Search

We revisit, and document new facts regarding, the financialization of U.S. energy markets in 2000–2010. We show that, after controlling for macroeconomic factors and physical energy market fundamentals, the strength of energy markets' co-movements with the U.S. stock market is positively related to the energy paper market activity of hedge funds that trade both asset classes. This relation weakens when credit risk is elevated. We find, in contrast, no link with the aggregate positions of commodity index traders in energy futures markets. Our findings have implications for the ongoing debate regarding the financialization of commodities. [ABSTRACT FROM AUTHOR]

Panama papers implicated celebrities round the globe for money laundering, tax evasion, and financing terrorist activities, on April 4th, 2016. Event study methodology is employed by using constant mean return model to gauge investors' behavior in forty-two stock exchanges before, on and after this event day. The study is believed to be first of its kind in the context of Panama papers. Results of 't - test' revealed statistically significant abnormal returns for three days, before and after the event day in two event windows (11, and 21 days), but on different dates, owing to accumulation effect. Thirty-three stock exchanges experienced statistically significant average abnormal returns (AAR), whereas, nine stock exchanges exhibited no response. Fifteen out of thirty-three exchanges witnessed cumulative average abnormal returns (CAAR) in positive direction, immediately after the event day for both event windows substantiating the underreaction hypothesis. Ten out of fifteen countries were found listed as 'more corrupt' on the Corruption Perception Index. The investors' apathy towards corruption in these countries, however, needs further research. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to provide a comprehensive conceptual framework and strong arguments with an intent to examine the stock market variables (predictors) indicating the money laundering (ML) and terrorism financing (FT) proceeds. Design/methodology/approach: This paper provides a comprehensive review of ML/FT through the stock market across developed, developing and emerging jurisdictions, sheds light on the existing literature and critically evaluates the gap in the relevant studies. Moving forward, this paper develops the conceptual framework and formulates hypotheses to explore the empirical relationship. Findings: This paper advocates and finds a basis to carry out much-needed empirical research between the ML/FT and stock market keeping in view the growing criminal cases in the developing countries. This paper suggests mining proxies from the publically available stock market data and the results of existing seminal research as variables of the study. These data and results carry information about the ML determinants. After developing hypothetical research providing concepts, this paper also finds that using a suitable methodology, preferable Bayesian logistic and linear regression models, it is possible to find the typologies and factors that can indicate and endorse the use of the stock market for ML/FT. Broadly, it is found that the significance of this study will be two-pronged: empirical development and policy implications. Research limitations/implications: This paper mainly focuses on the developing region, a newly emerging market and, peculiarly, a grey-listed region by the Financial Action Task Force (FATF). Practical implications: In light of the existing literature and to the best of the researchers' knowledge, this study will bring into focus the new age of the action research on the ML regime in the securities markets of the developing countries, hence, the emerging markets. Moreover, this research shall have a sheer significance for the policy measures on FATF recommendations on ML and FT, especially for the countries listed as "grey". Social implications: The research based on comprehensive review will help in controlling the social behaviours aiding the proceeds of ML. Originality/value: This research is extremely novel to the best of the researcher's knowledge. [ABSTRACT FROM AUTHOR]

A documentary review was carried out on the production and publication of research papers related to the study of Machine Learning Techniques and the Stock Market. The purpose of the bibliometric analysis proposed in this document is to know the main characteristics of the volume of publications registered in the Scopus database during the period 2016-2021, identifying a total of 576 publications. The information provided by the said platform was organized through tables and figures, categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics were described, the position of different authors regarding the proposed topic was referenced by employing a qualitative analysis. Among the main findings of this research, it is found that India, with 209 publications, was the country with the highest scientific production registered in the name of authors affiliated with institutions of that country. The Knowledge Area that made the greatest contribution to the construction of bibliographic material referring to the study of Machine Learning Techniques for Stock Market prediction was Computer Science with 459 published documents, and the type of publication that was most used during the aforementioned period was the Conference Article, representing 50% of the total scientific production. [ABSTRACT FROM AUTHOR]

A newly collected historical database for the Helsinki Stock Exchange (HSE) is used to analyse the number and structure of listed equity securities. The analysis shows that from its establishment in October 1912 to the end of 1981, a total of 849 different stock series and related issue rights have been listed on the HSE. Of these, 206 are normal stock series and they represent 167 different companies. The two largest industries represented on the HSE during most of the analysed period are metal and manufacturing and pulp and paper industries. Together they represented more than half of the listed companies throughout most of the sample period. [ABSTRACT FROM AUTHOR]

