Stock market investment and different behavioural patterns: an exploratory study

PurposeThe purpose of the research is to gain an understanding about how stock market investors impact various behavioural personality traits in various consumer groups with differing levels of motivation and capacity to absorb emerging stock market data.Design/methodology/approachThe research has used structural equation modelling (SEM) to test the validity of the theoretical model.FindingsThe current paper is the first study that uses stock market data from an emerging economy to examine the relationship between stock market investment and different behavioural patterns such as stock market attachment, trust, satisfaction and loyalty. The authors observe the presence of direct positive relationships between stock market investment and different behavioural personality traits. Moreover, the authors also observe that stock market attachment can be seen as an intermediary variable between stock investment involvement and satisfaction. The empirical findings also suggest the presence of indirect relationships between stock investment involvement and satisfaction and between stock market attachment and loyalty. The authors find that the indirect relationship between stock market attachment and loyalty occurs when the level of satisfaction is higher. Therefore, satisfaction appears to facilitate the relationship between stock market attachment and loyalty.Research limitations/implicationsOne major limitation of the study is data availability. More specifically, the study was conducted with customers of eight different banks in the province of Eskisehir, Turkey. From the 250 questionnaires distributed, 173 were returned, yielding a response rate of 69.2%.Practical implicationsBy identifying the trait characteristics of segments of stock market participants relative to their propensity to invest in stocks, it is possible to tailor messages that influence people to invest for the long term.Originality/valueThe paper deploys stock market data from an emerging economy to investigate the relationship between stock market investment and different surface traits such as stock market attachment, trust, satisfaction and loyalty. To the best of the authors' knowledge the current paper is the first such study.

Stock Market Investment and Inflation: Evidence from the United States and Canada

This paper examines the long-run relationship between goods prices and stock prices to understand whether stock market investment can help hedge against inflation in the United States (US) and Canada. This study employed an autoregressive distributed lag (ARDL) cointegration test developed by Pesaran, Shin, and Smith (2001), and finds evidence of a positive long-run economic relationship between stock prices and goods prices in both economies over the sample period 1960 to 2019. The long-run elasticity is above one for both economies implying that the developments in the goods market significantly affect the stock market. We undertake a suite of sensitivity checks and find robust evidence that the stock market investment can help hedge against inflation in the United States and Canada.

GET-RICH-QUICK SCHEMES’ IMPACT ON PUBLIC PERCEPTION OF STOCK MARKET INVESTMENT IN KAZAKHSTAN

At this moment, only 1% of people in Kazakhstan invest in the stock market. Apart from the lack of financial education, the increasing number of seemingly legitimate fraudulent financial schemes influence the public’s perception of the stock market. This research investigates the public perceptions of investment fraud, the difference between stock market investment and financial schemes, and the consequences of confusing the two concepts. Through a survey and an interview, the research yields qualitative and quantitative data. Although the majority of the people (75%) are aware that the stock market is not a scam, a higher majority (89%) believe it is risky. Fraudsters often capitalize on the victims’ ignorance. Often get-rich-schemes are unlicensed, unregulated, and lacks transparency while offering a high amount of returns for a small investment. Therefore, raising the public's awareness will encourage them to engage in stock market investing and avoid being scammed.

ARTIFICIAL INTELLIGENCE IN STOCK MARKET INVESTMENT

This study investigates the selection of stock from huge stock markets and by using good selection tools so that it will give a good return value. It helps investor to find an easy decision regarding their investment in stock market individually with effective collection of trading activities. Many artificial intelligence (AI) techniques are untested in the financial crisis scenario. This research really helpful to the investor in the stock selection and stock purchase decision. AI is also a one of the hottest topic for most industries, researchers and investors. The financial market is easy to analyze with multiple charts, due to the application of artificial intelligence.

Experience Effects in Finance: Foundations, Applications, and Future Directions

This article establishes four key findings of the growing literature on experience effects in finance: (i) the long-lasting imprint of past experiences on beliefs and risk taking; (ii) recency effects; (iii) the domain-specificity of experience effects; and (iv) imperviousness to information that is not experience-based. I first discuss the neuroscientific foundations of experience-based learning and sketch a simple model of its role in the stock market based on Malmendier et al. (2020a, b). I then distill the empirical findings on experience effects in stock-market investment, trade dynamics, and international capital flows, highlighting these four key features. Finally, I contrast models of belief formation that rely on “learned information” with models accounting for the neuroscience evidence on synaptic tagging and memory formation, and provide directions for future research.

Implicit theories of malleability in machines influence the perception and trust repair of intelligent agent

After an intelligent agent makes an error, trust repair can be attempted to regain lost trust. While several ways are possible, individuals' underlying perception of malleability in machines--implicit theory-- can also influence the agent's trust repair process. In this study, we investigated the influence of implicit theory of machines on intelligent agents' apology after the trust violation. A 2 (implicit theory: Incremental vs. Entity) X 2 (apology attribution: Internal vs. External) between-subject design experiment of simulated stock market investment was conducted (N = 150) via online. Participants were given a situation in which they had to make investment decisions based on the recommendation of an artificial intelligence agent. We created an investment game consist of 40 investment opportunities to see the process of trust development, trust violation, and trust repair. The results show that trust damaged less severely in Incremental rather than Entity implicit theory condition and External rather than internal attribution apology condition after the trust violation. However, trust recovered more highly in Entity-External condition. We discussed both theoretical and practical implications.

Developing a ranking methodology for Shari’ah indices: the case of Borsa Istanbul

Purpose This paper aims to develop a ranking methodology for the companies included in the Islamic indices in Turkey. Thus, this paper simplifies the decision-making process for investors with Islamic sensitivities to stock market investment when constructing their investment portfolio. Design/methodology/approach This paper uses a case study of 20 companies listed on Borsa Istanbul, drawing data from their 2017, 2018 and 2019 financial reports. These companies are scored and ranked according to their compatibility with the screening criteria used by Ziraat Katilim index in Turkey. In addition, this paper uses the quantitative screening process to calculate the ranking scores of these companies. Findings The findings show that some companies are highly compatible with the screening criteria, with ranking scores close to 100 points. However, some companies satisfied the criteria on the margin. This may not be a desirable result for some investors. Research limitations/implications Only 20 companies are included in the analysis. Since the conventional accounting system is used in Turkey, it was difficult to get exact information about the companies’ Shari’ah compatibility from the financial results. Practical implications The findings assist investors to determine which company is ethically more responsible than others within the Islamic framework. There are also implications for the companies in question, index providers and Shari’ah scholars. Social implications The findings aim to simplify the decision-making process of investors who have Islamic sensitivities to stock exchange market investment when they constitute their portfolio. Originality/value To the best of the authors’ knowledge, it is one of the first attempts to develop a ranking methodology for Shari’ah-screened stocks in Turkey even though Shari’ah screening has been on the agenda since the late 1990s. This paper also compares 11 indices based on their screening criteria.