Systematic illiquidity, characteristic illiquidity, and stock returns: Timeseries analysis

The objective of this research is to investigate the relationship between illiquidity and stock prices on the Tunisian stock exchange. While previous researches tended to focus on one form of illiquidity to examine this relationship, our study unifies three forms of illiquidity at the same time. Indeed, we simultaneously consider illiquidity as systematic risk, as a characteristic of the market, and as a characteristic of the stock. The aggregate illiquidity of the market is the average of individual stock illiquidity. The illiquidity risk is the sensitivity of the stock price to illiquidity shocks. Shocks of market illiquidity are estimated by the innovations in the expected market illiquidity. Results show that investors on the Tunisian stock exchange do not require higher returns when they expect a rise of market illiquidity, whereas investors on U.S markets are compensated for higher expected market illiquidity. In addition, shocks of market illiquidity provoke a fall in stock prices of small caps, while large caps are not sensitive to market illiquidity shocks. This differs slightly from results based on U.S. data where illiquidity shocks reduce all stock prices but most notably those of small caps. Robustness tests validate our findings. Our results are consistent with previous studies which reported that the “zero-return” ratio predicts significantly the return-illiquidity relationship on emerging markets.

Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

Corporate social responsibility and systematic risk: international evidence

PurposeThis paper aims to close gaps in the current literature according to whether there are differences regarding the relationship between corporate social performance (CSP) and systematic risk when diverse regions of the world are considered, and what the respective drivers for this relationship are. Furthermore, it tests the robustness to alternative measures for CSP and systematic risk.Design/methodology/approachThis study focuses on the impact of corporate social responsibility on systematic firm risk in an international sample. The authors measure CSP emerging from a company's social responsibility efforts by utilizing a CSP rating framework that covers a variety of dimensions. The instrumental variable approach is applied to mitigate endogeneity and identify causal relationships.FindingsThe impact of overall CSP on systematic risk is most distinct for North American firms and, in descending order, weaker in Europe, Asia–Pacific and Japan. Risk mitigation applies across all four regions. However, the magnitude of impact differs. While the most critical drivers in North America and Japan include product responsibility, Europe is affected most by the employees category and Asia–Pacific by environmental innovation.Practical implicationsThe findings help firms to control their cost of equity and investors may identify low-risk stocks by considering certain aspects of CSP.Originality/valueThis study distinguishes itself from previous literature addressing the connection between systematic risk and CSP by focusing on regional differences in an international sample, using the very transparent CSP measures of Asset4, identifying underlying impact drivers, and testing for robustness to alternative measures of systematic risk.

Holistic Security and Risk Intelligence: Are Current Risk Management Methods Leading to Breach?

How could we better prepare industry and governments against holistic, hybrid, or second-order attacks? <div>In this article we discuss the importance of addressing systemic and systematic risk management problems to provide holistic risk management and direct advances in technical security, utilising machine learning and artificial intelligence.</div>

Holistic Security and Risk Intelligence: Are Current Risk Management Methods Leading to Breach?

How could we better prepare industry and governments against holistic, hybrid, or second-order attacks? <div>In this article we discuss the importance of addressing systemic and systematic risk management problems to provide holistic risk management and direct advances in technical security, utilising machine learning and artificial intelligence.</div>

Decomposition of Natural Catastrophe Risks: Insurability Using Parametric CAT Bonds

Nat Cat risks are not insurable by traditional insurance mainly because of producing highly correlated losses. The source of such correlation among buildings of a region subject to a natural hazard is discussed. A decomposition method is proposed to split Nat Cat risk into idiosyncratic (and hence insurable) risk and systematic risk (carrying the correlated part). It is explained that the systematic risk can be transferred to capital markets using a set of parametric CAT bonds. Premium calculation is presented for insuring the decomposed risk. Portfolio risk-return trade-off measures for investing on the parametric CAT bond are derived. Multi-regional and multi-hazard parametric CAT bonds are introduced to reduce the risk of the investment. The methodology is applied on a region with about 3000 residential buildings subject to flood hazards.

Tanggung Jawab Sosial Dan Risiko Sistematis: Efek Moderasi Dari Fleksibilitas Keuangan Dan Investasi Penelitian Dan Pengembangan

The study is conducted with the aim of examining the effect of corporate social responsibility on systematic risk in companies listed on the IDX for the period of 2016-2020. This study adds financial flexibility and research and development investment as moderators which are still remain unexplored in Indonesia. This research is expected to be able to make investors consider social responsibility as a factor in making investment decisions. The data taken are stock prices, annual reports and sustainability reports which are secondary data. Data collection using purposive sampling method with certain criteria so that the number of samples in this study amounted to 43 companies. In testing the hypothesis using panel data regression analysis techniques with eviews. The results of the regression analysis show that the existence of corporate social responsibility has a significant positive effect on systematic risk. The moderating variable of financial flexibility does not affect the relationship between CSR and systematic risk. Then the research and development investment variables weaken the relationship between CSR and systematic risk. Therefore, management is expected to pay attention to R&D investment in making CSR policies. This study explains that R&D investment is one of the important roles in company sustainability.

Systematic risk in the biopharmaceutical sector: a multiscale approach

It is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in the biotech sector and ten asset classes using daily cross-country data from 1995 to 2019. We capture investors' heterogeneous investment horizons by decomposing time series according to frequencies. Using a maximal overlap discrete wavelet transform (MODWT) and a dynamic conditional correlation (DCC)-Student-t copula, diversification potentials are revealed, helping investors to reap the benefits of investing in biotech. Our findings indicate that the underlying assets exhibit nonlinear asymmetric behavior that strengthens during periods of turmoil.