Industry Returns Research Articles

How does investor sentiment impact systemic risk contagion across industries? Evidence from the Chinese stock market

ABSTRACT This study uses daily data across 24 sectors in China to investigate the contagion effect of systemic risk among industries. The data include industry returns, sentiment and marginal expected shortfalls (MES) from 1 January 2016 to 31 December 2022. A multi-source heterogeneous (MSH) data fusion method and an empirical Bayesian approach are employed to construct MSH data and build a complex network based on a graphical Gaussian model. The findings confirm high positive correlations and a significant clustering effect among industries in the Chinese stock market due to upstream and downstream supply chains and shareholding relationships. Moreover, the study reveals that systemic risk contagion is significantly associated with extreme events and the dissemination of investor sentiment. We calculate industry’s systemic risk receiver (SSR) index, rank the industries based on their SSR index values and select the 10 highest ranked as systemically important industries. This study offers valuable insights for regulators and policymakers seeking to bolster macro-prudential oversight and implement targeted supervision initiatives.

Implementing an In-House Digital Acknowledgment Solution for SAP MRA Returns in the Pharmaceutical Industry

This article comprehensively implements an in-house digital acknowledgment solution for pharmaceutical returns management using the SAP MRA (Material Returns Authorization) system. The solution addresses critical challenges in the pharmaceutical industry's return process, including regulatory compliance, operational efficiency, and cost optimization. Integrating SAP S/4 HANA, SAP PI/PO, and Microsoft Outlook provides a robust framework for handling return requests through multiple channels while maintaining stringent security standards. The implementation demonstrates significant improvements in processing times, customer satisfaction, and cost reduction through automation and digital transformation. The solution's architecture emphasizes scalability, security, and performance optimization, incorporating advanced features such as digital signatures, real-time monitoring, and automated workflow management. This article details the technical configuration, system architecture, end-to-end process implementation, and security framework while highlighting the business impact and future enhancement opportunities aligned with Industry 4.0 principles.

The Impact of Investor Sentiment on Stock Returns of Different Industries in China

The Impact of Investor Sentiment on Stock Returns of Different Industries in China

Financial regulatory policy uncertainty: An informative predictor for financial industry stock returns

We find that financial regulatory policy uncertainty is an informative indicator for predicting returns of financial industry stocks, outperforming popular predictive variables both in-sample and out-of-sample. Mean-variance investors can achieve substantial economic gains by allocating assets based on the information provided by the financial regulatory policy uncertainty index. Placebo tests suggest that other policy uncertainty indices do not provide predictive information for stock returns of the financial industry, and financial regulatory policy uncertainty cannot forecast stock returns of other industries. We demonstrate that the predictive power of financial regulatory policy uncertainty stems from the cash flow channel, potentially due to the inhibitory effect of uncertainty on firms’ economic activities.

A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures

The increasing popularity of Unmanned Aerial Vehicle (UAV) swarms is attributed to their ability to generate substantial returns for various industries at a low cost. Additionally, in the future landscape of wireless networks, UAV swarms can serve as airborne base stations, alleviating the scarcity of communication resources. However, UAV swarm networks are vulnerable to various security threats that attackers can exploit with unpredictable consequences. Against this background, this paper provides a comprehensive review on security of UAV swarm networks. We begin by briefly introducing the dominant UAV swarm technologies, followed by their civilian and military applications. We then present and categorize various potential attacks that UAV swarm networks may encounter, such as denial-of-service attacks, man-in-the-middle attacks and attacks against Machine Learning (ML) models. After that, we introduce security technologies that can be utilized to address these attacks, including cryptography, physical layer security techniques, blockchain, ML, and intrusion detection. Additionally, we investigate and summarize mitigation strategies addressing different security threats in UAV swarm networks. Finally, some research directions and challenges are discussed.

