Market Behavior Research Articles

Evolution of financial intermediation and performance consequences in China’s commercial banks

PurposeThis study evaluates the evolution from traditional to nontraditional financial intermediation (NTFI) in Chinese commercial banks from 2006 to 2021, analysing its impact on bank performance and risk.Design/methodology/approachAccounting measures are used to construct granular activity data, and factor analysis is employed to develop a financial intermediation evolution (FIE) index. The fully modified OLS (FMOLs) estimator is used for nonstationary data analysis, and difference-in-differences (DID) analysis is used for robustness check.FindingsChinese banks exhibit unique evolution in financial intermediation compared to developed economies, with greater inter-bank variations over time than intra-bank differences. From 2006 to 2017, three paths were identified: Investment, Fee and Repo models, with a fourth path (the Investment2 model) emerging post-2017. Only the Repo model enhances bank returns (ROA & ROE), while shifts towards NTFI increase liquidity and leverage risks across all models. The evolution of bank business models and their consequent implications on performance and risks are influenced by regulatory objectives and banks’ endeavours in regulatory arbitrage.Practical implicationsMajor stakeholders in the banking sector can gain a better understanding of financial intermediation and associated market behaviour, performance and risks, with significant implications for banking regulations and crisis management.Originality/valueThis study provides the first comprehensive overview of the evolution of Chinese commercial banks’ financial intermediation activities over an extended period, uncovering the unique characteristics distinct from the developed economies.

From analytics to empathy and creativity: Charting the AI revolution in marketing practice and education

The rapid advancement of artificial intelligence (AI) increasingly demands an understanding of its impact on marketing practice and education. Our hybrid literature review synthesizes 312 peer-reviewed articles on AI in marketing and consumer behavior, using scientometrics and the TCCM (Theory, Context, Characteristics, Methodology) framework. We identify five research areas: human–AI interaction in services, natural language processing (NLP) and computer vision for consumer insights, AI for e-commerce and decision support, marketing automation and creativity, and AI ethics. AI’s evolution is marked by a transition from analytical to empathetic and intuitive technologies like affective computing and generative AI. We highlight the changing dynamics between humans and AI, AI integration in marketing practices and education, and the transformed AI-enhanced marketing workplace. We underscore the significance of ethical considerations, the well-being of users, and the integration of generative AI tools. This review provides a comprehensive guide for forthcoming research, practical applications, and educational advancements in AI-enhanced marketing.

Psychological explanations for analysts' herding behavior: A study on the impact of information cascades and reputational concerns

This study investigates the influence of information cascades and reputational concerns on analysts' herding behavior in financial markets. Using a mixed-methods approach combining archival data analysis and surveys of 5,237 financial analysts covering S&P 500 companies from 2014 to 2023, we examine the interplay between these psychological factors. Our findings reveal that both information cascades and reputational concerns significantly impact herding behavior, with a notable synergistic effect when combined. Structural equation modeling demonstrates that information cascade strength positively correlates with herding behavior (β = 0.43, p < .001), partially mediated by perceived market consensus. Reputational concerns also show a strong positive association with herding (β = 0.51, p < .001), moderated by career stage. Importantly, the interaction between information cascades and reputational concerns (β = 0.24, p < .001) suggests that their combined effect is greater than the sum of their individual impacts. This effect varies across analyst characteristics and market conditions, with stronger influences observed in small-cap stocks and high-volatility periods. Time-series analysis reveals dynamic fluctuations in these effects over time, responding to significant market events. These results contribute to the theoretical understanding of financial market behavior by integrating social influence and career concern perspectives, offering insights for both scholars and practitioners in an increasingly complex market landscape.

