Search

Objective: The stock exchange with its operational mechanism, is a suitable place in a country's financial and economic structure and can work effectively in the direction of developing and expanding the country's liquidity resources. People consider the stock market to be related to behaviors such as greed, crises and assumptions. The increase in the number of active companies in the stock market, causes investors to face different options for investment, which has made it difficult and complicated to make appropriate investment decisions. Since people's decisions reflect their beliefs, distortions in beliefs will cause inappropriate decisions and subsequent harmful consequences for the decision maker. Therefore, the study of beliefs is considered as a fundamental issue because they have an important role in decision making. Behavioral finance theories challenge efficient market hypotheses by highlighting the important role of Investors' sentiment and psychological states in financial decisions. Behavioral financial studies the way people analyze and interpret information in order to make informed investment decisions and seeks to identify the effects of psychological processes on their beliefs and decisions. This field of knowledge is related to the psyche of investors and its role in financial decisions. All people have emotion that affects their decision-making and behavior. As an effective factor in their behaviors, investors' emotional tendency causes changes in the capital market and thus affects the volume of transactions and the stock market price. Another factor affecting the decision-making and investors' behavior is the published reports on companies' performance and financial status, such as the board activity report. These reports contain textual and quantitative information that includes a larger volume of the reports than quantitative information. One of the most important features of textual information is its tone, which in accounting refers to the amount of positive and negative text in reports. The tone of the reports published by managers is determined by the use of vocabulary and the combination of positive and negative words, which shows how management intends to inform outsiders. Therefore, the textual information used by management can be effective in investors' decisions. Method: The method of the present study is applied in terms of purpose and classification according to a method, which is descriptive-correlational, and the data analysis method is regression analysis and panel data method. The statistical population of the research is the companies in the Tehran Stock Exchange from 2009 to 2021. The statistical sample selected from the statistical population includes 151 companies (1963 year-company) in the studied period. The principal component analysis (PCA) method measures the investors' sentiments. Also, the tone of the board activity report is measured through text mining and using MAXQDA 2021. Results: The results show a positive and significant relationship between investor Sentiment and their trading behavior. Also, a negative and significant relationship was observed between the tone of the board activity report and investors' trading behaviour. Conclusion: The behavioral finance perspective discusses the behavior of investors and its effect on the capital market. One of the primary topics of this view is how to adapt the investors' sentiment to their decision-making and trading behavior. Investors' sentiments create a belief that causes them to react based on the same belief and behave differently based on this belief. These different behaviors, in total, cause a change in the price trend in the stock market. On the other hand, based on the signaling theory, to eliminate information asymmetry between insider and outsider users, managers publish information about the company in various reports, especially board activity reports, which are the most important form of reports. This type of report contains numerical and textual information. Textual information and numerical information are important in investors' decision-making. Managers can influence the behavior and reaction of investors by using the tone of textual information in reports. This research assumes that investors' sentiment is related to the formation of their trading behavior in the capital market (first hypothesis). Also, there is a relationship between the tone of textual information that managers use in their activity reports and investors' trading behaviour (The second hypothesis). The type of tone that managers use to disclose the textual information of the board activity report affects the decision-making of the users of this report and causes them to show behavior in the stock market that reduces the abnormal volume of transactions in total. Considering that no research has been done to study the effect of the tone of board activity report on the behavior of investors, the findings of this research can greatly help to identify the effects of the tone used by the management in disclosing the textual information of this type and also to understand the effect of investors' emotions on their trading behavior. [ABSTRACT FROM AUTHOR]

Using proprietary individual-level trading data around a natural experiment—the release of a smartphone trading app by a large investment advisor—this study investigates how smartphone trading technology affects retail investor behavior and mutual fund performance. App adoption by retail investors leads to an increase in investor attention and trading volume. App adopters' flows become more sensitive to short-term fund returns and market sentiment, resulting in higher aggregate flow volume among adopters. The funds more exposed to the shock experience a greater decline in abnormal returns, likely attributable to higher fund flow volume and liquidity costs. As a result, both adopters and nonadopters experience a decline in their mutual fund investment returns. This paper was accepted by Lin William Cong, finance. Funding: The W. Edwards Deming Center and the Eugene Lang Entrepreneurship Center at Columbia University provided funding to support this research. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2021.02099. [ABSTRACT FROM AUTHOR]

