Individual Trading Research Articles

More Lessons of the Accord: The 1986–87 Plumbers’ Union Dispute

The early phases of the Price and Incomes Accord sought to constrain money-wage increases in order to reduce the rate of inflation. Despite this, in 1986 and 1987 the Plumbers and Gasfitters Employees’ Union of Australia engaged in industrial action to pressure employers into increasing wages. Using this case study, we examine the capacity for individual trade union agency for wage increases in the context of Accord Mark II. The article draws on document analysis combined with interviews with six of the dispute’s key actors and close observers. It finds that trade union agency was quashed by various political and industrial actors and institutions. This threatened the financial survival of the NSW Australian Plumbers and Gasfitters Employees’ Union, which faced legal sanctions during the dispute.

Social media attention and retail investor behavior: Evidence from r/wallstreetbets

This paper studies the influence of Reddit social media attention on investor risk-taking and welfare using individual trading data from a large trading platform. We show that attention generated on the subreddit r/wallstreetbets (WSB) spurs uninformed trading, also in short positions. WSB attention increases overall risk levels as investors trade riskier stocks and allocate larger portfolio shares to new positions. WSB attention significantly reduces holding period returns (HPRs). Positions created when WSB attention is at its highest realize -8.5% HPRs, while the average HPR across all investments is positive. Investment decisions of retail investors are more likely to be based on emotional responses to angry and disgusted user contributions on Reddit rather than analyst investment advice.

투자심리와 주식 유동성 간의 관계

This paper empirically investigates the relation of investors' sentiment and stock liquidity. Particularly, this research explores whether investors' sentiment is related to stock liquidity and whether the level of individual trading and information environment in each firm affects the relation. Three hypotheses were drawn and tested. To test the hypotheses, stock prices and relevant data of firms listed on Korea Exchange(KRX) were put together, and the hypotheses were studied by panel and Fama-MacBeth regressions. This research discovers that investor sentiment has positive effects on stock liquidity. Further, this study finds that the level of individual trading and the level of information environment increases the positive relation between investors' sentiment and stock liquidity. This work finds individual trading and information has a certain role in the relationship between investors sentiment and stock liquidity. The findings suggest that increased investor sentiment may cause noise trading to increase. In addition, firms with more uninformed trading may have a more liquid stock market.

Predicting Counterfeits from Smartphone Multi-Images Using Deep Learning

Counterfeiting is a pervasive issue in the collectible card market, posing significant risks to collectors, investors, and the industry at large. The trade of counterfeit cards not only undermines the value of genuine collectibles but also deceives consumers who invest their resources into fraudulent items. Despite advancements in counterfeit detection technologies, many counterfeit cards remain undiagnosed and continue to circulate in the marketplace, primarily due to the reliance on traditional methods of verification, which are often time-consuming and require expert evaluation. This study aims to leverage the increasing capabilities of widely available consumer technologies, specifically smartphones, to develop an innovative approach for the early detection of counterfeit collectible cards. By utilizing the multi-image signals acquired from smartphone cameras, we hypothesize that significant differences in color, type font, and material reflectiveness associated with counterfeit cards can be identified through machine learning techniques, particularly convolutional neural networks (CNNs). To evaluate this hypothesis, we analyzed a comprehensive dataset of 22,298 individual trading cards collected through the Digital Grading Company smartphone app. The dataset consisted of user-submitted images, which were systematically categorized into training, development, and test datasets to facilitate model training and validation. A robust 34-layer CNN architecture was employed to analyze these multi-image signals and predict the prevalence of counterfeit cards. The model's performance was measured using the area under the receiver operating characteristic curve (AUC), providing a quantitative assessment of its discriminatory ability. Our results revealed that of the total cards analyzed, 6.0% were identified as counterfeit, with the CNN model achieving an AUC of 0.772 (95% CI 0.747 - 0.797) in the test dataset. This indicates a reasonable level of discrimination in detecting counterfeit cards based solely on the multi-image data. The findings suggest that deep learning technologies can significantly enhance counterfeit detection processes, providing collectors and industry stakeholders with a powerful tool to combat fraud. This study represents the first proof-of-concept demonstration of utilizing smartphone-based imaging for the detection of counterfeits in collectible trading cards. By validating the effectiveness of deep learning in this context, we pave the way for future research that can explore more sophisticated algorithms and techniques to further improve detection accuracy. Ultimately, our approach could lead to the development of accessible applications for collectors and consumers, empowering them to make informed decisions and protect their investments in the collectible market.

