Market Behavior Research Articles

Does Twitter happiness predict bullish markets? A study of the US stock markets

The evolving literature on using Twitter to capture investor sentiment suggests that stock market performance is linearly associated with Twitter's public mood. We investigate whether investor sentiment determined by the Twitter sentiment index (TSI) has a nonlinear predictive power for stock market behaviour. Using a dataset of seven US stock markets, we apply the cointegration techniques to show the relationship between Twitter sentiment and stock market behaviour. The results suggest a dynamic nonlinear cointegrating relationship. During the short run, Twitter happiness has a substantial effect on stock market indices, while in the long run, it has a moderating effect. Twitter happiness has a dominant effect on the US stock market indices than unhappy Twitter. These results highlight Twitter's growing role in predicting the stock market behaviour and show that the TSI acts as a better predictor of investor sentiment.

Intraday analyses on weather-induced sentiment and stock market behavior

We examine the intraday relationship between weather conditions and investor sentiment in the Korean equity market. By uncovering the intermediary role of weather-induced sentiment in affecting market variables, we suggest a potential link between weather conditions, sentiment, and market dynamics. High temperatures, humidity, and cloud coverage hurt investors’ moods, while strong winds and long sunshine durations have a positive impact. Though only cloud coverage marginally impacts stock returns, weather factors significantly affect market turnover, volatility, and illiquidity, especially when altering sentiment dynamics.

Western diplomacy and state access to capital

What determines government access to capital? Conventional explanations of state credit access rest on tenuous assumptions and fail to fully elucidate market behavior seen in recent years. This article argues that investors evaluate governments based on their diplomatic ties to the West. Specifically, diplomacy with major Western powers serves as a heuristic which signals to market actors that the debtor state is a trustworthy borrower with low default risk. Focusing on in-person diplomacy with the United States, I construct a new data set, the United States Economic Diplomacy (USED) Data, which codes over 5,000 meetings between the US President, US Treasury Secretary, and high-level foreign policymakers from 1990 to 2016. Among a sample of 110 countries, the statistical results uncover evidence of a significant but conditional “Western advantage” in sovereign bond markets. Greater diplomatic ties to the United States are associated with higher volumes of bond issuance. This advantage, however, manifests itself primarily when global capital is scarce and reverses as lower global interest rates incentivize greater risk-taking among investors.

A Study of the Mediating Effect and Path of Customer Fit in Repeat Purchase Behavior in the Residential Market Driven by Digital Marketing Technology

Improving the repeat purchase intention of customers of real estate enterprises from marketing strategies is increasingly important for real estate enterprises to achieve the goal of stable and increasing long-term profits. This paper, driven by digital marketing technology, takes customer repeat purchase behavior in residential commodity market as the research object. The variables of brand community value, customer fit and repeat purchase behavior are first defined and selected. Then the relationship model of brand community value, customer fit and repeat purchase behavior was established, questionnaire research was conducted on mature real estate community members, and data analysis and structural equation model fitting were carried out using SPSS and AMOS software in order to test the mediating effect of customer fit in repeat purchase behavior in the residential market as well as the path. The study concluded that the standardized coefficient of customer fit on customers’ repeat purchase intention is 0.36, which has a significant positive effect. Meanwhile, customer fit has a mediating effect between the five dimensions of brand community value and repeat purchase behavior, and all five dimensions have a partial mediating effect in the process of influencing repeat purchase intention.

Effect of Sellers' Ethical Behavior in Traditional Markets on Tourists’ Souvenir Purchase Intention. Does Ethnocentrism Matter?

This study investigates the direct impact of sellers' ethical behavior "SEB" in traditional markets on tourists’ souvenir purchase intention "TPI", as well as the indirect role of ethnocentrism "ETHNO" on the relationship between the other two variables. Self-administered surveys were provided to a randomly selected sample of tourists as part of the research, which used a quantitative technique. Structural equation modeling (SEM) analysis of 474 valid responses showed substantial positive correlations between "SEB" and "TPI", with "ETHNO" also having a positive effect on "TPI". The study also proved that "ETHNO" moderates the relation between "SEB" and "TPI". These insights offer souvenirs' sellers and policy-makers insightful advice on how to adapt their behaviors and marketing strategies to enhance the overall shopping experience and cater to the diverse cultural preferences of tourists.

