the progression of aortic aneurysm in the mouse model. Starting at 4 weeks of age, we subjected control and MFS mice to different levels of exercise intensity (8m/min, 10m/min, 15m/min, and 20m/min, corresponding to 55%, 65%, 75%, and 85% of VO2 max, respectively) on a treadmill for 30 minutes per day, five days a week for the duration of the study. At 24 weeks of age, aortic tissue were isolated and subjected to structural and functional studies using histology and wire myography in order to evaluate the effects of different exercise routines on elastin fragmentation and organization and aortic wall elasticity/stiffness. Our data shows that exercise training at the intensity levels between 55%-75% significantly reduces elastin fragmentation and disorganization, with less recovery observed in 85% MFS group. The reversibility of elasticity was also significantly restored in MFS mice subjected to 55%-75% intensity; however, the recovery was less pronounced in MFS mice subjected to 85% intensity. Furthermore, our data shows that smooth muscle cells (SMCs) contractilion in response to vasoconstrictor agent phenylephrine (100nM) is significantly reduced in MFS aorta (54.84 ± 1.63 mN/mm2) as compared to control (95.85 ± 3.04 mN/mm2). At 55% of intensity, exercise did not rescue SMCs contraction (63.45 ± 1.70 mN/mm2), while at higher intensity levels, SMCs contraction in response to phenylephrine was restored to levels similar to control aorta [65% (81.88 ± 4.57 mN/mm2), 75% (86.22 ± 3.84 mN/mm2), and 85% (83.91 ± 5.42 mN/mm2)]. This study provides the first time evidence that high intensity exercise (e.g. 85%) may not provide the most beneficial effects on aortic function (vasoconstriction) and structure (elastin fragmentation, aortic wall elasticity) during the progression of aortic aneurysm in MFS mice. On the other hand, based on our observations, medium intensity exercise (e.g. 65%) seems to provide the utmost protective effects on aortic structure and function in MFS mice. These findings provide new insights into the potential capacity, in which MFS patients could participate in various aerobic exercise routines, especially in young adults affected by cardiovascular complications particularly aortic aneurysm. This work was funded by Midwestern University Research Fund.

Keywords: aerobic exercise, aortic aneurysm, aortic wall elasticity, elastin fragmentation, Marfan syndrome

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Authors: K. O. Ogbedeh, T. T. Epidi, E. U. Onweremadu, E. E. Ihem

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Apart from the devastating nature of termites as pest of cassava, nearly all termite species have been implicated in soil fertility modifications. Elasticity of soil fertility indicators and pH in termite infested cassava field as influenced by tillage and organic manure sources in Owerri, Southeast, Nigeria was investigated in this study. Three years of of field trials were conducted in 2007, 2008 and 2009 cropping seasons respectively at the Teaching and Research Farm of the Federal University of Technology, Owerri. The experiments were laid out in a 3x6 split-plot factorial arrangement fitted into a randomized complete block design (RCBD) with three replications. The TMS 4 (2)1425 was the cassava cultivar used. Treatments consists three tillage methods (zero, flat and mound), two rates of municipal waste (1.5 and 3.0tonnes/ha), two rates of Azadirachta indica (neem) leaves (20 and 30tonnes/ha), control (0.0 tonnes/ha) and a unit dose of carbofuran (chemical check). Data were collected on pre-planting soil physical and chemical properties, post-harvest soil pH (both in water and KCl) and residual total exchangeable bases (Ca, K, Mg and Na). These were analyzed using a Mixed-model procedure of Statistical Analysis Software (SAS). Means were separated using Least Significant Difference (LSD.) at 5% level of probability. Result shows that the native soil fertility status of the experimental site was poor. However soil pH increased substantially in plots where mounds, A.indica leaves at 30t/ha and municipal waste (1.5 and 3.0t/ha) were treated especially in 2008 and 2009. In 2007 trial, highest soil pH was maintained with flat (5.41 in water and 4.97 in KCl). Control on the other hand, recorded least soil pH especially in 2009 with values of 5.18 and 4.63 in water and KCl respectively. Equally, mound, A. indica leaves at 30t/ha and municipal waste at 3.0t/ha consistently increased organic matter content of the soil than other treatments. Finally, mound and A. indica leaves at 30t/ha linearly and consistently increased residual total exchangeable bases of the soil.