Do industries lead the stock market? Evidence from an emerging stock market

Examining the gradual information diffusion hypothesis of Hong et al. (2007) in an emerging market context, we show that industry returns possess predictive information regarding the direction of the aggregate stock market in Borsa Istanbul, both in- and out-of-sample, and that the predictive power of industries is often magnified by crisis conditions. Several industries, including basic materials and consumer staples, have out-of-sample predictive power over the aggregate market, although out-of-sample predictability cannot be exploited to extract economic gains based on the direction of industry returns. Industry information emanating from the US stock market has significant spillover effects on Turkish industry returns, creating an opening for industry investment strategies for local investors. We show that foreign participation in the stock market facilitates the diffusion of information from US industries to Turkish industries and that the economic benefits from spillover effects can be as high as 60 percent on annual basis.

Industry return volatility and spillover effects in stock market under climate risk perception

ABSTRACT Utilizing alternative dimension reduction methods, this study constructs three Climate Risk Perception Indices (CRPIs) to quantify the impact of investor climate change sentiment on industry return volatility in the Chinese stock market. There are several findings in this study including (1) the CRPIs effectively capture the trend of climate change in China over the past decade. (2) The investor’s attention to climate change positively influences short-term stock market returns, exhibiting seasonal characteristics and industry heterogeneity. (3) The attention serves as an information sender for stocks in Telecommunication, Oil & Petrochemicals, and Environmental Protection. (4) The total spillover effect among climate-sensitive industries is significantly higher during extreme climate events. These conclusions provide empirical evidence for the stock market to respond effectively to climate risks.

Research on the Impact of Economic Policy Uncertainty and Investor Sentiment on the Growth Enterprise Market Return in China—An Empirical Study Based on TVP-SV-VAR Model

This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor sentiment index by applying the deep learning ERNIE (Enhanced Representation through Knowledge Integration) model, thereby capturing investors’ sentiment within the growth enterprise market. The dynamic interplay between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is scrutinized via the TVP-SV-VAR (time-varying parameter stochastic volatility vector auto-regression) model, and the asymmetric response of different industries’ stock returns within the growth enterprise market to economic policy uncertainty and investor sentiment shock. The findings of this research are that economic policy uncertainty exerts a negative influence on both investor sentiment and returns of the growth enterprise market. While it may trigger a temporary decline in stock prices, the empirical evidence suggests that the impact is of short duration. The influence of investor sentiment on the growth enterprise market returns is characterized by a reversal effect, suggesting that improved sentiment may initially boost stock prices but could lead to a subsequent decline over the long term. The relationship between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is time-variant, with heightened sensitivity observed during bull markets. Lastly, the effects of economic policy uncertainty and investor sentiment on the returns of different industries within the growth enterprise market are found to be asymmetric. These conclusions contribute to the existing body of literature on the Chinese capital market, offering a deeper understanding of the complex dynamics and the factors influencing market behavior.

Research on the Predictive Ability of Economic Policy Uncertainty on Stock Returns of Industries

In recent years, the emergence of economic policy uncertainty has had a certain impact on stock returns. It can be used as a research object to explore whether economic policy uncertainty can significantly predict future stock returns. This paper studies the predictive ability of economic policy uncertainty based on relevant data from major industries in the United States from January 1985 to December 2023, including Food industry (Food), Retail Stores industry (Rtail), Fabricated Products industry (FabPr), Drugs, Soap, Perfumes, Tobacco industry (Cnsum), Construction and Construction Materials industry (Cnstr), Consumer Durables industry (Durbl), Automobiles industry (Cars), Textiles, Apparel & Footwear industry (Clths), Machinery and Business Equipment industry (Machn), Chemicals industry (Chems), and Transportation industry(Trans), Utilities industry (Utils), Steel Works Etc industry (Steel), Mining and Minerals industry(Mines), Oil and Petroleum Products industry (Oil), and Banks, Insurance Companies, and Other Financials industry (Finan). Through both in-sample and out-of-sample examines, it is found that economic policy uncertainty is a reliable indicator for predicting stock returns, and its predictive performance is better in Food industry, Retail Stores industry, Fabricated Products industry, Transportation industry, Construction and Construction Materials industry, Consumer Durables industry, Automobiles industry, Textiles, Apparel & Footwear industry, Machinery and Business Equipment industry, Chemicals industry, and Drugs, Soap, Perfumes, Tobacco industry than in Utilities industry, Steel Works Etc industry, Mining and Minerals industry, Oil and Petroleum Products industry, and Banks, Insurance Companies, and Other Financials industry. Meanwhile, by replacing the measurement indicator of economic policy uncertainty, conducting long-horizon forecasting, and using different evaluation periods for robustness checks, it is found that the research conclusions are robust.

