Search results for: financial market prediction

Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Josiel Delgadillo, Johnson Kinyua, Charles Mutigwe

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The purpose of sentiment analysis is to determine the sentiment strength (e.g., positive, negative, neutral) from a textual source for good decision-making. Natural language processing in domains such as financial markets requires knowledge of domain ontology, and pre-trained language models, such as BERT, have made significant breakthroughs in various NLP tasks by training on large-scale un-labeled generic corpora such as Wikipedia. However, sentiment analysis is a strong domain-dependent task. The rapid growth of social media has given users a platform to share their experiences and views about products, services, and processes, including financial markets. StockTwits and Twitter are social networks that allow the public to express their sentiments in real time. Hence, leveraging the success of unsupervised pre-training and a large amount of financial text available on social media platforms could potentially benefit a wide range of financial applications. This work is focused on sentiment analysis using social media text on platforms such as StockTwits and Twitter. To meet this need, SkyBERT, a domain-specific language model pre-trained and fine-tuned on financial corpora, has been developed. The results show that SkyBERT outperforms current state-of-the-art models in financial sentiment analysis. Extensive experimental results demonstrate the effectiveness and robustness of SkyBERT.

Keywords: BERT, financial markets, Twitter, sentiment analysis

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Authors: Belayneh Yohannes

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In Ethiopia, onion production is increasing from time to time mainly due to its high profitability per unit area. Onion has a significant contribution to generating cash income for farmers in the Raya Azebo district. Therefore, enhancing onion producers’ access to the market and improving market linkage is an essential issue. Hence, this study aimed to analyze structure-conduct-performance of onion market and identifying factors affecting the market supply of onion producers. Data were collected from both primary and secondary sources. Primary data were collected from 150 farm households and 20 traders. Four onion marketing channels were identified in the study area. The highest total gross margin is 27.6 in channel IV. The highest gross marketing margin of producers of the onion market is 88% in channel II. The result from the analysis of market concentration indicated that the onion market is characterized by a strong oligopolistic market structure, with the buyers’ concentration ratio of 88.7 in Maichew town and 82.7 in Mekelle town. Lack of capital, licensing problems, and seasonal supply was identified as the major entry barrier to onion marketing. Market conduct shows that the price of onion is set by traders while producers are price takers. Multiple linear regression model results indicated that family size in adult equivalent, irrigated land size, access to information, frequency of extension contact, and ownership of transport significantly determined the quantity of onion supplied to the market. It is recommended that strengthening and diversifying extension services in information, marketing, post-harvest handling, irrigation application, and water harvest technology is highly important.

Keywords: oligopoly, onion, market chain, multiple linear regression

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Authors: Bita Mashayekhi, Hamid Kalhornia

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One of the most important roles of financial reporting is improving the firms’ investment decisions; however, there is not much supporting evidence for this claim in emerging markets like Iran. In this study, the effect of financial reporting transparency in investment efficiency of Iranian firms has been investigated. In order to do this, 336 listed companies on Tehran Stock Exchange (TSE) has been selected for time period 2012 to 2015 as research sample. For testing our main hypothesis, we classified sample firms into two groups based on their deviation from expected investment: under-investment and over-investment cases. The results indicate that there is positive significant relationship between financial transparency and investment efficiency. In the other words, transparency can mitigate both underinvestment and overinvestment situations.

Keywords: corporate governance, disclosure, investment decisions, investment efficiency, transparency

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Authors: Zain Khalid

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The paper outlines the trends of cyber financial crimes and frauds – approximating upto – in Pakistan after the enactment of The Prevention of Electronic Crimes Act in 2016. The paper elaborates on the newer methods that fraudsters have adopted after tighter preventive and counter measures were employed in Pakistan partly as a result of following the international finance related commitments, particularly the FATF regulations. The paper adopts case studies methods to highlight various aspects of the financial frauds and crimes committed and later investigated jointly by Pakistan’s one of the federal law enforcement agencies, the Federal Investigation Agency, and Mobilink Microfinance Bank , Pakistan’s premier microfinance bank. It additionally enriches the data through expert interviews – with crime investigators and the experts to carry out an in-depth analysis of the various factors involving the crime. This paper emphasizes the structural and situational factors that shape up the cyber financial crimes in Pakistan vis-à-vis digital illiteracy and lack of awareness among the users of financial services. This paper, thus, on the basis of findings and expert interviews, suggests policy reforms to reduce the instances of the financial crimes, especially in the remotest areas of the country.

