Market Behavior Research Articles

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

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.

Research on the Impact of Economic Policy Uncertainty and Investor Sentiment on the Growth Enterprise Market Return in China—An Empirical Study Based on TVP-SV-VAR Model

This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor sentiment index by applying the deep learning ERNIE (Enhanced Representation through Knowledge Integration) model, thereby capturing investors’ sentiment within the growth enterprise market. The dynamic interplay between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is scrutinized via the TVP-SV-VAR (time-varying parameter stochastic volatility vector auto-regression) model, and the asymmetric response of different industries’ stock returns within the growth enterprise market to economic policy uncertainty and investor sentiment shock. The findings of this research are that economic policy uncertainty exerts a negative influence on both investor sentiment and returns of the growth enterprise market. While it may trigger a temporary decline in stock prices, the empirical evidence suggests that the impact is of short duration. The influence of investor sentiment on the growth enterprise market returns is characterized by a reversal effect, suggesting that improved sentiment may initially boost stock prices but could lead to a subsequent decline over the long term. The relationship between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is time-variant, with heightened sensitivity observed during bull markets. Lastly, the effects of economic policy uncertainty and investor sentiment on the returns of different industries within the growth enterprise market are found to be asymmetric. These conclusions contribute to the existing body of literature on the Chinese capital market, offering a deeper understanding of the complex dynamics and the factors influencing market behavior.

What Deters Earnings Management? A Benford's Law Comparison Between Publicized Detection Methods and Increased Regulations

ABSTRACTThis paper examines the impact of public awareness and regulatory changes on earnings management over time. Despite scrutiny from regulators, auditors, and investors, the deterrents of earnings management remain unclear. I explore whether public awareness, through the dissemination of research, can influence market behaviors and reduce earnings management activities due to increased detection and penalties. Alternatively, I investigate if stricter regulation plays a role in curbing earnings manipulation. Using Benford's Law, I apply Thomas’ (1989) approach and compare data from 1950 to 1985, 1990 to 2002, and 2003 to 2020. My findings indicate that while public awareness alone does not deter earnings management, heightened regulation, particularly post‐Sarbanes‐Oxley Act (SOX), significantly reduces such practices. This study contributes to the literature by highlighting the comparative impact of public awareness and regulatory changes on earnings management.

Detecting anomalies in currency market of BRICS countries: insights into emerging economies

PurposeThe primary motive of the research paper is to investigate the efficiency of currency market of emerging economies (BRICS countries), by analyzing the presence and consistency of calendar anomalies – specifically the day-of-the-week (DOW) effect, the January effect and the Turn-of-the-month (TOM) effect. Daily data of around 23 years and 4 months were examined for the purpose of study. Segmented data of 8-year intervals were also analyzed to observe the changes in the pattern over a period. The findings are intended to offer insights into market behaviors and potential trading strategies for astute traders.Design/methodology/approachThe study employed a quantitative research design utilizing ordinary least squares regression with dummy variables and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) (1,1) model. To study the calendar anomalies, the daily closing price of five currency pairs has been taken from Bloomberg’s software. The Kruskal–Wallis test was conducted to validate and check the robustness of analysis.FindingsThere are mixed results regarding calendar anomalies in the BRICS currency markets. Analysis of aggregate data revealed that DOW effect is present in all the currencies except USDZAR, with Monday showing positive return. The segmented analysis showed the presence of DOW effect in USDCNY and USDINR, while other currencies lacked consistent patterns. The January effect was evident for Indian Rupees and Chinese Yuan but not for other currencies. TOM effect was present only in Chinese Yuan. Overall, the Indian Rupee and Chinese Yuan displayed inefficiencies, indicating potential for trading strategies, while other currencies appeared efficient and lacked consistent patterns.Originality/valueThe literature on currency pricing anomalies is sparse, particularly in the context of developing and underdeveloped economies. While some research exists on anomalies within the currencies of developed nations, there is a significant gap in studies focusing on emerging economies. This paper seeks to address this deficiency by investigating the presence of calendar anomalies in the currencies of BRICS countries, providing new insights into the behaviors of these emerging economies.

