Market Trends Research Articles

Applying the analytic hierarchy process for investigating key indicators of responsible innovation in the Taiwan software service industry

This study explores the critical factors of responsible innovation in the Taiwan software service industry. Based on a literature review, four main dimensions have been identified as the key factors, namely, financial capital, capabilities, social capital, and reputational assets. A hierarchical structure is constructed using these factors, and the Analytic Hierarchy Process (AHP) method is used to confirm the importance of factor weights. Questionnaires were distributed to various software companies and respondents from 35 companies. Analysis results indicate that reputational assets are highly valued, followed by social capital. The software server industry prioritizes these two indicators for responsible innovation development. Results highlight the importance of responsible innovation, assisting companies in understanding its significance and formulating strategies to enhance innovation capabilities, corporate governance, and alignment with environment and market trends. This study contributes to the evolving field of innovation management and offers valuable references for software service companies.

Integrated sustainability assessment of wood building products: The case of larch and chestnut cascading systems in Northern Italy

Wood is increasingly being appreciated in construction due to its valuable environmental attributes. This paper explores the environmental and market performance of two wood supply chains in Northern Italy. Larch and chestnut wood are extracted and processed to obtain beams, planks, MDF panels and energy. LCA is performed to evaluate the environmental impacts of 1m3 of extracted wood through a cradle-to-gate approach. Then, a biogenic carbon analysis is carried out using the EN 16449:2014 standard including a comparison of different end-of-life treatments. Also, OSB is proposed as an alternative path for wood chips and contrasted to the current energy scenario. Moreover, solid wood beams and planks are compared with engineered wood products (EWPs). Lastly, a market analysis is conducted to assess the market trends of the different wood products studied.The LCA shows similar results for both wood species across most impact categories, with slightly higher values for the chestnut system. Most impacts are related to the production of MDF boards and the energy valorization of wood chips. Biogenic carbon analysis shows a negative balance of emissions with −314 and −205 kg of CO2 eq for larch and chestnut, respectively. It also suggests that OSB manufacturing can be a valuable alternative to the energy use of wood chips and that the end-of-life treatment with better results is recycling. The comparison of beams and planks with engineered wood products supports that solid wood poses a better environmental alternative in similar applications. Market analysis shows stagnation in the apparent consumption of wood products in the European market and a slight growth in the Italian one between 2018 and 2022. Overall, the systems studied suggest that the potential environmental benefits of using wood in construction are not being matched by current market trends.

A review on coronary artery disease

Coronary Artery Disease (CAD) is currently one of the most widespread and deadly conditions in the world. Occurring due to atherosclerosis of the coronary artery and possessing a plethora of risk factors, the disease boasts an extremely large market size which is projected to continue increasing well into the future. Therefore, it is of utmost importance to conduct further research into the management of CAD and utilize this knowledge to develop new products and procedures to decrease its pervasiveness. The biological characteristics of healthy and diseased tissue, global and US market sizes, prevalence, trends, existing treatments, and products in clinical trials relating to CAD are discussed in detail below.

Investigating market trends and access implications for adalimumab following the loss of patent exclusivity: Lessons learned from biosimilar uptake in the U.S. biologic market

Investigating market trends and access implications for adalimumab following the loss of patent exclusivity: Lessons learned from biosimilar uptake in the U.S. biologic market

The Impact and Transformation of the Electric Vehicle Development on the Small Yacht Industry

With the global emphasis on environmental protection and energy sustainability, the rapid development of electric vehicle technology has gradually extended its influence to the maritime sector, particularly the small yacht industry. This paper aims to explore how the advancement of electric vehicle technology drives transformation in the small yacht industry, analyzing the environmental benefits, technological innovations, market trends, and challenges it brings. Through literature review, technical analysis, and market outlook, this paper provides theoretical support and strategic recommendations for the future development of the small yacht industry.

Inbound social media marketing and increased sales in SMEs: a correlational study in the pet food industry

New market trends have prompted Peruvian companies to apply techniques aimed at attracting potential customers, that is, to use inbound marketing, especially in the pet market, which has a profitable and competitive niche, driven mainly by the “pet-friendly” trend. This research aims to determine whether inbound marketing actions have a positive correlation with online sales. The methodology is quantitative, correlation, cross-sectional, and non-experimental. The target audience is men and women, between 18 and 35 years old who have made online purchases in the Peruvian pet food SME, Rapi Pet S.R.L. The sample is non-probabilistic and is composed of 248 people who completed a structured questionnaire of 24 questions. The results show positive perceptions of both variables with values higher than 70% after applying the barometer test, the reliability of the questionnaire was also established under Cronbach’s alpha of 0.922 and a correlation coefficient of 0.66 was obtained for the variables, as well as a p-value lower than 0.05 after performing the normality test. It is concluded that there is a positive relationship between inbound marketing actions and the increase of online sales in the company under study.