Keywords: elasticity, fertility, indicators, termites, tillage, cassava and manure sources

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Authors: Esmaeil Bahmyari

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The natural frequency analysis of the functionally graded (FG) rotating cylindrical shells subjected to thermal loads is studied based on the three-dimensional elasticity theory. The temperature-dependent assumption of the material properties is graded in the thickness direction, which varies based on the simple power law distribution. The governing equations and the appropriate boundary conditions, which include the effects of initial thermal stresses, are derived employing Hamilton’s principle. The initial thermo-mechanical stresses are obtained by the thermo-elastic equilibrium equation’s solution. As an efficient and accurate numerical tool, the differential quadrature method (DQM) is adopted to solve the thermo-elastic equilibrium equations, free vibration equations and natural frequencies are obtained. The high accuracy of the method is demonstrated by comparison studies with those existing solutions in the literature. Ultimately, the parametric studies are performed to demonstrate the effects of boundary conditions, temperature rise, material graded index, the thickness-to-length and the aspect ratios for the rotating cylindrical shells on the natural frequency.

Keywords: free vibration, DQM, elasticity theory, FG shell, rotating cylindrical shell

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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

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

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

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Authors: Tarah Bouaricha

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The impact of worst natural disasters is analysed in terms of insured losses which happened between 2010 and 2014 on S&P insurance index. Event study analysis is used to test whether natural disasters impact insurance index stock market price. There is no negative impact on insurance stock market price around the disasters event. To analyse the reaction of insurance stock market, normal returns (NR), abnormal returns (AR), cumulative abnormal returns (CAR), cumulative average abnormal returns (CAAR) and a parametric test on AR and on CAR are used.

Keywords: study event, natural disasters, insurance, reinsurance, stock market

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Authors: Anwar Hasan Abdullah Othman, Ahamed Kameel, Hasanuddeen Abdul Aziz

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In this study, an attempt has been made to investigate the relationship specifically the causal relation between fund unit prices of Islamic equity unit trust fund which measure by fund NAV and the selected macro-economic variables of Malaysian economy by using VECM causality test and Granger causality test. Monthly data has been used from Jan, 2006 to Dec, 2012 for all the variables. The findings of the study showed that industrial production index, political election and financial crisis are the only variables having unidirectional causal relationship with fund unit price. However, the global oil prices is having bidirectional causality with fund NAV. Thus, it is concluded that the equity unit trust fund industry in Malaysia is an inefficient market with respect to the industrial production index, global oil prices, political election and financial crisis. However, the market is approaching towards informational efficiency at least with respect to four macroeconomic variables, treasury bill rate, money supply, foreign exchange rate and corruption index.

Keywords: fund unit price, unit trust industry, Malaysia, macroeconomic variables, causality

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Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Gabriele Iannotta

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This paper investigates credit cycles by means of an experiment based on a Kiyotaki & Moore (1997) model with heterogeneous expectations. The aim is to examine how a credit squeeze caused by high lender-level risk perceptions affects the real prices of a collateralised asset, with a special focus on the macroeconomic implications of rising price volatility in terms of total welfare and the number of bankruptcies that occur. To do that, a learning-to-forecast experiment (LtFE) has been run where participants are asked to predict the future price of land and then rewarded based on the accuracy of their forecasts. The setting includes one lender and five borrowers in each of the twelve sessions split between six control groups (G1) and six treatment groups (G2). The only difference is that while in G1 the lender always satisfies borrowers’ loan demand (bankruptcies permitting), in G2 he/she closes the entire credit market in case three or more bankruptcies occur in the previous round. Experimental results show that negative risk-driven supply shocks amplify the volatility of collateral prices. This uncertainty worsens the agents’ ability to predict the future value of land and, as a consequence, the number of defaults increases and the total welfare deteriorates.

Keywords: Behavioural Macroeconomics, Credit Cycle, Experimental Economics, Heterogeneous Expectations, Learning-to-Forecast Experiment

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Authors: Yan Wang, Yasushi Asami, Yukio Sadahiro

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In urban context, urban nodes such as amenity or hazard will certainly affect house price, while classic hedonic analysis will employ distance variables measured from each urban nodes. However, effects from distances to facilities on house prices generally do not represent the true price of the property. Distance variables measured on the same surface are suffering a problem called multicollinearity, which is usually presented as magnitude variance and mean value in regression, errors caused by instability. In this paper, we provided a theoretical framework to identify and gather the data with less bias, and also provided specific sampling method on locating the sample region to avoid the spatial multicollinerity problem in three distance variable’s case.

Keywords: hedonic regression, urban node, distance variables, multicollinerity, collinearity

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Authors: Andrei-Marius Sandu

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This study aims to analyze the bovine meat and milk sector for the period 2007-2013. For the period analyzed, it is known that Romania has benefited from EU funding through the National Rural Development Programme 2007-2013. In this programme, there were measures that addressed exclusively the animal husbandry sector in Romania. This paper presents data on bovine production of meat, milk and livestock in Romania, but also data on the price and impact the European Funds implementation had on them.