A novel integrated prediction method using adaptive mode decomposition, attention mechanism and deep learning for coking products prices

Accurate prediction of coking product prices is crucial for enhancing production efficiency, cost optimization, and profit maximization in smart coking facilities. To address the volatility caused by nonlinear factors such as raw material costs, substitutes, macroeconomic indicators, sudden events, policy changes, and market behaviors, we propose a novel integrated prediction method for coking product price prediction. This method combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal decomposition, Bidirectional Encoder Representations from Transformers (BERT) for natural language processing, attention mechanisms (AT) to weigh feature importance, and an ensemble of Bidirectional Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, abbreviated BBG, for robust feature extraction. We design a feature selection strategy to avoid data leakage and improve the predictive ability of the model, and describe a method to maintain textual data information integrity when combining data from different sources. Experimental results on coke and methanol datasets show that our approach retains multi-source text richness improves predictive capability, and outperforms other state-of-the-art methods, providing an effective tool for developing smart coke plants.

Analyzing the Impact of COVID-19 on Indian Stock Market Efficiency: Sectoral Behavior

The study examines the impact of the COVID-19 pandemic on the Indian stock market, providing insights for informed investment decisions. It analyses efficiency and behavior across eight crucial sectors during two distinct periods: prepandemic (Period 1: February 15th, 2018 to February 14th, 2020) and pandemic (Period 2: February 17th, 2020 to February 15th, 2022). Addressing a literature gap on sector-wise pandemic effects, the study contributes valuable knowledge to investors and policymakers. Utilizing the variance-ratio methodology enhances understanding of market efficiency. The findings reveal distinct behavior in the Indian stock market between Period 1, characterized by stable, normally distributed returns, and Period 2, marked by increased volatility and extreme events, likely influenced by the pandemic.

A novel model for rural household photovoltaic market trading: Utilizing cooperative alliances within a peer-to-peer framework

In the face of diminishing traditional subsidy models and the constraints of grid-dependent transactions, this study explores trading models as a catalyst for rural electrification and sustainable development in China. Highlighting the emergence of rural photovoltaic (PV) prosumers, we propose trading models through cooperative alliances to facilitate market engagement within a peer-to-peer (P2P) framework. By applying the Shapley value for the equitable distribution of internal revenues, the study elucidates the complex interrelations between rural prosumers and the electrical grid, fostering a better understanding of their mutual interests. Further, we introduce a dynamic game-theoretic model to investigate the evolving market behaviors under diverse strategic influences among rural prosumer groups and the grid. Using numerical simulations based on real-world data from Shanghang County, Fujian Province, we analyze surplus and deficit electricity production scenarios within these alliances. The findings underscore the effectiveness of the cooperative alliance trading model in aligning individual, collective, and societal benefits. This comparative analysis of strategic outcomes validates the model's feasibility and role in advancing sustainable rural electrification strategies.

Tastes on Wheels: Exploring Consumer Value Perceptions, Enjoyment and Choice Behavior in the Food Truck Market

Tastes on Wheels: Exploring Consumer Value Perceptions, Enjoyment and Choice Behavior in the Food Truck Market

What influences the development of wine-producing regions? New economic opportunities in the eastern foothills of the Helan Mountains in China