Youth Employment Challenges and Opportunities in Pakistan: An Econometric Analysis

The involvement of young people in Pakistan's job market is the subject of this dissertation. This work highlights two main benefits of the research conducted using micro data. First, it breaks out the labor market behaviors of teenagers in Pakistan by age group. Then, using econometric approaches, it explains what variables influence youth work chances in Pakistan. According to the research, several young people start working at a young age, which may affect their future productivity and income. At the beginning of their professions, young people frequently have a higher unemployment rate; nevertheless, this number decreases with time. In addition, there were notable disparities in the labor market outcomes for male and female teenagers based on area. Unemployment rates for young women are higher than those for young men in several regions of the nation. Although young people in Baluchistan are more prepared to enter labor than their counterparts in other regions, the study's main conclusion shows that their employment prospects are poorer. Several characteristics, the findings of a logistic regression analysis demonstrate that the job chances of youth in Pakistan are highly impacted by age, gender, marital status, emigration, geography, educational achievement, and family characteristics. In addition, studies show that Pakistani teenagers are a heterogeneous group, reflecting a wide variety of personal traits and perspectives on employment that are influenced by their home context. Policymakers should see Pakistani youth as something other than a monolithic entity. Unemployment is a problem in Pakistan due to several factors.

Unveiling housing preferences amidst flood risks: an empirical study in an emerging market

Purpose This study investigates the interplay between flood risk, financial constraints and buyer behavior in the housing market. It aims to discern how flood experience influences housing choices, particularly among financially constrained buyers. Design/methodology/approach Using hedonic pricing models and probit analysis, this research examines the implicit prices of house characteristics and evaluates the determinants of housing choices, considering factors such as flood risk, financial status and past flood experience. Findings The study underscores that lower house prices incentivize buyers to opt for homes in flood-prone areas, thereby increasing their vulnerability to flood risks. Consequently, financial constraints exacerbate this situation, compelling low-income buyers to accept higher flood risks. However, flood experience fosters risk awareness among buyers, encouraging them to steer clear of flood-prone areas, albeit with lesser efficacy among financially constrained individuals. Originality/value This research contributes to the understanding of how flood risk and financial limitations shape housing decisions. It highlights the role of flood experience in influencing buyer behavior and emphasizes the need for targeted policies to mitigate flood-related risks in the housing market, particularly among economically vulnerable populations.

Swipe, view, engage: the interplay of athlete social media content and viewer attitudes on sports perception and interest

PurposeInfluencers in physical activity and sports have gained significant prominence in promoting health and well-being via digital social networks. This research investigates the impact of athlete influencers on sports perceptions, with a focus on korfball. Using korfball as a representative sport minimizes biases, allowing a nuanced exploration of athlete-generated content’s direct influence on viewer attitudes.Design/methodology/approachA questionnaire survey with 319 respondents explores variables like athlete attitude, attractive appearance, social media profile attitude, emotional engagement toward korfball, korfball perception, korfball interest and general sports attitude. Structural equation modeling analysis examines relationships among these variables.FindingsThe study delves into the complex interplay among athlete attitude, social media attitude and emotional engagement toward sports, emphasizing the pivotal role of authenticity in fostering deeper connections. Contrary to expectations, athlete attractiveness did not significantly influence viewer priorities, signaling a notable shift towards valuing authenticity and performance. Furthermore, the research explores how general sports attitudes moderate relationships between sports perceptions and interest, offering critical insights for sports sociology and marketing.Originality/valueThis study innovates by focusing on korfball, emphasizing authenticity over attractiveness and providing insights into the evolving dynamics of sports marketing and consumer behavior in the digital age. This research evidences the profound impact of the digital age on sports engagement, laying a foundation for future studies and practical applications in sports sociology, marketing and consumer behavior.