Increasing market volatility and the profound impacts of climate change require a comprehensive understanding of how weather affects stock market performance. This paper aims to investigate the effect of eight weather conditions (clear sky, precipitation, pressure, temperature, relative humidity, specific humidity, wind direction, and wind speed) on the returns of major African stock markets (Botswana, Cote d’Ivoire, Kenya, Mauritius, Morocco, Namibia, Nigeria, Rwanda, South Africa, Tanzania, Tunisia, Uganda and Zambia) over the period from January 2, 1998 to December 30, 2023. Using daily data and a GJR-GARCH (1,1) model with an AR process, the findings reveal that weather conditions influence all African stock markets. Specifically, the markets are categorized according to their sensitivity to weather conditions into three groups: highly affected (5-7 coefficients with 0.001≤ p

The proliferation of FinTech apps has democratized access to financial services, empowering individuals and businesses to take greater control of their finances. These apps have catalyzed financial innovation, disrupted traditional business models, and fostered competition in the financial industry. Moreover, FinTech apps have the potential to drive economic growth, promote financial literacy, and advance financial inclusion on a global scale. FinTech applications have played an important role in the creation of an arena where information is collected, stored, and processed to make the best decisions of investment. This has boosted the investment and increased the activity in capital markets when considered from the investors’ point of view. Capital markets have always been an important place for investors to invest in long-term securities with a maturity of more than one year. Primary as well as secondary markets are part of it. People invest in securities expecting a return in the form of capital gains and/or income. The role of FinTech applications in facilitating various investment decisions has also opened doors to retail investors in capital markets. Traditionally, retail investors have been overlooked because they are perceived not to have enough capital to invest, be too risk-averse, and be too costly to service. Both quantitative and qualitative data have been used. 150 online surveys were used to gather primary data. The outcome from this study showed a major shift in stock market investors towards digital connection due to various reasons. Some cultural and behavioral effects have been found among the investors for the financial transactions.

This study introduces a new indicator to measure the lottery-like degree of financial products by considering their idiosyncratic volatility and skewness. The findings indicate that investors tend to purchase funds that hold lottery-like stocks, while investors prefer lottery-like funds when the market sentiment is high. However, funds holding lottery-like stocks and those exhibiting lottery traits experience relatively lower adjusted returns in the next quarter. Interestingly, when the market sentiment is high, investors can obtain better performance only from funds that hold lottery-like stocks, indicating that investors may be better off selecting lottery-holding funds rather than lottery-like funds during periods of high sentiment. Overall, our study sheds light on investor behavior and the impact of market sentiment on fund flows and performance in lottery-like funds. [ABSTRACT FROM AUTHOR]

Addressing the issue of information asymmetry in crowdfunding, this paper developed a new model for predicting crowdfunding performance, based on the decision utility rules for processing uncertain information in prospect theory and combining the analysis of crowdfunding project information disclosure with investor utility. To tackle the issue of excessive feature selection in practical applications, it introduced a sparsity-based feature selection method using neural networks, which could help crowdfunding platforms to focus on core features for better understanding and predicting investor behavior. Empirical analysis of over 150 000 projects on the Kickstarter platform shows that models considering investors perception of risk and prospect utility have better predictive and explanatory power for crowdfunding performance. The research results not only provide a new perspective for the prediction and evaluation of crowdfunding projects, but also offer powerful tools for crowdfunding platforms and fundraisers to establish models for analyzing backers backing behavior. [ABSTRACT FROM AUTHOR]

The article analyzes search queries in the Russian and American segments of Google. The author proposes a methodology for selecting and classifying search queries that reflect investor sentiment, which potentially influence the activity of the population in the financial market. The article calculates sentiment indices for the United States and Russia, demonstrating a high correlation with the national stock indices S&P500 and IMOEX. The author summarizes that financial market quotes can be determined as indicators of investor sentiment, which in turn are formed on the basis of a complex of economic and noneconomic factors. [ABSTRACT FROM AUTHOR]

The fundamental theory of the Efficient market hypothesis (EMH), which states that market participants are rational, has received a lot of criticism. The complexity of behavior in the capital market is still a black box, especially when psychological biases influence aggressively on decision-making amid uncertainty. Experimental research on finance and capital markets in the form of AI using machine learning seeks to predict the results of more complex interactions. This multidisciplinary approach offers efforts to explain social phenomena from the micro level to macro descriptions which are built artificially through the computational world. The processing modeling approach is preferred because it includes the complexes that emerge from the behavior and interactions of individuals in the real world. Agent Based Model (ABM) is an AI approach in the form of computational simulation that performs a bottom-up approach by combining irrational–rational agent interactions through networks in microenvironments. Using the ABM approach through Netlogo computing, this study proves that AI can be used to analyze investor behavior in the capital market. [ABSTRACT FROM AUTHOR]