The unknown nature of the future requires us to question our decisions and seek reliable methods. The artificial neural networks approach, which is one of the methods used to best predict the future and one that is important for decision making has been thought of, particularly in recent years, as a method with a high level of validity in the fields of economy and financial prediction. The Istanbul Stock Exchange (ISE), at which millions of national and international investors operate, is among the developed stock exchanges of the world. The ISE has the attributes of being appropriate for making predictions regarding financial returns, without any sector differentiation, as a whole. In this study, it was aimed to predict monthly stock yields of 14 different paper companies dealing with the ISE (Istanbul Stock Exchange) by using artificial neural network. Four different variables (the gold price, ISE daily trading volume, exchange rate purchase-sale average, and monthly deposit interest rates by utilizing) and 127 months data were used. Results show that the monthly stock yields of the paper sector can be predicted correctly to account for 95% of the variability of data with the artificial neural network model, and the average absolute percentage failure value was 6.85%. [ABSTRACT FROM AUTHOR]

The paper "Corporate managers, price noise and the investment factor" investigates the impact of flows between the bond and equity funds on investment factors over the period 1984 to 2015. The paper "To jump or not to jump: momentum of jumps in crude oil price volatility prediction" finds a phenomenon, "momentum of jumps" (MoJ) and proposes a strategy that significantly outperforms the individual models and a series of competing strategies to enhance the predictability of crude oil price volatility. The paper "A theory of very short-time price change: security price drivers in times of high-frequency trading" analyzes and identifies four parameters considered to affect price changes. [Extracted from the article]

This article identifies a group of paintings by Edgar Degas as "bureau pictures," representations of workplaces, creative or commercial, in which piles of paper are spread on desks. Through his depiction of paper, as well as his affective casting of the way in which human figures relate to their piles of paper, Degas explores the intertwined gestures of creative and bureaucratic labor. Paper's tendency to offer a medium for fragmentation was represented in contemporary literature by Théophile Gautier and Émile Zola. In these texts and in Degas's images, papery fragmentation takes two forms: either as a creative scattering symptomatic of an anti-classical aesthetic informed by Romanticism, or as an instrument of bureaucracy and administration, as in Zola's representation of the stock market and the department store. Ultimately, in the bureau pictures, Degas does not take a definitive stance, but rather dwells on the way in which the two approaches intermingle, such that it is impossible to distinguish clearly between an artist's or an author's work and that of a clerk, at least at the level of the gestures and the handling of pieces of paper. In a move typical of Modernist aesthetics, however, paint and compositional choices strike back in order to submerge paper in the user's coin (corner), as a "true fragment" impressed with the mark of artistic temperament. [ABSTRACT FROM AUTHOR]

Blockchain technology (BCT) is regarded as one of the most important and disruptive technologies in Industry 4.0. However, no comprehensive study addresses the contributions of BCT adoption (BCA) on some special business functionalities projected as financial variables like BCA integrity, transparency, etc. Therefore, the primary objective of this study was to close this theoretical gap and determine how BCA has contributed to the four business sectors that were selected since FinTech had the greatest potential in these domains. The PRISMA approach, a systematic literature review model, was used in this work to make sure that the greatest number of studies on the topic were accessed. The PRISMA model's output helped identify relevant publications, and an analysis of these studies served as the foundation for this paper's findings. The findings reveal that BCA for companies with a disrupting financial technology (FinTech) attitude can help in securing corporate transaction transparency; offer knowledge, same-data, and information sharing; enhance fidelity, integrity, and trust; improve organizational procedures; and prevent fraud with cyber-hacking protection and fraudulence suspension. Moreover, blockchain's smart contract utilization feature offers ESG and sustainability functionality. This paper's novelty is the projection to four business sectors of the three-layer research sequence: (i) financial variables operated as BCA functionalities, (ii) issues, risks, limitations, and opportunities associated with the financial variables, and (iii) implications, theoretical contributions, questions, potentiality, and outlook of BCA/FinTech issues. And the ability of managers or practitioners to reference this sequence and make decisions on BCA matters is considered a key contribution. The proposed methodology provides business practitioners with valuable insights to reevaluate their economic challenges and explore the potential of blockchain technology to address them. This study combined a systematic literature review (SLR) with qualitative analysis as part of a hybrid research approach. Quantitative analysis was carried out on all 835 selected papers in the first step, and qualitative analysis was carried out on the top-cited papers that were screened. The current work highlights the key challenges and opportunities in established blockchain implementations and discusses the outlook potentiality of blockchain technology adoption. This study will be useful to managers, practitioners, researchers, and scholars. [ABSTRACT FROM AUTHOR]