Evaluating the Value of a Geology Degree: An Economic and Societal Analysis

In an era where the economic returns on investment in higher education are increasingly scrutinized, the question arises: Is a geology degree worth it? This paper tends to provide answers to this inquiry through comprehensive economic analysis to evaluate the financial viability and broader benefits of obtaining a geology degree. The analysis begins by examining the economic landscape for geologists, including their earning potential, employment opportunities, and career advancement trajectories. Data from national labor statistics, industry reports, and academic studies are synthesized to provide a nuanced understanding of the financial outcomes associated with a geology degree. Factors such as geographic location, industry specialization, and level of education are considered to capture the variability in economic outcomes among geology graduates. This paper also explores the non-monetary benefits of a geology degree, such as job satisfaction, intellectual fulfilment, and contributions to societal challenges like natural resource management, environmental conservation, and hazard mitigation. These aspects contribute to a holistic evaluation of the degree's value proposition, balancing economic returns with intrinsic rewards. Furthermore, the analysis addresses trends in the geology job market, including the impacts of technological advancements, environmental regulations, and global economic shifts on career opportunities for geologists. Insights into emerging fields within geology, such as geotechnical engineering, hydrogeology, and environmental consulting, are also discussed to illuminate evolving career pathways and specialization opportunities. Through this multidimensional examination, the paper aims to provide valuable insights to prospective students contemplating a geology degree, educators shaping curricula, and policymakers concerned with higher education and workforce development. By integrating economic principles with empirical data and qualitative perspectives, this study offers a robust framework for understanding a geology degree's economic implications and broader societal contributions in contemporary contexts.

Research and Analysis of the Development of Live E-commerce on Xiaohongshu

This article analyzes the competitiveness and development prospects of the buyer e-commerce model on Xiaohongshu in the social e-commerce field. Firstly, by examining Xiaohongshu's community ecosystem, buyer resources, social shopping experience, and innovative brand partnerships, the unique advantages and business value of Xiaohongshu's buyer e-commerce model are revealed. Secondly, combining market demands and trends analysis with buyer resource and operational cost analysis, the feasibility and development prospects of Xiaohongshu's buyer e-commerce model are discussed. Lastly, from the perspective of competitor analysis and summarizing prospects, recommendations are provided to further optimize buyer resources, strengthen brand partnerships, control operational costs, enhance user experience, and improve community management. These suggestions aim to consolidate Xiaohongshu's leading position in the social e-commerce field, create better shopping experiences and marketing platforms for users and brand merchants, and achieve sustainable business growth.

The Impact of Geopolitical Conflict and Fed Rate Hikes on Asset Pricing

In the current context of weak economic recovery, ongoing Russia-Ukraine conflict, and consecutive interest rate hikes by the Federal Reserve, global financial and commodity markets have experienced intensified turbulence. To investigate the impact of the Russia-Ukraine conflict and the Federal Reserve interest rate hikes on asset pricing, this study first analyzed the U.S. stock market, bond market, and commodity market based on data from the CEIC database. From the comprehensive stock and year-on-year data after each round of interest rate hikes, it is evident that both the Russia-Ukraine conflict and the Federal Reserve interest rate hikes have had a significant negative impact on investors' financial assets. Additionally, due to the continuous interest rate hikes by the Federal Reserve, funds from the stock market have gradually shifted to the bond market. Furthermore, after analyzing the impact of the Russia-Ukraine conflict and the Federal Reserve interest rate hikes on the U.S. stock market, bond market, and commodity market, the study further explored the influence of these factors on asset pricing using the discounted cash flow (DCF) valuation model. Finally, the study provided an outlook on future market trends.

Research on the Pricing Model of Financial Derivatives

Based on the importance of the pricing model of financial derivatives, this paper points out the defects and risks of the current model and proposes some possible solutions. The research method is mainly to summarize the framework of the existing model by searching the academic literature and commercial examples related to the pricing model. The final conclusion is that an obvious flaw of the pricing model is that it is based on an idealized market state and cannot predict hidden market trends, but through random volatility, consideration of market friction, and the development of mathematical technology, the market can refine the pricing model. Research and forecast risk premium, market liquidity and asset price changes in advance when considering pricing. In addition, the market is also supported to consider the application of large machines in the portfolio as well as the issue of options panels, so as to deepen the understanding of the movement of financial products.

