Factors effective on behavior of common stocks in Mashhad's stock exchange (as well as a comparative study regarding behavior of INVESTORs in Tehran's stock exchange) This research deal with factors effective on behavior of INVESTORs of common stocks in Mashhad's stock exchange (as well as a comparative study regarding behavior of INVESTORs in Tehran's stock exchange) The literature of the subject reviews applications of behavioral finance, new finance, behavioral economic model. The previously conducted studies had noticed to the influence of these variables on investment to the lesser extent. Further more a comparative study among stock exchange markets on behavioral finance had not been performed until that time.The method of this research, is applied in term of objective, and is descriptive in term of data acquisition. the information have been collected from INVESTORs of Mashhad and Tehran's stock exchange markets through questionnaire. Statistical society includes all INVESTORs presenting in both above said stock exchange markets. the desired sample was selected among the society by simple random method.In this research we are seeking a reply to this question that whether the purchaser of stocks in Mashhad stock exchange follow a special trend of thinking concerning stock selection or not.The principal variables of this research are: domesticity managers for the company, behavior of other INVESTORs, to prophesy, immediate opportunity, rumors, domesticity of bargained goods, sub material, final goods, buy and storage of shares v.s buy and immediate sell.Findings resulted by the research reveals that DECISION taken by the INVESTORs in the both stock exchange could be influenced by a series of particular behavioral factors. Such factors are more emphasized specially within INVESTORs at Mashhad's stock exchange.

Investment attraction and fostering INVESTOR engagement represent critical focal points for national and local governments. In recent years, interdisciplinary fields such as behavioral economics have explored novel approaches to understanding investment attraction and its associated challenges. Traditional economic analyses often fail to capture the complexities of INVESTOR DECISION-MAKING due to methodological limitations and unrealistic assumptions. Behavioral economics research illuminates the presence of cognitive biases that individuals exhibit when entering investment projects, many of which are predictable. These findings present fresh opportunities for gaining deeper insights into INVESTOR behavior and optimizing policies related to investment attraction. Accordingly, this study aims to identify behavioral factors and cognitive biases influencing urban investment attraction, offering valuable insights for municipal planners nationwide. The study adopts an applied research design, utilizing a survey methodology. The targeted population in 2023, includes employees of municipal investment organizations, professionals in investment-related sectors, researchers specializing in investment studies, and experienced INVESTORs. The sample size was determined based on Morgan's table, with 105 participants selected through random sampling. The research findings highlight that cognitive biases, namely overconfidence, framing effect, loss aversion, optimism, regret aversion, and halo effect, exert the most substantial influence on INVESTORs' choices and DECISION-MAKING processes. By delving into behavioral economics, this study contributes to a deeper understanding of the cognitive biases that shape INVESTOR behavior in urban investment projects. The implications derived from these findings hold immense value for municipal planners, enabling them to devise more effective strategies to attract INVESTORs and optimize investment policies.