Predictive Patterns and Market Efficiency: A Deep Learning Approach to Financial Time Series Forecasting

This study explores market efficiency and behavior by integrating key theories such as the Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH), Informational Efficiency and Random Walk theory. Using LSTM enhanced by optimizers like Stochastic Gradient Descent (SGD), Adam, AdaGrad, and RMSprop, we analyze market inefficiencies in the Standard and Poor’s (SPX) index over a 22-year period. Our results reveal “pockets in time” that challenge EMH predictions, particularly with the AdaGrad optimizer at a size of the hidden layer (HS) of 64. Beyond forecasting, we apply the Dominguez–Lobato (DL) and General Spectral (GS) tests as part of the Martingale Difference Hypothesis to assess statistical inefficiencies and deviations from the Random Walk model. By emphasizing “informational efficiency”, we examine how quickly new information is reflected in stock prices. We argue that market inefficiencies are transient phenomena influenced by structural shifts and information flow, challenging the notion that forecasting alone can refute EMH. Additionally, we compare LSTM with ARIMA with Exponential Smoothing, and LightGBM to highlight the strengths and limitations of these models in financial forecasting. The LSTM model excels at capturing temporal dependencies, while LightGBM demonstrates its effectiveness in detecting non-linear relationships. Our comprehensive approach offers a nuanced understanding of market dynamics and inefficiencies.

Advancements in Stock Market Prediction Techniques: A Comprehensive Survey

The stock market, known for its unpredictable nature and complex workings, has been the focus of significant study. Conventional models frequently fail to fully capture the complex and diverse nature of stock market behavior. With the increasing complexity of financial markets, there is a rising demand for advanced strategies that can offer investors precise predictions and essential insight. There are several studies in this regard, from which we chose 24 modern studies from the year 2018 to the year 2023 in various ways to come out with results that are the focus of a new start in the prediction. This review collected different machine learning methods and deep learning techniques, including deep neural networks, which were then used to build powerful prediction models. Integrating behavioral finance theory with quantitative indicators and financial news provides a unique perspective on stock market dynamics. Also, The Efficient Market Hypothesis (EMH) highlights the significant impact of future information, such as news, on the valuation of stocks. Combining deep learning techniques in forecasting and examining the stock market represents an essential stage in financial research, it offers a more intricate comprehension of the intricacies of stock market dynamics. However, obstacles still need to be overcome, and it is essential to use prudence when applying reported accuracies to actual trading situations. The convergence of behavioral finance, efficient market hypothesis, and deep learning present opportunities for more investigation, offering a more comprehensive method of forecasting stock market trends

Analyzing the Intersection of Labor Market Dynamics and Economic Growth: A Bibliometric Study

This study conducts a bibliometric analysis to examine the intersection of labor market dynamics and economic growth, focusing on key themes, trends, and emerging areas of research. Using VOSviewer, we analyze 980 publications from 1959 to 2024, identifying central themes such as "labour market," "human capital," and "economic development," which play critical roles in shaping the relationship between workforce dynamics and economic growth. The findings highlight how technological advancements, globalization, and demographic shifts have influenced labor market structures, wage dynamics, and industry-specific developments over time. Emerging topics, such as the relationship between labor markets and energy consumption, point to the growing importance of sustainability in economic discussions. The results provide valuable insights into the evolution of labor market studies and offer directions for future research, particularly in the context of green energy transitions and workforce adaptability in an age of rapid technological change. This analysis contributes to a more comprehensive understanding of the complex interactions between labor market behavior and economic performance.