Stock Market Prediction is a challenging and trending topic for researchers in recent years. Although it contains significant risk, it is frequently utilized in investment schemes that promise big returns. The returns on stocks are quite erratic. They are influenced by a number of variables, including prior stock prices, current market trends, financial news, social media, etc. There are many methods used to forecast stock value, including technical analysis, fundamental analysis, time series analysis, and statistical analysis, however none of these methods has been demonstrated to be a reliable forecasting method. In order to improve the accuracy of stock price prediction, a variety of machine learning approaches and algorithms are examined in this study. [ABSTRACT FROM AUTHOR]

Purpose: This paper participates in the debate on market efficiency and correct approach for asset pricing through a comprehensive review of literature in favor, as well as against the long held belief of market efficiency. The purpose of this paper is to understand emerging trends in behavioral finance and establish its future potential as a mainstream alternative theory of asset pricing. Design/methodology/approach: The review and discussion of literature is mainly divided into three different sections that are –theories supporting efficient market hypothesis (EMH); studies providing evidences from the stock market on the failure of EMH and studies on behavioral finance, discussing separately investors' behavioral biases keeping in mind their effect on stock prices; and providing empirical evidences on the effect of investor sentiment on stock prices. Findings: The review of literature from both the point of views has helped in understanding the market efficiency issue and changing dynamics of asset pricing approach. This is achieved by highlighting the gaps in the concept of market efficiency and also suggesting how these gaps can be bridged with a superior approach such as behavioral finance. Through further discussion of emerging trends in behavioral finance, the paper also points out gaps and how these can be abridged, for behavioral finance to be accepted as a mainstream alternative approach to EMH. Originality/value: This is an extensive and one of a kind study that discusses market efficiency through discussion of EMH and behavioral finance side by side. With the help of such a study, researchers can precisely understand the need and can focus on the future course of action to make behavioral finance a mainstream approach to asset pricing. [ABSTRACT FROM AUTHOR]

This article studies sin stocks on the European market over the period 2006–2020. In addition to stocks in alcohol, tobacco, gambling, and defense sectors that are traditionally considered sin stocks, we analyze stocks in carbon-intensive sectors that have newly evolved as sin stocks, such as oil and gas, metals and mining, uranium, and coal. To investigate the return performance of sin stocks, we run time-series regressions with factor models and Fama–MacBeth cross-sectional regressions. Our results suggest that there is no robust sin premium for new sin stocks or traditional sin stocks, either in comparison with the market or their peer stocks. Our analysis on ownership characteristics shows that investors in sin stocks are mainly investment managers, corporations, and individuals. While new sin stocks are popular among institutional investors, traditional sin stocks are less held by norm-constrained investors. Finally, our subsample analyses indicate some potential changes in the market perception of the new sin stocks after the Paris Agreement. [ABSTRACT FROM AUTHOR]

Auction rate preferred stock (ARPS) is often regarded as an alternative to other near-cash instruments such as commercial paper while the dividend exclusion for ARPS offers tax advantages to corporate purchasers. The mean default risk premium for ARPS, relative to commercial paper, is estimated at 83 basis points during stable financial markets. This default premium appears to surge during unstable equity markets, having jumped by 192 basis points in November 1987. Lower-rated ARPS shows even larger changes, with yields 40-50 basis points above yields on high-rated ARPS, adjusted for the normal risk premium differential. The perceived risk change of ARPS underscores how quickly market participants re-evaluate default risk, and even the importance of the priority order among debt and equity claimants. Findings suggest ARPS and commercial paper are not an acceptable substitute for commercial paper during times of unsettled equity markets. [ABSTRACT FROM AUTHOR]

