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Article
Publication date: 13 February 2024

Elena Fedorova and Polina Iasakova

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

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Abstract

Purpose

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

Design/methodology/approach

The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.

Findings

The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.

Originality/value

First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”

Details

The Journal of Risk Finance, vol. 25 no. 2
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 12 April 2013

Pauline M. Shum and Jisok Kang

Leveraged and inverse ETFs (hereafter leveraged ETFs) have received much press coverage of late due to issues with their performance. Managers and the media have focused…

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Abstract

Purpose

Leveraged and inverse ETFs (hereafter leveraged ETFs) have received much press coverage of late due to issues with their performance. Managers and the media have focused investors' attention on the impact of compounding, when the funds are held for more than one day. The aim of this paper is to lay out a framework for assessing the performance of leveraged ETFs.

Design/methodology/approach

The authors propose a simple way to disentangle the effect of compounding and that of the management of the fund and the trading premiums/discounts, all of which affect investors' bottom line. The former is influenced by the effectiveness and the costs of the manager's (synthetic) replication strategy and the use of leverage. The latter reflects liquidity and the efficiency of the market.

Findings

The paper finds that tracking errors were not caused by the effects of compounding alone. Depending on the fund, the impact of management factors can outweigh the impact of compounding, and substantial premiums/discounts caused by reduced liquidity during the financial crisis further distorted performance.

Originality/value

The authors propose a framework for practitioners to evaluate the performance of leveraged ETFs. This framework highlights a very topical issue, that of the impact of synthetic replication, which all leveraged ETFs use. Financial regulators such as the SEC and the Financial Stability Board have all taken issue with synthetically replicated ETFs. In leveraged ETFs, this issue is masked by the effects of compounding. The framework the authors propose allows investors to disentangle the two effects.

Details

Managerial Finance, vol. 39 no. 5
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 11 December 2020

Lee A. Smales

COVID-19 has had an immense impact on global stock markets, with no sector escaping its effects. Investor attention towards COVID-19 surged as the virus spread, the number of…

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Abstract

Purpose

COVID-19 has had an immense impact on global stock markets, with no sector escaping its effects. Investor attention towards COVID-19 surged as the virus spread, the number of cases grew and its consequences imposed on everyday life. We assess whether this increase in investor attention may explain stock returns across different sectors during this unusual period.

Design/methodology/approach

We adopt the methodology of Da et al. (2015), using Google search volume (GSV) as a proxy for investor attention to examine the relationship between investor attention and stock returns across 11 sectors.

Findings

Our results demonstrate that heightened attention towards COVID-19 negatively influences US stock returns. However, relatively speaking, some sectors appear to have gained from the increased attention. This outperformance is centred in the sectors most likely to benefit (or likely to lose least) from the crisis and associated spending by households and government (i.e. consumer staples, healthcare and IT). Such results may be explained by an information discovery hypothesis in the sense that investors are searching online for information to enable a greater understanding of COVID-19's impact on relative stock sector performance.

Originality/value

While we do not claim that investor attention is the only driver of stock returns during this unique period, we do provide evidence that it contributes to the market impact and to the heterogeneity of returns across stock market sectors.

Details

Review of Behavioral Finance, vol. 13 no. 1
Type: Research Article
ISSN: 1940-5979

Keywords

Open Access
Article
Publication date: 13 October 2017

Halil Kiymaz and Koray D. Simsek

The purpose of this paper is to examine the performance of US mutual funds that invest primarily in emerging market equities and bonds.

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Abstract

Purpose

The purpose of this paper is to examine the performance of US mutual funds that invest primarily in emerging market equities and bonds.

Design/methodology/approach

The study adopts the Morningstar classification of mutual funds and uses the Lipper US Mutual Fund Database through FactSet to obtain monthly returns and various metrics for emerging market equity and bond mutual funds covering the period from January 2000 to May 2017. Several descriptive statistics for these funds are reported as well as various risk-adjusted performance measures. Alphas are computed for different sub-periods using different factor models to mitigate potential biases.

Findings

The results show that diversified emerging market funds generate some significant alphas for their investors during the study period. Emerging market bond funds, on the other hand, do not provide any significant positive alphas; mostly alphas are negative. An analysis of sub-period performance suggests that these funds do not consistently provide excess returns, showing great variations from one period to another.

