Measurement of extreme market risk: Insights from a comprehensive literature review

Chakraborty, Gourab; Chandrashekhar, G. R.; Balasubramanian, G.

doi:10.1080/23322039.2021.1920150

Cited by 4 publications

(4 citation statements)

References 135 publications

(118 reference statements)

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“…The findings of this study align with the significant risk premium factor reported by Pal (2021) and supported by . However, the result regarding the market risk premium contradicts the finding of Chakraborty et al (2021), who reported an insignificant effect of the market risk.…”

Section: Capmcontrasting

confidence: 94%

“…However, contrasting findings have also emerged, with some studies indicating that market risk does not significantly affect mutual fund performance. For instance, Chakraborty et al (2021) found no significant impact of market risk on mutual fund performance in the Indian context. Moreover, certain studies have delved…”

Section: Mutual Fund Performance and Market Riskmentioning

confidence: 97%

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Analyzing the Performance of Pakistan Equity Mutual Funds Using Multifactor Models: Pre-COVID Analysis

Danial,

Iftikhar,

Shah

2023

BMS

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Previous literature has primarily focused on examining the determinants that impact mutual fund returns in developed and emerging economies. However, there is a dearth of empirical research on how multifactor models are associated with mutual fund returns in underdeveloped economies. The inconsistent literature regarding the efficiency of the literature posed a question of whether the fund managers can generate extra returns for the investors. To address this research gap, this study aims to analyze mutual fund returns using multifactor models, specifically the Capital Asset Pricing Model (CAPM), Fama-French three-factor model, Carhart four-factor model, Treynor-Mazuy and Fama Net-Selectivity. The first three models analyze the funds with four factors: market factor, growth factor, value factor and momentum factor. The results of the study indicate that the risk premium factor plays a crucial role in understanding mutual fund returns, as it exhibits significant value across all three models. However, the value and size factors were found to be insignificant in both the Fama-French and Carhart models. Additionally, the momentum factor was not significant in the Carhart model. Notably, the significant alpha observed in all multifactor models suggests the skill of mutual fund managers, which was further confirmed using the Treynor-Mazuy and Fama Net-Selectivity models to assess their timing and selectivity abilities in actively managed funds. Furthermore, the finding of the study suggests the inefficiency of the market. The investors could earn extra return from their investments. However, the managers play important role in generating the abnormal returns as suggested by Treynor-Mazuy and Fama Net-selectivity model significance. In addition to that, investors should consider the market risk premium in their investment returns.

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Section: Capmcontrasting

confidence: 94%

Section: Mutual Fund Performance and Market Riskmentioning

confidence: 97%

Analyzing the Performance of Pakistan Equity Mutual Funds Using Multifactor Models: Pre-COVID Analysis

Danial,

Iftikhar,

Shah

2023

BMS

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“…The past financial market turmoil, such as the 2007-2008 Global Financial Crisis or March 2020 market turmoil caused by a global health crisis, has shown that rare extreme market events can have severe consequences and spillover effects for world economies [108]. Therefore, it is important for financial risk management portfolio diversification to control and monitor extreme downside market risk [109].…”

Section: Extreme Condition Detection In Cryptocurrenciesmentioning

confidence: 99%

Applying Artificial Intelligence in Cryptocurrency Markets: A Survey

2022

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The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.

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“…Financial risk, encompassing leverage, liquidity, and volatility, is crucial in shaping a firm's financial decisions and strategies (Lotfaliei, 2021;Elkhal, 2019). Understanding how financial risk interacts with company growth to influence earnings management practices can provide valuable insights into the underlying mechanisms (Chakraborty et al, 2021;Elshandidy and Zeng, 2022).…”

Section: Introductionmentioning

confidence: 99%

Earnings Management: Boon or Bane for Growth in Indonesia Consumer Sector?

Hamdani,

Priyanto,

Suhandi

2024

IJFR

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This study investigates the relationship between earnings management and financial risk on company growth in the Primary Consumer Goods Industry sector listed on the IDX from 2017 to 2021. The findings show that earnings management practices positively and significantly impact company growth, indicating that companies implementing management profits tend to experience higher growth (Growth Companies). Enhancing positive and efficient earnings management practices can improve company performance and overall business growth. The statistically significant effect indicates that this relationship is not coincidental, emphasizing the strong relationship between earnings management and firm growth. However, financial risk does not significantly affect company growth and cannot moderate the relationship between earnings management and company growth. This observation highlights the complexity of the impact of financial risk on corporate growth, which various unmeasured factors, including changes in interest rates, market fluctuations, and credit risk, may influence. Other internal and external factors that affect the relationship between financial risk and firm growth may not be considered in this regression model, limiting its ability to capture the subtleties of this relationship fully. Nonetheless, this study shows that Financial Risk and Earnings Management significantly affect the dependent variable, Company Growth. The results show that companies that apply better earnings management practices in their financial reporting experience better or higher growth. However, this study had limitations, and future research should explore a different sector, as this may yield different results. In addition, future research may consider including control variables such as firm size, liquidity, leverage, industry growth, and other economic factors to understand their impact on firm growth better

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Measurement of extreme market risk: Insights from a comprehensive literature review

Cited by 4 publications

References 135 publications

Analyzing the Performance of Pakistan Equity Mutual Funds Using Multifactor Models: Pre-COVID Analysis

Analyzing the Performance of Pakistan Equity Mutual Funds Using Multifactor Models: Pre-COVID Analysis

Applying Artificial Intelligence in Cryptocurrency Markets: A Survey

Earnings Management: Boon or Bane for Growth in Indonesia Consumer Sector?

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