The Influence of Green Marketing Communication and Brand Credibility through the Mediation of Environmental Attitudes and Moderation of Income Levels on Purchasing Decisions of Green Marketing Products in Aceh

The issue of environmental sustainability has become increasingly important, particularly in the context of green product marketing, making it crucial to understand the factors that influence green product purchasing decisions. This study aims to clarify how brand credibility and green marketing communication affect consumer behavior through environmental attitudes and the moderating impact of income levels. Primary data from 300 green product consumers, gathered via a structured questionnaire and analyzed using Structural Equation Modeling (SEM-PLS), reveal several key findings. Brand credibility significantly enhances both environmental attitudes and purchasing decisions, with environmental attitudes playing a critical mediating role in these relationships. Green marketing communication also positively impacts environmental attitudes and purchasing decisions. Additionally, income levels positively affect purchasing decisions but negatively moderate the interaction between environmental attitudes and purchasing decisions. These results underscore the essential role of environmental attitudes in mediating the effects of brand credibility and green marketing communication on purchasing decisions, as well as the nuanced influence of income levels on consumer behavior regarding sustainable products.

Twitter Economic Uncertainty and Herding Behavior in ESG Markets

Twitter Economic Uncertainty and Herding Behavior in ESG Markets

Analysis on the Influencing Factors of the Performance of Jindi Real Estate Market

Market performance refers to the price, output, profit, product quality, technical progress and other final economic results of enterprises in a certain market structure through certain market behavior, as well as their impact on the overall social welfare, reflecting the efficiency of the real estate market operation and the advantages and disadvantages of resource allocation. The quality of economic benefits directly reflects the size of the core competitiveness of an industry, and is also the foundation of the existence of the industry. Therefore, it is very important to evaluate the performance of the real estate market. This paper studies the development and influencing factors of Jindi real estate industry, constructs the evaluation index of market performance of Jindi real estate industry, and makes an empirical analysis on the development of Jindi real estate industry by using reasonable evaluation methods. Find out the advantages and disadvantages of Jindi real estate industry development, and formulate appropriate policies and real estate development strategies for Jindi according to the specific development situation.

Impact of Climate Risks and Global Uncertainties on Technology and Environmental Protection Stocks: A Study on China

This study aims to examine the relationship between CUM, CPU, CU, and UCT with the FTSE China Technology Index (FTXIN410) and SZSE Environmental Protection Index (SZEPI) using ARDL and NARDL methods. To test the robustness of the model, three additional parametric methods are employed: FMOLS, DOLS, and CCR. The technology and environmental protection sectors in China are crucial for the country's sustainable economic future. Understanding the impact of global and local uncertainties on these sectors is critical for predicting sectoral trends and future market dynamics. Climate uncertainties and global uncertainties can impact investors' returns and market behaviors. In critical sectors such as technology and environmental protection, uncertainties need to be closely monitored for their implications on investment decisions and market stability. The results of the study indicate a long-term relationship between China’s climate uncertainties and global uncertainties with technology and environmental protection indices.

Market Behaviors under the Stock Index Circuit Breaker using an Agent-based Approach

Market Behaviors under the Stock Index Circuit Breaker using an Agent-based Approach

Polytope Fraud Theory

Polytope Fraud Theory (PFT) extends the existing triangle and diamond theories of accounting fraud with ten abnormal financial practice alarms that a fraudulent firm might trigger. These warning signals are identified through evaluation of the shorting behavior of sophisticated activist short sellers, which are used to train several supervised machine learning methods in detecting financial statement fraud using published accounting data. Our contributions include a systematic manual collection and labeling of companies that are shorted by professional activist short sellers. We also combine well-known asset pricing factors with accounting red flags in financial features selections. Using 80 % of the data for training and the remaining 20 % for out-of-sample test and performance assessment, we find that the best method is XGBoost, with a Recall of 72 % and F1-score of 82 %. Other methods have relatively lower performance, demonstrating the robustness of our results. This shows that the sophisticated activist short sellers, from whom the algorithms are learning, have excellent accounting insights, tremendous forensic analytical knowledge, and sharp business acumen. Our feature importance analysis indicates that potential short-selling targets share many similar financial characteristics, such as bankruptcy or financial distress risk, clustering in some industries, inconsistency of profitability, high accrual, and unreasonable business operations. Our results imply the possible automation of advanced financial statement analysis, which can both improve auditing processes and effectively enhance investment performance. Finally, we propose the Unified Investor Protection Framework, summarizing and categorizing investor-protection related theories from the macro-level to the micro-level.