The formation and development of wine regions require unique natural conditions and hundreds of years of technical and cultural accumulation. The development of the wine industry at the eastern foot of Helan Mountain in China, in addition to the appropriate geographical environment, the technical migration, and talent flow supported by consistent policies, have improved the allocation ability of resource factors and conversion efficiency, making it a world-renowned new wine-producing region in just 40 years. This particular mechanism deserves attention as new industries develop in other parts of the world. Using the methods of spatial autocorrelation, hot spot analysis, and geographic detector, this paper comprehensively analyzes the growth and spatial evolution characteristics of wineries in the eastern foot of Helan Mountain in China and deeply discusses the mechanism of the interaction between the localization of global floating capital and China's unique regional economic policies. It is found that the local selection of global floating capital has a spatial locking effect on the formation of CHMWR, and the rolling support policies of China's central and local governments promote the rapid expansion and regional spread of the number and scale of wineries, with significant spatial agglomeration and spatial diffusion. The profound mechanism of such changes lies in the dual interaction of the local selection of global liquid capital and the Chinese government's wine-producing policies adapted to the geographical environment. In this process, a series of internal and external factors such as the innovation of grape planting and wine-making technology, consumer market, and government behavior have accelerated the continuous growth of the wine-producing region at the eastern foot of Helan Mountain in Ningxia. This paper adds a new understanding and deep understanding of the interaction between the local implantation of economic globalization and the macro-control policies of the Chinese government.

Don’t put all the eggs in one basket: examining adaptive selling behavior in salespeople’s performance

AbstractIn a B2B selling context, salespeople are encouraged to perform adaptive selling strategies to attend to diversified customer needs and achieve sales performance (i.e., market share) and relational performance (i.e., trust, loyalty, and satisfaction) by performing adaptive selling behavior or showing the ability to recognize customers’ emotions. However, based on the resource allocation theory, the current research proposes that the two aforementioned factors seem to interfere with each other to affect the final performances. Results show that from Study 1, adaptive selling behavior, which is consequential of salespeople’s interpersonal mentalizing skills, is positively associated with relational performance, and the relationship is moderated by emotional intelligence. From Study 2, perceived service climate, a consequence of perceived transformational leadership is positively associated with sales performance. There was a significant interaction between adaptive selling behavior and service climate on sales performance, indicating that salespeople who do not perceive that firms emphasize services, they have sufficient resources to apply adaptive selling strategies. Conversely, if they were encouraged to focus on providing service, this might affect their adaptive selling on sales performance. Based on the results, we provide insights for B2B salespeople and managers to focus on one salient goal to achieve the best outcome.

On-demand insurance: idea, development, impact on market equilibrium

The article presents the definition, idea, development, and current state of a new business model known as on-demand insurance which has been in existence since the early 21st century. Moreover, the article does not only describe the premises behind the development of this new method of creating value for clients of insurance companies and the opportunities that appear in the market but also addresses the challenges that insurance providers can encounter. Based on the analysis and available source materials, the most interesting types of these insurance products and the entities that include such products in the portfolios were presented. In addition, some new regulatory challenges and trends were described. The purpose of this study is to analyse the behaviour of the insurance market after the introduction of on-demand products in light of the classical definition of equilibrium in a competitive insurance market under conditions of information asymmetry. The authors analysed the economic implications of implementing on-demand insurance, demonstrating its potential to enhance the overall welfare of the insured population by increasing utility for certain risk groups of clients.

Tourists' Intention to Visit Royal Cemetery as a Dark Tourism Destination: The Role of Destination Image, Self-Congruence, and Destination Personality

This study seeks to investigate the effect of destination image, self-congruence, and destination personality on the visitors' intentions to visit the Imogiri Royal Cemetery, Yogyakarta Special Region, Indonesia. The cemetery is considered a historical and cultural place wherein a few kings and sultans who once ruled Java lie buried. Thousands of tourists visit this place throughout the year, especially on dates that have great significance in the Islamic and Javanese calendars. Data were collected from a convenience sample comprising of 220 tourists in Yogyakarta. Results show that destination image is an antecedent to destination personality, while self-congruence and destination personality each positively relate to the visit intentions of tourists. Conversely, one’s destination image does not predict self-congruence. In contributing to the literature at the intersection of royal and dark tourism, this underpins sustainability in tourist tourism, destination marketing, and consumer behaviour. The implications of such a study are found to fall in line with the United Nations SDGs, specifically Goal 8: Decent Work and Economic Growth, regarding the promotion of sustainable tourism, which brings in income and provides employment; and Goal 11: Sustainable Cities and Communities, due to the fostering of conservation and promotion at the cultural heritage sites.