This study examines the investor behavior of mutual funds in China. Our results show little evidence about the redemption puzzle. We further refute the disposition effect, that is, investors do not tend to redeem funds with superior past performance. Additionally, market conditions are crucial for investment decisions. In bull markets, investors are prone to taking risks while being conservative and prudent during bad times. Considering investor heterogeneity, we find that institutional investors value the funds’ past performance metrics, assigning greater weight to historical performance. Both institutional investors and individual investors show stronger sensitivity to complicated performance indicators in bad times than good times. Furthermore, we find that investors in China chase for the star funds. We also perform tests on the existence of the smart money effect, and the results show that investors can screen good funds from bad ones.

The literature on overconfidence has witnessed prolific growth since the beginning of the century. This context underscores the necessity to comprehend and categorize an increasingly diverse body of overconfidence research within the financial domain. This study reviews existing literature on financial overconfidence from its inception to the present with a detailed review of 132 articles from 84 journals by examining theories, context, and methods (TCM) used in overconfidence research. Our review unpacks significant themes (i.e. determinants of overconfidence, overconfidence and risk-taking, overconfidence measures and type of investors, overconfidence in a volatile market, overconfidence, and personal financial behavior). We propose a pertinent research framework to investigate the less investigated aspects of financial overconfidence and suggest future research direction.

In order to promote the healthy development of China's fund market and alleviate the phenomenon of "funds make money, investors don't", this paper explores the impact and economic consequences of fund signals on investors' decisions in China. An analysis of quarterly Chinese fund data from January 2010 to December 2022 finds that Chinese investors tend to rely on strong signals and flow their money to highly rated or high-yielding funds. Sustainability is an integral part of ESG ratings, which have gradually become a focal point in the international investment market, but the lack of a platform to display ESG ratings in China has prevented investors from effectively utilizing these ratings in their investment decisions. The study also found that individual investors prefer short- and medium-term returns, while both institutional and individual investors chase ratings, with individual investors relying more on these signals. In addition, strong signals are not effective in predicting fund performance returns, while ESG ratings show significant positive predictive effects, validating the irrational preference of Chinese investors to blindly follow strong signals. [ABSTRACT FROM AUTHOR]

We examine the connectedness of aerospace and defense companies and their relation to measures of geopolitical risk. With hierarchical clustering, we find stable and localized company clusters. Increasing geopolitical risk leaves these clusters intact but strengthens inter-cluster connectedness. Focusing on intraday data, we find that for most companies, instantaneous news arrival in form of jumps impacts realized volatility significantly. Further, we show that different measures of geopolitical uncertainty (GPR and COVOL) have differing impact on short-term predictions of intraday volatility, underlying the importance to distinguish between different sources of uncertainty. We provide evidence that investors react instantaneously to increases in geopolitical risk with some persistence of these shocks. The COVOL index holds significant informational content for short- and medium-term predictions of realized volatility of global aerospace and defense companies. • Aerospace and defense companies form localized clusters. • Increasing geopolitical risk increases the intercluster connectedness. • Different measures of geopolitical risks (GPR and COVOL) affect realized volatility predictions. • Instantaneous news arrival in form of volatility jumps are causally related to changes in geopolitical risk. • We provide evidence that investors react instantaneously to increases in geopolitical risk. [ABSTRACT FROM AUTHOR]

The use of text readability indicators and sentiment factors to asset pricing is practical in investment risk management yield pursuit. We analyzed the polarity of textual sentiment of Management Discussion and Analysis (MD&A) in 10-Ks for firms in the U.S. by constructing Sentiment Entropy, a new sentiment indicator based on L&M Dictionary, which captures the same characteristics of positive and negative emotions from the perspective of polarity. The empirical results show that the polarity of sentiments increases investment risk measured by return volatility, which differs from the traditional one-way emotion indicator. We found through a mediator effect test that emotional polarity significantly increases investment risk by stimulating large changes in trading volume, which is consistent with conventional wisdom. Challenging conventional findings, we find that positive and negative emotions in disclosures may have homogenous effects on the market, providing inspirations for understanding the role of textual sentiment on investor behavior. [ABSTRACT FROM AUTHOR]

