Market Trends Research Articles

Promoting financial inclusion for SMEs: Leveraging AI and data analytics in the banking sector

Financial inclusion is crucial for the growth and sustainability of small and medium-sized enterprises (SMEs), which are significant contributors to economic development and job creation. However, traditional banking models often fall short in serving the unique needs of SMEs due to perceived high risks and insufficient credit history. Leveraging artificial intelligence (AI) and data analytics, the banking sector can transform its approach to financial inclusion, offering tailored financial products and services that cater specifically to SMEs. AI and data analytics enable banks to analyze vast amounts of data from diverse sources, including transactional histories, social media activity, and market trends. This holistic view of an SME's financial health and business potential allows for more accurate risk assessment and credit scoring. Machine learning algorithms can identify patterns and predict creditworthiness, enabling banks to extend credit to SMEs that may have been overlooked by traditional models. Additionally, AI-driven insights facilitate the development of customized financial products, such as flexible loan terms and dynamic interest rates, that align with the cash flow cycles and operational realities of SMEs. Automated processes and AI-powered chatbots enhance customer service, providing SMEs with timely support and financial advice, thereby improving their banking experience. Data analytics also play a critical role in fraud detection and prevention, ensuring the security of transactions and building trust among SME clients. By continuously monitoring and analyzing transaction data, banks can quickly identify and mitigate fraudulent activities, protecting SMEs from financial losses. Moreover, AI and data analytics support the creation of financial literacy programs tailored to the specific needs of SMEs, empowering business owners with the knowledge and tools to make informed financial decisions. This educational aspect is vital in fostering a sustainable financial ecosystem for SMEs. In conclusion, the integration of AI and data analytics in the banking sector holds significant promise for promoting financial inclusion among SMEs. By providing more accessible, customized, and secure financial services, banks can support the growth and success of SMEs, ultimately contributing to broader economic development and financial stability.

Behavioral Insights Into Investment Decision-Making: Evidence From the Nepal Stock Exchange

This paper provides a reliable foundation for investigating how people's behaviors influence investment decisions. The study surveyed 286 individual investors who participated in the Nepal Stock Exchange, employing a purposive sampling procedure and self-administered structured Likert scale questionnaire. The results indicate that behavioral factors, including stock affordability, information availability, third-party opinions, and herding effect, significantly influence individual investors’ rationality in making investment decisions. The study concludes that in a developing and expanding environment, factors such as disposable income, market trends, and external opinions notably shape investment decisions when individuals choose to invest in the stock market. The alignment of our findings with existing literature reinforces the robustness of our results. The implications of these findings extend to investors, financial advisors, and policymakers alike. Recognizing the influence of behavioral factors can aid in developing strategies to promote informed decision-making and mitigate the adverse effects of herding behavior.

Nutraceuticals: An alternative to pharmaceuticals

The study is aimed to highlights the different aspects of nutraceuticals including market trends, difficulties, and the processing methods used to harness natural resources. A very broad "nutraceutical" rise has been caused by the nutraceuticals' reach for healthcare management of chronic disorders. The majority of settings had a negligible impact on inadequate eating and lifestyle choices. Research on nutraceuticals is very important and reasonably priced. The healthcare and pharmaceutical industries are facing difficulties as a result of its financial system. It has been found that nutraceuticals contribute to the prevention of infection and cancer and have a positive effect on immune system and cardiovascular health. Based on their composition and mode of action, nutraceuticals are categorized into classes. The many kinds of nutraceuticals and their potential therapeutic effects in illness, such as their anti-inflammatory, anti-cancer, antioxidant, and anti-lipid properties, will be discussed in this review. Additionally Over the last fifty years, lifestyles have been significantly impacted by urbanization, the rate of industrialization, and rapid change. which resulted in the formation of a junk food eating habit.The ability to eat healthily is impacted by these practices, which gradually reduce the quantity and quality of nutrients consumed. Dementia, starvation, and degenerative diseases are becoming more common as a result of the population’s changed eating habits. In recent years, people’s worries about their health and ability to receive healthcare have grown. Nutraceuticals are essential for boosting immunity without compromising the body’s natural defenses. Nutraceuticals are present in active foods, designer meals, medical diets, dietary supplements, and other diet regimens that offer additional health benefits. Nutrient-rich foods help with a number of bodily functions, including the prevention and treatment of diseases. The medicinal plant offers a range of phytochemical qualities to address different medical issues.