Linear and nonlinear causality in financial markets

Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although their limited explanatory power is well known. In this paper, we present a much more general framework for assessing co-dependencies by identifying linear and nonlinear causalities in the complex system of financial markets. To do so, we use two different causal inference methods, transfer entropy and convergent cross-mapping, and employ Fourier transform surrogates to separate their linear and nonlinear contributions. We find that stock indices in Germany and the U.S. exhibit a significant degree of nonlinear causality and that correlation, while a very good proxy for linear causality, disregards nonlinear effects and hence underestimates causality itself. The presented framework enables the measurement of nonlinear causality, the correlation–causality fallacy, and motivates how causality can be used for inferring market signals, pair trading, and risk management of portfolios. Our results suggest that linear and nonlinear causality can be used as early warning indicators of abnormal market behavior, allowing for better trading strategies and risk management.

Applying Fractal Theory: Solving the Geometric Challenge of Price Change and Scaling in Economics

<i>Background</i>: Price changes in economics present significant geometric challenges due to sharp discontinuities, which cannot be efficiently described by continuous processes like Brownian motion. Traditional models often rely on linear assumptions, yet financial data frequently exhibit irregular, complex patterns. Fractal theory, a mathematical framework, offers a more accurate way to describe these fluctuations by revealing the underlying self-similar structures in price changes and scaling phenomena. This study explores the use of fractal geometry to gain deeper insights into market behavior. <i>Objective</i>: The objective is to demonstrate that an alternative model, constructed based on geometric scaling assumptions, offers a more accurate description of price changes in competitive markets. <i>Method</i>: The study combined the scaling principle from fractal geometry with a stable Levy model to formulate an integrated model. The logarithmic transformation of the model was applied over successive price changes to observe the behavior of market prices. <i>Result</i>: The scaling principle asserts that no specific time interval (such as a day or a week) holds inherent significance in competitive markets. Instead, these time features are compensated or arbitrated away, supporting the idea that market behavior is self-similar across different time scales. <i>Conclusion</i>: The scaling principle provides a more reliable framework for modeling price changes and is recommended for consideration in economic analyses.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

Integrating Fintech-Driven Technical Analysis into Value Investing: A Study of Moving Averages in Cryptocurrency Market Dynamics

This research explores the application of fintech-driven technical analysis, specifically focusing on moving averages within value investing. Using Coinbase as a case study, the study examines the effectiveness of moving averages in volatile market conditions where traditional metrics are less applicable. Historical stock data was exported from Yahoo Finance and analyzed using Excel, emphasizing the 30 vs. 10 moving average combination. This combination was chosen to balance short-term and long-term market perspectives. The analysis revealed that while moving averages effectively highlight trends and momentum shifts, they are lagging indicators and require supplementary analysis to account for external factors such as news and regulatory changes. Despite not being a precise predictive tool, moving averages provided valuable insights into market behavior, supporting the argument that fintech tools can complement traditional value investing strategies by offering real-time analysis and dynamic approaches to market assessment.

Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions

Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). Two data sources feed the model: specifically, NIFTY-50 Stock Market and real-time Twitter sentiment. Through Natural Language Processing (NLP), the raw data is pre-processed and key features are extracted before they are fused into a unified dataset using a cross-domain transformer, namely CDSFT, and then Circle-Inspired Optimization Algorithm (CIOA) selects the most important features from this dataset. This decreases the complexity of the model without losing essential information. Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. The proposed model Siagra-ConSA-HSOA achieved 99.9% accuracy and 99.8% recall in the testing stage, meaning that such a model performs better than the current approaches both in prediction accuracy and efficiency. Thus, this is a glimmer that the model shall be able to overcome some of the main problems with the current techniques used in predicting the behavior of the stock market.GitHub Repository: https://github.com/jramans2/Siamese-GCN-SplitAttention-Stock-Prediction.git