The proliferation of FinTech apps has democratized access to financial services, empowering individuals and businesses to take greater control of their finances. These apps have catalyzed financial innovation, disrupted traditional business models, and fostered competition in the financial industry. Moreover, FinTech apps have the potential to drive economic growth, promote financial literacy, and advance financial inclusion on a global scale. FinTech applications have played an important role in the creation of an arena where information is collected, stored, and processed to make the best decisions of investment. This has boosted the investment and increased the activity in capital markets when considered from the investors' point of view. Capital markets have always been an important place for investors to invest in long-term securities with a maturity of more than one year. Primary as well as secondary markets are part of it. People invest in securities expecting a return in the form of capital gains and/or income. The role of FinTech applications in facilitating various investment decisions has also opened doors to retail investors in capital markets. Traditionally, retail investors have been overlooked because they are perceived not to have enough capital to invest, be too risk-averse, and be too costly to service. Both quantitative and qualitative data have been used. 150 online surveys were used to gather primary data. The outcome from this study showed a major shift in stock market investors towards digital connection due to various reasons. Some cultural and behavioral effects have been found among the investors for the financial transactions. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to review, systematize and integrate existing research on disposition effect and investments. This study conducts bibliometric analysis, including performance analysis and science mapping and thematic analysis of studies on disposition effect. Design/methodology/approach: This study adopted a thematic and bibliometric analysis of the papers related to the disposition effect. A total of 231 papers published from 1971 to 2021 were retrieved from the Scopus database for the study, and bibliometric analysis and thematic analysis were performed. Findings: This study's findings demonstrate that research on the disposition effect is interdisciplinary and influences the research in the domain of both corporate and behavioral finance. This review indicates limited research on cross-country data. This study indicates a strong presence of work on investor psychology and behavioral finance when it comes to the disposition effect. The findings of thematic analysis further highlight that most of the research has focused on prospect theory, trading strategies and a few cognitive and emotional biases. Practical implications: The findings of this study can be used by investors to minimize their biases and losses. The study also highlights new techniques in machine learning and neurosciences, which can help investment firms better understand their clients' behavior. Policymakers can use the study's findings to nudge investors' behavior, focusing on minimizing the effects of the disposition effect. Originality/value: This study has performed the quantitative bibliometric and thematic analysis of existing studies on the disposition effect and identified areas of future research on the phenomenon of disposition effect in investments. [ABSTRACT FROM AUTHOR]

The one-stop platform of stock investment has a positive effect on technological developments in the world of stock investment. That triggers changes in investor behavior in buying stock. This research investigates the influence of the "stream" feature as User Generated Content (UGC) media as well as the relationship between the Fear of Missing Out (FoMO) phenomenon and investor's financial literacy in buying stocks. This research implements partial least squares-structural equation modeling (PLS-SEM) to conduct quantitative research. Sample data collection was collected using non-probability sampling. This research is limited to Indonesian stock investors, using the Stockbit platform for stock investment, and the research was conducted cross-sectionally. This research found that FoMO effects that investors felt can significantly influence purchasing attitudes, subjective norms, and perceived behavioral control. Meanwhile, the quality of information contained in UGC has a significant positive effect on purchase attitudes and financial literacy can reduce the influence of investor FoMO on purchasing intentions. This research contributes to the extended theory of planned behavior in explaining stock investment behavior and enriches understanding of the influence of UGC, FoMO, and financial literacy on investment decisions. This research also contributes to Stockbit management, psychology practitioners, content creators, and financial consultants in the realm of stocks.

The pandemic, followed by the Russian aggression against Ukraine, caused rapid changes in the economy. European countries experienced unprecedented price increases, which resulted in a significant increase in the cost of capital. This resulted primarily in limited access to capital and a significant reduction in investments in the real estate market. In addition, investors began to withdraw capital from investments in the real estate market to other assets, encouraged by their rising rates of return. The article presents how the indicated circumstances translated into the financial efficiency of companies from the Real Estate sector. Listed companies of the European Economic Area in the years 2019-2022 were analyzed. Changes in the main accounting measures and market measures for individual countries as well as the characteristics of real estate market participants were analyzed.

