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Digital currency investment has emerged as a result of global transformation toward technology-driven human lives. In Asia, Malaysia as an Islamic country is one of the early adopters with a high level of awareness on cryptocurrency. This paper aims to investigate the factors affecting the investment decision in cryptocurrency among potential investors in Malaysia.

Data was collected from 200 individuals aged 18 years and over. The hypotheses were tested using the partial least squares – structural equation modeling technique.

Results showed that attitude toward risk and perceived behavioral control have a significant positive effect on the investor’s investment decision in cryptocurrency. Interestingly, machine learning forecasting enhances the relationship between perceived benefits and the investment decision in cryptocurrency.

Results benefit investors and practitioners on the significant determinants of investment decision in cryptocurrency in emerging market.

Despite having high volatility and complexity in price determination, and being decentralized, cryptocurrency has managed to attract many investors due to reasons less explored. The outcome of this study extends the theory of planned behavior and confirms the role of machine learning forecasting as a moderator in the context of cryptocurrency investment.

This study explores whether herd behavior exists for equity crowdfunding investors in China and whether this herding is rational.

Based on signaling theory and social learning theory, two hypotheses were proposed. This study employed two approaches to collect data. First, this paper analyzed 3,041 investments on an equity crowdfunding platform in China using Python programming and built a panel data model. Second, based on a unique experiment design, this study conducted several relevant herd behavior simulation experiments.

We found that investors in the Chinese equity crowdfunding market exhibit herd behavior and that this herding is rational. Project attributes play a negative role in moderating the relationship between the current investment amount and cumulative investments. Experimental results further support our findings.

This study contributes to the emerging literature on herding in crowdfunding by focusing on equity crowdfunding in China. We are the first to explore whether Chinese equity crowdfunding investors exhibit rational herding behavior. The study is also original in applying social learning theory to equity crowdfunding and in using both actual crowdfunding campaigns and experimental approaches to collect data. This study has valuable implications to practice.

The purpose of this paper is to examine the evolutionary nature of herding phenomenon in the context of a frontier stock market, the Colombo Stock Exchange of Sri Lanka.

This study applies the cross-sectional absolute deviation methodology for daily frequencies of data of all the common stocks listed during the period from April 2000 to March 2018. The regression coefficients are estimated by using both the ordinary least square and the quantile regression procedures.

The findings reveal significant changes to the pattern of herding over different market periods, each with specific characteristics. Herding is strongly evident in up and down market days in the 2000-2009 period, during which the market was highly uncertain with the impact of the political instability of the country due to the Civil War on the stock trading. Even after this Civil War period, herd tendency is strongly manifested toward the up market direction as a result of the investors’ optimism about the country’s economy and political stability, which caused to a speculative bubble in the market. After that, it is turned into negative herding due to the panic selling occurred in view of the uncertainty of the inflated prices, which led to a market crash. Notably, herding appears to be consistently absent over the period after the crash, despite the presence of herd motives such as high market uncertainties triggered by political instability and economic crisis during that period.

The findings suggest that herd behavior is an evolving phenomenon in financial markets. Consistent with the adaptive market hypothesis, the absence of herding evident after the market crash could be attributed to the investors’ learning of the irrationality of herding/negative herding for adapting to market conditions. As a result, herding and negative herding tendencies declined and disappeared at the aggregate market level.

This study contributes to the literature by providing novel evidence on the evolutionary nature of behavioral biases, particularly herding, as predicted by the adaptive market hypothesis. With the application of the quantile regression procedure, in addition to customary used ordinary least squares approach, it also provides robust evidence on this phenomenon.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

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.

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.

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.

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.

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.

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.

Recent studies have shown that low-volatility shares outperform high-volatility shares. Given the conventional finance theory that risk drives return, this study aims to investigate and attempt to explain the presence of the low-risk anomaly (LRA) in South Africa.

Using share prices from 1990 to 2016, various buy-and-hold strategies are constructed to determine the return to an investor attempting to capitalise on such an anomaly. These strategies involve combinations relating to a price filter, the calculation of risk and volatility, value-weighting or equal-weighting of portfolios and the window period to construct said portfolios.

It was found that the LRA exists on the Johannesburg Stock Exchange (JSE_=) when using univariate sorts, without controlling for the size or value effect. When using multivariate portfolio sorts (size and volatility or value and volatility), it was found that the LRA does not exist on the JSE under the majority of risk proxies, but particularly prevalent when downside risk is used. This loosely points towards a potential “inverse momentum” effect where low-return portfolios outperform their counterparts.

In general, it is established that the risk–return relationship is non-linear and deterministic under traditional proxies, but improves to being somewhat, but not completely, linear under a Kalman filter. The Kalman filter, which can be considered a proxy for learning, does not remove the anomaly in its entirety, indicating that behavioural approaches are needed to explain such phenomena.

The purpose of this paper is to examine how policy instability is priced in interest rates. Policy instability refers to the likelihood that the current policy will be changed in the future in the absence of political power shifts.

Chinese government’s experimental policy-making approach provides an ideal set of frequent policy flip-flops which allows us to identify the effect of policy changes.

Conditional on the bureaucratic quality of policymaking, a good-quality policy reversal is related to reductions in interest rate term spread and volatility; a bad-quality policy reversal is related to increases in the spread and volatility. The bureaucratic quality is multi-dimensional and the moderating effect is stronger on interest rates when it is measured more precisely.

First, we can use the interest rate dynamics to infer the policy risk premium, which is a more objective market indicator of the bureaucratic quality of the policy change. Second, the study is among the first that documents the pricing of policy instability can be moderated by the bureaucratic quality. The results indicate that it is important for a government to be responsive and consistent in liberalizing the financial market. It will lead to reduced cost of capital and volatility for investors and firms in the economy. Third, given that the bureaucratic quality is multi-dimensional and produces stronger impact jointly, a country shall continue to improve on different aspects of the bureaucratic quality. Although the study is based on the empirical evidence from Chinese policy environment, the results can be broadly applied to any developing economies that intend to liberalize the market to spur economic growth.

The purpose of this paper is to analyze investor reactions to ethical screening by pension plan managers.

The author presents a sample consisting of data corresponding to 573 pension plans in relation to such aspects as financial performance, inception date, asset size, number of participants, custodial and management fees, and whether their managers adopt ethical screening or give part of their profits to social projects. On this data the author implements the fixed effects panel data model proposed by Vogelsang (2012).

The results obtained indicate that investors/consumers prefer traditional or solidarity pension plans to ethical pension plans. Furthermore, the findings show that ethical investors/consumers are more (less) sensitive to positive (negative) lagged returns than caring and traditional consumers, causing traditional consumers to contribute to pension plans that they already own.

The author does not know what types of environmental, social and corporate governance criteria have been adopted by ethical pension plan managers and the weight given to each of these criteria for selecting the stock of the firms in their portfolios that could influence in the investors’ behaviour.

The results obtained in the current paper show that investors invest less money in ethical pension plans than in traditional and solidarity pension plans; this could be due to the lack of information for their part. To solve this, management companies could increase the transparency about their corporate social responsibility (CSR) investments to encourage investors to invest in ethical products so these lead to raising CSR standards in companies, and therefore, sustainable development.

The Spanish socially responsible investment retail market is still at an early phase of development, and regulators should promote it in order to encourage firms to adopt business activities that take into account societal concerns.

This paper provides new evidence in a field little analysed. This paper contributes to the existing literature by focusing on examining the behaviour of pension funds investors whose investment time horizon is in the long-term while previous literature focus on analysing behaviour of mutual fund investors whose investment time horizon is in the short/medium term what could cause different investors’ behaviour.