Keywords: financial crimes, cyber crimes, digital literacy, terrorism financing, banking sector

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Authors: Hananto Prakoso, Jean-Pierre Orfeuil

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The relationship between city size, urban transport efficiency (speed), employment proximity (distance) and accessibility of labour market is rarely examined especially in developing countries. This paper reveals the relationship using 2 points of views (active population and company). Then the analysis is divided according to 3 transport modes (car, public transport and motorcycle) and takes into account the vehicle ownership rate. We employ data across 111 districts in 4 big cities of Indonesia. In our result, speed indicator contributed positively to accessibility of labour market while distance elasticity is negative. In absolute value, elasticity of speed indicator is higher than that of distance.

Keywords: labour market, travel time, travel cost threshold, transportation

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Authors: Wael Bakhit, Salma Bakhit

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This paper employs a quarterly time series to determine the timing of structural breaks for interest rates in USA over the last 60 years. The Chow test is used for investigating the non-stationary, where the date of the potential break is assumed to be known. Moreover, an empirical examination of the financial sector was made to check if it is positively related to deviations from an assumed interest rate as given in a standard Taylor rule. The empirical analysis is strengthened by analysing the rule from a historical perspective and a look at the effect of setting the interest rate by the central bank on financial imbalances. The empirical evidence indicates that deviation in monetary policy has a potential causal factor in the build-up of financial imbalances and the subsequent crisis where macro prudential intervention could have beneficial effect. Thus, our findings tend to support the view which states that the probable existence of central banks has been a source of global financial crisis since the past decade.

Keywords: Taylor rule, financial imbalances, central banks, econometrics

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Authors: JingWei Yu, Hong Yang Yu

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At present, the increasingly serious air pollution in various cities of China has made people pay more attention to the air quality index(hereinafter referred to as AQI) of their living areas. To face this situation, it is of great significance to predict air pollution in heavily polluted areas. In this paper, based on the time series model of LSTM, a spatiotemporal prediction model of PM2.5 concentration in Mianyang, Sichuan Province, is established. The model fully considers the temporal variability and spatial distribution characteristics of PM2.5 concentration. The spatial correlation of air quality at different locations is based on the Air quality status of other nearby monitoring stations, including AQI and meteorological data to predict the air quality of a monitoring station. The experimental results show that the method has good prediction accuracy that the fitting degree with the actual measured data reaches more than 0.7, which can be applied to the modeling and prediction of the spatial and temporal distribution of regional PM2.5 concentration.

Keywords: LSTM, PM2.5, neural networks, spatio-temporal prediction

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Authors: Luisa Unda, Kamran Ahmed, Paul Mather

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We examine the effect of board characteristics on the performance and asset quality of credit unions in Australia, using a large sample covering the period 2004-2012. Credit unions are unique in that they are customer-owned financial institutions and directors are democratically elected by members, which is distinctly different from other financial institutions, such as commercial banks. We find that board remuneration, board expertise, and attendance at board meetings have significantly positive impacts on credit union performance and asset quality, while board members who hold multiple directorships (busy directors), have a significant negative impact on credit union performance. Financial performance also improves with larger boards and long-tenured directors in credit unions. All of these relations hold after we control for alternative measures of performance, credit union characteristics and endogeneity problem.

Keywords: credit unions, corporate governance, board of directors, financial performance, Australia, asset quality

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Authors: Sadaf Sahar, Usman Qamar, Sadaf Ayaz

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In the software development lifecycle, the quality prediction techniques hold a prime importance in order to minimize future design errors and expensive maintenance. There are many techniques proposed by various researchers, but with the increasing complexity of the software lifecycle model, it is crucial to develop a flexible system which can cater for the factors which in result have an impact on the quality of the end product. These factors include properties of the software development process and the product along with its operation conditions. In this paper, a neural network (perceptron) based software quality prediction technique is proposed. Using this technique, the stakeholders can predict the quality of the resulting software during the early phases of the lifecycle saving time and resources on future elimination of design errors and costly maintenance. This technique can be brought into practical use using successful training.