Innovative Social Groups in the Institutional Changes of the Russian Countryside

The article considers the formation of innovative social groups in the social structure of the Russian countryside, broadcasting the managerial style of thinking and lifestyle of an active subject of agricultural labor. The current model of the agricultural market was formed under the powerful institutional pressure of market reforms. These objective social processes transformed the mentality of rural residents, gave rise to various labor practices, including employment in the informal sector, from which innovative market models of behavior in the agricultural labor market emerged. Using the example of farms and rural entrepreneurs, innovative layers in the labor market are shown, the criteria for which are an increase in agricultural land, production growth using new technologies and digital equipment, access to export markets. The information base consists of data from large-scale panel studies of Rosstat 2011–2022 and the All-Russian agricultural censuses of 2006, 2016, 2021. Institutional changes in the agricultural labor market have been identified, within the framework of which innovative groups are being formed. The development of innovative technologies used in agriculture requires the involvement of highly qualified labor. However, there is an acute shortage of personnel in the agricultural labor market. The wages of a significant segment of jobs in the rural market do not ensure economic stability. The survival strategy remains the main factor in the socio-economic behavior of rural residents, which determines the employment of most of them in non-agricultural sectors in the agricultural labor market. The crisis of agricultural organizations and the arrival of high-tech capitalist forms of production in agriculture cause the migration of the rural population to cities. Despite significant government support for agriculture, there is a shortage of engineering specialties and skilled workers. All these processes hinder the emergence and development of new innovative groups in rural areas. The foreign experience of the functioning of an institutionalized system of information networks and consulting for farmers and small businesses in agriculture is also presented.

The influence of European MiCa regulation on cryptocurrencies

This research investigates the relationship between impending regulatory measures, specifically the introduction of European MiCa regulations, and their influence on cryptocurrency markets. Analysing stock market responses, we highlight significant shifts in liquidity, variance, and return dynamics post-MiCa-related announcements. Specifically, such announcements are associated with significant negative cryptocurrency returns and elevated liquidity. The findings reveal defined heterogeneity across different cryptocurrency sub-classes, each uniquely affected by its inherent attributes and susceptibility to regulatory changes. This work offers insights into the nature of market responses to regulatory intervention, providing an invaluable perspective on the equilibrium between cryptocurrency market behaviour and the evolving European regulatory landscape.

Sustainable energy practices and cryptocurrency market behavior

We examine the effects of energy usage on the return, volatility and jump processes in the Bitcoin (BTC) and Ethereum (ETH) markets. Our main finding indicates that while BTC returns respond significantly to changes in electricity consumption, the effect of electricity consumption on ETH returns is negligible. We attribute this discrepancy to BTC's relative energy inefficiency, which contrasts with ETH's commitment to transitioning to a more energy-efficient mining protocol over our sample period. Additionally, we show that the effect of electricity consumption on cryptocurrencies is mitigated by media coverage and policy uncertainty regarding the state of the economy, environment, and cryptocurrency markets. Our results suggest that when trading cryptocurrencies, investors consider their relative energy efficiency and price electricity consumption differently, highlighting the influence of the transition towards more sustainable energy practices on investor preferences and investment decisions in cryptocurrency markets.

The Relationship between Electronic Marketing and Consumer Behavior "An Applied Study on a Sample of Customers of Al-Rafidain Bank – Iraq"

The current research aims to understanding the relationship between e-marketing and consumer behavior through study applied to a sample of Rafidain Bank customers, and the current research focuses on surveying the opinions of a sample of customers dealing with Rafidain Bank in three Iraqi governorates (Babil, Karbala, and Najaf), numbering (325) Customers. the researcher examined, through a set of hypotheses, the effects of the independent variable, which is electronic marketing, on consumer behavior, and consequently the effects of the latter on the bank’s profitability. A questionnaire was designed to survey the opinions of the individuals in the aforementioned research sample, according to a scale to be respected the quintet, and these phrases included five axes distributed between the study variables, demographic information of the study sample, elements of electronic marketing, elements supporting electronic marketing, indicators of the efficiency of banking services, and finally consumer behavior. The descriptive analysis resulted in tables that displayed the initial statistical results and analytical results that address the research hypotheses, whether negative or positive. the study reached the following conclusions the most important of which is that The dimensions of consumer behavior are arranged as follows: education, attitudes, perception, motives, and finally, the study proved the existence of a strong relationship between the independent variable and the mediator on the bank's profitability clearly, so the recommendations were confirmed that this relationship should be under the attention of the relevant parties in the upper and middle management and that they should take it into account when working on developing the marketing mix strategies for banking services at Rafidain bank to enhance its pioneering role in banking work at the level of Iraq and the world.