DYNAMIC CAPABILITIES AS A SUCCESS-FACTOR OF THE LONG-TERM BUSINESS ALLIANCE: THE CASE OF SIEMENS AND ATOS

Strategic alliances offer numerous benefits, including access to new markets, shared resources, and enhanced capabilities, which collectively contribute to a firm’s competitive edge. However, maintaining these partnerships over time remains a challenge due to the dynamic nature of collaborative relationships. The article is devoted to the analysis of dynamic capabilities of partnership and its role in the long-term development of the business alliance between Siemens and Atos. The specificity of dynamic capabilities is identified, namely the characteristics of "sensing," "seizing," and "reconfiguring" capabilities at the stages of partnership development between Siemens and Atos. Three stages of development of dynamic capabilities of the business alliance between Siemens and Atos are defined: the founding stage (2011-2014), the expansion stage (2015-2020), and the current stage (2021-2025). The founding stage involved mutual investments and the adoption of performance programs to enhance operational efficiency. The expansion stage saw the alliance focusing on Industry 4.0, cybersecurity, and digital strategy, with increased funding and expanded scope. The current stage emphasizes sustainability and advanced technologies like quantum computing and blockchain, along with joint initiatives and participation in research programs. It is found that over the decade of collaboration, the alliance has demonstrated the ability to adapt and evolve according to market and technological changes, based on the abilities of both partners to identify new opportunities, analyze market trends, and accordingly transform the alliance's business model. Thus continuous adaptation, investment in dynamic capabilities, and cooperative efforts with external partners are vital for maintaining mutually beneficial partnerships. The Siemens-Atos alliance serves as a case study illustrating how these capabilities can be effectively leveraged to achieve sustained success in a dynamic business environment.

Quality Matrix-Based Product Innovation in Birkinpet Pet Harness Apparel

This study employs the Quality Function Deployment (QFD) method to develop BirkinPet pet harness apparel, focusing on customer needs and competitive advantages. A House of Quality (HoQ) matrix was constructed using responses from 96 participants, evaluating the relationship between Customer Importance and Technical Importance to identify key technical priorities for immediate implementation in BirkinPet’s production process. The primary HoQ matrix results indicate that lightweight, comfort, and safety (481.5), design for various activities (416.1), and ergonomic and comfortable design (389.9) are the most important attributes to customers. The main technical priorities include ensuring the product is safe and strong (4.99) and providing a variety of sizes and options (4.86). The novelty lies in the comprehensive and structured application of QFD, emphasizing innovation in designing products that align with current market trends and customer preferences. By integrating comfort, safety, and flexible design, BirkinPet successfully creates a high-quality product that meets customer needs, enhancing customer satisfaction and loyalty while strengthening its competitive position in the pet accessories market.

Optimization of Foreign Market Entry Through Adaptation to Changing Market Trends in The Destination Country

This research is qualitative research at Artistica Jewelry which aims to determine the optimization of foreign markets through adapting to changing trends, to determine the challenges faced by companies in adapting to changing trends. How new growth opportunities emerge in foreign markets as a result of the adaptations faced by the company. This research analyzes secondary data, including company reports and related literature. Data collection techniques use interview, observation and documentation techniques. The research results reveal that Artistica Jewelry Surabaya has succeeded in expanding its market share in the target country market by adapting to consumer desires, innovating in product design. The challenges faced by companies in adapting include: consumer tastes changing rapidly, aspects of technology and innovation, the rapid development of e-commerce, competitive competition, as well as aspects of sustainability and ethics in business which are currently intensely discussed by people. Opportunities that arise as a result of adaptation are being able to gain a competitive advantage in increasingly complex international markets. This provides useful insights for other companies looking to strengthen their presence in foreign markets.

Soy Protein-Based Wound Dressings: A Review of Their Preparation, Properties, and Perspectives.

Wound healing is a major challenge worldwide, and people have been researching wound dressings that can promote wound healing for decades. Natural biobased materials, such as polysaccharides and proteins, have been widely used in the development of wound dressings. Among them, soy protein-based materials have attracted the interest of a wide range of researchers due to their safety, biocompatibility, controlled degradation, and ability to be mixed with other materials. However, there has been a lack of comments on these soy protein-based wound dressings. This work reviews various forms of soy protein-based wound dressings, such as hydrogels, films, and others, which could be prepared through physical/chemical cross-linking with synthetic or natural polymers. The important role played by soy protein-based materials in the wound healing phase and their properties will be examined, such as their anti-inflammatory, antioxidant, angiogenesis-promoting, cellular biocompatibility, self-healing ability, adhesion, antimicrobial, and tunable mechanical properties. Additionally, insights into the market prospects and trends for soy protein dressings are provided, clarifying the enormous development potential of soy protein as a new type of wound repair material.