Behavioral finance challenges traditional economic theories by demonstrating how cognitive and emotional biases systematically influence INVESTOR DECISIONs. This study conducts a meta-analysis of 61 empirical studies (24 domestic, 37 international) to compare the effects of 12 prominent behavioral biases, selected based on their prevalence and diversity in the literature. Employing a random-effects model in CMA2 to account for heterogeneity, we quantify the biases' relative impacts using effect sizes, Z-scores, and hypothesis testing. Results reveal that mental accounting exhibits the strongest effect size, underscoring its dominant role in distorting financial DECISION-MAKING. Overconfidence, loss aversion, and anchoring also demonstrate significant—though variable—influences. These findings consolidate fragmented behavioral finance research, offering empirical clarity on the comparative weight of key biases in investment behavior.Keywords: Investment, Behavioral Finance, Bias, Capital Market, Meta-AnalysisJEL classification: E22، D03، O16 IntroductionPrices in financial markets frequently diverge from fundamental values, even with rational participants, challenging traditional theories like the Efficient Market Hypothesis (EMH) and Modern Portfolio Theory (MPT). Classical finance assumes a structured DECISION-MAKING process comprising problem recognition, solution identification, alternative evaluation, and optimal choice selection, but empirical evidence reveals systematic irrationalities, which behavioral finance addresses by incorporating psychological perspectives to demonstrate how cognitive and emotional biases distort INVESTOR behavior. This study synthesizes research on twelve key biases through meta-analysis, confirming their significant impact across markets, where cognitive biases include overconfidence (overestimating knowledge), anchoring (relying on initial reference points), herd behavior (following crowds), representativeness (using stereotypes over data), availability heuristic (overweighting recent information), and mental accounting (categorizing money subjectively), while emotional biases feature loss aversion (fearing losses more than valuing gains), regret aversion (avoiding potential regret), self-attribution (blaming failures on externals), optimism bias (overestimating success), and self-control issues (failing long-term planning). These biases stem from bounded rationality, time constraints, emotions, social pressures, and information asymmetry, with Prospect Theory further explaining irrationalities by showing how INVESTORs assess gains and losses asymmetrically, particularly when facing losses. By understanding these biases, markets can develop tools to mitigate their effects and foster more rational DECISION-MAKING, as recognizing behavioral biases in investment DECISIONs proves crucial for both INVESTORs and policymakers to help mitigate irrational choices and avoid unexpected financial risks, while this research aligns with global behavioral finance studies in emphasizing the need for bias-aware strategies to enhance DECISION-MAKING stability.MethodsThis meta-analysis synthesizes empirical research on INVESTOR behavioral biases through a rigorous four-step methodology: (1) systematic literature review to identify relevant studies, (2) effect size calculation using standardized metrics, (3) heterogeneity testing via Q-statistics and I² to assess consistency, and (4) model selection (fixed- or random-effects) based on heterogeneity levels, with inclusion criteria requiring studies to examine at least one of 12 key biases (e.g., overconfidence, loss aversion), report statistical outcomes (effect sizes, p-values), and cover diverse markets including traditional assets and cryptocurrencies. Heterogeneity testing determined model selection, where studies demonstrating consistency (Q p ≥ 0.05) utilized a fixed-effects model while those showing significant variation (Q p < 0.05) employed a random-effects model, with effect size analysis testing two competing hypotheses: H₀ (bias X has no significant effect on DECISIONs) and H₁ (bias X has a significant effect), where a Z-test p-value > 0.05 supported H₀ while p < 0.05 rejected it, thereby confirming a bias's influence. The study's key contributions include establishing a unified framework for assessing bias impacts across markets, maintaining methodological rigor through adaptive modeling approaches, and enabling cross-market comparisons that distinguish universal versus context-specific effects, while the findings consolidate fragmented behavioral finance research to offer INVESTORs and policymakers actionable insights for mitigating bias-driven risks, ultimately quantifying how cognitive and emotional biases shape investment behavior across different environments to provide robust, generalizable conclusions for improving financial DECISION-MAKING. Conclusion and discussionThis meta-analysis confirms that cognitive and emotional biases universally impact INVESTOR DECISION-MAKING across global markets. The study used a random-effects model, justified by confirmed heterogeneity (I² and Q statistics with p<0.05). Results demonstrate all examined biases significantly influence financial behavior. Key findings reveal several important patterns. Overconfidence bias was universally validated. Anchoring bias showed mixed results, with one dissenting study. Herding and representativeness biases received unanimous support. While some studies contested loss aversion, availability, and regret aversion biases, the majority confirmed their significance. Notably, mental accounting emerged as the most influential bias. The research highlights how bias manifestation varies based on personal experiences and environmental factors. This suggests their prominence depends on context. Emotional biases appear particularly susceptible to recent events and temporal factors. Several limitations should be noted. There are insufficient quantitative studies on lesser-known biases and emerging markets. Future research should prioritize four key directions: First, longitudinal studies of bias evolution. Second, examining interaction effects between multiple biases. Third, investigating market-specific manifestations (traditional vs. crypto markets). Fourth, developing evidence-based mitigation strategies. The cryptocurrency boom presents a critical research gap, as most studies focus on traditional markets. Interdisciplinary collaboration with psychologists could yield practical interventions. Additionally, real-time behavioral tracking in digital markets may uncover new bias patterns. These advancements would help INVESTORs and policymakers counteract systematic DECISION-MAKING errors. Such tools are especially valuable in today's increasingly complex financial ecosystems. The consistency of findings across domestic and international studies underscores how fundamental behavioral biases are to financial DECISION-MAKING processes.

Evaluating the performance of companies using their financial ratios is a challenging task that is expected to become more straightforward by reducing the dimensionality of the data. The purpose of this study is to evaluate the performance of companies using a hybrid model for investment-related DECISION MAKING through which the mean value of various financial ratios are calculated based on the INVESTOR's risk-taking behavior so that the number of all criteria is reduced to one single value for each alternative. To do so, a sample of 172 companies listed in Tehran Stock Exchange was selected from 2008 to-2018. Firstly, the financial ratios were prioritized using DECISION trees regression analysis (type CART) and TOPSIS Technique. The results showed that Gross Profit Margin and Debt to Equity Ratio are the most and the least important factors, respectively. Then, using OWA (Ordered Weighted Averaging Aggregation) operator, the role of INVESTOR’, s risk-taking behavior was investigated, and the results showed that INVESTOR’, s risk-taking behavior changes the outcome of the DECISION-MAKING process significantly.

