Purpose: The purpose of this paper is to look at the impact of two most commonly used heuristics, namely, representative bias and availability bias on investment decision making and to check that either locus of control interact with the said relations through theoretical proposal and then verification through empirical evidence.Design/methodology/approach: The study is a quantitative research using a survey questionnaire for its data collection. Data are collected from 227 investors operating at Islamabad, Lahore, and Sargodha in Pakistan and analyzed using structural equation modeling while the interaction effect is analyzed through simple linear regression following the rules set by Baron and Kenny (1986).Findings: The results reveal that both of the heuristics under study significantly cause investors to deviate from rational decision making while the locus of control have no significant moderating effect.Originality/value: The proposed model provides insight on how the behavioral factors can lead investors to suboptimal decision making. This study is first of its kind to quantify the degree of irrationality caused by these factors. The findings of this study are practically useful for individual investors, investment managers, and also for policy makers.

Given the sudden rise of artificial intelligence and declining standardization, the study focuses on the link between contextualized workplace coordination and administrative performance by preserving knowledge and utilizing generative AI models. The study employed a survey questionnaire design to collect data from project employees working at software houses in Islamabad. Hypothesis testing is done by utilizing partial least square regression through SMART-PLS. The findings indicate that the mandatory implicit and explicit coordination practices through accountability at organizational levels lead to increased knowledge sharing and administrative performance in Pakistan. The study concluded that knowledge-sharing acts as a mediator, hence implicit and explicit knowledge coordination is only fruitful in the occurrence of effective knowledge-sharing systems in the organization. The infusion of generative AI models by organizational employees is found to act as a moderator. It has significant potential to reduce the drawbacks associated with a lack of knowledge-sharing systems. Aside from being a pioneer study in this contextualized setting of HR analytics, these findings also have practical significance for HR managers, employees, and other stakeholders. The findings can be utilized to better ensure effectiveness and efficiency in organizational settings while managers can overcome any shortcomings in the system through the infusion of artificial intelligence in their HR analytics practices.

This study uses data from the KSE-100 stock index from 2009 to 2021 to investigate the influence of technological advancements, investors' sentiments, and behavioral factors on stock market inefficiency. The study employs a threshold regression model to distinguish between different thresholds of these factors and a Wald test is used to determine the significance of the variances among thresholds. The results indicate that technological advancements, investors' sentiments, and behavioral factors significantly affect the predictability of returns in the KSE-100 index. This study is unique in the context of Pakistan and provides a comprehensive framework for understanding stock market operations. The findings have practical applications for individuals, researchers, managers, institutions, and policymakers seeking to better understand the impact of technology, society, and behavioral biases on stock markets. Furthermore, this study provides unique insights into addressing the issue of market inefficiency by identifying the factors responsible for such behavior.

Purpose: Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.Design/methodology/approach: Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.Findings: The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.Originality/value: This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

The current study focuses on some of the most commonly relied upon biases in decision making. The study aimed at understanding the influence of herding, overconfidence, anchoring, and loss aversion on the decision-making style of investor besides it also investigates the role of financial literacy, since the traditional paradigm of finance is of the view that the knowledge of finance is directly associated with the degree of irrational outcomes. To explore this linkage data from investors trading at Lahore, Karachi, and Islamabad is gathered. Structural equation modeling is used for establishing the proposed associations. The results revealed that behavioral biases significantly impact the decision-making of investors. The results of moderation analysis presented that financial literacy plays a major role in de-biasing decision making. These findings can be extremely useful for investors, policymakers, and investment professionals. Not only to make optimal decision-making but also by providing a deeper understanding of the daily life stock market behavior.

Purpose: Predicting stock movement is of vital interest to managers and investors. The study aims to validate a similar model by identifying accounting variables that significantly predict the stock return at Pakistan Stock Exchange (PSX). Design and Methodology: The study utilized panel data extracted from the financial statements of nonfinancial firms included in the KSE-100 index from 2005 to 2020. A pooled regression model with a fixed effect for basic variables and a random effect for anomaly variables is used to test their impact on future stock returns. Findings: The regression analysis indicates that there exists a significant impact of basic accounting variables in forecasting expected return, return yield and return growth. The results for anomaly variables also significantly predict expected return and yield but are unable to predict earning growth significantly. Implications: The proposed model provides a tool for investors to take advantage of any mispricing existing in the stock market. The study statistically linked accounting anomaly variables with stock returns at Pakistan Stock Exchange. In a developing economy like Pakistan, where market mispricing is high this knowledge can be utilized by researchers, managers, and policymakers to foresee the direction of the business sector in Pakistan.