This paper examines how overconfidence in financial literacy, as measured by MiFID test scores, impacts investors' trading behavior. Our theoretical model suggests that overconfident investors exhibit a higher demand for risky assets, potentially underperform compared to rational investors, and face higher transaction costs, thereby benefiting brokers. Furthermore, the impact of overconfidence intensifies with its level. We then test these propositions empirically using a unique brokerage dataset. Our findings reveal that overconfident investors engage in more frequent trading and incur higher transaction costs, with both effects increasing as overconfidence levels rise. However, we do not find evidence supporting the notion that they perform worse than rational investors. In fact, their performance tends to improve with higher levels of overconfidence. Additionally, we explore whether stress resulting from a mismatch between MiFID test scores and subjective financial literacy can mitigate the impact of overconfidence. Our data lends some support to this hypothesis. [ABSTRACT FROM AUTHOR]

Capital markets are essential for a country's development. However, the effort to develop a sustainable capital market may be challenged by many uncertainties, such as pandemics, politics, and investor behavior. This study aims to unveil the future of Indonesia's capital market and provide implications and options for stakeholders to develop a best-practice capital market. The scenario personarrative method, including cluster analysis, is used in this research as it can complement each other by signifying options to different types of investors. Data were gathered from interviews with 25 experts and a questionnaire survey of 438 respondents. With an analysis emphasizing the government's political direction and regulator ability, four transformative scenarios were constructed: (1) thin bull in the pastureland, (2) facing Torero in the bullring, (3) train the circus bull, and (4) world-champion bull. Eight personas were described, each with unique characteristics. Finally, 32 behavioral narratives were constructed based on the four scenarios and eight personas with the options provided. These results benefit stakeholders when deciding on acting in Indonesia's capital market. It is recommended that stakeholders carefully consider ensuring that Indonesia's capital market can achieve the world champion bull scenario in 2030. [ABSTRACT FROM AUTHOR]

Do weather patterns in South Asia have an impact on investment decisions? This question is particularly relevant given the region's vulnerability to natural disasters such as floods, cyclones, and droughts. To explore this issue, we apply regression and generalized method of moments using yearly data of weather, stock indices, borrowing opportunity, economic growth, interest, and inflation rates from 1995 to 2021. Based on data availability, this study has taken Bangladesh, India, Pakistan, and Sri Lanka as sample countries. The research results indicate that weather significantly influences investment decisions and that consideration of weather factors can mitigate risk and enhance investment return. [ABSTRACT FROM AUTHOR]

This study examines the investor behavior of mutual funds in China. Our results show little evidence about the redemption puzzle. We further refute the disposition effect, that is, investors do not tend to redeem funds with superior past performance. Additionally, market conditions are crucial for investment decisions. In bull markets, investors are prone to taking risks while being conservative and prudent during bad times. Considering investor heterogeneity, we find that institutional investors value the funds' past performance metrics, assigning greater weight to historical performance. Both institutional investors and individual investors show stronger sensitivity to complicated performance indicators in bad times than good times. Furthermore, we find that investors in China chase for the star funds. We also perform tests on the existence of the smart money effect, and the results show that investors can screen good funds from bad ones. Impact Statement: This study examines the investor behavior of mutual funds in China. Our results show little evidence about the redemption puzzle. We further refute the disposition effect, that is, investors do not tend to redeem funds with superior past performance. Additionally, market conditions are crucial for investment decisions. In bull markets, investors are prone to taking risks while being conservative and prudent during bad times. Considering investor heterogeneity, we find that institutional investors value the funds' past performance metrics, assigning greater weight to historical performance. Both institutional investors and individual investors show stronger sensitivity to complicated performance indicators in bad times than good times. Furthermore, we find that investors in China chase for the star funds. We also perform tests on the existence of the smart money effect, and the results show that investors can screen good funds from bad ones. Overall, this study offers valuable insights into the decision-making processes of Chinese mutual fund investors, providing a more nuanced understanding of their behavior compared to established financial theories. [ABSTRACT FROM AUTHOR]

The literature on overconfidence has witnessed prolific growth since the beginning of the century. This context underscores the necessity to comprehend and categorize an increasingly diverse body of overconfidence research within the financial domain. This study reviews existing literature on financial overconfidence from its inception to the present with a detailed review of 132 articles from 84 journals by examining theories, context, and methods (TCM) used in overconfidence research. Our review unpacks significant themes (i.e. determinants of overconfidence, overconfidence and risk-taking, overconfidence measures and type of investors, overconfidence in a volatile market, overconfidence, and personal financial behavior). We propose a pertinent research framework to investigate the less investigated aspects of financial overconfidence and suggest future research direction. Impact statement: The significance of this paper lies in its potential to deepen our understanding of how psychological biases and significant overconfidence influence investment behavior and market outcomes. By synthesizing findings from multiple studies, this literature review highlights common themes, identifies gaps in knowledge, and suggests avenues for future research. Ultimately, insights gained from such a review can inform investors, financial professionals, and policymakers about the importance of recognizing and addressing overconfidence in investment decision-making processes. This understanding can lead to more informed and rational investment strategies, potentially mitigating the adverse effects of overconfidence on individual investors and market efficiency. [ABSTRACT FROM AUTHOR]