Unveiling Consumer Perceptions and Preferences in the Era of Disruptive Innovation of New Energy Vehicles Market in China

This study examines Chinese consumers' perceptions and responses to disruptive innovations within the New Energy Vehicles (NEVs) market, utilizing a questionnaire survey to explore awareness, perceived value of technological advancements, and the impact of these perceptions on adoption decisions. The survey, encompassing a diverse demographic of 154 respondents, highlights a female majority with a significant representation from the "25-34" age group, indicating a young, educated demographic with a considerable interest in NEVs. Analysis reveals a moderate familiarity with NEVs, a high valuation of technological progress, and a clear recognition of charging infrastructure availability as critical factors influencing adoption. Primary barriers identified include the high initial cost and limited charging infrastructure, with the data suggesting a strong potential for NEVs market growth if these concerns are addressed. Additionally, the survey uncovers optimistic future expectations among consumers, closely tied to government policies, technological advancements, and evolving market trends. This research contributes to understanding consumer attitudes in the rapidly changing NEVs landscape, offering insights for stakeholders to foster adoption and align strategies with consumer preferences and needs, underscoring the necessity for focused educational and marketing efforts to mitigate existing barriers and leverage consumer interest in technology.

Using Machine Learning to Predict the Stock Market Trend

For a long time, people have been trying to predict the direction of stocks, employing various methods. This paper explores the application of machine learning algorithms in predicting stock market trends, aiming to address the challenges of traditional methods and leverage the advantages of data-driven approaches. Highlights the limitations of conventional techniques and elucidates the potential of machine learning in capturing complex market dynamics. This paper discusses various machine learning algorithms and their characteristics, emphasizing their adaptability and scalability in analyzing vast amounts of data. Through empirical analysis and comparative evaluations, this paper demonstrates the efficacy of machine learning in enhancing prediction accuracy and informing investment decisions. This paper explores using machine learning for stock market prediction, overcoming traditional limitations and leveraging data-driven approaches. It discusses various algorithms, showing their adaptability and scalability, and demonstrates their efficacy in enhancing prediction accuracy. Our study contributes to the growing body of research on stock price prediction by leveraging advanced computational techniques to maneuver through the ever-changing financial markets.

Effectiveness of AI-based Machine Learning Algorithms in Predicting Global Market Movements

The challenging yet traditional field of stock market forecasting has garnered significant interest from data scientists and economists. Given the high risks associated with trading, where investors can lose part or all of their initial capital, there is a growing need for advanced methods to support investment decisions. By leveraging the capability to predict future stock performance patterns by machine learning or AI, the return on investment for short-term trading can potentially be increased. This research reviews the structures of various AI-based machine learning techniques with a case study of Apple stock data spanning from 2010 to 2021 and the effectiveness of the algorithms in terms of various real-time factors. Among several machine learning algorithms, the SVM algorithm is shown to be the most accurate, with an accuracy rate of 76% for the case study. However, the effectiveness of machine learning systems in predicting stock prices is influenced by several factors. The inherent volatility and unpredictability of financial markets make it challenging for models to anticipate sudden, unforeseen events. Overfitting is a common issue where models that perform well on historical data fail to generalize to new, unseen data because they capture noise instead of genuine market trends. Data quality issues, such as noisy or incomplete data, further complicate predictions. Additionally, the complexity of machine learning models can hinder interpretability, making it easier for analysts to trust and utilize their outcomes. This research underscores the necessity for careful and ongoing refinement of ML techniques in stock price forecasting.

Rubber Farmers' Strategies to Face Unstable Market Trends (Case Study of Smallholder Rubber Farmers in North Sumatra)

Rubber Farmers' Strategies to Face Unstable Market Trends (Case Study of Smallholder Rubber Farmers in North Sumatra)

Machine Learning Based Stock Market Trend Prediction and Analysis

Machine Learning Based Stock Market Trend Prediction and Analysis

Performance Analysis of Selected Mutual Fund Schemes: A Case Study

This study aims to evaluate the performance of selected mutual fund schemes offered by Aditya Birla Sun Life Mutual Fund. By leveraging secondary data from April 2022 to March 2023, this research analyzes the Net Asset Value (NAV) and corresponding returns of five mutual fund schemes. Utilizing statistical tools and the Sharpe Index Model, the study assesses risk-adjusted returns to rank the schemes and provide insights into their relative performance. The findings serve as a valuable resource for individual investors, organizations, and regulators, facilitating informed investment decisions and highlighting market trends. The research underscores the significance of mutual funds as a viable investment vehicle for diverse portfolios and examines the efficacy of specific schemes in achieving their investment objectives.