Using daily data since 2017, we disentangle China-specific structural shocks driving Chinese financial markets and examine spillovers across global markets. The novelty of this paper consists of simultaneously identifying China shocks with shocks emanating from the United States and shocks to global risk sentiment - two major forces driving global financial markets - to ensure that China spillover estimates do not reflect common factors. Our results show that shocks originating in China have material impacts on global equity markets, although spillovers are much smaller than those following shocks in the United States, or those triggered by shifts in global risk sentiment. By contrast, shocks from China account for a significant proportion of variation in global commodity prices, more on a par with those of the United States. Nevertheless, spillovers from China can be significantly amplified in an environment of heightened global volatility, or when the shocks are large. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Management / Revista de Administração da UFSM is the property of Brazilian Journal of Management / Revista de Administracao da UFSM and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article investigates empirically the comovements of the conditional mean and volatility of stock returns. It extends the results in the literature by demonstrating the role of the commercial paper-Treasury yield spread in predicting time variation in volatility. The conditional mean and volatility exhibit an asymmetric relation, which contrasts with the contemporaneous relation that has been tested previously. The volatility leads the expected return, and this time series relation is documented using offset correlations, short-horizon contemporaneous correlations, and a vector autoregression. These results bring into question the value of modeling expected returns as a constant function of conditional volatility. [ABSTRACT FROM AUTHOR]

We analyse the impact of the introduction of a new technology on the National Stock Exchange in India that allowed trading of stocks without the need to transfer paper share certificates (demat trading). We document a decrease in the bid-ask spread and an increase in trading volume following its introduction particularly for those stocks that were previously illiquid. We present evidence that suggests that the primary channel for the increase in liquidity was the elimination of the risk of being sold forged securities as the clearing system took on the risk of reimbursing buyers of forged shares at the introduction of demat trading. [ABSTRACT FROM AUTHOR]

Initial Public Offering (IPO) is a kind of public offering in the form of companies' share which are sold to investors. An IPO is underwritten by one or more investment banks. These banks help in listing the shares on stock exchanges. The purpose of this research paper is to get a comprehensive analysis of previous research studies on the performance of IPO, which may provide the present growth and scope of research work in the field. 106 research studies on IPOs have been reviewed and outlined all the studies based on of their tools, methodology, year of the study, sample period, sample countries taken, type of studies, and sources of the study. For the present study, research articles published from 1998 to 2018 were selected for review. The outcomes of the analysis show that the number of studies on IPO rose continuously over the last seven years. The conclusion drawn from this paper may provide helpful facts regarding the following reviews of the IPO to investors, researchers, shareholders, and policymakers for taking important decisions. This study can be further refined by including the other classifications such as Google trend. [ABSTRACT FROM AUTHOR]

The objective of this paper is to analyze the differential effect of Brexit on the British stock market and money market before and after the Brexit referendum. Prior work. Many research articles have emerged on the economic effect of Brexit, but these papers have largely been predictive in nature; this paper builds on these prior research and examines what exists rather than being predictive. Approach. Stock market data and exchange rate data were collected from the London Stock Exchange. The data were analyzed with the usage of paired t-test of difference in means. Results: Findings from the analysis show a negative stock value within three days after the referendum, but further analysis show a positive stock value increase within twenty one days after the Brexit referendum. Additionally, the t-test results show that the British Pound fluctuated toward a weaker trajectory than the pre-Brexit period. Implications: Policy makers should shorten the negotiation period for exiting international integrations to reduce protracted loss of investments. Future searchers should observe more days to expand this paper's new result between stock and money market fluctuations. Value: This paper contributes by examining the difference in fluctuation between stock and money market effects of Brexit. [ABSTRACT FROM AUTHOR]

The Capital Asset Pricing Model (CAPM) is a widely used and tractable model relating financial risk and return. However, it has been less successful when taken to the data. In their seminal paper, Frazzini and Pedersen propose a Betting Against Beta (BAB) factor to take advantage of this fact. In their construction, the BAB factor is a portfolio which longs low-beta stocks and shorts high-beta stocks. Since its construction, the BAB factor has been widely cited and used both in financial academics and in industry. This paper proposes a novel betting on and against beta (BOAB) strategy, which bets on beta on days with macroeconomic announcements and bets against beta on days without. Here, macroeconomic announcement days are defined as days when inflation, unemployment, or interest rate decisions are released. Our findings confirm the persistent positive beta-return relationship observed on macroeconomic announcement days. We show that compared to the BAB strategy, BOAB delivers higher average daily excess returns over the time period 1964 to 2021 when applied to the U.S. stock market. This outperformance of the BOAB strategy is robust to different constructions of the BAB factor and is economically and statistically significant when compared with the usual asset pricing factors such as value, size, and momentum. The outperformance of the BOAB strategy could be used to inform agents' portfolio allocation choices. Limitations are also discussed. Plain Language Summary: Macroeconomic announcements and trading strategies Trading strategies are important because they impact how financial market participants invest and manage risk-return tradeoffs. This paper proposes a betting on and against beta (BOAB) strategy, which takes long positions on beta on a subset of trading days and short positions on other days. We determined which days to take long beta positions by testing the prediction of asset pricing models on days with macroeconomic announcements. We carefully constructed macroeconomic announcement days and used the Fama-MacBeth two-step testing procedure to evaluate predictions of the CAPM model. We conclude that the BOAB strategy outperforms the BAB factor, especially due to its outperformance on days with macroeconomic announcements. In the process, we consider varying ways of constructing the betting against beta factor to address critiques of the original BAB factor. We also demonstrate that the large, positive returns of the BAB factor has diminished since the publication of the original BAB paper. [ABSTRACT FROM AUTHOR]