Capstone Project Data Analytics Untuk Menigkatkan Penjualan Secara Efektif

Amidst the fierce competition in the coffee shop industry in New York, many businesses have experienced a decline in sales, especially in certain products. This research aims to discuss the Capstone Data Analytics project to increase product sales at coffee shops in New York, focusing on the decline in sales of packaged chocolate products. Through a descriptive quantitative analysis approach, sales data from three branches of the coffee shop were evaluated to understand the factors influencing the sales decline. Findings showed that high prices and stagnant sales trends were the main causes of sales decline. Recommendations include price optimization, increased promotion, product diversification, and understanding market trends to increase sales by 2-5% in the following year.

The rise of Soybean in international commodity markets: A quantile investigation

The complex interplay between agricultural and energy commodities has been a subject of interest in past research, gaining more relevance recently due to geopolitical events such as the conflict between Ukraine and Russia. This conflict has systematically driven up the prices of both energy and agricultural commodities. Deeply understanding the dynamic interconnections between these commodities and the cascading events resulting from the war is crucial for comprehensive market analysis. Our study leverages the connectedness or risk of spillover based on a Quantile Vector Autoregression (QVAR) model, allowing us to track connectedness over time through the examination of extreme quantiles. This approach facilitates the identification of shocks triggered by exogenous events, such as the Russian-Ukrainian war, which are often observable in these extreme quantiles or tails. The investigation encompasses several agricultural commodities: Wheat, Barley, Soybean, Soybean Oil, Soybean Meal, and Sunflower Oil, along with energy commodities represented by Crude Oil and Natural Gas. Furthermore, we considered the prices of crucial fertilizers, DAP & Urea, given their significance in agricultural production. The timeframe for our study extends from January 2010 to January 2023, providing a comprehensive review of market trends during various geopolitical scenarios. This research contributes valuable insights into the intersection of global events, agricultural trends, and energy commodity markets. The study revealed that Soybean and its derivatives consistently play a leading role in the market, with Soybean being the primary shock transmitter. This is particularly true for the upper Quantile, where Soybean and Soybean Meal's influence remains stable. On the other hand, Soybean Oil's, Barley, and Wheat risk of spillover has increased, especially during the Ukraine-Russia conflict. Finally, spillover appears symmetric, with both extreme tails exhibiting around 91–87 % connectedness, while the median Quantile is under 49 %. We observed a diminution in network complexity, manifested as a decline in network connectedness, in correlation with extreme quantiles. Policymakers can use this information to draft proactive measures, ensuring stability and sustainability in both domestic and international markets.

Application of Sentiment Analysis to Explore the Connection Between Public Sentiment and Stock Price Movement by Python

This study aims to research the relationship between sentiments of public and the trend of the stock. Specially, it focuses on analyzing how positive or negative sentiment correlated to the trend of stocks. In methodology, this research investigates the predictive power of public sentiment on stock market trend by analyzing users comments on Reddit by utilizing Python Reddit API Wrapper (PRAW) to systematically web-scrape relevant financial discussion from a subreddit called stocks. After the data collecting, preprocessing step is conducted to removing noise and standardize text. Eventually, sentiment analysis is applied to analyze the sentiments of comments by using Natural Language Processing (NLP) techniques. This approach allows for an in-depth examination of the correlation between the sentiments expressed in Reddit comments and subsequent stock price fluctuations by comparing the outcome and the actual price movement. Eventually, through the comparison between the sentiment analysis result and actual stocks movement, the sentiment of public comments shares the same trend with the market movement.

Umetna inteligenca v modni in oblačilni industriji

In recent times, there has been significant interest in incorporating artificial intelligence (AI) in the fashion and apparel industry, as it has the ability to bring about significant transformations across different aspects of the sector. This comprehensive review offers a systematic examination of current AI applications in fashion, and discusses implications and future opportunities. The study investigates AI uses in the sector, with a focus on virtual try-on technology, personalized recommendations, AI-driven design and supply chain optimization. This analysis explores the benefits and constraints of these applications, and is supported by real-world case studies showcasing successful deployments. The review examines how AI influences consumer insights and market trends, and highlights the effectiveness of sentiment analysis and social media monitoring as tools for understanding consumer preferences and the shaping of brand perception. It also emphasizes AI's role in analysing market trends, enabling the identification of emerging fashion trends through AI-driven market research tools. This study provides a glimpse into future possibilities and advancements AI can introduce to fashion, including potential integration with augmented reality (AR), innovative applications in fashion shows and events, and the potential to revolutionize traditional business models. This review consolidates primary findings on the introduction of AI in the fashion and apparel sector, and emphasizes AI's potential to enhance consumer experiences, sustainability practices and market efficiency.

