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Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices.

The authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district.

The findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation.

The analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a property-focused lexicon and panel analysis over a longer time horizon.

The findings suggest a role for Twitter-derived sentiment in predictive models for local variation in property prices as it can be observed in real time.

This is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices.

This study aims to investigate the impact of financial and behavioural factors on investment decisions in the cryptocurrency market within the Gulf Cooperation Council (GCC).

The study uses the cross-sectional absolute deviation methodology developed by Chang et al. (2000) to determine the existence of herding behaviour during extreme conditions in the cryptocurrency market of four GCC countries: Bahrain, Saudi Arabia, Kuwait and UAE. In addition, a questionnaire survey was distributed to 322 investors from the GCC cryptocurrency markets to gather data on their investment decisions.

The study finds that the herding theory, prospect theory and heuristics theory account for 16.5% of the variance in investors' choices in the GCC cryptocurrency market. The regression analysis results show no multicollinearity problems, and a high F-statistic indicates the general model's acceptability in the results.

The study's findings suggest that behavioural and financial factors play a significant role in investors' choices in the GCC cryptocurrency market. The study's results can be used by investors to better understand the impact of these factors on their investment decisions and to develop more effective investment strategies. In addition, the study's findings can be used by policymakers to develop regulations that consider the impact of behavioural and financial factors on the GCC cryptocurrency market.

This study adds to the body of literature in two different ways. Initially, motivated by earlier research examining the impact of behaviour finance factors on investment decisions, the authors look at how the behaviour finance factors affect investment decisions of the GCC cryptocurrency market. To extend most of these studies, this study uses a regime-switching model that accounts for two different market states. Second, by considering the recent crisis and more recent periods involving more cryptocurrencies, the authors have contributed to several studies examining the impact of behavioural financial factors on investment decisions in cryptocurrency markets. In fact, very few studies have examined the impact of behavioural finance on cryptocurrency markets. Therefore, to the best of the authors’ knowledge, this study is the first of its kind to investigate how behavioural finance factors influence investment decisions in the GCC cryptocurrency market. This allows to better illuminate the factors driving herd behaviour in the GCC cryptocurrency market.

The purpose of this study is to prove that narratives can be a adequate foundation for human behavior in general and economic behavior in particular using the Donald Duck universe as an example.

By using a content analysis, the authors examine 208 stories of the Donald Duck universe to prove that economic behavior is already embedded in modern narratives of the 20th century.

This analysis shows that behavioral finance effects are identified in a total of 52.4% of the analyzed comics. This study furthermore distinguishes the main comic characters Donald Duck and Scrooge McDuck and finds that eight of the nine considered behavioral finance biases can be detected in both. The most striking effect for Donald Duck is overconfidence and for Uncle Scrooge loss aversion.

Collectively, these comics provide potential exemplars for behavioral finance. Regardless of whether these comics depict human nature or merely reflect human behavior during that time, they inevitably contribute to the understanding that psychological and sociological influences determine behavior in addition to economic factors that can be used for academic teaching.

In summary, comics, such as the Donald Duck universe, are suitable narratives for behavioral finance.

It has been argued in the literature that structural changes in the financial markets, such as integration, have the potential to cause herding behavior or correlated behavioral patterns in traders. The purpose of this study is to investigate whether there is any financial herding behavior in the Latin American Integrated Market (MILA), a transnational stock market composed of Chile, Peru, Colombia and Mexico stock exchanges and whether there is any ARCH or GARCH effect in the herding behavior models.

This study uses the modified return dispersion approach on daily index return data. The sample period is from January 03, 2002 to May 07, 2019. The data are obtained from the MILA database. To count time-varying volatilities in herding models, the authors run ARCH family regression with GARCH (1,1) settings. Hwang and Salmon (2004) model is used as a robustness test.

The authors found strong herding behavior under the general market conditions and moderate and partial herding behavior under some specified markets circumstances, such as bull and bear markets and high-low volatility states. Moreover, the pre-MILA period exhibits more herding behavior than the post-MILA period. The empirical results show that most of the ARCH and GARCH effects are statistically significant, implying that the past information of stock returns and market volatility significantly affect the volatility of following periods, which can also explain the formation of herding tendency among investors. Finally, the results of the robustness tests (Hwang and Salmon, 2004) confirm herding in all periods, except full sample period for Mexico and post-MILA period for Mexico and Colombia.

This study investigates the herding behavior in the MILA market in terms of market return, volatility and timing. A limitation of the paper is that the authors have not included other factors on the formation of herding behavior, such as macroeconomic factors, effects of regional or international markets and policy influences. The authors will explore the issue in the extension of the paper.

As MILA is the first virtual integration of stock exchanges without merging, the study provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are useful for academics, investors and policymakers in their investment and decision makings.

The paper provides useful findings and draws good inferences of herding behavior in the MILA market in terms of market return, volatility and timing which are not only useful in practical implications, but also in social implications.

This study contributes to the herding literature by examining four different hypotheses in respect of the unique case of transnational stock exchange without fusions or corporate mergers, where each market maintains its independence and regulatory autonomy. The authors also contribute to the literature by including both ARCH and GARCH effects in the herding behavioral models along the Hwang and Salmon (2004) approach.

Determining the impact of behavioral influences on the stock market has significant implications for investment analysis and portfolio management. Behavioral biases are parameters that need to be considered in investment decision-making. The purpose of this study is to inform Bangladeshi investors about behavioral biases that they may encounter when making investment decisions in the prevailing frontier environment.

Through the chi-square test, one-way ANOVA, paired-samples t-test and descriptive analysis based on the facts collected from 281 respondents of the Dhaka Stock Exchange (DSE), the study has found that individual investors of Bangladesh often make investment decisions emotionally rather than based on theories.

