Search results

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.

This study aims to investigate the effect of fear sentiment with a novel data set on Bitcoin’s (BTC) return, volatility and transaction volume. The authors divide the sample into two subperiods to capture the changing dynamics during the COVID-19 pandemic.

The authors retrieve the novel fear sentiment data from Thomson Reuters MarketPsych Indices (TRMI). The authors denote the subperiods as pre- and post-COVID-19 considering January 13, 2020, when the first COVID-19 confirmed case was reported outside China. The authors use bivariate vector autoregressive models given below with lag-length k, to investigate the dynamics between BTC variables and fear sentiment.

BTC market measures have dissimilar dynamics before and after the Coronavirus outbreak. The results reveal that due to the excessive uncertainty led by the outbreak, an increase in fear sentiment negatively affects the BTC returns more persistently and significantly. For the post-COVID-19 period, an increase in fear also results in more fluctuations in transaction volume while its initial and cumulative effects are both negative. Due to extreme uncertainty caused by the COVID-19 pandemic, investors may trade more aggressively in the initial phases of the shock.

The authors are convinced that the results in this paper have more far-reaching implications for other markets regulated by the states. BTC provides a natural benchmark to understand how fear sentiment drives and impacts the markets isolated from any interventions. Hence, the results show that in the absence of regulatory frameworks, market dynamics are likely to be more volatile and the fear sentiment has more persistent impacts. The authors also highlight the importance of using micro, asset-specific sentiment measures to capture market dynamics better.

BTC is not associated with any regulatory authority and is not produced by the governments and central banks. COVID-19 as a natural experiment provides an opportunity to explore the pure effects of market sentiment on BTC considering its decentralized and unregulated features. The paper has two main contributions. First, the authors use BTC-specific fear sentiment novel data set of TRMI instead of more general market sentiments used in the existing studies. Next, this is the first study to examine the association between fear and BTC before and after COVID-19.

The purpose of this research is to study how knowledge‐based systems are applied in an industrial environment. This paper attempts to propose a system, object‐based knowledge integration system (OBKIS) which supports the early stages of product development.

This proposed system characterizes the “dynamic” information exchange capability through its distinct features to include executable tasks within product information script that is being utilized in various functional groups, thereby introducing the action items to be carried out in relevant areas. To achieve the “dynamic” information exchange capability, object technology, which is favorable to the creation of inter‐related modularized data objects, is incorporated into the product information script to facilitate the active information interchange process. The universal extensible markup language (XML) is also adopted to facilitate data exchange between the database and the knowledge base in order to make real time data and knowledge available throughout the enterprise.

Further research on developing the well‐structured XML schema is needed in order to provide a well‐understood syntax and self‐defined mark‐up language to suit particular needs of product data exchange. For verification and measurement, it is suggested that one should evaluate the system in terms of data reliability, transformation accuracy and effectiveness for improving product design process.

The implications of these for information flows and management of product data during product development are discussed. In order to validate the feasibility of the proposed system, a prototype is developed for a local company so as to provide linking between the system design concept and system implementation in a practical environment.

The significance of this research is that a new product data schema for the initial phase of product development is formulated and the proposed system supports invoking various behaviors for the same message and overriding the pre‐defined inherited operation such that a flexible correlation can be formulated in the iterative product design process.

The current study proposes an integrative framework for examination of determinants of stock returns in US market based on the five-factor Fama and French (FF) model, macroeconomic variables and investor sentimental factors. The study is based on both value weighted and equally weighted monthly portfolio returns of all CRSP firms which are incorporated in the United States and listed on the NYSE, AMEX or NASDAQ.

The study applies PLS-SEM methodology to examine the major determinants of portfolio return.

The study suggests that investor sentiments are the major driving forces which positively influence the portfolio stock returns. The macroeconomic factors, the FF Factors and Momentum factor have negative influences on portfolio stock returns.

The study is the first of its kind which aim to determine the determinants of portfolio returns using the PLS-SEM methodology.

The purpose of this paper is to review and discuss the literature focusing on defining and measuring sentiments so as to understand their role in stock market behavior.

Critical review of the literature by analyzing myriad scholarly articles. The study is based on an analysis of 81 scholarly articles to critically analyze the approach toward defining and measuring market sentiments. The articles have been examined to identify and critique different classification of sentiment measures. A discussion is built to scrutinize the sentiment measures under the purview of theoretical underpinnings of the investor sentiment theory as well.

With more than five decades of research, the sentiment construct in finance literature is still ill-defined. Myriad empirical proxies of sentiment measures have led to conflicting results. The sentiment construct defined in financial theories needs to be revisited from the lens of sentiments defined in psychology.

The study is limited to analyzing the role of individual and institutional sentiments in equity markets. There is a need to explore sentiments with respect to different investment styles and strategies along with the type of investors.

Developing a suitable sentiment proxy can result in devising profitable trading strategies for investors. Understanding factors driving investor sentiments will help regulators to become more proactive and frame better policies.

This paper has leveraged psychology literature to highlight the limitations in development of sentiment construct in finance literature. By identifying stylized facts from reviewing the empirical literature, it highlights areas for future research.