Keywords: software quality, fuzzy logic, perception, prediction

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Authors: Pepur Sandra, Bulog Ivana, Rimac Smiljanić Ana

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During the COVID-19 pandemic, stress and family financial fragility arose worldwide. Economic and health uncertainty created new pressure on the everyday life of families. The work from home, homeschooling, and care of other family members caused an increase in unpaid work and generated a new division of intrahousehold. As many times before, women have taken the higher burden. This paper analyzes family stress and finance during the COVID-19 pandemic. We propose that women's inclusion in paid and unpaid work and their financial literacy influence family finances. We build up our assumptions according to the two theories that explain intrahousehold family decision-making: traditional and barging models. The traditional model assumes that partners specialize in their roles in line with time availability. Consequently, partners less engaged in payable working activities will spend more time on domestic activities and vice versa. According to the bargaining model, each individual has their preferences, and the one with more household bargaining power, e.g., higher income, higher level of education, better employment, or higher financial knowledge, is likely to make family decisions and avoid unpaid work. Our results are based on an anonymous and voluntary survey of 869 valid responses from women older than 18 conducted in Croatia at the beginning of 2021. We found that families who experienced delays in settling current obligations before the pandemic were in a worse financial situation during the pandemic. However, all families reported problems settling current obligations during pandemic times regardless of their financial condition before the crisis. Women from families with financial issues reported higher levels of family and personal stress during the pandemic. Furthermore, we provide evidence that more women's unpaid work negatively affects the family's financial fragility during the pandemic. In addition, in families where women have better financial literacy and are more financially independent, families cope better with finance before and during pandemics.

Keywords: family financial fragility, stress, unpaid work, women's financial literacy

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Authors: Gerald Abdesaken, Thomas O. Miller

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This paper analyzes the performance of gold as a safe-haven investment. Assuming high market volatility as an impetus to seek a safe haven in gold, the return of gold relative to the stock market, as measured by the S&P 500, is tracked. Using the Chicago Board Options Exchange (CBOE) volatility index (VIX) as a measure of stock market volatility, various criteria are established for when an investor would seek a safe haven to avoid high levels of risk. The results show that in a vast majority of cases, the S&P 500 outperforms gold during these periods of high volatility and suggests investors who seek safe haven are underperforming the market.

Keywords: gold, portfolio management, safe haven, VIX

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Authors: Mohammad Irfan

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The purpose of this paper is to investigate the relationship among the key macroeconomic variables and Islamic stock market in India. This study is based on the time series data of financial years 2009-2015 to explore the consistency of relationship between macroeconomic variables and Shariah Indices. The ADF (Augmented Dickey–Fuller Test Statistic) and PP (Phillips–Perron Test Statistic) tests are employed to check stationarity of the data. The study depicts the long run relationship between Shariah indices and macroeconomic variables by using the Johansen Co-integration test. BSE Shariah and Nifty Shariah have uni-direct Granger causality. The outcome of VECM is significantly confirming the applicability of best fitted model. Thus, Islamic stock indices are proficiently working for the development of Indian economy. It suggests that by keeping eyes on Islamic stock market which will be more interactive in the future with other macroeconomic variables.

Keywords: Indian Shariah Indices, macroeconomic variables, co-integration, Granger causality, vector error correction model (VECM)

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Authors: Gonzalez Carlos, Martinez Fransisco

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There are various types of analysis that allow us to involve seismic phenomena that cause strong requirements for structures that are designed by society; one of them is a probabilistic analysis which works from prediction equations that have been created based on metadata seismic compiled in different regions. These equations form models that are used to describe the 5% damped pseudo spectra response for the various zones considering some easily known input parameters. The biggest problem for the creation of these models requires data with great robust statistics that support the results, and there are several places where this type of information is not available, for which the use of alternative methodologies helps to achieve adjustments to different models of seismic prediction.

Keywords: GMPM, 5% damped pseudo-response spectra, models of seismic prediction, PSHA

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Authors: Haruna Maama

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Environmental, social and governance (ESG) reporting is gaining considerable attention despite being voluntary. Meanwhile, it consumes resources to provide ESG reporting, raising a question of its value relevance. The study examined the impact of ESG reporting on the market value of listed firms in SSA. The annual and integrated reports of 276 listed sub-Sahara Africa (SSA) firms. The integrated reporting scores of the firm were analysed using a content analysis method. A multiple regression estimation technique using a GMM approach was employed for the analysis. The results revealed that ESG has a positive relationship with firms’ market value, suggesting that investors are interested in the ESG information disclosure of firms in SSA. This suggests that extensive ESG disclosures are attempts by firms to obtain the approval of powerful social, political and environmental stakeholders, especially institutional investors. Furthermore, the market value analysis evidence is consistent with signalling theory, which postulates that firms provide integrated reports as a signal to influence the behaviour of stakeholders. This finding reflects the value placed on investors' social, environmental and governance disclosures, which affirms the views that conventional investors would care about the social, environmental and governance issues of their potential or existing investee firms. Overall, the evidence is consistent with the prediction of signalling theory. In the context of this theory, integrated reporting is seen as part of firms' overall competitive strategy to influence investors' behaviour. The findings of this study make unique contributions to knowledge and practice in corporate reporting.