Keywords: European funds, measures, national rural development programme, price

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Authors: Ira Joseph

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The LNG paradox addresses the issue of how the most expensive form of gas supply, which is LNG, will grow in an end user market where demand is most competitive, which is power generation. In this case, LNG demand growth is under siege from two entirely different directions. At one end is price; it will be extremely difficult for gas to replace coal in Asia due to the low price of coal and the age of the generation plants. Asia's coal fleet, on average, is less than two decades old and will need significant financial incentives to retire before its state lifespan. While gas would cut emissions in half relative to coal, it would also more than double the price of the fuel source for power generation, which puts it in a precarious position. In most countries in Asia other than China, this cost increase, particularly from imports, is simply not realistic when it is also necessary to focus on economic growth and social welfare. On the other end, renewables are growing at an exponential rate for three reasons. One is that prices are dropping. Two is that policy incentives are driving deployment, and three is that China is forcing renewables infrastructure into the market to take a political seat at the global energy table with Saudi Arabia, the US, and Russia. Plus, more renewables will lower import growth of oil and gas in China, if not end it altogether. Renewables are the predator at the gate of gas demand in power generation and in every year that passes, renewables cut into demand growth projections for gas; in particular, the type of gas that is most expensive, which is LNG. Gas does have a role in the future, particularly within a domestic market. Once it crosses borders in the form of LNG or even pipeline gas, it quickly becomes a premium fuel and must be marketed and used this way. Our research shows that gas will be able to compete with batteries as an intermittency and storage tool and does offer a method to harmonize with renewables as part of the energy transition. As a baseload fuel, however, the role of gas, particularly, will be limited by cost once it needs to cross a border. Gas converted into blue or green hydrogen or ammonia is also an option for storage depending on the location. While this role is much reduced from the primary baseload role that gas once aspired to land, it still offers a credible option for decades to come.

Keywords: natural gas, LNG, demand, price, intermittency, storage, renewables

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Authors: Norul Hisham Hamid, Amir Affan, Ummi Hani Abdullah, Paridah Md. Tahir, Khairul Akmal Azhar, Rahmat Nawai, W. B. H. Wan Sulwani Izzati

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The dimensional stability and static bending properties of epoxy composite reinforced with amorphous and crystalline silica were investigated. The amorphous and crystalline silica was obtained by the precipitation method from carbonisation process of the rice husk at a temperature of 600 °C and 1000 °C for 7 hours respectively. The epoxy resin was mixed with 5%, 10% and 15% concentrations of amorphous and crystalline silica. The mixture was stirred for 10 minutes and cured at 28 °C for 72 hours and oven dried at 80 °C for 72 hours. The scanning electron microscope image showed the silica sized of 10-30nm was obtained. The water absorption and thickness swelling of epoxy/amorphous silica composite was not significantly different with silica concentration ranged from 0.08% to 0.09% and 0.17% to 0.20% respectively. The maximum modulus of rupture (85 MPa) and modulus of elasticity (3284 MPa) were achieved for 10% silica concentration. For epoxy/crystalline silica composite; the water absorption and thickness swelling were also not significantly different with silica concentration, ranged from 0.08% to 0.11% and 0.16% to 0.18% respectively. The maximum modulus of rupture (47.9 MPa) and modulus of elasticity (2760 MPa) were achieved for 10% silica concentration. Overall, the water absorption and thickness swelling were almost identical for epoxy composite made from either amorphous or crystalline silica. The epoxy composite made from amorphous silica was stronger than crystalline silica.

Keywords: epoxy, composite, dimensional stability, static bending, silica

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Carlos Tapia Cortez, Serkan Saydam, Jeff Coulton, Claude Sammut

