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We theorize the critical role of angel investors' affective experiences and first impressions in the context of entrepreneurial finance. We develop a model and propositions to illustrate why angel investors make the decision to continue screening, thus explaining why certain investment proposals make it, while others do not.

Drawing on affective events theory and the literature on affective experiences, we theorize how the perceptions of pitches that trigger positive or/and negative physiological arousal, short-lived emotions, and associated thoughts are different, thus allowing us to build new theory of how these different experiences can influence the outcome of the evaluation process in the initial screening stage.

Our model suggests that the initial evaluation unfolds in five stages: perception of an entrepreneurial pitch, physiological arousal, emotions, first impression, and a decision to continue screening. When different manifestations of physiological arousal and subsequent emotions set the tone of first impressions, they can be either a positive, negative, or mixed experience. While positive and mixed first impression can lead to selection, negative first impression can lead to rejection.

We illustrate what is of value for angel investors when they look for new investments, and why certain entrepreneurial pitches lead to the decision to continue screening, while others do not. We propose that what angel investors feel is particularly important in situations where they are not yet making the ultimate decision to invest money but are involved in decisions about whether to continue to spend time to investigate the investment proposal.

The main purpose of this research is to empirically test the impacts of emotional intelligence score and emotional intelligence processing on the performance of investor’s portfolio.

A mail questionnaire survey was conducted on a sample of 983 international investors. The total number of usable responses received was 197 giving a response rate of 20.4 per cent. From 197 investors, 46 accepted to complete the experiment study during two trading hours for each investor from January first until February 19, 2015.

The results reveal a positive impact of emotional intelligence on portfolio performance. Additional analysis shows that the emotional intelligence process has a significant impact on the portfolio performance. The higher impact is revealed when the investors understand the markets tendency, manage their own emotions, take their financial decisions and finally control their personnel emotions during market fluctuations. The lower impact is detected when investors take reactive decisions after perceiving the markets tendency. This research also reveals that the investors have high capacity to manage and control their emotions during market fluctuations especially who are characterized by high emotional intelligence level.

The first limit of this research is the exploration of limited number of investors and financial operations during limited period. Therefore, the results could not be generalized, and further studies should include larger samples during larger period. The second limitation concerns the used variables to measure the portfolio performance and the emotional intelligence level. For future studies, it will be preferred to use other quantitative and qualitative variables lead to measure the different analytical dimensions of portfolio performance and emotional intelligence.

The results hold implications for investors that seek to enhance efficiently and effectively the portfolio performance. It also prompts investors to focus on effort that can improve the management and the control of personnel emotions.

This paper presents one of the first empirical studies that attempt to explore how emotional intelligence and, particularly, emotional process serve to sustain the performance of portfolio during market fluctuations.

This study aims to establish the factors affecting the financial investment decision-making of an investor, with specific reference to investors’ emotions and how various events such as festivals, the pandemic and sports matches affect their investors’ investment decision-making. The authors further intend to understand the role of these investor emotions in creating stock market anomalies.

Twenty-nine semistructured exploratory interviews with fund managers from the top 10 asset management companies in India, who deal with individual investors regularly, were taken. The interviews were conducted to identify and describe the underlying ideas and sentiments that influence an individual’s investment behavior.

Although risk and return are the primary motivators of investment decisions, fund managers’ daily interactions with individual investors are affected by unpredictability and technical ambiguity, and investing is an inherently emotionally arousing process, according to the findings of the in-depth interviews.

To the best of the authors’ knowledge, this study is one of the first studies in Indian market to report the views of financial professionals about the emotional aspect of investors in making an investment decision. With most of the research conducted using quantitative methods, the current study brings in the perspective of financial professionals using primary data.

Emotion plays a significant role in both institutional and individual investors’ decision-making process. Emotions affect the perception of risk and the assessment of monetary value. However, there is a lack of empirical evidence available that addresses how investors’ emotions affect commodity market returns. The purpose of this paper is to investigate whether media-based emotions can be used to predict future commodity returns.

The authors examine the short-term predictive power of media-based emotion indices on the following five days’ commodity returns. The research adopts a proprietary data set of commodity-specific market emotions, which is computed based on a comprehensive textual analysis of sources from newswires, internet news sources and social media. Time series econometrics models (threshold generalized autoregressive conditional heteroskedasticity and vector autoregressive) are employed to analyze 14 years (January 1998-December 2011) of daily observations of the CRB commodity market index, crude oil and gold returns, and the market-level sentiments and emotions (optimism, fear and joy).

The empirical results suggest that the commodity-specific emotions (optimism, fear and joy) have significant influence on individual commodity returns, but not on commodity market index returns. Additionally, the research findings support the short-term predictability of the commodity-specific emotions on the following five days’ individual commodity returns. Compared to the previous studies of news sentiment on commodity returns (Borovkova, 2011; Borovkova and Mahakena, 2015; Smales, 2014), this research provides further evidence of the effects of news and social media-based emotions (optimism, fear and joy) in the commodity market. Additionally, this work proposes that market emotion incorporates both a sentimental effect and appraisal effect on commodity returns. Empirical results are shown to support both the sentimental effect and appraisal effect when market sentiment is controlled in crude oil and gold spot markets.