Religious practices, fasting, and individuals’ trading behavior: a natural experiment

Purpose This study aims to investigate the effect of seasonality caused by fasting as a religious practice on trading activity. Design/methodology/approach The authors use an unbiased sample of daily trading by individuals and institutions on the Boursa Kuwait. The authors use panel data on trading activities and Tobit regression models to examine the effect of Muslims’ religious practice of fasting during the holy month of Ramadan on trading behavior. Findings The authors find that during the holy month of Ramadan, Muslims’ religious practice of fasting leads to a decline in the frequency of both overall stock market trading and the ratio of individual trading volume to total trading volume. The authors find a significant decrease in individual buy-side trading as a proportion of total trading volume and simultaneously a significant increase in institutional buy-side trading. Practical implications This study’s findings have important implications for the main players in stock markets of countries with a Muslim majority. Market-makers should be aware of the significant increase in the proportion of institutional buy-side trading volume to total trading volume to minimize the cost of trading with better-informed traders (adverse selection). Originality/value To the best of the authors’ knowledge, this is the first study that investigates individuals’ trading activity during Ramadan.

Short-term contrarian in the carbon emission market

We consider the short-term contrarian effects in daily data from carbon emission markets. Technical analysis is conducted with three contrarian trading rules on four carbon emission markets of China. Various individual and joint test statistics are developed for testing the contrarian profits from individual trading path as well as a group of trading paths. Empirical evidence shows that the contrarian profits are statistically and economically significant across the technical rules. Particularly, the contrarian moving average group trading generates average returns of 3.8 to 13.8 % per month across the different carbon markets during the period of 2013–2023 and the corresponding Sharpe ratios range from 0.9 to 2.01, while the Sharpe ratios from the buy-and-hold strategy are all below 0.12. Further tests support that the contrarian profits are not eliminated by transaction costs, and not dominated by extreme returns. We also emphasize on the explanations of the contrarian profit. Among the considered variables, the market volatility is the most significant explanatory variable for contrarian profits across four markets, whereas the market illiquidity and non-trading breaks are also significant in certain markets. The finding supports the explanation of the behavioural bias of overreaction for the short-term contrarian effect. Other explanations, including short-selling constraint, clearing stress, price limits, government interventions, etc. are also discussed in the paper.

Subjective Well-Being of Chief Executive Officers and Its Impact on Stock Market Volatility During the COVID-19 Pandemic in Poland: Agent-Based Model Perspective

This study aimed to connect the behavioral corporate finance perspective (micro level) with complexity theory via agent-based modeling to analyze the impact of selected psychological factors of chief executive officers (CEOs) on stock market volatility (macro level). Specifically, we wanted to explore whether Polish CEOs’ subjective well-being (SWB) influenced their managerial decisions during the COVID-19 pandemic and how it might be related to the volatility of stock prices during this critical period in Poland. Our study was based on a survey of Polish CEOs who managed companies listed on the Warsaw Stock Exchange. In particular, 255 CEOs completed the Satisfaction with Life Scale, the Positive and Negative Affect Scale, and a business survey on the impact of the COVID-19 pandemic on company management. Using the results of this survey, we built an agent-based model to investigate how CEOs’ decision-making, stemming from their SWB levels, influences the perception of prices by individual traders and, in turn, how it is translated into aggregate stock market volatility. The results indicate the pathways through which the microscopic-level SWB of CEOs influences market price formation at a macroscopic level. The findings obtained from our model may shed new light on the rational expectations theory applied to stock market volatility during the financial crisis.

Nonfinancial 8-K disclosures and individual investors' trading during earnings announcement window

PurposeThis study investigates the influence of nonfinancial 8-K disclosures released during the earnings announcement window on the abnormal trading activities of individual investors.Design/methodology/approachWe employ regression analysis in this empirical study to examine the impact of nonfinancial 8-K filings on individual investors' abnormal trading activities.FindingsOur results reveal that individual investors exhibit higher levels of abnormal trading activities when firms release nonfinancial 8-Ks during the (0,1) window of earnings announcements. This effect is observed for both buyer-initiated and seller-initiated transactions and is particularly pronounced for firms reporting an operating loss. Negative sentiment in 8-Ks significantly intensifies such effect. Additionally, we find that buy-sell consensus increases significantly with concurrent nonfinancial 8-Ks. This suggests that 8-Ks may reduce information noise, leading individuals to trade with greater conviction.Originality/valueOur study examines the joint influence of nonfinancial 8-Ks and earnings announcements on individual investors' trading activities, thereby providing a novel perspective on the mechanisms through which 8-K filings affect individual investors' trading behaviors.