Keywords: environmental accounting, ESG accounting, signalling theory, sustainability reporting, sub-saharan Africa

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Authors: Romain Ben Taleb, Aurélie Montarnal, Matthieu Lauras, Mathieu Dahan, Romain Miclo

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To be competitive and sustainable, any company has to maximize its value. However, unlike listed companies that can assess their values based on market shares, most Small and Medium Enterprises (SMEs) which are non-listed cannot have direct and live access to this critical information. Traditional accounting reports only give limited insights to SME decision-makers about the real impact of their day-to-day decisions on the company’s performance and value. Most of the time, an SME’s financial valuation is made one time a year as the associated process is time and resource-consuming, requiring several months and external expertise to be completed. To solve this issue, we propose in this paper a value-oriented metamodel that enables real-time and dynamic assessment of the SME’s value based on the large definition of their assets. These assets cover a wider scope of resources of the company and better account for immaterial assets. The proposal, which is illustrated in a case study, discusses the benefits of incorporating assets in the SME valuation.

Keywords: SME, metamodel, decision support system, financial valuation, assets

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Authors: Shiva Kumar, G. S. Vijay, Srinivas Pai P., Shrinivasa Rao B. R.

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In the present study RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tech and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.

Keywords: radial basis function networks, emissions, performance parameters, fuzzy c means

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Authors: Adiyam Amare

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This research explores the handloom weaving quality and fashion development process for traditional Ethiopian costumes, particularly focusing on the challenges and opportunities within the contemporary global fashion market. Through a qualitative approach, including interviews and direct observations, the study identifies key factors affecting the handloom industry, such as quality improvement, market integration, and cultural preservation. The findings suggest that enhancing production quality, modernizing techniques, and fostering global market participation can significantly improve the competitiveness of Ethiopian traditional garments in the global fashion industry.

Keywords: fashion, culture, design, textile

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Authors: Ishay Wolf

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In this study, the financial position of pension actors in the market during the pension system transition toward a more funded capitalized scheme is explored, mainly via an option benefit model. This is enabled by not considering the economy as a single earning cohort. We analytically demonstrate a socio-economic anomaly in the funded pension system, which is in favor of high earning cohorts on at the expense of low earning cohorts. This anomaly is realized by a lack of insurance and exposure to financial and systemic risks. Furthermore, the anomaly might lead to pension re-reform back to unfunded scheme, mostly due to political pressure. We find that a minimum pension guarantee is a rebalance mechanism to this anomaly, which increases the probability to of the sustainable pension scheme. Specifically, we argue that implementing the guarantee with an intra-generational, risk-sharing mechanism is the most efficient way to reduce the effect of this abnormality. Moreover, we exhibit the convergence process toward implementing minimum pension guarantee in many countries which have capitalized their pension systems during the last three decades, particularly among Latin America and CEE countries.

Keywords: benefits, pension scheme, put option, social security

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Authors: Nidhin Dani Abraham, T. K. Sri Shilpa

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Asset liability management is an important aspect in banking business. Moreover, the today’s banking is based on BASEL III which strictly regulates on the counterparty default. This paper focuses on prediction and analysis of counter party default risk, which is a type of risk occurs when the customers fail to repay the amount back to the lender (bank or any financial institutions). This paper proposes an approach to reduce the counterparty risk occurring in the financial institutions using an appropriate data mining technique and thus predicts the occurrence of NPA. It also helps in asset building and restructuring quality. Liability management is very important to carry out banking business. To know and analyze the depth of liability of bank, a suitable technique is required. For that a data mining technique is being used to predict the dormant behaviour of various deposit bank customers. Various models are implemented and the results are analyzed of saving bank deposit customers. All these data are cleaned using data cleansing approach from the bank data warehouse.