This paper adopts the valence-arousal approach and cognitive appraisal approach to explain financial anomalies caused by investors’ emotions. Additionally, this is the first paper to explore the predictive power of investors’ emotions (optimism, fear and joy) on commodity returns.

The economic system is an expectation's feedback system, thus decisions made by economic agents are based on their expectations about the future state of the economy. These decisions affect actual realization of economic variables and this process leads to the new expectations. For a long period of time, economics was based on the erroneous belief that economic agents apply rational calculations to economic and financial decisions. The main purpose of the current paper is to present the theoretical model explaining emotion's component of expectations in the process of financial decision making.

The research is based on the generally accepted scientific qualitative and quantitative methods, including monographic method.

The paper shows how the expectations and subjective beliefs of different financial market participants could be translated into prices. After describing the main investor's categories, it is possible to model their subjective beliefs about the current price evolution on the stock exchange and formulate the demand strategy of each investor's group. Finally, the model shows mathematical considerations how prices result from demands, considering that they are set by the market maker.

The paper shows how emotions impact investors' beliefs and could be transmitted into prices. A particular agent category – the emotional investor – was formulated, who exclusively follows his intuition and whose presence influences market prices. So, there is no doubt that an appropriate rational strategy requires the adoption to the new kind of market agent and theoretical considerations presented in the paper could contribute to this process.

This article aims to examine how investor moods and aggressiveness differ in their state and influence investor stock market performance associated with the moon phase. The mechanisms and impact of full moon gravity on investor stock trading performance are explored through an experimental approach and econometrics model.

A time-series quasi-experimental study, using the full moon and new moon time periods, was coupled with a psychometric test of investors' behaviours, administered through an online survey, similar to a pre-post experiment. Confirmation of the results was achieved by using an econometric model, adopted from Dichev and Janes.

This research found that investor psychology is influenced by the full moon, but no effect was recorded during the new moon phase. Confirmed by the paired t-difference test, the small correlation, in addition to the quantitative model, the results show the full moon impacts market behaviour during its orbital phase. Consequently, the authors surmise that the full moon does influence investor cognition and emotion disarray, mood disorders, and aggressiveness, resulting in poor stock trading performance.

The need for an active investment strategy is the major implication of this study. During the full moon phase, investors tend to be more aggressive and moody and seek hedonic utility instead of the traditional economics utility, meaning that they tend to follow the sentiment of the market.

This paper fulfils an identified need to study how the full moon affects investor stock trading performance.

The authors study the explanatory power of investor rationality and irrationality for value and momentum portfolios. We also examine the relationships during financial crisis events, namely, the US subprime mortgage crisis (2007–2009) and the European debt crisis (2011–2013).

This study examines the influence of investors’ rationality and irrationality on the US stock market, using the multiple linear regression model and the stepwise regression model. Technically, the stepwise regression uses the machine-learning technique, with specific testing methods — forward selection, backward selection and stepwise selection — to find the best-fit model, according to Akaike’s Information Criterion (AIC). Thus, in this study, we will show the best model, as tested by the stepwise regression model.

Our empirical results contribute to the importance of reasons and emotions for stock-market returns and conclude that rationality and irrationality simultaneously explain the value and momentum portfolios, as well as the ETF portfolios. Also, the rational and irrational explanatory powers differ, depending on portfolios and different periods. Rational factors usually explain the volatility of the return to a greater extent than irrational factors. Moreover, during a financial crisis, the irrational factors remarkably increase their importance in explaining returns, especially for the ETF portfolios.

We expect this study’s contribution will show not only academic contribution but also benefit many stakeholders in the financial market. Investors and traders can identify various irrational factors of trading — for example, taking a long position during the panic in the market following the indicators in the models. Managers also reconsider the cost of the company by adding irrational factors when computing the equity’s expected return. Similarly, stock exchanges can adequately adjust their circuit breaker during a pessimistic-investor period. Finally, regulators can evaluate a complete picture of the stock market by adding irrational factors into their considerations.

The purpose of this paper is to study and describe several biases in investment decision-making through the review of research articles in the area of behavioral finance. It also includes some of the analytical and foundational work and how this has progressed over the years to make behavioral finance an established and specific area of study. The study includes behavioral patterns of individual investors, institutional investors and financial advisors.

The research papers are analyzed on the basis of searching the keywords related to behavioral finance on various published journals, conference proceedings, working papers and some other published books. These papers are collected over a period of year’s right from the time when the most introductory paper was published (1979) that contributed this area a basic foundation till the most recent papers (2016). These articles are segregated into biases wise, year-wise, country-wise and author wise. All research tools that have been used by authors related to primary and secondary data have also been included into our table.