Deficit aversion: Mercantilist ideas and individual trade preferences

AbstractWhat factors affect trade preferences? This article focuses on current‐account balances, which despite being de‐emphasized by mainstream economic theory, play an outsized role in political rhetoric regarding the costs and benefits of free trade. This article shows that individual preferences over trade openness reflect the mercantilist belief that when a country is running a current‐account deficit, trade reduces that country's aggregate employment prospects and diminishes its status on the world stage. This article shows that current‐account balances are an important driver of individual trade preferences. The theory's predictions are borne out by hierarchical analysis of cross‐national observational survey data, and further supported by the results of an original survey priming experiment in the United States. These results contribute to a growing literature emphasizing the effect of macroeconomic factors on preferences.

Aesthetics of Trademarks with an Asian Flavour

Trademarks should be considered not only as an intellectual property right but also as aesthetic creations influenced by various philosophies. A trademark can thus be understood as a word, a phrase, a symbol, a shape, a configuration or any other device introduced to distinguish the products of individual traders or manufacturers. Trademarks also have an aesthetic dimension. This means that aesthetics of trademarks can be revealed through the letters and morphemes as well as their literary, mythological, and religious associations. Trademarks play a crucial role in some commercial sectors. The aim of this article is therefore to present the aesthetics of trademarks through the lens of East Asian philosophies such as Confucianism and Daoism, as well as Japanese Zen. This analysis brings us to considerations of traditional aesthetic functionality. This means that the brand is the main decorative feature of the product or is the product itself. It can therefore be influenced by different philosophies, and the above cases even confirm this theory.

Research on Energy Trading Mechanism Based on Individual Level Carbon Quota

High economic growth is accompanied by substantial consumption of fossil energy and significant negative externalities on the ecological environment. The global warming effect resulting from environmental pollution caused by energy has brought energy carbon emissions into the forefront of social attention. Establishing a carbon trading market is an essential measure to achieve the “double carbon” goal, with individual and household carbon emissions accounting for 70% of China’s total emissions. Constructing an individual-level carbon trading market will facilitate the efficient realization of this goal. However, addressing the challenge of handling vast amounts of data and network congestion in relation to frequent but small-scale individual carbon trading has become an urgent issue that needs to be resolved. In light of this, the present study designs a digital technology-based framework for the carbon market trading system and proposes an individual carbon asset price-based model for carbon market trading, aiming to establish a research framework for the carbon quota market. Furthermore, blockchain technology is employed as the underlying technology in the proposed carbon trading market model to cater to individual-level carbon trading services and achieve optimal matching between carbon quota suppliers, thereby enhancing profitability of the carbon trading platform. The numerical results obtained from the model demonstrate that in absence of government subsidy mechanisms, individual-level carbon trading can effectively reduce total consumer emissions. The present study successfully overcomes the carbon lock-in effect of consumer groups and achieves the generation and trading of individual carbon assets despite capital constraints. This study facilitates accumulation and trade of individual carbon resources, reduces overall consumer emissions, enhances environmental benefits at societal level, and provides a foundation for governmental decision-making.

The state as a market maker: The rise (and fall) of Brazilian securitization in the 21st century

This paper analyses housing securitization in Brazil during 2007–2020 and provides three contributions to debates on the relations between the state and financialization in emerging markets. First, we highlight that the Brazilian state has gone far beyond the common role of market enabling, characterized by regulatory rollout and fiscal incentives. Instead, by acting as a market maker on the demand and supply side, it has provided the required liquidity and depth for the emerging segment of residential securitization. Unlike the financial disintermediation through private market makers such as brokerage houses and individual traders that provide risk-reduction, Brazilian securitization has unfolded through the issuance and acquisition of securities by the state’s housing bank and its associated social housing fund. Second, unlike the speculative Minsky type of booms and busts that characterize privately-driven markets, state market making generates a trajectory that is associated with the capacity of governments to roll-out markets for residential securitization. While the social-developmental stance of the Lula and Dilma administrations have boosted securitization, the market-oriented austerity politics of the Temer and Bolsonaro administrations have quickly dried up secondary markets. Finally, while securitization was initially a means to the end of social housing, it seems increasingly the other way around. More specifically, considering that thick and consolidated secondary markets ultimately depend on the quality and stability of the underlying loans and income streams that originate operations, stricter risk reduction and guarantees clauses have gradually penetrated the social housing segment and threaten to undermine the right to secure tenure.