Impact of Exposure to Online Advertising on The Intention to Download Online Loan Applications Among Young Adults (18-27 Years) in Indonesia

This study examines the impact of online loan advertisements on the behavior of Generation Z (Gen-Z) in Indonesia, specifically their propensity to download loan applications. Data from Gen-Z individuals aged 18-27 were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate the effects of informativeness, entertainment, irritation, and incentives on advertisement value. Findings show that these advertising elements, especially incentives, significantly influence inspire-to behavior. Mediating factors such as advertisement value, perceived risk, and trust also play a significant role. The respondent profile indicates a majority of males, Bachelor's degree holders, and students. The study suggests that enhancing marketing strategies in the online loan sector requires understanding the factors influencing Gen-Z's decisions. It also highlights the importance of reducing unacceptable advertising elements. Future research should consider a more diverse sample and updated theoretical models. This study contributes to digital marketing and consumer behavior literature, providing insights for more effective and responsible online loan advertising in Indonesia.

Digital Marketing and Consumer Nutritional Behavior

Digital marketing involves promoting and selling products and services online and it is becoming extremely critical for retailers to gain new customers. Digital marketing uses online platforms, e-mail communications and messaging services, advertisements, and multi-modal online techniques. Digital marketing techniques can be used in parallel with machine learning and artificial intelligence to provide more effective results in analyzing customer behavior. Artificial intelligence (AI) has become an important and popular tool in the 21st century, affecting almost every sector, including engineering, science, medicine, business, finance, and marketing. However, AI’s dominance in every field of life, especially in digital marketing, may cause some ethical concerns related to consumers. In addition to these moral concerns, digital marketing creates doubts about its negative impact on human health. For this reason, understanding consumer behavior and providing products according to consumer needs has become indispensable for companies today. Digital marketing is everywhere in our daily lives and is also frequently used in the food industry. Through digital marketing, food industries promote the consumption of foods that are particularly high in energy and poor in nutrients. Consequently, individuals exposed to digital marketing in the food industry may be prone to obesity-related problems. This study analyzes the relationship between digital marketing, AI, and consumer behavior specifically on eating habits along with references from the literature.

Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

PurposeThe Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.Design/methodology/approachHybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.FindingsOur study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.Originality/valueTo the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Application of Cluster Analysis in the Evaluation of Automotive Fuel Prices Marketed in Goiás

Objective: The aim of this study is to present economic indicators constructed through cluster analysis, aiming to identify which gas station brands offer the lowest overall fuel prices. Theoretical Framework: The increasing demand for low-cost products, particularly automotive fuels, is a constant in the Brazilian market, significantly impacting both the economy and consumers' daily lives. This scenario underscores the analysis of prices as a crucial tool for understanding market behavior. Method: Data were obtained from the open data portal of the National Agency of Petroleum, Natural Gas and Biofuels (ANP), focusing on the historical price series of regular gasoline, premium gasoline, and ethanol in the state of Goiás. Cluster analysis was employed to group eight gas station brands into three distinct clusters. Results and Discussion: Findings reveal that Rodoil offers the lowest overall fuel prices, whereas Alesat, Ciapetro, and Raízen have the highest overall prices. These discoveries provide valuable insights into the competitive dynamics of the fuel market in Goiás. Research Implications: The significant debates generated in society by the rising prices of gasoline, ethanol, and other products such as cooking gas and diesel underscore the urgent need to revise fuel pricing policies in the country. Originality/Value: This study contributes uniquely by applying cluster analysis to identify price patterns among different gas station brands, offering an innovative approach to understanding competitiveness in the fuel distribution sector in Goiás.

Dynamic Pricing and Energy Management of Virtual Power Plant Based on Source-Charge Uncertainty

With the intermittent, random and volatile distributed renewable energy mass influx into the grid and power market, the power system will face greater challenges. As a small energy autonomous system that integrates distributed generation, energy storage and load, virtual power plant connects the power market and end users, and it is also an important subject of power market reform. Its operating efficiency directly determines the effectiveness of market reform. However, virtual power plant faces the market risk of double uncertainty of internal renewable energy output and load demand during operation, so it needs to optimize the market behavior to protect its own interests. Therefore, the paper takes virtual power plant as the research object, and discusses the dynamic pricing strategy of virtual power plant considering the double uncertainty of source and charge and real-time market linkage from the aspects of theoretical analysis, modeling construction and simulation analysis, to provide reference for decision pricing of virtual power plant and formulation of electricity market reform policy.