Exploring Market Dynamics: A Qualitative Study on Asset Price Behavior, Market Efficiency, and Information Role in Investment Decisions in the Capital Market

This qualitative study explores market dynamics, asset pricing behavior, market efficiency, and the role of information in investment decisions within the financial markets. The research aims to provide insights into the underlying motivations, perceptions, and experiences of market participants, offering a comprehensive understanding of these complex phenomena. Employing qualitative methods such as semi-structured interviews and textual analysis, data was collected from a diverse range of participants including investors, financial analysts, and market regulators. The study found that market dynamics are influenced by various factors including investor sentiment, economic indicators, regulatory changes, and technological advancements. Behavioral biases among investors, such as herd mentality and overconfidence, challenge traditional theories like the efficient market hypothesis (EMH), indicating the presence of market inefficiencies. Moreover, information plays a central role in shaping investor perceptions and driving market trends, though concerns exist regarding the reliability of information sources in the era of social media and algorithmic trading. The study underscores the importance of investor education, diversified investment strategies, transparency, and regulatory interventions in fostering fair, efficient, and resilient financial markets. Moving forward, addressing these issues will be crucial for informed investment decision-making and advancing our understanding of financial market dynamics.

Forecasting Stock Market Performance: An Ensemble Learning-Based Approach

There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance’s conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities that represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary “Buy” (1) or “Not Buy” (0) recommendation for each stock. We present approaches for a variety of machine learning models – Logistic Regression, Random Forest Classifiers, and Extreme Gradient Boosting models and do a statistical comparison on the balanced accuracies of each machine learning model. This paper aims to detail our understanding of how one can leverage the public sentiment of a stock to gauge its future value.

THE SITUATION ON THE UKRAINIAN RESIDENTIAL REAL ESTATE MARKET IN THE PRE-WAR AND WAR PERIODS

The War and the Ukrainian Housing Market: Overview and Forecasts: The article examines the domestic housing market dynamics in the pre-war and war periods. Quantitative indicators of the housing stock of Ukraine are analyzed, and their dynamics and structure are highlighted. The State Program "eRestoration": The Way to Housing Restoration: The features of implementing the "eRestoration" program aimed at compensating damaged and destroyed housing are considered. Trends in price changes in the primary, secondary, and rental housing markets in different regions of Ukraine are substantiated and conceptualized. Resumption of construction: Dynamics and prospects: The article examines aspects of the resumption of construction in Ukraine: the number of residential complexes sold in 2023 and apartments put into operation are analyzed. The state's role in the recovery of the housing market emphasizes the critical role of the state in stimulating the development of the housing market and providing affordable housing for people who lost it due to the war. Housing market: challenges and prospects. 1. Functioning of the housing market: the housing market as a system of economic relations; its role in stimulating savings, redistribution of resources, and investment; the impact of the war on the domestic housing market. 2. Market trends in 2023: growing demand for ready or almost ready housing; higher real estate prices, especially in the western regions; internal and external population migration; slow construction and accumulation of pent-up demand. 3. Future forecasts: stabilization of the market in the absence of escalation of the war; conversion of pent-up demand into new transactions; price correction; opportunities for further market development; stimulation of demand for housing and commercial real estate; development of urban infrastructure. 4. The role of the state: the leading role of the state in the formation of the regulatory framework; implementation of programs to eliminate imbalances in housing provision; elimination of the adverse effects of the war; 5. Conclusion: The restoration and development of the housing market of Ukraine is a complex task that requires joint efforts of the state, businesses, and citizens. Improving the uniqueness of the text: new subheadings were added to improve the structure of the text; more dynamic and emotional words and phrases were used; author's opinions and analytics were added; specific examples and statistics were provided; complex sentences were rephrased to improve readability; a different writing style was used to emphasize the author's handwriting.

Market intelligence applications leveraging a product-specific Sentence-RoBERTa model

Market intelligence, which collects and analyzes market trends and competitive landscape, is crucial for business success in the market. In particular, defining market scope is important because the market analysis outcomes vary depending on where the market boundaries are. However, this has been challenging due to the difficulty in scoping a number of similar products into the target market appropriately. To address this challenge, this study devised a novel method for acquiring reliable market intelligence utilizing the Sentence-RoBERTa (SRoBERTa) model, a pre-trained language model (PLM) specialized in computing sentence similarity. The SRoBERTa model was fine-tuned with 26,248,771 data on product relations and built a product-specific SRoBERTa model (named Pro-SRoBERTa) for effectively identifying the products that fall within the market scope. It outperformed the untrained model by up to 82.8 % on the hierarchical product categorization task. Finally, the Pro-SRoBERTa model was applied to determine the scope of the market followed by obtaining diverse market intelligence. The analysis module was successfully implemented in the market information system using a large-scale, real-world dataset.