The result shows that risk aversion and risk perception are the two most influential emotional dimensions that impact investors' decisions. The findings are consistent with the other researchers and highlight the fact that investors hardly act according to the norms recommended in the financial theories.

The findings are grounded on a small portion of investors at DSE on some particular days, which is not sufficient to study individual investors' entire complex decision-making behavior from various angles. Many respondents were reluctant and even confused to disclose their behavioral aspects. These, along with biased and careless answers, may impede the identification of the actual scenario of the behavioral responses in decision-making that demand further study.

The novelty of this study is unique in that it examined investors of the DSE, who are considered to be a representative in a frontier market like Bangladesh. Since this market is not very resilient, small investors need to be aware of the biases of behavioral factors to survive.

This paper applies a volume-price probability wave differential equation to propose a conceptual theory and has innovative behavioral interpretations of intraday dynamic market equilibrium price, in which traders' momentum, reversal and interactive behaviors play roles.

The authors select intraday cumulative trading volume distribution over price as revealed preferences. An equilibrium price is a price at which the corresponding cumulative trading volume achieves the maximum value. Based on the existence of the equilibrium in social finance, the authors propose a testable interacting traders' preference hypothesis without imposing the invariance criterion of rational choices. Interactively coherent preferences signify the choices subject to interactive invariance over price.

The authors find that interactive trading choices generate a constant frequency over price and intraday dynamic market equilibrium in a tug-of-war between momentum and reversal traders. The authors explain the market equilibrium through interactive, momentum and reversal traders. The intelligent interactive trading preferences are coherent and account for local dynamic market equilibrium, holistic dynamic market disequilibrium and the nonlinear and non-monotone V-shaped probability of selling over profit (BH curves).

The authors will understand investors' behaviors and dynamic markets through more empirical execution in the future, suggesting a unified theory available in social finance.

The authors can apply the subjects' intelligent behaviors to artificial intelligence (AI), deep learning and financial technology.

Understanding the behavior of interacting individuals or units will help social risk management beyond the frontiers of the financial market, such as governance in an organization, social violence in a country and COVID-19 pandemics worldwide.

It uncovers subjects' intelligent interactively trading behaviors.

This paper aims to investigate whether managerial overconfidence has an impact on investment inefficiency beyond its influence on the use of internal financing or whether internal financing behaves as a full intermediary.

The study employed three dependent variables, namely business investment scale, overinvestment and underinvestment, and analyzed data from 282 firms across five different industries listed in 11 Middle East/North Africa (MENA) countries between 2013 and 2019 using regression analysis via least square dummy variable (LSDV).

The findings indicate that while internal financing can provide funding for investment opportunities and address capital shortages, it may also result in overinvestment, particularly in companies led by overconfident managers.

Stakeholders, including shareholders and board of directors, should pay attention to the chief executive officer (CEO)'s behavioral aspects such as overconfidence in decision-making while undertaking new investment projects. Additionally, regulators and policymakers in emerging markets like MENA should re-evaluate the corporate governance framework, devise a corporate governance index and promote boardroom gender diversity as it can significantly reduce risk.

This study adds to the limited research on the impact of managerial overconfidence on investment efficiency in the MENA region. By focusing on this region, which has unique economic, political and social characteristics, the study provides new insights into the role of behavioral biases in investment decision-making in emerging markets.

This study investigates the effect of unproven energy reserve news on the volatility of energy firms' stocks. Thus, investors' perception of unproven energy reserves is revealed. Additionally, the study aims to determine whether the effect of the news changes according to time and volatility level.

The general autoregressive conditional heteroskedasticity (GARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models consist of the energy reserve exploration news in Turkey for the period 2009–2022 and the volatility of 14 energy stocks.

The results indicate energy exploration news's negative and significant effect on volatility. According to empirical results, energy stock volatility is most affected in the first ten days. Besides, the results show that the significant models of energy reserve news in low-volatility stocks are proportionally higher than in high-volatility stocks.

Only unproved reserve news is included in the analysis, as sufficient confirmed reserves could not be reached during the sampling period. Further studies can compare proven and unproved reserve news effects. Additionally, a similar analysis can be conducted between Turkey and another country with a similar socio-economic character to examine different investor behaviors.

This research includes indications on managing investors' reactions to unproven energy reserve news.

This study contributes to the literature by analyzing unproven reserves. Contrary to previous studies, examining stock volatility also makes the study unique.

Purpose: Investor sentiment in financial markets has a close relationship with the general mood prevailing in the environment such as economic, social and political life. Future economic expectations are important for both investors and policymakers. Investor sentiment and macroeconomic variables are likely to affect each other. Emerging countries are particularly sensitive to interest and foreign exchange risk. Turkey is an important emerging country. The effects of interest rate and exchange fluctuations are high in this country. The aim of this study is to reveal the relationship between investor sentiment and interest and foreign exchange rates in Turkey.

Methodology: This study investigates the relationship between economic confidence index, exchange rates and interest rates in Turkey during the period between January 2012 and November 2019 using monthly data sets. The economic confidence index is used to represent the investor sentiment in the study. Interest rate variables are the deposit interest rates and the commercial credit interest rates. The representative of the US dollar currency variables is included in the analysis. This chapter used the time series vector error correction model approach of stationarity test, cointegration test and Granger causality test.

Findings: According to the causality test, there is a two-way relationship between economic confidence index and exchange rate, and there is uni-directional causality from commercial credit interest rate to economic confidence index. The results show that foreign exchange and commercial credit interest rate variables are carefully monitored by market players and are effective and influential in the formation of future expectations.

Originality/value: The study shows the direction of the relationship between economic confidence foreign exchange and commercial credit interest rate. Policymakers can shape expectations by taking into account the direction of the relationship.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.