Objective: Investigation and behavioral finance analysis of INVESTORs and market participants in the nascent field of financial management is one of the topics. Integration can be considered classical economics and sciences of psychology and DECISION-MAKING. While taking note of the recent developments on the stock exchange, have all the internal and external factors considered in the nuances and micro-sighted because this process of thought, conscience, and a mentally wide variety of people (INVESTORs) that has this is a subjective understanding is vital for transparency and a better understanding of the market. Since the 1980s, logical investment hypotheses and efficient markets have been ruined by appearing in behavioural finance literature. The mentioned literature states that some biases cause DECISION-MAKING problems while trading shares. Tversky and Kahneman, in the 70s, in a series of articles, achieved the development of applications of psychological knowledge in finance and economy. This research gradually became known as behavioral finance in the 90s as a field of financial matters. In the behavioural finance paradigm, the prevalent assumption of viewing a human being as a rational entity who is always satisfied in optimizing his/ her benefits is questionable. Behavioral finance asserts that the attitude of INVESTORs to the capital market is a function of investment psychology, ideas and position about risk. Research shows that many micro and macro factors that are measurable and non-measurable affect the behavior of INVESTORs. The advocates of Behavioral finance knowledge strongly believe that awareness of psycho-cognitive" tendencies in the investment realm is essential and requires serious improvement in expanding the scope of studies. However, academicians and professionals who are advocates of classic financial schools do not believe in examining the behavioral aspects of humans and their impact on financial DECISIONs as an independent field of study. Yet, both quantitative and qualitative advancement of experimental studies within this field indicates the importance of research on behaviors in financial markets. Financial literacy is essential in understanding INVESTORs’ behavior in advanced and emerging stock markets. While previous studies have shown a positive association between improved financial literacy levels and the quality and performance of INVESTORs’ DECISIONs, the dynamics of this process have not been adequately examined. This study aims to identify influential factors in individual INVESTOR's DECISION-MAKING and designing a model in the capital market. Due to the necessity of the subject and the lack of a comprehensive model that shows the factors influencing INVESTORs' DECISION-MAKING in the Iran capital market, the present study developed this model. Method: The methodology of this research is a development, exploratory and qualitative research in terms of result, objective and method, respectively. The research method is based on the strategy of the data foundation theory method with the coding method and paradigm model of Strauss and Corbin. The statistical population of the present study includes university experts and experts in the capital market who have continued to collect information through interviews until saturation; also, a sample of the snowball method has been used based on the opinions of 50 experts and the Strauss and Corbin system approach. The data collection tool was a structured interview, and the focus group continued with the subject matter experts until the theoretical level was reached. Also, using open coding, axial coding and selective coding, the concepts, categories, specifications and dimensions of the categories that were classified into contextual factors, causal conditions, strategies, intervening conditions, and outcomes were extracted and presented and Approved by the expert opinion of the expert. Results: This study provides new insight into the factors affecting DECISION-MAKING. The research data were analyzed using open, axial and selective coding methods, which is a component of the grounded theory approach with the aid of Maxqda 2018 software; finally, the integrated model of developing INVESTOR DECISION-MAKING is provided, which resulted in the identification of 2 categories of causal conditions, 6 categories of phenomena/context, 9 categories of intervening conditions, 7 categories of action strategies and 7 categories of consequences related to the main phenomenon of research. Conclusion: Based on the research results, individual INVESTORs can improve the quality of their DECISION-MAKING and make more effective investment DECISIONs by implementing the presented strategies and identifying and reducing their mental biases. These strategies include capital market knowledge, consulting with the expert capital market, extensive training in stock exchange concepts, employing specialized and experienced brokers in the capital market, reforming the structural system of the stock exchange, transparency of financial information, expansion of the secondary market, security of investment, Providing the ground for the INVESTOR not to leave the capital market.

Orthodox microeconomics DECISION-MAKING models are based on optimization but in the real world, most of DECISIONs are institutionally and based on habits, rules and predetermined solutions. In this paper we have combined some concepts from cognitive-behavioral economics and new institutional economics to characterize institutional DECISION-MAKING. Evidences from cognitive and behavioral economics show that the mental costs of direct optimization, even in simplest forms, are high and agents tend to make DECISIONs institutionally to reduce this mental costs. Coase and Williamson argue that "non-zero transaction costs" can result “ hierarchy” replaced instead of “ market” and similarly we argue that "non-zero DECISION MAKING costs" can result “ institutional DECISION MAKING” replaced instead of “ direct optimization” . Religious behavior is a kind of institutional behavior (following religious rules and habits) and considering this view that institutional DECISION MAKING and direct optimization both are important and both have distinctive roles in DECISION MAKING process can influence microfoundations of Islamic economics.