Dynamics between Bitcoin Market Trends and Social Media Activity

Dynamics between Bitcoin Market Trends and Social Media Activity

Optimizing risk management in oil and gas trading: A comprehensive analysis

The oil and gas trading sector is inherently volatile, influenced by geopolitical events, market dynamics, and regulatory changes. Effective risk management is crucial for mitigating these uncertainties and ensuring operational stability and profitability. This paper presents a comprehensive analysis of optimizing risk management in oil and gas trading, highlighting strategies, tools, and best practices to manage and mitigate risks. Risk management in oil and gas trading encompasses a broad spectrum of activities, including market risk, credit risk, operational risk, and compliance risk. To optimize risk management, firms must employ a multifaceted approach that integrates advanced analytics, robust risk assessment frameworks, and strategic decision-making processes. Key strategies include the use of financial instruments such as futures, options, and swaps to hedge against price volatility. These instruments enable traders to lock in prices and margins, thereby reducing exposure to market fluctuations. Advanced analytics and data-driven decision-making are essential components of effective risk management. By leveraging big data, machine learning, and artificial intelligence, traders can gain deeper insights into market trends, forecast price movements, and identify potential risks. Predictive analytics allows for proactive risk management, enabling firms to anticipate and respond to market changes swiftly. A comprehensive risk assessment framework is also critical. This involves regular monitoring and evaluation of risk exposure, stress testing, and scenario analysis. Such practices help identify vulnerabilities and ensure that risk mitigation measures are in place. Additionally, fostering a risk-aware culture within the organization is vital. This includes training and educating employees on risk management practices and encouraging transparent communication and reporting of risks. Compliance risk, driven by regulatory changes, is another significant aspect of risk management in oil and gas trading. Staying abreast of regulatory developments and ensuring compliance with international and local regulations is essential to avoid legal penalties and reputational damage. Implementing robust compliance programs and leveraging technology to monitor regulatory changes can help firms stay compliant. In conclusion, optimizing risk management in oil and gas trading requires a holistic approach that combines financial hedging, advanced analytics, comprehensive risk assessment, and compliance management. By adopting these strategies, firms can effectively mitigate risks, enhance decision-making, and achieve sustainable growth in a highly volatile industry. Keywords: Optimizing, Risk Management, Oil and Gas, Trading, Analysis.

Anomaly Detection in Kuwait Construction Market Data Using Autoencoder Neural Networks

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market’s complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder’s effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry.

Trading Strategy of the Cryptocurrency Market Based on Deep Q-Learning Agents

ABSTRACT As of December 2021, the cryptocurrency market had a market value of over US$270 billion, and over 5,700 types of cryptocurrencies were circulating among 23,000 online exchanges. Reinforcement learning (RL) has been used to identify the optimal trading strategy. However, most RL-based optimal trading strategies adopted in the cryptocurrency market focus on trading one type of cryptocurrency, whereas most traders in the cryptocurrency market often trade multiple cryptocurrencies. Therefore, the present study proposes a method based on deep Q-learning for identifying the optimal trading strategy for multiple cryptocurrencies. The proposed method uses the same training data to train multiple agents repeatedly so that each agent has accumulated learning experiences to improve its prediction of the future market trend and to determine the optimal action. The empirical results obtained with the proposed method are described in the following text. For Ethereum, VeChain, and Ripple, which were considered to have an uptrend, a horizontal trend, and a downtrend, respectively, the annualized rates of return were 725.48%, −14.95%, and − 3.70%, respectively. Regardless of the cryptocurrency market trend, a higher annualized rate of return was achieved when using the proposed method than when using the buy-and-hold strategy.

Optimizing Genetic Algorithm With Momentum Strategy for Technical Trading Rules: Evidence From Futures Markets

ABSTRACTThis paper introduces an innovative genetically optimized dynamic composite strategy for achieving profitability in futures markets. Utilizing daily data from 35 actively traded futures contracts (1984–2022), we highlight the potential advantages of integrating the momentum effect into dynamic moving average strategies. This enhancement can boost the strategy's capability to capture and capitalize on market trends, ensuring consistent and stable returns. The developed dynamically composite technical trading strategy aspires to be a valuable reference for investors and the finance academic community, contributing to advancements in the field.