Purpose - The primary concern of SRI in Indonesia began in 2009 along with the establishment of the Sri Kehati Index (SKI). Since its launch, historically Sri Kehati index has shown better performance compared to several major indices such as the Composite Stock Price Index (CSPI), LQ45, Jakarta Islamic Index, and so on. However, the number of works of literature revealed that the majority of research focuses on the demographic of investors. While investigating related to the role of their religion still small. Moreover, the product of SRI in Indonesia has been established and the type of investor has been classified. Thus examining the behavior of Muslim investors is a necessity. Methodology - Self-administered questionnaire was used to collect the data. Then, the questionnaire was shared through an online form nationwide. 205 respondents were collected. Hence, partial least square structural equation modeling (PLS-SEM) was applied to analyze the data. Findings - The result revealed that socially-oriented investment, environmental-oriented investment, and religiosity have a positive significant influence on the intention to be a socially responsible investment. While financial-oriented investment has an insignificant influence on the intention to be a socially responsible investment. Moreover, religiosity moderates between socially oriented and environmentally oriented investment negatively. Originality - The present paper contributes to the existing literature by giving new evidence of Muslim investor intention in SRI in Indonesia. Research Implications - This research provides some practical contributions for policymakers to enhance the infrastructures of SRI. [ABSTRACT FROM AUTHOR]

Purpose: The disposition effect remains one of the most significant investor behavior puzzles. This study aims to consolidate the knowledge, explore current dynamics, elicit trends and offer future research directions to demystify the disposition effect. Design/methodology/approach: This study applies the hybrid review method. It first used bibliometric analysis (212 documents), followed by content analysis (54 articles) to analyze the breadth and depth of literature on the disposition effect. Findings: This study presents performance analysis and science mapping. It identifies five main research streams: evidence, implications and mitigation techniques; theoretical explanations; investor biases and hedonic framing; attributes, beliefs and preferences; and implications for asset pricing and market efficiency. This study further offers future research directions for disposition effect research. Research limitations/implications: This study deploys sequential bibliometric and content analysis. A meta-analysis of quantitative articles could provide specific insights regarding the disposition effect. Besides, this study is based on Scopus-indexed journals only. Practical implications: This study benefits investors and portfolio managers as they learn effective ways to guard against the disposition effect. Policymakers may tweak tax laws to incentivize long-term holding, and regulators can run investor education campaigns to minimize the disposition effect's consequences effectively. Originality/value: To the best of the authors' knowledge, this is probably the first hybrid review of high-quality, contemporary articles on the disposition effect that offers science mapping, research streams, future research directions and a succinct summary of theories, contexts, characteristics and methods deployed in the field of research. [ABSTRACT FROM AUTHOR]

This study aims to analyze and classify the evolution of crowdfunding in Portugal from 2014 to 2020, addressing the central question, "What is the evolution of literature on crowdfunding and its research focuses in Portugal?". Additionally, it investigates, through the sub-question, if crowdfunding is perceived as an alternative form of financing. The methodology employs a systematic review, covering four thematic areas: (1) research focus—concepts; (2) research method—quantitative/qualitative identification; (3) geographical area—countries of study; (4) innovation—future research areas. The research begins with Google Scholar, followed by a more specific search of the B-On database, focusing on the Portuguese context. Results highlight the scarcity of research in Portugal, emphasizing the nascency of crowdfunding in the country. The study reveals the importance of investor behavior, influenced by platform security and regulations. Growth in crowdfunding in Portugal is anticipated, attracting multidisciplinary interest but emphasizing the need for more comprehensive studies. Despite limitations in data availability, the study provides valuable insights for entrepreneurs seeking alternative financing in Portugal, demonstrating crowdfunding as an alternative financing method. Integration of crowdfunding with technology, especially blockchain, is suggested as a potentially disruptive system, paving the way for future research and innovations. [ABSTRACT FROM AUTHOR]

