Abstract: A crucial component to the success of any financial advising relationship is for the financial professional to understand the perceptions, preferences and thought-processes carried by the financial clients they serve. Armed with this information, financial professionals are more quickly able to understand how they can tailor their approach to best match the individual preferences and needs of each personal investor. Our research explores the use of a quantitative assessment tool in the financial services industry to assist in the identification of the personal investor’s consumer behaviors, especially in terms of financial risk tolerance, as it relates to their financial decision making. Through this process, the Unitifi Consumer Insight Tool (UCIT) was created and refined to capture and categorize personal investor financial behavioral categories and the financial personality tendencies of individuals prior to the initiation of a financial advisement relationship. This paper discusses the use of this tool to place individuals in one of four behavior-based financial risk tolerance categories. Our discoveries and research were aided through administration of a web-based survey to a group of over 1,000 individuals. Our findings indicate that it is possible to use a quantitative assessment tool to assist in predicting the behavioral tendencies of personal consumers when faced with consumer financial risk and decisions.
Abstract: Comparing with prior studies mainly focused on the
effect of a certain event (it may be the initial announcement of bad
news or the repeated announcements of identical bad news) on stock
price, the aim of this study is to explore how investors react to
subsequent bad news with identical content. Empirical results show
that as a result of behavioral pitfalls, investors underreact to the initial
announcement of the bad news (i.e., unknown bad news) and overreact
to the repeated announcements of the identical bad news (i.e., known
bad news).
Abstract: In this paper, we provided a literature survey on the
artificial stock problem (ASM). The paper began by exploring the
complexity of the stock market and the needs for ASM. ASM
aims to investigate the link between individual behaviors (micro
level) and financial market dynamics (macro level). The variety of
patterns at the macro level is a function of the AFM complexity. The
financial market system is a complex system where the relationship
between the micro and macro level cannot be captured analytically.
Computational approaches, such as simulation, are expected to
comprehend this connection. Agent-based simulation is a simulation
technique commonly used to build AFMs. The paper proceeds by
discussing the components of the ASM. We consider the roles
of behavioral finance (BF) alongside the traditionally risk-averse
assumption in the construction of agent’s attributes. Also, the
influence of social networks in the developing of agents interactions is
addressed. Network topologies such as a small world, distance-based,
and scale-free networks may be utilized to outline economic
collaborations. In addition, the primary methods for developing
agents learning and adaptive abilities have been summarized.
These incorporated approach such as Genetic Algorithm, Genetic
Programming, Artificial neural network and Reinforcement Learning.
In addition, the most common statistical properties (the stylized facts)
of stock that are used for calibration and validation of ASM are
discussed. Besides, we have reviewed the major related previous
studies and categorize the utilized approaches as a part of these
studies. Finally, research directions and potential research questions
are argued. The research directions of ASM may focus on the macro
level by analyzing the market dynamic or on the micro level by
investigating the wealth distributions of the agents.
Abstract: This study investigates the investors- behavioral
reaction to the investment rating change announcements from the
views of behavioral finance. The empirical results indicate that
self-interest does affect the intention of securities firms to release
investment ratings for individual stocks. In addition, behavioral
pitfalls are also found in the response of retail investors to investment
rating change announcements.
Abstract: This study explored the relationship between
psychological traits, demographics and financial behavioral biases for
individual investors in Taiwan stock market. By using questionnaire
survey method conducted in 2010, there are 554 valid convenient
samples collected to examine the determinants of three types of
behavioral biases. Based on literature review, two hypothesized
models are constructed and further used to evaluate the effects of big
five personality traits and demographic variables on investment biases
through Structural Equation Model (SEM) analysis. The results
showed that investment biases of individual investors are significantly
related to four personality traits as well as some demographics.
Abstract: The survival of publicly listed companies largely
depends on their stocks being liquidly traded. This goal can be
achieved when new investors are attracted to invest on companies-
stocks. Among different groups of investors, individual investors are
generally less able to objectively evaluate companies- risks and
returns, and tend to be emotionally biased in their investing
decisions. Therefore their decisions may be formed as a result of
perceived risks and returns, and influenced by companies- images.
This study finds that perceived risk, perceived returns and trust
directly affect individual investors- trading decisions while attitude
towards brand partially mediates the relationships. This finding
suggests that, in courting individual investors, companies still need to
perform financially while building a good image can result in their
stocks being accepted quicker than the stocks of good performing
companies with hidden images.