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Strategic mineral commodities (SMC) both energetic and metals have long been fundamental for human beings. There is a strong and long-run relation between the mineral resources industry and society's evolution, with the provision of primary raw materials, becoming one of the most significant drivers of economic growth. Due to mineral resources’ relevance for the entire economy and society, an understanding of the SMC market behaviour to simulate price fluctuations has become crucial for governments and firms. For any human activity, SMC price fluctuations are affected by economic, geopolitical, environmental, technological and psychological issues, where cognition has a major role. Cognition is defined as the capacity to store information in memory, processing and decision making for problem-solving or human adaptation. Thus, it has a significant role in those systems that exhibit dynamic equilibrium through time, such as economic growth. Cognition allows not only understanding past behaviours and trends in SCM markets but also supports future expectations of demand/supply levels and prices, although speculations are unavoidable. Technological developments may also be defined as a cognitive system. Since the Industrial Revolution, technological developments have had a significant influence on SMC production costs and prices, likewise allowing co-integration between commodities and market locations. It suggests a close relation between structural breaks, technology and prices evolution. SCM prices forecasting have been commonly addressed by econometrics and Gaussian-probabilistic models. Econometrics models may incorporate the relationship between variables; however, they are statics that leads to an incomplete approach of prices evolution through time. Gaussian-probabilistic models may evolve through time; however, price fluctuations are addressed by the assumption of random behaviour and normal distribution which seems to be far from the real behaviour of both market and prices. Random fluctuation ignores the evolution of market events and the technical and temporal relation between variables, giving the illusion of controlled future events. Normal distribution underestimates price fluctuations by using restricted ranges, curtailing decisions making into a pre-established space. A proper understanding of SMC's price dynamics taking into account the historical-cognitive relation between economic, technological and psychological factors over time is fundamental in attempting to simulate prices. The aim of this paper is to discuss the SMC market cognition hypothesis and empirically demonstrate its dynamic-cognitive capacity. Three of the largest and traded SMC's: oil, copper and gold, will be assessed to examine the economic, technological and psychological cognition respectively.

Keywords: commodity price simulation, commodity price uncertainties, dynamic-cognition, dynamic systems

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Authors: Victoria Masi Haruna Karatu, Nik Kamariah Nikmat

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In recent times awareness about the environment and green purchase has been on the increase across nations due to global warming. Previous researchers have attempted to determine what actually influences the purchase intention of consumers in this environmentally conscious epoch. The consumers too have become conscious of what to buy and who to buy from in their purchasing decisions as this action will reflect their concern about the environment and their personal well-being. This trend is a widespread phenomenon in most developed countries of the world. On the contrary evidence revealed that only 5% of the populations of Nigeria involve in green purchase activities thus making the country lag behind its counterparts in green practices. This is not a surprise as Nigeria is facing problems of inadequate green knowledge, non-enforcement of environmental regulations, sensitivity to the price of green products when compared with the conventional ones and distrust towards green products which has been deduced from prior studies of other regions. The main objectives of this study is to examine the direct antecedents of green purchase intention (green availability, government regulations, perceived green knowledge, perceived value and green price sensitivity) in Nigeria and secondly to establish the mediating role of perceived behavioral control on the relationship between these antecedents and green purchase intention. The study adopts quantitative method whereby 700 questionnaires were administered to lecturers in three Nigerian universities. 502 datasets were collected which represents 72 percent response rate. After screening the data only 440 were usable and analyzed using structural equation modeling (SEM) and bootstrapping. From the findings, three antecedents have significant direct relationships with green purchase intention (perceived green knowledge, perceived behavioral control, and green availability) while two antecedents have positive and significant direct relationship with perceived behavioral control (perceived value and green price sensitivity). On the other hand, PBC does not mediate any of the paths from the predictors to criterion variable. This result is discussed in the Nigerian context.

Keywords: Green Availability, Green Price Sensitivity, Green Purchase Intention, Perceived Green Knowledge, Perceived Value

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Authors: Chiao-Yi Chen, Dung-Ying Lin

Abstract:

With the growing awareness of environmental protection and the implementation of government carbon reduction policies, the number of electric vehicles (EVs) has rapidly increased, leading to a surge in charging demand and imposing significant challenges on the existing power grid’s capacity. Traditional urban power grid planning has not adequately accounted for the additional load generated by EV charging, which often strains the infrastructure. This study aims to optimize grid operation and load management by dynamically adjusting EV charging prices based on real-time electricity supply and demand, leveraging consumer demand elasticity to enhance system efficiency. This study uniquely addresses the intricate interplay between urban traffic patterns and power grid dynamics in the context of electric vehicle (EV) adoption. By integrating Hsinchu City's road network with the IEEE 33-bus system, the research creates a comprehensive model that captures both the spatial and temporal aspects of EV charging demand. This approach allows for a nuanced analysis of how traffic flow directly influences the load distribution across the power grid. The strategic placement of charging stations at key nodes within the IEEE 33-bus system, informed by actual road traffic data, enables a realistic simulation of the dynamic relationship between vehicle movement and energy consumption. This integration of transportation and energy systems provides a holistic view of the challenges and opportunities in urban EV infrastructure planning, highlighting the critical need for solutions that can adapt to the ever-changing interplay between traffic patterns and grid capacity. The proposed dynamic pricing strategy effectively reduces peak charging loads, enhances the operational efficiency of charging stations, and maximizes operator profits, all while ensuring grid stability. These findings provide practical insights and a valuable framework for optimizing EV charging infrastructure and policies in future smart cities, contributing to more resilient and sustainable urban energy systems.