Impact of Climate Policy Uncertainty (CPU) and global Energy Uncertainty (EU) news on U.S. sectors: The moderating role of CPU on the EU and U.S. sectoral stock nexus

This article accounts for the impact of positive and negative shocks of the news-related Climate Policy Uncertainty (CPU) and the novel Economist Intelligence Unit's report-based global Energy Uncertainty (EU) on the U.S. sectoral stock returns by using the ARDL and NARDL approaches with dynamic multiplier simulations. We also utilize both the DCC-GARCH and ADCC-GARCH approaches to extract the symmetric and asymmetric dynamic conditional correlations between the EU and the U.S. sectoral stock returns and then regress these conditional correlation series on the CPU through series of quantile regressions. Overall, the findings suggest that only the positive CPU shocks negatively impact the U.S. sectoral stock returns of Consumer Services, Financials, Industries, Telecommunication and Utilities in the long-term, whereas the negative CPU shocks insignificantly predict the U.S. sectoral returns. The findings also report that only the negative EU shocks increase the U.S. sectoral stock returns of Consumer Services, Financials, Health Care, Industries, Moreover, the positive (negative) EU shocks cause the U.S. sectoral returns of Materials and Technology to decrease (increase) in the long-term. Portfolio managers may consider diversifying their portfolios to include sectors least susceptible to negative impacts from the CPU and EU shocks such as Health Care and Oil & Gas. Our findings also show that CPU shocks moderate the dynamic conditional correlations between the EU and the U.S. sectoral returns of Consumer Services, Materials, Health Care, Telecommunication, Oil and Gas and Utility. Fund managers should contemplate augmenting the allocations to the Financials, Industrials, and Technology sectors owing to their diminished interconnectivity with the EU during periods of heightened CPU.

The Influence of ROA, ROE, NPM, and GPM on Stock Returns of Technology Companies Listed on the Indonesian Stock Exchange

This research examines the influence of Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), and Gross Profit Margin (GPM) on the stock returns of technology businesses listed on the Indonesia Stock Exchange. This analysis comprises all legally registered technological businesses listed on the Indonesian Stock Exchange from 2019 to 2022. Currently, the website idx.co.id displays about 43 technological businesses that have been officially registered. This study focuses on ROA) and NPM as crucial metrics for predicting stock returns in the technology industry. It specifically highlights the possibly negative impact of ROE. The minimal impact of GPM implies that investors may place more emphasis on evaluating possible stock returns in the technology sector using other metrics. It is advisable to do more analysis on the factors that influence returns in order to have a thorough comprehension.

Exploring the Effects of Nitrogen Fertilization Management and Soil Factors on the Yield and Quality of Flue-Cured Tobacco in China—A Quantitative Research

Tobacco, a pivotal economic crop in China, faces the challenge of securing high-quality raw materials for its industry due to unbalanced and inefficient nitrogen (N) application. To assess the impact of fertilizer management and soil factors on the yield and quality of flue-cured tobacco (FCT), a meta-analysis was conducted across 82 peer-reviewed research studies. The findings demonstrated that both fertilizer management and soil properties exerted a significantly greater influence on yield (63.13% and 62.05%, respectively) than the proportion of superior and medium tobacco (PSMT) (23.57% and 23.83%, respectively). Multiple models were conducted to analyze the N application rate for maximum yield and PSMT, respectively, resulting in an optimum range from 90 to 100 kg N ha−1. The highest yield and PSMT increments were observed with fertilizer timing (FT) applied twice, a basal fertilizer ratio (BFR) exceeding 50%, and a soil pH below 6.5. The nicotine content escalated with increasing N application rates (NR) and soil nitrogen content, peaking at NR over 120 kg N ha−1 and soil total nitrogen (TN) above 2 g kg−1. Stepwise regression modeling indicated that nicotine content was positively influenced by fertilizer management factors (including NR, fertilizer timing, and BFR), as well as initial soil nitrogen content (AN and TN). However, it was negatively correlated with available potassium (AK). Therefore, the results of this meta-analysis suggest that effective fertilizer management, slightly acidic soils enriched with AK, and lower N supply capacity are crucial for enhancing leaf quality while reducing nicotine content. This approach promises improved economic and environmental returns for the tobacco industry in China.