Türkiye bulunduğu coğrafi konum itibariyle deprem kuşağı üzerinde yer almakta olup tarih boyunca büyük depremlere sahne olmuştur. Bu depremler çok sayıda can kaybına yol açmakla birlikte ekonomik açıdan da büyük zararlara sebep olmuştur. 2023 yılının Şubat ayında Kahramanmaraş ve Hatay merkezli sırasıyla Mw7.7, Mw7,6 ve Mw6.4 büyüklüğünde depremler yaşanmıştır. Bu depremlerde 50 binin üzerinde insan yaşamını kaybetmiş ve 100 binin üzerinde insan ise yaralanmıştır. Deprem, 11 ilde (Adana, Adıyaman, Diyarbakır, Elâzığ, Gaziantep, Hatay, Kahramanmaraş, Kilis, Malatya, Osmaniye ve Şanlıurfa) büyük yıkımlara neden olmuştur. Çalışmanın amacı depremden sonra gayrimenkul sektöründe yatırım davranışlarındaki farklılıkları tespit etmektir. Bu amaçla Türkiye genelinde bir anket çalışması uygulanmıştır. Çalışmanın ampirik bulgularına göre deprem öncesi ve sonrası gayrimenkul yatırım davranışları arasında önemli farklılıklar tespit edilmiştir. Katılımcıların deprem sonrasında yatırım yaparken binanın lokasyonu, iç ve dış tasarımı, fiyatı gibi faktörlerden ziyade deprem yönetmeliğine uygun yapılıp yapılmadığı gibi faktörleri dikkate aldığı gözlemlenmiştir. Ayrıca katılımcıların deprem sonrasında çok katlı yapılar yerine müstakil veya az katlı yapıları tercih ettikleri tespit edilmiştir.

The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.

Copyright of Journal of Research in Economics, Politics & Finance / Ekonomi, Politika & Finans Arastirmalari Dergisi is the property of Journal of Research in Economics, Politics & Finance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Financial literacy is explained as the cognitive understanding of financial indicators and risk aversion, risk perception and investor behavior. Perhaps the investor behavior may vary depending on several factors such as gender, age, income level, social status, education etc. This research aims to highlight the effect of gender on financial market perception among Turkish investors. The outputs of two surveys the first for the last quarter of 2022 and the second for the first quarter of 2023, are analysed and compared. Therefore, two consecutive quarters are compared by gender for investment behaviors. This reserach observes some factors such as stress level, portfolio holding times, investment decisions and expectations regarding cryptocurrency markets. The methodology follows the Cronbach Alpha, Kolmogorov-Smirnov and Shapiro-Wilk Normality, and Mann-Whitney U tests respectively. The findings support gender differences in perception and investment behavior. [ABSTRACT FROM AUTHOR]

The financial sector, which has sparked increasing organizational and scientific interest in recent years, plays a vital role in the Turkish economy. After enduring multiple economic downturns, consumers have become more cautious when considering financial investments, making it challenging for financial institutions to formulate effective marketing strategies. This study aims to shed light on investor behavior in Tukish markets. The results of two surveys are examined: the first conducted in the final quarter of 2022, and the second in the first quarter of 2023. This article delves into various variables, including stress levels, portfolio holding times, investment choices, and attention to cryptocurrency markets. The methodology employs the Mann-Whitney U test, Cronbach's Alpha, Kolmogorov-Smirnov, and Shapiro-Wilk normality tests. The findings from the two surveys are compared. Based on the analysis results, it can be inferred that respondents' investment preferences and risk tolerance have evolved over time. The results demonstrate a spectrum of portfolio diversification tendencies. [ABSTRACT FROM AUTHOR]

Stock market performance is determined by supply and demand of individual, institutional, and foreign investors, who increasingly use media such as news articles for decision-making. We present a bidirectional long short term memory model to forecast trading trends based on statistically significant investor-specific topics from financial news datasets. The application of this study shows three valuable results: (i) topics significantly meaningful to each investor type differ, (ii) investors show different decision-making trends for the same news topics and different sensitivity levels, and (iii) news topics significantly associated with investors' responses differ according to the stock market and sensitivity. [ABSTRACT FROM AUTHOR]

Pakistan's political instability has pushed its economic system to the brink of collapse. Considering this political turmoil, this study addresses the behavior of liquidity providers against microblogging-opinionated information. The behavioral perspective was quantified through multiple linear regressions, the Bayesian theorem, and the vector error correction technique. Before this political crisis, sentiment indicators were linked to the liquidity-conditional cost for the same trading session. In the political uncertainty environment, pessimistic opinions were the sole concern of the liquidity providers during the same trading session. The liquidity facilitator was observed to price the liquidity in light of pessimistic sentiments. The Bayesian theorem suggested a higher posterior probability for the occurrence of the liquidity-facilitating cost in response to the pessimistic sentiments. Nevertheless, the past time series changes for the sentiment indicators were irrelevant in determining changes in cost-based liquidity for the next trading session.