A new era of understanding of human emotions, behavior and sentiments has been started which was earlier dominated by the study of financial markets. Moreover, this area is not only attracting the, attention of academicians but also of the various corporates, financial intermediaries and entrepreneurs thus adding to its importance. The study is more inclined toward the study of individual and institutional investors and financial advisors’ investors but the behavior of intermediaries through which some of them invest should be focused upon, narrowing down population into various variables, targeting the expanding economies to reap some unexplained theories. This study has identified 17 different types of biases and also summarized in the form of tables.

The study is based on some of the most recent findings to have a quick overview of the latest work carried out in this area. So far very few extensive review papers have been published to highlight the research work in the area of behavioral finance. This study will be helpful for new researches in this field and to identify the areas where possible work can be done.

Practical implication of the research is that companies, policymakers and issuers of securities can watch out of investors’ interest before issuing securities into the market.

Under the Social Implication, investors can recognize several behavioral biases, take sound investment decisions and can also minimize their risk.

The essence of this paper is the identification of 17 types of biases and the literature related to them. The study is based on both, the literature on investment decisions and the biases in investment decision-making. Such study is less prevalent in the developing country like India. This paper does not only focus on the basic principles of behavioral finance but also explain some emerging concepts and theories of behavioral finance. Thus, the paper generates interest in the readers to find the solutions to minimize the effect of biases in decision-making.

The purpose of this study is to gauge the awareness and perception of Indian individual investors about a new fintech innovation known as robo-advisors in the wealth management scenario. Robo-advisors are comprehensive automated online advisory platforms that help investors in managing wealth by recommending portfolio allocations, which are based on certain algorithms.

This is a phenomenological qualitative study that used five focussed group discussions to gather the stipulated information. Purposive sampling was used and the sample comprised investors who actively invest in the Indian stock market. A semi-structured questionnaire and homogeneous discussions were used for this study. Discussion time for all the groups was 203 min. One of the authors moderated the discussions and translated the audio recordings verbatim. Subsequently, content analysis was carried out by using the NVIVO 12 software (QSR International) to derive different themes.

Factors such as cost-effectiveness, trust, data security, behavioural biases and sentiments of the investors were observed as crucial points which significantly impacted the perception of the investors. Furthermore, several suggestions on different ways to enhance the awareness levels of investors were brought up by the participants during the discussions. It was observed that some investors perceive robo-advisors as only an alternative for fund/wealth managers/brokers for quantitative analysis. Also, they strongly believe that human intervention is necessary to gauge the emotions of the investors. Hence, at present, robo-advisors for the Indian stock market, act only as a supplementary service rather than a substitute for financial advisors.

Due to the explorative nature of the study and limited participants, the findings of the study cannot be generalised to the overall population. Future research is imperative to study the dynamic nature of artificial intelligence (AI) theories and investigate whether they are able to capture the sentiments of individual investors and human sentiments impacting the market.

This study gives an insight into the awareness, perception and opinion of the investors about robo-advisory services. From a managerial perspective, the findings suggest that additional attention needs to be devoted to the adoption and inculcation of AI and machine learning theories while building algorithms or logic to come up with effective models. Many investors expressed discontent with the current design of risk profiles of the investors. This helps to provide feedback for developers and designers of robo-advisors to include advanced and detailed programming to be able to do risk profiling in a more comprehensive and precise manner.

In the future, robo-advisors will change the wealth management scenario. It is well-established that data is the new oil for all businesses in the present times. Technologies such as robo-advisor, need to evolve further in terms of predicting unstructured data, improvising qualitative analysis techniques to include the ability to gauge emotions of investors and markets in real-time. Additionally, the behavioural biases of both the programmers and the investors need to be taken care of simultaneously while designing these automated decision support systems.

This study fulfils an identified gap in the literature regarding the investors’ perception of new fintech innovation, that is, robo-advisors. It also clarifies the confusion about the awareness level of robo-advisors amongst Indian individual investors by examining their attitudes and by suggesting innovations for future research. To the best of the authors’ knowledge, this study is the first to investigate the awareness, perception and attitudes of individual investors towards robo-advisors.

The purpose of this paper is to present an affect account that identifies emotions driving sell preferences in stock markets that result in the disposition effect (winning stocks hold too short and losing stocks too long) and to specify how stock prices are influenced.

The affect account is derived based on analyses of previous research showing the disposition effect, proposed explanations of the effect, and basic emotion research. An individual-level analysis is performed of the consequences for stock market prices.

The main proposal is that investors prefer to sell when price increases make the increasing balance of hope and fear equal to a faster increasingly balance of anticipated elation and disappointment, and when price decreases make the faster increasingly negative hope-fear balance equal to the increasing negative elation-disappointment balance. Steepness in slope of the negative hope-fear balance accounts for whether a loser is never sold (an extreme disposition effect), sold later than a winning stock (the usually observed disposition effect), or sold earlier than a winning stock (a reverse disposition effect). The individual-level analysis suggests that the affect-driven disposition effect would intensify or attenuate trends in stock prices depending on the demand-supply balance.

A conceptual contribution to research of emotion influences on stock trading and specifically to explanations of the disposition effect on sell decisions by less sophisticated and experienced investors.