Liquidity risk and the beta premium

Gong, Cynthia M.; Luo, Di; Zhao, Huainan

doi:10.1111/jfir.12263

Cited by 7 publications

(3 citation statements)

References 77 publications

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“…Racicot et al (2019) finds that illiquidity factor is important in the asset pricing model in the dynamic context. This is supported by Gong et al (2021), which finds that the liquidity factor could improve the performance of the Fama and French three-factor and five-factor models. Conversely, Safiuallah and Shamsudin (2021) finds that the inclusion of liquidity and interest rate factors does not improve the pricing ability of the model.…”

Section: Research Themes and Discussionmentioning

confidence: 71%

How Well has Fama-French Five-Factor Model Explained Asset Returns? - A Systematic Literature Review

Sohor,

Low

2024

IJARAFMS

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Despite producing mixed findings, the Fama and French's (2015) model has been widely used to explain stock returns. This paper conducts a systematic literature review of the Fama and French's (2015) five factor model to evaluate its empirical validity in estimating the average returns of stocks. The review revolves around four major themes: performance of the fivefactor model in explaining stock returns and whether it outperforms the three-factor model in stock returns estimation; predictive power of the two new factors in the five-factor model namely profitability and investment factors; value factor's redundancy for describing average stock returns after accounting for profitability and investment factors; and the exploration of other factors that could enhance the model's explanatory power. The paper used the PRISMA (The Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method as a search strategy to conduct the systematic reviews. Highly cited articles published the Web of Science database from 2015 to 2022 were explored and a final sample of 61 were used in the review. The reviews find that in most studies, the Fama-French five-factor model is superior to the three-factor model in predicting the average stock market return at the national and regional levels. On the predictive power of profitability and investment factors, the findings were mixed. Similar conclusion was reached for the redundancy of value factor in explaining stock returns. Several other factors have also been found to increase the explanatory power of the five-factor model. The systematic literature of this paper evaluates several specific research questions related to Fama-French models, thereby providing researchers an understanding of the research topic and its evolution over time. Future review could be further expanded further to include articles from the Scopus-indexed journals. Insights from this paper are also useful for investors engaging in factor investing and in formulating asset allocation strategy.

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Section: Research Themes and Discussionmentioning

confidence: 71%

How Well has Fama-French Five-Factor Model Explained Asset Returns? - A Systematic Literature Review

Sohor,

Low

2024

IJARAFMS

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“…Therefore, when a firm proposes to make a major cross-industry M&A, investors and analysts in the capital market are prone to be optimistic (Wang et al , 2010), which is reflected in the fact that the market return rate is higher than that of intraindustry M&A. The research of Yang et al (2019) also supports the above view. They found that there is a premium of 8.42% in diversified M&A of listed firms in China.…”

Section: Related Literature and Hypothesis Developmentmentioning

confidence: 92%

Technology-oriented M&A, market reaction and innovation output

Yao

Yue

2023

NBRI

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Purpose The purpose of technology mergers and acquisitions (M&A) is to achieve innovation. The authors use the data from the China Patent Research System of China National Intellectual Property Administration to classify technical correlations into three types: similar, complementary and nonrelatedness (cross-sectoral category). And the authors explore three issues: the market reaction to technology-oriented M&A, the impact of technology-oriented M&A on goodwill and how technology-oriented M&A affects innovation. Design/methodology/approach The authors use data from China Patent Research System of China National Intellectual Property Administration to classify technical correlations into three types: similar, complementary and nonrelatedness (cross-sectoral category). And the authors explore three issues: the market reaction of technology-oriented M&A, the impact of technology-oriented M&A on goodwill and how technology-oriented M&A affects innovation. The empirical research shows that the cross-sectoral M&A is popular in the market and is positively correlated with cumulative abnormal return (CAR) and premium rate of M&A. However, the technology-similarity M&A, which is committed to in-depth exploration of original technology, is negatively correlated with CAR and goodwill. Findings The empirical research shows that cross-sectoral M&A is popular in the market and is positively correlated with CAR and premium rate of M&A. However, the technology-similarity M&A, which is committed to in-depth exploration of original technology, is negatively correlated with CAR and goodwill. In addition, empirical results show that there is an inverted U-shaped relationship between technology-oriented M&A and innovation output, and the inflection points are 41.8%, 48.9% and 38.8%, respectively. Originality/value The research contributions of this paper are as follows: first, most domestic studies simply and roughly measure the degree of technical relevance based on whether the firms belong to the same industry and whether there is common knowledge between them, but the authors provide a more accurate measure of technology-oriented M&A. Second, in the research on the economic consequences of technology-oriented M&As, a large number of literatures have mainly focused on the innovation performance of the acquirer after deals, including the number of patent applications, the number of patent citations, innovation output, etc., and they pay less attention to its impact on the market reaction and goodwill.

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“…Ross et al (1995) believes that financial risk is usually accompanied by the rapid development and expansion of enterprises because there must be some excessive debt-raising investment behaviors in this process. The impact of relevant national laws and regulations has brought about unstable student sources, annual differences in government funding, and lack of financial management supervision internal factor Xu, 2017 Property rights agency is not clear enough, extracorporeal circulation of income budget, project bidding for personal gain, vicious cycle of repaying old loans with new ones, and evaluation by the risk control department has not been implemented Huang, 2006 The salary system lacks incentives, and it is difficult for the school-run industry to achieve positive growth Wu, 2012 Infrastructure that exceeds the capacity of colleges and universities, rigid personnel system Liu & Liu, 2010 It is related to the inaccurate positioning of the school, blind comparison, cost accounting, and control system that need to be improved Huang, 2016 Financing by means of debt, resulting in debt risk Fu, 2011 Cash flow risk Gong, 2006 Lack of risk awareness and credit awareness, poor openness and transparency of financial information Zhang, 2018 School infrastructure investment, excessive investment, resulting in investment risk Li, 2015 Related to poor financial management and debt management Dong, 2015 Debt risk, investment risk, liquidity risk and institutional risk Wang, 2012 Universities debt continues to expand Cao, 2011 Imperfect internal control, imperfect management system, wrong investment decision-making, low level of capital management, indifferent risk awareness and low quality of personnel Ling & Ni, 2013 Unreasonable debt structure and huge interest…”

Section: Literature Reviewmentioning

confidence: 99%

Structure and Measurement of University Financial Risk

Dong,

Cai

2024

EUrASEANs

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Since the 1990s, higher education in China has expanded significantly. In 2020, the gross enrollment rate will reach 54.4%, and the total number of students in higher education accounts for one-fifth of the total scale of higher education in the world, ranking first in the world in terms of scale. The scale of higher education in China has grown, and the continuous increase in investment in higher education funds has increased the difficulty of controlling financial risks. Strengthening the prevention and control of financial risks in colleges and universities and strengthening the construction of internal control will be of great significance to the healthy development of universities. To understand the sources and causes of financial risks in universities, this study interviews the financial staff of S University and constructed a structural map of financial risks in universities using the grounded theory method to obtain data. The researchers compiled a questionnaire to study the financial risk of universities, distributed and received back 308 questionnaires at S University. By analyzing the survey data, the constructed structure of the financial risks of the university was verified.

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Liquidity risk and the beta premium

Cited by 7 publications

References 77 publications

How Well has Fama-French Five-Factor Model Explained Asset Returns? - A Systematic Literature Review

How Well has Fama-French Five-Factor Model Explained Asset Returns? - A Systematic Literature Review

Technology-oriented M&A, market reaction and innovation output

Structure and Measurement of University Financial Risk

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