A Critical Study of Psychology of Retail Investors

This research critically examines the psychology of retail investors, focusing on the behavioural finance approach to investment decision-making. Behavioral finance challenges the traditional assumption that investors are always rational, proposing instead that psychological, social, and cultural factors influence financial behaviours. The study aims to explore how demographic factors such as gender, age, and investment experience, along with macroeconomic conditions like interest rates and inflation, shape retail investors' decision-making processes. By analysing both quantitative and qualitative data from surveys and questionnaires, the study seeks to uncover patterns in how emotions, cognitive biases, and social networks affect investment choices across different groups. The scope of this research extends to identifying significant variations in investment behaviour based on demographics and cultural influences. Moreover, the study will test hypotheses related to gender differences in the influence of peers and interest rate changes on investment decisions. Through a mixed-methods approach, this research aspires to provide valuable insights for financial institutions and individual investors, enhancing their understanding of the psychological drivers behind market behaviour and investment strategies.

The role of social influence in mediating the relationship between relative advantage and purchase intentions for smartphones: A study in Kathmandu, Nepal

This research explores smartphone usage dynamics in Kathmandu, Nepal, focusing on how relative advantage, social influence, and purchase intention interact in the context of the city's growing technological landscape. To address the identified gap in understanding the mediating role of social influence, descriptive and exploratory research methods are used. A survey involving 412 smartphone users was conducted both in person and online. The study reveals that relative advantage significantly impacts both social influence and purchase intention. Social influence is found to partially mediate the relationship between relative advantage and purchase intention. Specifically, individuals who perceive a greater relative advantage in a smartphone, thereby further enhancing their purchase intention. The research provides valuable insights into the factors driving consumer behavior in Kathmandu's smartphone market. It demonstrates that while relative advantage directly affects purchase intentions, social influence also plays a critical role in mediating this relationship. These findings can guide marketers in refining their product placement, promotional strategies, and community engagement efforts, ensuring they address both the perceived advantages of smartphones and the influence of social factors on consumer decisions.

The social ledger: qualitatively decoding interaction-driven investment decisions among Indian investors and fund managers

Purpose This study aims to investigate the antecedents of social interaction among Indian retail investors and fund managers to understand how these factors influence investment decisions. By identifying and examining these antecedents, the study aims to shed light on the social dynamics that shape investment behavior in the Indian financial market. Design/methodology/approach The researchers have mainly adopted an interpretive strategy for the present study. Qualitative data elicited through semistructured interviews with six retail investors and two fund managers were subjected to qualitative thematic analysis. Findings Our research found several factors that make Indian retail investors and fund managers connect and make financial decisions. Peers can improve a person’s investing performance through social facilitation, and discussing investment suggestions and lessons learned can affect a group’s investment behavior. Social norms also influenced investors’ financial decisions, demonstrating compliance. Investor closeness increased information sharing. Finally, the fear of missing out (FOMO), a psychological phenomenon where people fear missing out on rewarding experiences, encouraged social engagement as investors sought appealing prospects. Research limitations/implications The researchers interviewed eight carefully selected interviewees across the divide between retail investors and fund managers. Adopting other grouping criteria, conducting a focus group discussion with more respondents or adopting a mixed-methods approach may increase our understanding of the investment decision behaviors of Indian retail investors and fund managers. Practical implications The findings have far-reaching consequences, from deepening our knowledge of investors’ motivations and actions to directing individual savers, informing the development of financial literacy initiatives, influencing fund management practices and inspiring additional research in this study area. Originality/value This research, including retail investors and fund managers, significantly contributes to the literature on investment decisions and behavioral finance, particularly in the context of Indian investors and managers. This study’s unique perspective and comprehensive approach make it a valuable addition to the field, sparking interest and further exploration among academics, practitioners and investors alike.