Trading Behavior Research Articles

A Research on Unlocking Investment Rationality: Exploring the Role of Behavioral Finance in Mitigating Investment Biases in Guntur

Behavioral finance is a growing academic discipline that investigates the cognitive, emotional, and psychological factors influencing financial decision-making. Unlike traditional finance, which assumes rationality in decision-making, behavioral finance emphasizes how biases, heuristics, and emotions can lead to deviations from logical behavior in financial markets. This field explores the behavior of individuals and institutions in financial markets, particularly focusing on why and how their actions diverge from conventional economic theories. Key concepts include heuristics, which are mental shortcuts used for quick decision-making, and biases such as overconfidence, anchoring, and confirmation bias that influence financial choices. Additionally, prospect theory is a cornerstone of behavioral finance, highlighting how individuals evaluate potential losses and gains differently, often leading to risk-averse or risk-seeking behavior. The present study also examines the role of emotions and social factors in financial markets, such as fear, greed, and peer influence. These elements significantly impact trading behavior, market trends, and investment decisions. For instance, emotional reactions during financial crises or bubbles can lead to irrational decisions, such as panic selling or speculative investments. Behavioral finance bridges the gap between psychology and economics, offering a more comprehensive understanding of financial behaviors. By studying these psychological influences, researchers aim to design better decision-making frameworks and improve market efficiency. Keywords: Behavioral Finance, Biases, Decision-Making, Emotions, Financial-Markets, Heuristics, Market Efficiency, Prospect Theory, Psychology, Social Factors

Factors Influencing The Behaviour Of Traders In Waste Management At Nanggalo Market Padang City 2023

Waste is solid waste material from human activities at home, schools, shops, public places, markets and so on. One of the markets where there is still a lot of waste in Padang City is the Nanggalo Market in Padang City. Nanggalo Market is one of the traditional markets that sells the daily needs of the community in the market there are still many traders who litter and scatter, for this reason research was conducted at the Nanggalo Market. The purpose of this study was to determine the factors that influence the behaviour of traders in waste management in Nanggalo Market, Padang City in 2023. This research design is quantitative, conducted at Nanggalo Market, Padang City. The research was conducted from December 2022 to June 2023. The population was 408 traders in Nanggalo Market in Padang City with a sample of 80 traders, and the data analysis used was chi-square test statistics. The results showed that there was a significant relationship between knowledge and traders' behaviour in waste management (p<0.024), there was a significant relationship between attitudes and traders' behaviour in waste management (p<0.024), there was a significant relationship between facilities and traders' behaviour (p<0.004), there was a significant relationship between knowledge and traders' behaviour (p0.004), there is a significant relationship between government policy and traders' behaviour (p<0.000), and there is no significant relationship between the role of cleaning staff and traders' behaviour (p>0.182). It can be concluded that there are several factors that are related and some are not related to the behaviour of traders in waste management at Nanggalo Market, Padang City in 2023.

Overnight versus Intraday Momentum and Investor Heterogeneity: Evidence from the Korean Equity Market

I provide aheterogeneity-based explanation for robust overnight and intraday momentum, as well as offsetting cross-period reversals in the Korean equity market from 2014 to 2017, using investor-type trade flow data. I demonstrate that retail investors' attention-based trading on overnight returns and domestic institutions' arbitrage trading on intraday returns drive these relationships, employing Fama-Macbeth (1973) cross-sectional regressions. Finally, I show that the trading behavior of sentiment-prone retail investors weakens when overall market sentiment is pessimistic, particularly during down markets and financial crises. This study has significant implications for the literature on investor heterogeneity and stock prices.

Money, inflation tax, and trading behavior: Theory and laboratory experiments

Money, inflation tax, and trading behavior: Theory and laboratory experiments

The Role of Short Selling in the Korean Stock Market : Review and Implications of Empirical Research

Issues related to the short-selling system in the Korean stock market continue to be debated among academics, individual investors, and financial regulatory authorities. This study comprehensively reviews the major findings of empirical research on short-selling in the Korean stock market and offers policy implications for future research directions on short-selling. Although short-selling studies in Korea exhibit mixed empirical findings, depending on the characteristics of the data used, sample periods, measurement methods of short-selling, selection of related variables, and research design, a large body of previous studies reported that short selling increases the market efficiency and price discovery of overvalued stocks in the Korean stock market. Considering the areas lacking research on short-selling in the Korean stock market, the following research topics are proposed for future research: first, the behavior of arbitrage and hedge trading by short-sellers; second, the information channels of short-sellers; and third, the influence of short-sellers on managerial decision-making in corporate finance.