This study examines whether and how the interaction between extreme market movements and the disposition effect can affect subsequent return spreads between stocks with large paper losses and those with large paper gains for a sustained period of time. We study all the A-shares listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange. Based on the prospect theory, our results show that after extreme negative market movements, the disposition effect enhances significant subsequent return spreads; however, it generates insignificant subsequent return spreads when extreme market movements are positive. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including forecasts and earnings by Anderson and colleagues, the targets of private equity market by Osborne, Katselas and Chapple, and takeover from private operating versus non-operating bidders by Bugeja and Sinelnikov.

The quest for sustainable economic development alternatives is revolutionising the automobile industry toward a more economic and sustainable products. This paper examines the relationship between green (electric) vehicle products and stock market performance. The prior work inclination is on the innovation and diffusion theories, which provides a lens to examine green innovation in auto industry. The paper adopts a fusion of review and quantitative approach. Data were from the International Energy Agency and Investing databases. A fixed effect panel analysis examine how the stock market reacts to two main types of electric vehicles - the Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV). The result adds to new knowledge namely, the BEV product relates to the stock market significantly at P=0.00002 but negatively. However, the PHEV has a positive and significant relationship with the stock market at a P=0.00001. The results offer practical implications including inter alia, manufacturers' introduction of more PHEV products into the consumer market has a high propensity to improve the stock market performance; this provides a useful information for stock market analysis and auto industry strategic planning. It also offers a good innovation case material for business schools. This paper contributes the first modelling of two main types of green car on stock market performance. [ABSTRACT FROM AUTHOR]

This research paper provides a comprehensive review of the emerging trends in AI-based stock market prediction. The paper highlights the key concepts, approaches, and techniques employed in AI-based stock market prediction and discusses their strengths and limitations. Key topics covered include deep learning, natural language processing, sentiment analysis, and reinforcement learning. This paper also presents case studies and evaluates the performance of different AI-based models in predicting stock market trends. Overall, this research paper provides valuable insights into the latest advancements in AI-based stock market prediction and their potential implications for investors, financial analysts, and policy makers. [ABSTRACT FROM AUTHOR]

The prediction of time-varying volatility plays an important role in financial data. In the paper, a comprehensive analysis of the mean return and conditional variance of NSE index is performed to use GARCH, EGARCH and TGARCH models with Normal innovation and Student’s t innovation. Conducting a bootstrap simulation study which shows the Model Confidence Set (MCS) captures the superior models across a range of significance levels. The experimental results show that, under various loss functions, the GARCH using Student’s t innovation model is the best model for volatility predictions of NSE among the six models. [ABSTRACT FROM AUTHOR]

The article presents a list of forthcoming research papers to be published in the journal, including "Examining the Relationships Between Capital, Risk and Efficiency in European Banking," by Yener Altunbas, Santiago Carbo, Edward P. M. Gardener and Philip Molyneux, "The Validity of Economic Value Added Model: An Empirical Application," by Dimitris Kyriazis and Christos Anastassis and "Dispersed Trading and the Prevention of Market Failure: Blackout at the Copenhagen Stock Exchange," by David Porter, Carsten Tanggaard, Daniel G. Weaver and Wei Yu.

This paper offers a concise analysis of the strategies currently in use for stock price prediction by retail investors. The price may increase or decrease according to the quarterly results, financial news flow, technical behavior, or market sentiment resulting from recent developments worldwide. This paper discussed the accuracy of many proposed approaches and methodologies for predicting stock price movement. The Support Vector Machine (SVM) is the foundation of the approaches, with additional parameters and variables. [ABSTRACT FROM AUTHOR]