Economic behavior highlights the importance of differences in individual characteristics like cognition and emotional regulation strategy in predicting the selection and performance of economic choices. However, the literature on value premium has overlooked the effect of cognition and emotional regulation strategy in assessing individual preference to select value versus growth stocks. We fill this gap by employing dual-process and emotional regulation theories to investigate the impact of intuitive cognition (Type 1), analytical cognition (Type 2), expressive suppression, and cognitive reappraisal on individual preferences for the selection of value versus growth stocks. Results confirm that individuals with higher reliance on Type 1 (or Type 2) and expressive suppression (or cognitive reappraisal) exhibit lower (or higher) preferences for the selection of value versus growth stocks. These results imply that emotion alters an individual's decision-making, and both emotion and cognition are inherently intertwined from inception to action. Our findings have implications for investors to avoid (or seek) investment in emotion-driven fundamentally weak overvalued firms (or fundamentally strong undervalued firms) via regulating emotional inhibitors to engage in thorough decision-making. [ABSTRACT FROM AUTHOR]

Behavioral finance designates a field of study that analyzes the behavior of individuals in general and of investors in particular. More, it follows to look into how decisions are taken in a financial context. The investigation of investor behavior considers a series of approaches that range from traditional finance to modern finance and behavioral finance (and even neurofinance). Nowadays, investors are constantly exposed to a vast amount of information, including quantitative financial data and financial news presented in the media. They can also be influenced by the opinions and recommendations of their peers in the social environment. Processing this information can be a difficult task, and market participants often use heuristics to simplify the process. Although the human brain is capable of learning and adapting, the decision-making process involves complex cognitive, emotional, and motivational factors that must be carefully considered. Therefore, this paper aims to enhance our understanding of the financial decision-making process by exploring multiple perspectives proposed by the literature, including the neuroscientific, psycho-emotional, and cultural ones. By undertaking a holistic approach into understanding financial decision-making, this paper follows to provide insights that could help investors make better-informed decisions in the future. Ultimately, a deeper understanding of the factors that underlie financial decision-making could have far-reaching implications for the field of finance and not only. [ABSTRACT FROM AUTHOR]

In financial-behavioral theory, analyzing the impact of investor attitude and behavior on investment decisions can lead to a fair distribution of service to capital market actors. Establishing such equity requires identifying and prioritizing the important factors that influence investor decision-making behaviors. Therefore, the present study has attempted to identify the factors influencing investor attitude and behavior in investment decision making, and by their conceptual and operational definition, and the formulation of their relationship, develop a comprehensive decision making model based on investor behavior and attitude in Tehran Stock Exchange. In order to formulate the research model and fit it, 350 questionnaires were distributed by non-random sampling method and 225 questionnaires were completed and collected. Factor analysis was used to test the data obtained from the questionnaire. The main components of the research include understanding of their performance, financial ability, cognitive bias, behavioral attitude and investor optimal decision making. Findings of the study indicate that understanding of their performance is negatively and significantly correlated with behavioral attitude and positively and significantly correlated with financial ability. Also, other research results show that behavioral attitude has a negative and significant relationship with investor optimal decision, but financial ability has a positive and significant relationship with investor optimal decision.

Behavioral economics and artificial intelligence (AI) have been two rapidly growing fields of research over the past few years. While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. This study provides a bibliometric analysis of the literature on AI and behavioral economics to gain insight into research trends in this field. We conducted this bibliometric analysis using the Web of Science database on articles published between 2012 and 2022 that were related to AI and behavioral economics. VOSviewer and Bibliometrix R package were utilized to identify influential authors, journals, institutions, and countries in the field. Network analysis was also performed to identify the main research themes and their interrelationships. The analysis revealed that the number of publications on AI and behavioral economics has been increasing steadily over the past decade. We found that most studies focused on customer and consumer behavior, including topics such as decision-making under uncertainty, neuroeconomics, and behavioral game theory, combined mainly with machine learning and deep learning techniques. We also identified several emerging themes, including the use of AI in nudging and prospect theory in behavioral finance, as well as undeveloped themes such as AI-driven behavioral macroeconomics. The findings suggests that there is a need for more interdisciplinary collaboration between researchers in behavioral economics and AI. We also suggest that future research on AI and behavioral economics further consider the ethical implications of using AI and behavioral insights in decision-making. This study can serve as a valuable resource for researchers interested in AI and behavioral economics.