Influence of packaging, husbandry, feeding practices, and price transparency on consumer segments preferences for milk in Germany: A conjoint and latent class analysis

Despite a slight decline in average consumption in recent years, milk remains a significant component of diets in Western countries, including Germany. Concurrently, there has been growing awareness of the impact of food choices on climate, environment, and animal welfare. This study examines how various attributes of milk—such as packaging, husbandry practices, feeding methods, price, and price transparency—affect consumer decisions, using fresh milk as a case study.We conducted a choice experiment with 250 participants, who selected from product alternatives varying in price, milk features, cow husbandry and feeding practices, packaging types, and labels. Contrary to previous studies, which identified price as a primary decision criterion, our findings indicate that price was secondary for most participants, except for a specific segment.Label influence was minimal, while packaging had a significant impact on consumer choices. Interestingly, most respondents preferred glass packaging, despite its potential environmental drawbacks, suggesting a need to explore whether this preference is based on misconceptions about its environmental benefits.The type of husbandry emerged as the most influential factor in consumer decisions. In terms of animal welfare, there was a strong preference for pasture milk, particularly for "Year-round grazing with mother-bonded calf rearing." This preference not only explains the marketing trend towards pasture milk but also presents an opportunity for sustainable farms to differentiate themselves and attract customers amidst a decline in pasture-based milk production.

Predictive Analysts Using Different Algorithm Machine And Deep Learning For Financial Market: Review Paper

This paper presents the development and effectiveness of deep and machine learning techniques in forecasting stock market trends. This paper review focus on key developments from 2020 to 2024 involves the integration of ensemble learning, sentiment analysis from social media, and various predictive algorithms such as LSTM, CNN, and ANN. These techniques improves predication accuracy by efficient processing large data groups and detect nonlinear patterns in stock price trends. in addition, the paper presented background about various machine learning algorithms such as KNN, naive Bayes, linearregression decision trees, SVM, randomforests and deep learning models suchas neural network,LSTM and CNN .the findings of this study to providing valuable insights to researchers and practitioners who aim to improve investment strategies and improve predictive accuracy By looking at the appropriate algorithm

Moving Toward Economic and Digital Sustainability in Marketing Analytics

Despite analytical advancements, firms have yet to realize the full potential of big data marketing analytics (BDMA) because the poor quality data restricts customer predictions and insightful decisions. Technology and market uncertainty create challenges in understanding business needs, choosing analytical tools, determining customer insights, and market trends. This study aims to assess the quality of marketing analytics, including technology and information quality. Data were collected from 236 North American respondents working in firms with at least limited experience in the deployment of BDMA. The analysis tool was PLS-SEM. The findings supported the hypothesis that technology and market uncertainty negatively influence the quality of analytical outcomes. This study makes a significant theoretical and methodological contribution to BDMA literature by assessing the quality of analytics as an integrated formative construct.

The Procedure of Extracting Natural Gas liquids in Cryosift Method

The extraction of Natural Gas Liquids (NGLs) plays a pivotal role in the modern energy landscape, serving as both a significant economic driver and a critical component of energy supply chains. This project investigates the comprehensive processes involved in NGL extraction, which includes the separation of ethane, propane, butanes, and natural gasoline from natural gas and crude oil. Key techniques such as cryogenic distillation, absorption, and fractionation are explored in depth, highlighting their operational principles, efficiency, and technological advancements. In addition to the technical aspects, this project examines the environmental impacts of NGL extraction and the strategies employed to mitigate these effects. Innovations in technology and process optimization are discussed, emphasizing their role in reducing carbon footprints and enhancing sustainability. The economic dimension of NGL extraction is also analyzed, with a focus on market trends, supply chain logistics, and the factors influencing global demand and pricing. Case studies from leading NGL extraction facilities are used to illustrate best practices, challenges, and the implementation of cutting-edge technologies. Through a multi-faceted approach, this project aims to provide a detailed understanding of the NGL extraction industry. It underscores the importance of NGLs in the global energy market, the technological advancements driving the industry forward, and the economic and environmental considerations that shape its future. The findings offer valuable insights for stakeholders, policymakers, and industry professionals seeking to navigate the complexities of NGL extraction and leverage its benefits for sustainable development. Key Words: Cryogenic Distillation, Absorption, Fractionation