Investor Sentiment and Stock Market Performance in the Big Data Environment

This study investigates the influence of social media sentiment on stock market performance by analyzing investor comments on the Snowball platform for Yunnan Baiyao (00538.SZ) and CICC (601995.SH) (China International Capital Corporation) between July and September 2024. Using web-scraping technology and sentiment analysis, the research categorizes comments into positive, neutral, or negative, applying regression models to investigate the relationship between the number of posts reflecting investor sentiment and stock trading performance, while controlling for variables such as company assets, P/E ratio, and market turnover rate. The results reveal that both positive and negative sentiments significantly affect stock trading behavior, with negative sentiment having a stronger impact. Additionally, company size and market fundamentals consistently influence trading volume and turnover rate. The study concludes by recommending that investors rely more on fundamental analysis rather than short-term sentiment fluctuations to achieve long-term stability in their investments.

A Reputation-Based Pricing Strategy for Distributed Diverse Entity Systems: Enhancing Market Efficiency Through Real-Time Reputation Updates

Although existing studies address the reduction of default rates by adjusting electricity trading rankings based on reputation values, the mechanisms for penalizing electricity trading defaults remain incomplete. Therefore, this paper proposes a real-time reputation-based pricing method for distributed diverse entity systems to mitigate electricity trading defaults. First, a reputation reward and penalty mechanism evaluates the trading behavior of diverse entities. Next, a ‘price-dominant, reputation-auxiliary’ pricing concept guides the process. Following this, a reputation-driven pricing strategy model for distributed adjustable resources allows for bid adjustments based on real-time market dynamics. Upon electricity trading completion, the reputation values of all entities are recalculated and disclosed, enabling entities to adjust future pricing and electricity trading quantities to optimize their profits. This method effectively reduces default rates while alleviating the impact of market electricity tradings on peak-to-valley fluctuations. Finally, simulations conducted on the MATLAB 2018b platform confirm the rationality and feasibility of the proposed real-time reputation-based pricing strategy within distributed diverse entity systems.

Public attention, investor sentiment and stock markets: Evidence from Chinese listed firms

ABSTRACT The COVID-19 pandemic’s onset introduced unforeseen disruptions to the Chinese stock market. This study uses all A-share companies from 20 January to 26 April 2020, as research samples to explore the impact of public attention on investor sentiment and the stock market during the pandemic. Our findings indicate that the heightened public attention with pandemic-related information triggers disorderly trading behaviours, which ultimately leads to a decline in stock returns, enhanced stock market volatility, and a climb in trading volume. Mechanism tests further find that the level of public concern about COVID-19 mainly affects investor sentiment, which in turn has a negative impact on the stock market. Additionally, heterogeneity analyses reveal that during the COVID-19 pandemic, this negative effect of public attention on stock market is more pronounced in labour-intensive firms and the tertiary sector. Our conclusions provide practical insights and guidance for financial regulatory agencies to maintain stock market stability during sudden public events.

Market participants' trading behavior toward anomalies: Evidence from the Korean market

Market participants' trading behavior toward anomalies: Evidence from the Korean market

COVID-19 and investors' trading behavior: Evidence from the New Zealand equity market

COVID-19 and investors' trading behavior: Evidence from the New Zealand equity market

Retail Investor Protection in the Age of Digital Trading: Regulatory Challenges and Solutions

The research analyzes the regulatory challenges posed by these platforms and to develop effective solutions. The study examines existing regulatory frameworks, such as the Dodd-Frank Act, MiFID II, and the Commodity Exchange Act, as well as market data on trading behaviors and feedback from retail investors and regulatory bodies. Key findings include the identification of significant regulatory gaps, particularly in the areas of anti- money laundering (AML) and know-your-customer (KYC) requirements, and the lack of clarity in the classification of digital assets as securities or commodities. The significance of these findings lies in their implications for enhancing investor protection and market stability. The broader implications suggest that a more integrated and technology-driven regulatory framework could enhance transparency, prevent market manipulations, and ensure a level playing field for all market participants. This research contributes to the ongoing discourse on digital asset regulation to safeguard retail investors in the digital trading era.