The Failure of Silicon Valley Bank: A Test of Market Efficiency

The unexpected collapse of Silicon Valley Bank (SVB) on March 10, 2023, sent shockwaves through the tech industry. SVB, a long-standing financial institution serving tech startups for nearly four decades, suddenly shifted from solvency to insolvency within 48 hours, marking the second-largest bank failure in U.S. history. This event provides an excellent opportunity to test the Efficient Market Hypothesis (EMH), particularly its semi-strong form, which posits that rapid information absorption prevents significant stock price gains in response to new information. This research uses the standard event study methodology in the finance literature to investigate SVB’s collapse and its impact on the stock prices of 30 banks traded on the New York Stock Exchange. The study aims to discern whether stock returns exhibited reactions prior to, on, or after the public announcement of SVB’s failure, thereby assessing market efficiency. Using historical stock and S&P 500 index data, the study analyzes holding period returns, performs regression analyses for pre-event periods, and calculates average excess returns. Results indicate statistically significant negative impacts on stock prices surrounding and on the event date. Furthermore, consistent with behavioral finance theory, a decline in adjusted stock prices approximately 7 days before the event suggests anticipatory market behavior, in line with semi-strong market efficiency. SVB’s case emphasizes the role of external factors, regulatory changes, and industry concentrations in shaping market responses. This research contributes empirical evidence to the discourse on market efficiency, highlighting the need for a nuanced understanding of market behaviors during crises. Lessons from SVB’s collapse will inform regulatory and risk management strategies, impacting future discussions on market efficiency. Likewise, the study results support the semi-strong form efficient market hypothesis and suggest the possibility of trading on this information up to 7 days prior to the announcement consistent with the behavioral finance literature (Bacon & Howell 2021). This study provides valuable insights into market dynamics during unprecedented events, influencing future discussions on regulation, risk management, and market efficiency.

Analysing the Correlation between Social Media Marketing and Consumer Purchase Behaviour

This study analysis of social media marketing includes use of platforms to communicate with consumers, promote items, and influence their decisions. The process by which customers choose which products or services to purchase is known as consumer purchase behaviour. Data was provided for this study, which observed exactly how social media marketing distresses customer buying behaviour, by consuming 350 participants, 150 marketers, and 200 customers. It is understood that many social media marketing features distress and unfair customer decisions. Hypotheses remained established to observe the relationships between commitment to social media marketing content, frequency of commercials, influencer content, and various social media platforms with consumer purchase behavior. Using SPSS logistic and linear regression analyses remained engaged to evaluate these relationships. Results expose that arrangement with social media marketing comfortable and influencer content positively moves buying behaviour. Linear regression analysis verifies these outcomes, proving the power of these relationships and the importance the effect of standard buying behavior. As that, the study delivers proposals concerning the way to use social media marketing approaches to increase consumer appointment and sales. Based on these insights, the study recommends optimizing social media marketing techniques to maximize customer interaction and improve financial outcomes.

The Digital Echo: The Influence of Digital Marketing Channels on Brand Awareness and Consumer Purchase Decisions

This study explore the impact of the digital marketing strategies ( Social Media Marketing, Email Marketing, Content Marketing and Online Advertising) on brand awareness while investigating to what extent does this resulting effect subsequent affects consumer purchase decisions. Understanding these relationships can allow businesses to tailor their marketing programs to produce higher brand visibility and an increased flow of consumers. The study employed quantitative research design and data was collected using online survey questionnaire from 265 respondents. Data analysis was performed using SmartPls software. The results of the study suggest that Social Media Marketing, Email Marketing, Content marketing and Online Advertising all have positive relationship in designed model with brand awareness. Moreover, the research demonstrates that consumers are more likely to purchase based on brand recognition. This study found strong relationships between social media, email, content, online advertising, brand awareness, and purchasing decisions. Brand awareness increases purchasing intention and consumer loyalty. A personalized and integrated digital marketing strategy is needed since different digital marketing channels have different effects on brand awareness and buying decisions. This study adds to the literature by showing how digital marketing methods affect brand awareness and purchasing decisions. This study helps marketers optimize their digital marketing strategies and improve brand performance in the digital age by explaining the complex relationship between digital marketing and customer behavior. It also emphasizes the need for a comprehensive strategy to digital marketing, since digital channels influence consumer perceptions and buying behavior.