Electronic Payments System and Banking Industry’s Return in Nigeria: A Time-Varying Granger Causality Approach

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The paper is motivated by the growth of the electronic payments system and its relevance in enhancing the banking industry's earnings. Consequently, the paper examines the causal relationship between the electronic payments system and the banking industry's returns in Nigeria. The paper offers some important contributions to the literature involving the use of an approach that allows for data-driven identification of the change points in the electronic payments system and the banking industry's returns nexus. The paper discovered three important findings. First, the causal relationship between the electronic payments system and the banking industry's returns in Nigeria changes with time changes. Second, there is evidence of causality between the electronic payments system and returns on assets and equity, however, the causality was more evident under the recursive window. Third, the causal relationship was more evident in some specific periods such as 2020Q4, all quarters of 2015 to 2016, and 2020 to the end of 2023. The paper recommends that policymakers should revisit and reinvigorate the specific cashless policies instituted in 2012-2016 that aided higher returns to the banking industry. Similarly, the Central Bank of Nigeria and other banking industry players should intensify efforts to encourage the use of electronic payments due to their usefulness and ability to enhance the banking industry's earnings.</span></p>

Pengaruh Return In Asset, Debt To Equity Ratio dan Current Ratio Terhadap Harga Saham pada Industri Manufaktur Sub Sector Makanan dan Minuman Periode 2019-2022

This study's goal was to examine how the Production Industry's Return on Assets, Debt to Equity Ratio, along with Current Ratio affected share prices in the foods and beverages subsector from 2019 to 2022. Purposive sampling is used in this research to obtain financial along with annual reports from the Indonesia Stock Exchange's official website. The analysis approach used is multiple regression. This study obtained 72 data. The findings demonstrated that the current ratio, debt to equity ratio, as well as return on assets overall substantially increased stock prices. While the coefficient of determination (Adjusted R2) value is 23.8%, which means that the contribution of return on assets, debt to equity ratio along with current ratio in explaining the stock price is 23.8%.

Pengaruh Earning Per Share dan Price Earning Ratio Terhadap Return Saham Pada Perusahaan Industri Wood and Lumber yang Go Public di Bursa Efek Indonesia

The capital market is a vehicle that can mobilise long-term funds from the public to be channelled into productive sectors. The purpose of the capital market in Indonesia is not only to mobilise funds from the public so that they can be channelled into productive sectors, but also to realise income distribution through ownership of company shares. This goal can be achieved gradually with the increasing number and types of securities traded in line with the increasing number of institutions that support the implementation of the capital market. This study aims to determine the effect of earning per share and price earning ratio (PER) on stock returns in Wood and Lumber industry companies that go public on the Indonesia Stock Exchange for the period 2005-2007. The analysis method used in this research is multiple linear regression with SPSS version 12.0. The results of this study can be concluded that: (1) Earning per share has a positive effect on stock returns, because the significance value is smaller than 0.05 and one of the considerations of investors besides risk is the return they will get in the form of a share of net income distributed to shareholders, (2) Price earning ratio has no effect on stock returns, because the significance value is greater than 0.05.

Stock return predictability using economic narrative: Evidence from energy sectors

This paper applies the Narrative-based Energy General Index (NEG) to forecast stock returns in the energy industry. The index is constructed using natural language processing (NLP) techniques applied to news topics from The Wall Street Journal. The results indicate that NEG outperforms in predicting future returns of the energy industry in both in-sample and out-of-sample, and the predictive power surpasses that of other macroeconomic variables. The asset allocation exercise demonstrates the substantial economic value of NEG. Furthermore, we document that NEG not only exhibits superior predictive power for energy sector returns but also provides valuable insights for the whole stock market.