Financial knowledge acquisition and trading behavior: empirical evidence from an online information tool

Financial knowledge acquisition and trading behavior: empirical evidence from an online information tool

Evolutionary game for incentivizing social cooperation of prosumers in transactive energy communities

Transactive energy communities offer significant potential for the smart grid’s transition to renewable energy. However, they typically rely on fixed trading contracts between prosumers, which limits flexibility and engagement. Extensive research has been conducted on the mechanisms for implementing transactive energy communities, but there is still limited focus on prosumers’ trading behaviours and their social cooperation in peer-to-peer trading. In this work, we are exploring the potential of evolutionary game theory for modelling the evolution of trading behaviours among community prosumers over time aiming to define a blockchain market mechanism that promotes social cooperation-based P2P interactions. The model, inspired by the Hawk-Dove game, incentivizes prosumers’ cooperative behaviours (Doves) and penalizes prosumers with self-centred trading behaviours (Hawks), fostering social cooperation despite the temptation of higher payoffs from defection. In trading, Doves avoid conflicts and are open to cooperation, while Hawks are not. During a market session, if both prosumers choose the Hawk trading strategy, they are penalized with higher trading costs. If one chooses the Hawk and the other Dove, the Hawk gains all the benefits, and the Dove gains nothing. If both choose Dove, they share the benefits equally. We have evaluated the effectiveness of the solution with a blockchain-based energy marketplace considering realistic scenarios using energy data from prosumers. Results suggest that matched pairs of prosumers in P2P energy transactions gradually shift towards altruistic behaviours. Additionally, at the end of the market session, over 92% of the transactions involve altruistic behaviours, therefore, the prosumers are effectively incentivized to change their behaviour. Moreover, it may significantly increase traded energy in market sessions by up to 50% driven by cooperative trading behaviours of prosumers which unlock higher levels of energy flexibility. Finally, it outperforms the market clearing mechanism in transactions energy prices by up to 3%.

Institutional Trading and Managers' Complex Disclosure: Evidence From Conference Calls

ABSTRACTThis study investigates the impact of managerial linguistic disclosures on institutional trading behaviors during earnings conference calls from 2006 to 2016. Utilizing the linguistic Fog decomposition methodology on textual analysis, it analyzes how the linguistic complexity of managerial communication influences institutional ownership changes. Findings indicate that both obfuscation and informativeness of managerial discourse significantly affect institutional trading, with effects varying based on the informativeness of post‐call analyst reports. This impact is more pronounced under conditions of earnings quality uncertainty, suggesting a heightened reliance on managerial cues by institutional investors during such periods. Additionally, the study differentiates responses among investor types: transient investors are more sensitive to linguistic cues, while dedicated and quasi‐indexer investors focus on the informational content of disclosures. These insights enhance understanding of how managerial communication and analyst report informativeness shape institutional trading behaviors through linguistic complexity's influence on institutional ownership.

The COVID-19 pandemic and feedback trading dynamics: Unveiling global patterns

This study analyzes how the COVID-19 pandemic impacted stock markets worldwide using the COVID-19 Global Fear Index (GFI) devised by Salisu and Akanni (2020). We examine feedback trading behaviors in stock indices across 70 countries, revealing a complex relationship between pandemic-sentiment and feedback trading. Our study finds that GFI primarily motivates negative feedback trading in many developed countries, particularly those in higher latitudes, while the relationship between pandemic-sentiment and feedback trading is complex and varies across regions. Notably, China and India deviate from these patterns, exhibiting no significant feedback trading effects. These results highlight how regional differences shape financial market responses to the COVID-19 crisis. This analysis offers valuable insights into the pandemic's nuanced impact on global financial markets, emphasizing the distinct reactions across diverse geographic regions.