Factors Affecting Share Traders' Investment Decisions: Investigating the Psychological, Social, and Economic Factors That Influence Share Traders' Investment Choices and Risk Tolerance

Deep Dive into Share Trader Decision-Making: A Psychological, Social, and Economic Exploration This research delves into the intricate world of share trader decision-making, specifically focusing on the interplay between psychology, social dynamics, and economic factors. It aims to shed light on how these multifaceted influences shape investment choices and risk tolerance, particularly among the burgeoning generation of young adult traders (Gen Z).  Beyond Rationality: the Behavioral Dimension Investment decisions are often depicted as exercises in cold, calculated logic. However, the field of behavioral finance challenges this notion, highlighting the significant role of psychological biases. This study builds upon this established knowledge by exploring how these psychological factors, along with social and economic considerations, converge to influence trading decisions and risk tolerance within the Gen Z demographic.  Methodology: Unveiling the Underlying Factors To gather valuable insights, the study will employ a survey methodology utilizing a five-point Likert scale questionnaire. Disseminated through social media platforms, the survey aims to capture data from a broad range of participants. The primary target audience will be Gen Z respondents (aged 18-21), with a subset of participants from older generations included for comparative analysis. The questionnaire will be meticulously crafted to assess psychological factors (e.g., overconfidence, fear of missing out), social influences (e.g., peer pressure, online communities), economic considerations (e.g., market trends, interest rates), and risk tolerance.  Hypotheses: A Framework for Understanding The study proposes a set of four core hypotheses to guide the investigation:  Psychological Influence: Psychological factors, such as overconfidence or anchoring bias, significantly impact share traders' investment decisions.  Social Dynamics in Play: Social factors, including group dynamics and the influence of online communities, exert a substantial influence on share traders' decisions.  Economic Considerations as a Guidepost: Economic factors, encompassing market trends, interest rates, and company performance, provide valuable guidance for share traders' decision-making processes.  The Moderating Effect of Initial Trades: Initial trade decisions act as a moderator, influencing the relationship between the aforementioned factors and an individual's risk tolerance.  Data Analysis: Unveiling the Relationships The collected data will be meticulously analyzed using structural equation modeling (SEM) software like SPSS AMOS. This powerful technique allows researchers to delve deeper by evaluating:  Confirmatory Factor Analysis: This analysis technique assesses the strength and validity of the relationships between the observed variables (survey questions) and the underlying latent variables (psychological factors, social factors, etc.). It essentially confirms that the survey questions are effectively capturing the intended constructs.  Path Coefficients: Path coefficients quantify the direct effects of each factor (psychological, social, economic) on risk tolerance. Additionally, the analysis will explore whether initial trade decisions moderate these effects, meaning they influence the strength of the relationship between the factors and risk tolerance.  Expected Outcomes: Illuminating the Path Forward This research aspires to achieve the following key outcomes:Demystifying Decision-Making: Identify the relative influence of psychological, social, and economic factors on Gen Z share traders' decisions.  Understanding Risk Tolerance: Elucidate how these factors interact and contribute to the development of risk tolerance among young adult investors.  Empowering Traders: Equip individual traders with valuable insights to bolster their decision-making processes and risk management strategies.  Informing Financial Literacy: Provide insights for policymakers and educators to design financial literacy programs and regulations that cater to the specific needs and preferences of young adult investors.  Acknowledging Limitations: A Call for Further Exploration The study acknowledges inherent limitations, such as the potential for self-reported bias in survey responses. Additionally, the initial focus on a specific age group (Gen Z) within a limited geographical area (India) necessitates further research to explore potential cultural and demographic variations in financial decision-making. This research serves as a springboard for future investigations, paving the way for a more comprehensive understanding of the nuanced interplay between psychological, social, and economic factors in shaping financial decision-making across diverse demographics and cultural contexts.

Wealthy individual investors and stock markets' tail risk.