Keywords: dynamic pricing, demand elasticity, EV charging, grid load balancing, optimization

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Authors: M. Zasada, A. Markiewicz, A. Erkiert-Polguj, E. Budzisz

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The epidermis is a keratinized stratified squamous epithelium covered by the hydro-lipid barrier. Therefore, active substances should be able to penetrate through this hydro-lipid coating. L-ascorbic acid is one of the vitamins which plays an important role in stimulation fibroblast to produce collagen type I and in hyperpigmentation lightening. Vitamin C is a water-soluble antioxidant, which protects skin from oxidation damage and rejuvenates photoaged skin. No-needle mesotherapy is a non-invasive rejuvenation technique depending on electric pulses, electroporation, and ultrasounds. These physicals factors result in deeper penetration of cosmetics. It is important to increase the penetration of L-ascorbic acid, thereby increasing the spectrum of its activity. The aim of the work was to assess the effectiveness of pure L-ascorbic acid activity in anti-aging therapy using a needle-free and micro-needling mesotherapy. The study was performed on a group of 35 healthy volunteers in accordance with the Declaration of Helsinki of 1964 and agreement of the Ethics Commissions no RNN/281/16/KE 2017. Women were randomized to mesotherapy or control group. Control group applied topically 2,5 ml serum containing 20% L-ascorbic acid with hydrate from strawberries, every 10 days for a period of 9 weeks. No-needle mesotherapy, on the left half of the face and micro-needling on the right with the same serum, was done in mesotherapy group. The pH of serum was 3.5-4, and the serum was prepared directly prior to the facial treatment. The skin parameters were measured at the beginning and before each treatment. The measurement of the forehead skin was done using Cutometer® (measurement of skin elasticity and firmness), Corneometer® (skin hydration measurement), Mexameter® (skin tone measurement). Also, the photographs were taken by Fotomedicus system. Additionally, the volunteers fulfilled the questionnaire. Serum was tested for microbiological purity and stability after the opening of the cosmetic. During the study, all of the volunteers were taken care of a dermatologist. The regular application of the serum has caused improvement of the skin parameters. Respectively, after 4 and 8 weeks improvement in hydration and elasticity has been seen (Corneometer®, Cutometer® results). Moreover, the number of hyper-pigmentated spots has decreased (Mexameter®). After 8 weeks the volunteers has claimed that the tested product has smoothing and moisturizing features. Subjective opinions indicted significant improvement of skin color and elasticity. The product containing the L-ascorbic acid used with intercellular penetration promoters demonstrates higher anti-aging efficiency than control. In vivo studies confirmed the effectiveness of serum and the impact of the active substance on skin firmness and elasticity, the degree of hydration and skin tone. Mesotherapy with pure L-ascorbic acid provides better diffusion of active substances through the skin.

Keywords: anti-aging, l-ascorbic acid, mesotherapy, promoters

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Authors: Marlen Acosta Alamo, Lisa Manne, Richard Veit

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Habitat loss due to land use change is one of the leading causes of biodiversity loss worldwide. This form of habitat loss is a non-random phenomenon since the same environmental factors that make an area suitable for supporting high local biodiversity overlap with those that make it attractive for urban development. We aimed to compare the effect of two non-random habitat loss predictors on the richness, abundance, and rarity of nature-affiliated and human-affiliated North American breeding birds. For each group of birds, we simulated the non-random habitat loss using two predictors: the House Price Index as a measure of the attractiveness of an area for humans and the Normalized Difference Vegetation Index as a proxy for primary productivity. We compared the results of the two non-random simulation sets and one set of random habitat loss simulations using an analysis of variance and followed up with a Tukey-Kramer test when appropriate. The attractiveness of an area for humans predicted estimates of richness loss and increase of rarity higher than primary productivity and random habitat loss for nature-affiliated and human-affiliated birds. For example, at 50% of habitat loss, the attractiveness of an area for humans produced estimates of richness at least 5% lower and of a rarity at least 40% higher than primary productivity and random habitat loss for both groups of birds. Only for the species abundance of nature-affiliated birds, the attractiveness of an area for humans did not outperform primary productivity as a predictor of biodiversity following habitat loss. We demonstrated the value of the House Price Index, which can be used in conservation assessments as an index of the risks of habitat loss for natural communities. Thus, our results have relevant implications for sustainable urban land-use planning practices and can guide stakeholders and developers in their efforts to conserve local biodiversity.

Keywords: biodiversity loss, bird biodiversity, house price index, non-random habitat loss

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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