Originality/value

The emerging market funds provide US investors with an alternative source of exposure for their portfolios. Emerging markets differ from developed markets on a wide range of market and economic characteristics, including size, liquidity, and regulation. This study contributes to the scarce literature on these types of funds and provides a comprehensive performance assessment against various benchmarks during a period that encompasses significant bear and bull markets across the world.

Details

Journal of Capital Markets Studies, vol. 1 no. 1
Type: Research Article
ISSN: 2514-4774

Keywords

Article
Publication date: 1 January 2009

Nikiforos T. Laopodis

The purpose of this paper is to re‐examine the relationship between real investment and stock prices for the USA for 1960‐2005 in view of distinct economic regimes during the…

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Abstract

Purpose

The purpose of this paper is to re‐examine the relationship between real investment and stock prices for the USA for 1960‐2005 in view of distinct economic regimes during the 40‐year period.

Design/methodology/approach

The paper employs simple models of investment, checks for cointegration, and applies the value at risk (VAR) methodology.

Findings

First, it was found that during the 1960‐1990 period investment and the stock market exhibited a good relationship and shared a common stochastic trend. Second, during the 1990‐2005 period this relationship broke down. Finally, extending the model to include the long‐term interest rate did not produce significant impacts on or feedbacks from and to either variable. It is concluded that the 1990‐2005 period has been distinct from the previous periods in that the stock market did not always abide by the fundamentals such as interest rates and/or investment expenditures. It is thus concluded that the high stock market growth rates of the 1990s have adversely impacted real investment expenditures.

Practical implications

Lack of influence of the real long‐term interest rate on either the investment of the stock price equations for the 1990‐2005 period. This implies that both investment and the stock market did not “take into account” a fundamental variable, the discount rate, instead they had a run on their own (especially the stock market).

Originality/value

The value of the paper is in showing that interest rates and investment expenditures do not always move as economic theory predicts or that economic fundamentals do not always rule.

Details

Managerial Finance, vol. 35 no. 1
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 20 February 2007

Johan Knif and Seppo Pynnönen

The purpose of the paper is to study the relationship between stock return correlation and volatility.

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Abstract

Purpose

The purpose of the paper is to study the relationship between stock return correlation and volatility.

Design/methodology/approach

Utilizing a logit‐type regression model, the paper analyzes the incremental effect of volatility on the level of correlation. The focus of the paper is set on the impact of the volatilities involved in the definition and calculation of the correlation as well as on the effects of external volatilities from other markets.

Findings

In the paper, an explicit model was constructed to investigate the contribution of the level of volatility on mutual correlations of the markets. The empirical results strongly support the findings that high volatility tends to increase correlations between the markets (see for example). An analysis of the small Nordic markets further showed that the local volatilities may play a role in the change of the level of correlation. However, it is the general world‐wide volatility level that mainly drives the changes in the correlations.

Originality/value

Particularly, the results of the paper show that market correlations tend to be dependent on the general world‐wide volatility rather than on local volatilities of single markets. This approach gives us important information about the behavior of the correlation with respect to the level of each market's risk as well as to the general global market‐risk level. The results can be directly utilized by portfolio managers in planning portfolio diversification strategies in accordance with the expected future volatility.

Details

Managerial Finance, vol. 33 no. 3
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 1 March 2021

Hazem Al-Najjar, Nadia Al-Rousan, Dania Al-Najjar, Hamzeh F. Assous and Dana Al-Najjar

The COVID-19 pandemic virus has affected the largest economies around the world, especially Group 8 and Group 20. The increasing numbers of confirmed and deceased cases of the…

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Abstract

Purpose

The COVID-19 pandemic virus has affected the largest economies around the world, especially Group 8 and Group 20. The increasing numbers of confirmed and deceased cases of the COVID-19 pandemic worldwide are causing instability in stock indices every day. These changes resulted in the G8 suffering major losses due to the spread of the pandemic. This paper aims to study the impact of COVID-19 events using country lockdown announcement on the most important stock indices in G8 by using seven lockdown variables. To find the impact of the COVID-19 virus on G8, a correlation analysis and an artificial neural network model are adopted.

Design/methodology/approach

In this study, a Pearson correlation is used to study the strength of lockdown variables on international indices, where neural network is used to build a prediction model that can estimate the movement of stock markets independently. The neural network used two performance metrics including R2 and mean square error (MSE).