Infrastructure and the patterns of trade: An empirical look at China

Transport infrastructure plays an important role in economic growth, particularly for developing countries. Recently a burgeoning literature explores how transport infrastructure facilitates economic growth via the channel of trade. In this paper, we study this question in the context of China, but with several distinctions. First, we distinguish firms from different regions in China, and see how their trade behavior responds differently to changes in infrastructure. Second, we consider both export and domestic sales, with an eye toward the interplay between the two channels. Our estimation results reveal the presence of both regional heterogeneity and sales channel competition. For in-land provinces, an improvement in their transport infrastructure raises firms’ exports at the cost of domestic sales. Interestingly, the results are reversed for coastal provinces, mainly driven by non-coastal cities of coastal provinces (rather than coastal cities). We also find negative spatial spillover, in the sense that neighboring provinces tend to compete with rather than complement each other. Our analysis has important policy implications under the current geopolitical tension and policy initiatives (e.g. dual circulation in China).

Follow the gaze: How social attention shapes gendered trading choices

We explore how visual attention differentially impacts the trading behavior of men and women. In the laboratory, eye-tracking technology measures information gaze during a sequential trading game in which participants are asked to buy or sell an asset. Before making a decision, traders receive information on the trading decisions of other participants (others' decisions) and the redemption value of the asset (private information). Research documents that women, compared to men, pay more attention to social cues. In this study, women are more attention-driven when making financial decisions, as compared to men. We conclude that attentional priority is a cognitive mechanism that can account for the increased tendency of women, compared to men, to follow the social cues of others with disparate information and tournament incentives.

Retail and Institutional Investor Trading Behaviors: Evidence from China

We study two important questions regarding trading dynamics in China: How do retail and institutional investors trade, and what are the underlying factors for these behaviors? Different from the United States, China's stock market has two prominent features: dominance of retail investors and active participation by the government. After reviewing nearly 100 previous studies, we reach three conclusions. First, there is substantial heterogeneity in retail investors. Small retail investors have low financial literacy, exhibit behavioral biases, and not surprisingly, negatively predict future returns, whereas large retail investors and institutions are capable of processing information and positively predict future returns. Second, the macro- and firm-level information environment in China is slowly but gradually improving, which greatly affects trading behaviors of different investors, especially the more sophisticated institutional investors and large retail investors. Finally, the Chinese government actively adjusts their regulations on the stock market to serve the dual goals of growth and stability. Many regulations are effective, while some may generate unintended consequences.

The Impact of Earning Press Releases Sentiment on Abnormal Stock Returns and Volatility

This dissertation investigates the impact of sentiment in earnings press releases on abnormal stock returns and volatility based on 150 countries from 1 January 2000 to 31 December 2016. Using Henry’s dictionary method for sentiment analysis, the study finds a negative association between optimistic tones in press releases and abnormal stock returns, implying that highly positive sentiment may trigger lower market reactions. Additionally, the results indicate that positive sentiment in press releases leads to reduced abnormal volatility both immediately and consistently over a period of 5 days, 10 days, and 30 days. These findings remain robust even after accounting for the impact of the 2008 financial crisis. By addressing both short-term and long-term effects, this research contributes to the literature on enhancing the understanding of how effectively investors incorporate sentiment from press releases into their trading behaviours.

Statistical Predictions of Trading Strategies in Electronic Markets

We build statistical models to describe how market participants choose the direction, price, and volume of orders. Our dataset, which spans 16 weeks for four shares traded in Euronext Amsterdam, contains all messages sent to the exchange and includes algorithm identification and member identification. We obtain reliable out-of-sample predictions and report the top features that predict direction, price, and volume of orders sent to the exchange. The coefficients from the fitted models are used to cluster trading behavior and we find that algorithms registered as Liquidity Providers exhibit the widest range of trading behavior among dealing capacities. In particular, for the most liquid share in our study, we identify three types of behavior that we call (i) directional trading, (ii) opportunistic trading, and (iii) market making, and we find that around one-third of Liquidity Providers behave as market markers.