Keywords: data mining, asset liability management, BASEL III, banking

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Authors: Ali Abu Ali, Mohammad Salhab

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Introduction: In the Middle East and North Africa (MENA) microfinance struggles to find momentum. The Lebanese economy has been struggling through the years due to domestic and external, political and social instability. Although as of 2014 there are around 23 MFIs that are mainly subsidized by the USAID, operating in the country, the Lebanese microfinance market is mostly dominated by three microfinance institutions: Al Majmoua, Vitas, and Al Quard Al Hassan Association. Methodology: A quantitative approach using a standardized questionnaire would analyse the perception of the average Lebanese towards microfinance. A questionnaire was designed and validated. Results: Almost half of the respondents earn a monthly income ranged between $100 and $600. Almost 52% of the respondents were university graduates, around 25% finished secondary and high school, and 12% hold a masters or MBA degree. Topic understanding towards microfinance differs across Lebanese areas. The highest percentage of respondents who claim that microfinance offers financial services to low income people are the residents of Beirut (35.1%), Bekaa (30.8%), and South of Lebanon (24.7%). Higher levels of topic understanding were associated with lower levels of age range. Al Quard el Hassan foundation was regarded as the most known micro financial institution operating in Lebanon. In general, Lebanese people tend to believe that microfinance can play an important role in reducing unemployment rates and poverty levels in Lebanon. When people were asked what would motivate you to get a loan from MFIs, most of the respondent (57.4%) across all the Lebanese region claimed that it was the need for money to satisfy a need such as paying back a loan, to fix something at home, or for self-consideration like buying a car. Conclusion: Our findings showed that in general Lebanese tend to have a positive perception towards microfinance. However, most Lebanese perceive microfinance as the process of just providing loans without specifying for whom it is intended. We advise that government introduces laws to regulate the microfinance market.

Keywords: microfinance, economics, finance, business, analysis, theory

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Authors: Teguh Sugiarto

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Background: This study aims to explore the fraud that occurred in the financial statements using the Benford distribution law of 1st and 2nd case study of PT AKR Corporindo Tbk. Research Methods: In this study the authors use the first digit of the analysis and the analysis of the second digit of Bedford’s law. Having obtained the results of the analysis of the first and second digits, authors will make the difference between implementations using the scale above and below 5%. The number that has the level of difference in the range of 5% above or below, then a financial report in may, to analyse in the followup to the direction of the audit investigation, and authors assume happens a confusion in the financial statements. Findings: From research done, we found that there was a difference in the results of the appearance of the first digit of the number with the proper use of Benford's law, according to PT AKR Corporindo financial reports Tbk for the fiscal year 2006-2010, above and below the level the difference in set 5%. Conclusions: From the research that has been done, it can be concluded that on PT AKR Corporindo financial report 2006, 2007, 2008, 2009 and 2010, there is a level difference of appearance of numbers according to Benford's law is significant, as presented in the table analysis.

Keywords: Benford law, first digits, second digits, Indonesian company

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Authors: Istijabatul Aliyah, Bambang Setioko, Rara Sugiarti

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Surakarta is one of the cities which are formed with the concept of Javanese cosmology. As a traditional town of Java, Surakarta is known as ‘the paradise’ of traditional markets. Since its establishment, Surakarta is formed with Catur Gatra Tunggal or Four Single-Slot concept (palace, square, mosques, and markets). Current development in Surakarta downtown today indicates that traditional markets have improved themselves in both physical and non-physical aspects. The efforts start from the market façade revitalization, restoration and the overall development of market; up to social activities, competition between traders or large celebrations in the neighbourhood market. This research was conducted in Surakarta, which is aimed at: identifying the role of traditional market revitalization efforts in the development of a city. This study employs several methods of analysis, namely: 1) Spatial analysis for mapping the distribution of traditional markets in the city constellation, 2) Category-Based Analysis (CBA) to classify the revitalization of traditional markets that has an influence in the development of the city, and 3) Interactive Method of Analysis. The results of this research indicate that the presence of a constellation of traditional markets in Surakarta is dominated by the presence of Gede Market, not only as the oldest traditional market, but also as a center of economic and socio-cultural activities of the community. The role of traditional market revitalization in the development of a town is as an Urban Catalyst towards a MICE city in the sense that the revitalization effort, even done in a relatively short time and not yet covering the overall objects, is able to establish brand image of Surakarta as a city of culture which is friendly and ready to be MICE tourism city.