Findings

The results of stock indices prediction showed that R2 values of all G8 are between 0.979 and 0.990, where MSE values are between 54 and 604. The results showed that the COVID-19 events had a strong negative impact on stock movement, with the lowest point on the March of all G8 indices. Besides, the US lockdown and interest rate changes are the most affected by the G8 stock trading, followed by Germany, France and the UK.

Originality/value

The study has used artificial intelligent neural network to study the impact of US lockdown, decrease the interest rate in the USA and the announce of lockdown in different G8 countries.

Details

Journal of Chinese Economic and Foreign Trade Studies, vol. 14 no. 1
Type: Research Article
ISSN: 1754-4408

Keywords

Article
Publication date: 1 June 2023

Ashraf M. Noumir, Michael R. Langemeier and Mindy L. Mallory

The average U.S. farm size has risen dramatically over the last three decades. Motives for this trend are the subject of a large body of literature. This study incorporates farm…

Abstract

Purpose

The average U.S. farm size has risen dramatically over the last three decades. Motives for this trend are the subject of a large body of literature. This study incorporates farm size risk and return analysis into this research stream. In this paper, cross-sectional and temporal relations between farm size and returns are examined and characterized.

Design/methodology/approach

Relying on farm level panel data from Kansas Farm Management Association (KFMA) for 140 farms from 1996 to 2018, this article examines the relationship between farm size and returns and investigates whether farm size is related to risk. Two measures of farm returns are used: excess return on equity and risk-adjusted return on equity. Value of farm production and total farm acres are used as measures of farm size.

Findings

Findings suggest a significant and positive relationship between farm size and excess return on equity as well as farm size and risk-adjusted return on equity. However, this return premium associated with farm size is not associated with additional risk. Stated differently, farm size can be viewed as a farm characteristic that is associated with higher return without additional risk.

Practical implications

These findings provide further support for ongoing farm consolidation.

Originality/value

The results suggest the trend towards consolidation in production agriculture is likely to continue. Larger farms bear less risk.

Details

Agricultural Finance Review, vol. 83 no. 3
Type: Research Article
ISSN: 0002-1466

Keywords

Article
Publication date: 1 December 2004

Kathryn Wilkens, Nordia D. Thomas and M.S. Fofana

We examine the stability of market prices for 35 technology and 35 industrial stocks for the period December 31, 1993 to October 31, 2002. A phase portrait plot of the detrended…

Abstract

We examine the stability of market prices for 35 technology and 35 industrial stocks for the period December 31, 1993 to October 31, 2002. A phase portrait plot of the detrended log prices and de‐meaned returns of the two sectors shows a chaotic pattern in the stock prices indicating the presence of nonlinearity. However, when we compute the Lyapunov exponents, negative values are obtained. This shows that the price fluctuations for the 70 stocks result primarily from diffusion processes rather than from nonlinear dynamics. We evaluate forecast errors from a naïve model, a neural network, and ARMA models and find that the forecast errors are correlated with average changes in closed‐end fund discounts and other sentiment indexes. These results support an investor sentiment explanation for the closed‐end fund puzzle and behavioral theories of investor overreaction.

Details

Managerial Finance, vol. 30 no. 12
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 17 May 2018

Chase Gooding and E. Frank Stephenson

The purpose of this paper is to examine the effect of CVS’s decision to stop tobacco sales on the company’s share price.

Abstract

Purpose

The purpose of this paper is to examine the effect of CVS’s decision to stop tobacco sales on the company’s share price.

Design/methodology/approach

The paper uses event study methodology to examine the same day effect of CVS’s announcement and the one-year later effect of CVS’s announcement. Competing pharmacy retail chains’ stock performance is included for comparison purposes.

Findings

CVS’s shares fell by about one percentage point on the day of the company’s announcement while competitors’ share prices increased. A year later, however, CVS’s share price had increased by about twice as much as competitors’ share prices.

Originality/value

The finding that a company can make a decision that harms its short-run share price in exchange for a long-run share appreciation suggests that short-termism may not be as significant a concern as some critics of corporate management suggest.

Details

Journal of Entrepreneurship and Public Policy, vol. 7 no. 2
Type: Research Article
ISSN: 2045-2101

Keywords

11 – 20 of 655