Interaction effects in the cross-section of country and industry returns

We comprehensively examine the interaction effects in the cross-sectional predictability of country and industry returns. Using nearly five decades of data from 68 countries, we construct all possible double-sorted portfolios based on 44 portfolio characteristics and uncover numerous significant interactions. An out-of-sample value-weighted strategy that selects the top long-short country (industry) interactions generates a monthly World CAPM alpha of 0.33% (0.62%) with a Sharpe ratio of 0.58 (0.75). The strongest interactions stem from implementing momentum and technical analysis signals in small and illiquid countries or industries. Furthermore, the return patterns mainly emanate from frontier and weakly integrated markets—highlighting the role of market frictions and segmentation in the occurrence of abnormal returns. Consistent with these interpretations, the interactions decline over time as global markets mature and become more integrated.

Impact of sentimental factors on stock portfolio returns –an empirical analysis

This study aims to introduce an integrated model for understanding the influence of various sentimental factors in conjunction with macroeconomic factors on portfolio returns across ten industry sectors within the US market. These sentimental factors are categorized into market-wide, consumer, and individual stock market factors to assess their impact on industry portfolio returns. Employing the Autoregressive Distributed Lag (ARDL) model, the study evaluates the effects of macroeconomic and sentimental factors on stock market portfolio returns. The findings reveal a negative relationship between short-term interest rates and portfolio returns in specific industry sectors like manufacturing, telecom, and wholesale/retail. The study finds a positive relationship between the Hi-tech sector's risk spread and portfolio returns. Market sentimental factors positively influence portfolio returns of durable, non-durable, utility, and other sectors. Individual sentimental factors negatively impact portfolio returns in hi-tech, utility, durable, energy, and other sectors. The stock market-related individual, sentimental factor of the number of IPOs has a positive impact on portfolio returns in the energy sector and a negative impact on portfolio returns in other sectors. Consumer sentimental factors are significant positive determinants for portfolio returns in durable, energy, telecom, health, and other sectors. Discounts on closed-end funds may provide vital fundamental information regarding lower future earnings for stocks in the durable and energy sectors. The study provides valuable insights for investors to optimize their portfolio strategies in response to macroeconomic and sentimental factors within specific industry sectors.

Mutual Fund Herding in Industries: The Turkish Case

ABSTRACT This study investigates the industry herding of mutual funds traded in Turkey and the effects of industry herding on industry valuations. Using a sample of monthly portfolio holdings of 37 equity-intensive mutual funds traded in Borsa Istanbul (BIST), we employ the LSV and Sias herding measures. We find significant industry herding with the LSV measure and no overall industry herding with the Sias measure. We show that fund flows of underlying investors, individual stock herding, and the investing style of the mutual funds are not relevant drivers of industry herding. Furthermore, we document that industry herding is not a factor that destabilizes industry returns. These findings are especially crucial for investors who trade in concentrated markets since they raise questions regarding aspects such as the lower number of investors and stocks, as well as the information content of industry components, which have been claimed to have an impact on herding behavior in such a setting.

Are Industry Returns Informative about Other Industries and Fundamentals?

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">This paper examines the information content of selected US industries focusing on the dynamic linkages among these industries, the stock market and a number of fundamental variables. The period of investigation spans from January 1960 to December 2021. The empirical strategy includes several methodologies such as regressions, vector autoregressions and volatility models. The idea is to investigate the dynamic linkages among these series at both the mean and the volatility levels. The results point to significant industry returns’ explanatory power for many predictors of economic activity including the stock market. Further, time-varying analysis of the linkages among the industries and the stock market’s returns reveal that certain industries such as <em style="mso-bidi-font-style: normal;">Oil</em> and <em style="mso-bidi-font-style: normal;">Financials</em> provide consistent information leadership over other industries and across decades. Further, upon assessing the industry–market return volatility spillovers, it was found that a market risk–return profile may not always be economically significant and timely for investors. Finally, crises, financial or otherwise, affect industries but to differing degrees.</span></p>