Keywords: traditional market revitalization, urban catalyst, MICE tourism, Surakarta

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Authors: Hong Fan, Lifang Gao, Feng Zhan

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This paper examines the influence of local gambling attitudes on a firm's long-term financial performance. Firms located in gambling-prone regions may be more willing to take risks, thus spending more on innovative projects. However, firms in such regions may also be likely to choose projects impulsively and allocate resources inefficiently. By studying Chinese publicly listed firms from 2010 to 2017, we find that firms in more gambling-prone regions invest more in R&D. Both local gambling attitudes and firms’ R&D spending are positively associated with firms’ long-term financial performance. More importantly, our study reveals that the positive impact of R&D spending on firms’ long-term financial performance is weakened by gambling-friendly attitudes, probably because firms in gambling-prone regions are more likely to overinvest in risky projects. This effect is stronger for larger firms, state-owned enterprises (SOEs), firms with more government subsidies, and firms with weaker internal control.

Keywords: regional gambling attitudes, long-term financial performance, R&D, risk, local bias

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Authors: Gaurav Kumar Sinha

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The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

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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: Hashim Zameer

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Effective tourism management plays a vital role in promoting sustainability and supporting ecosystems. A common principle that has been in practice over the years is “first pollute and then clean,” indicating countries need financial resources to promote sustainability. Financial development and the tourism management both seems very important to promoting sustainable development. However, without institutional support, it is very difficult to succeed. In this context, it seems prominently significant to explore how institutional quality, tourism development, and financial development could promote sustainable development. In the past, no research explored the role of tourism development in sustainable development. Moreover, the role of financial development, natural resources, and institutional quality in sustainable development is also ignored. In this regard, this paper aims to investigate the role of tourism development, natural resources, financial development, and institutional quality in sustainable development in China. The study used time-series data from 2000–2021 and employed the Bayesian linear regression model because it is suitable for small data sets. The robustness of the findings was checked using a quantile regression approach. The results reveal that an increase in tourism expenditures stimulates the economy, creates jobs, encourages cultural exchange, and supports sustainability initiatives. Moreover, financial development and institution quality have a positive effect on sustainable development. However, reliance on natural resources can result in negative economic, social, and environmental outcomes, highlighting the need for resource diversification and management to reinforce sustainable development. These results highlight the significance of financial development, strong institutions, sustainable tourism, and careful utilization of natural resources for long-term sustainability. The study holds vital insights for policy formulation to promote sustainable tourism.

Keywords: sustainability, tourism development, financial development, institutional quality

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Authors: Sylwia Frydrych, Jörg Prokop, Michał Buszko

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This study investigates the complex relationship between corporate ESG (Environmental, Social, Governance) performance and stock price risk within the European market context. By analyzing a dataset of 435 companies across 19 European countries, the research assesses the impact of both combined ESG performance and its individual components on various risk measures, including volatility, idiosyncratic risk, systematic risk, and downside risk. The findings reveal that while overall ESG scores do not significantly influence stock price risk, disaggregating the ESG components uncovers significant relationships. Governance practices are shown to consistently reduce market risk, positioning them as critical in risk management. However, environmental engagement tends to increase risk, particularly in times of regulatory shifts like those introduced in the EU post-2018. This research provides valuable insights for investors and corporate managers on the nuanced roles of ESG factors in financial risk, emphasizing the need for careful consideration of each ESG pillar in decision-making processes.

Keywords: ESG performance, ESG factors, ESG pillars, ESG scores

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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Authors: Igor Perechuda, Wojciech Cieśliński

Abstract:

The paper identifies the features of Polish sports clubs in the particular organizational forms: profit and nonprofit. Identification and description of these features is carried out in terms of financial efficiency of the given organizational form. Under the terms of the efficiency the research allows you to specify the advantages of particular organizational sports club form and the following limitations. Paper considers features of sports clubs in range of Polish conditions as legal regulations. The sources of the functioning efficiency of sports clubs may lie in the organizational forms in which they operate. Each of the available forms can be considered either a for-profit or nonprofit enterprise. Depending on this classification there are different capabilities of increasing organizational and financial efficiency of a given sports club. Authors start with general classification and difference between for-profit and non-profit sport clubs. Next identifies specific financial and organizational conditions of both organizational form and then show examples of mixed activity forms and their efficiency effect.

Keywords: financial efficiency, for-profit, non-profit, sports club

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