This paper employs a unique data set to analyze the trading behavior of wealthy individual investors across Mainland China and their impact on Chinese stock markets' tail risk. Results show that the wealthy individual investors' trading behavior can explain Chinese stock markets' tail risk, and the daily investment portfolios based on the network density of wealthy individual investors have significant excess returns. This paper also investigates the determinants of wealthy individual investors' trading behavior with the social network method and the spatial econometric model, and reveals that wealthy individuals benefit from the spillover effect of their trading behavior through the investor networks. The results of this paper not only reveal micro evidence for the formation mechanism of asset prices, but also provide insight into the behavior of wealthy individual investors.

Culture imprint and gambling preference: Evidence from individual investors' trading in the Chinese stock market

Using the stock transaction records of individual investors from a large brokerage firm in China, we explore the impact of Confucian culture on the gambling preferences of individual investors. After a series of tests, we find a significant negative relationship between Confucianism and the lottery-type stock preference of individual investors, indicating that Confucian culture inhibits gambling preferences in stock investment. This effect is more pronounced for female investors, middle-aged investors and investors in regions with higher gambling sentiment. Investor risk preference mediates the relationship between Confucian culture and lottery-type stock preference.

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study.

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

Review of Recent Research Directions and Practical Implementation of Low-Frequency Algorithmic Trading

Financial trading has undergone substantial technological evolution, with automation taking center stage, leading to approximately 80% of US market trades being executed by computer systems, predominantly by large financial institutions. The rise of algorithmic trading, poised to engage smaller entities, international markets, and individual traders, drives this article’s exploration of research in this field. Providing a comprehensive overview, it outlines the evolution of trading practices and defines algorithmic trading as a computer-powered tool aiding investment decisions. The article details the steps involved in algorithmic trading, covering opportunity identification, quantitative research, implementation, testing phases, and continuous monitoring. It also examines prevalent programming languages and open-source platforms facilitating algorithm development. Focusing on trading frequencies across financial instruments, it delves into high-frequency trading as a subset, alongside methodologies like technical and fundamental analysis, time series analysis, option trading strategies, and machine learning techniques used in algorithm creation. Categorized by trading frequencies, analytical approaches, involved financial instruments, and analysis objectives, the reviewed papers contribute insights into algorithmic trading’s diverse landscape and methodologies, offering valuable perspectives for industry participants and researchers alike.

Trend Factor in China: The Role of Large Individual Trading

Abstract We propose a novel trend factor for the Chinese stock market that incorporates both price and volume information to capture dominant individual trading, momentum, and liquidity. We find that volume plays a more significant role in the trend factor for China than for the United States, reflecting the greater retail participation in China. By incorporating this trend factor into the 3-factor model of Liu et al. (2019), we propose a 4-factor model that explains a wide range of stylized facts and 60 representative anomalies. Our study highlights the important role of individual trading in asset pricing, especially in China. (JEL G12, G14, G15)

Understanding integration issues in intelligent transportation systems with IoT platforms, cloud computing, and connected vehicles

<p>The fast improvements in information and communication technology have altered the transportation business, resulting in intelligent transportation systems (ITS) that aim to improve transportation network safety, efficiency, and sustainability. Cloud computing, networked automobiles, and IoT platforms are driving this shift. Infrastructure for current civilization must include transport. The effectiveness of transport infrastructure is vital for all countries’ individual mobility, trade, and economic development. Traffic congestion, fuel price increases, and CO<sub>2</sub> emissions have all increased in recent years, posing problems for modern civilization. It is crucial to improve transportation efficiency and safety. To develop a sustainable, intelligent transportation system, it is necessary to seamlessly integrate and interoperate with cutting-edge technologies like linked cars, the Internet of Things (IoT), and cloud computing. To respond to difficulties facing the transportation industry, which include rising fuel costs, high CO<sub>2</sub> emissions, expanding congestion in traffic, and greater dangers on the highway, integration concerns must be concluded.</p>

Individual investor trading and stock returns after the Covid-19 pandemic: Evidence from Korea

This paper reveals a significant attenuation in the previously observed positive association between individual investors’ purchases and stock returns following the Covid-19 pandemic. To investigate these changes, I utilize a comprehensive dataset of daily stock transactions, categorized by investor groups, from the Korean main board market. By employing a difference-in-difference regression model, I find that stocks influenced by the influx of amateur investors or attention-based trading post-pandemic are the key drivers behind this transformation. These findings suggest that policymakers might need to consider implementing systematic liquidity provision programs, such as market-making activities, to fill the liquidity gap created by individual investors.