When the U.S. Stock Market Becomes Extreme?

Hedging downside risk before substantial price corrections is vital for risk management and long-only active equity manager performance. This study proposes a novel methodology for crafting timing signals to hedge sectors’ downside risk. These signals can be integrated into existing strategies simply by purchasing sector index put options. Our methodology generates successful signals for price corrections in 2000 (dot-com bubble) and 2008 (global financial crisis). A key innovation involves utilizing sector correlations. Major price swings within six months are signaled when a sector exhibits high valuation alongside abnormal correlations with others. Utilizing the price-to-earnings ratio for identifying sectors’ high valuations is more beneficial than the bond–stock earnings yield differential. Our signals are also more efficient than those of standard technical analyses.

Section: Signalmentioning

confidence: 99%

When to Hedge Downside Risk?

Giannikos,

Guirguis,

Kakolyris

et al. 2024

“…This is confirmed by the current situation in the markets around the world. The financial crisis had a determining impact on financial markets, causing stocks to sell sharply worldwide (Aboura 2014;Jiang et al 2021;Gennaro and Nietlispach 2021).…”

Section: Introductionmentioning

confidence: 99%

Accounting Quality and Audit Attributes on the Stock Price Crashes in an Emerging Market

Salehi

Zimon

Hashim

et al. 2022

This study addresses the relationship between accounting quality and audit attributes (i.e., audit quality, auditor industry specialization, audit concentration, and audit fees) with companies’ SPCRs listed on the Iraqi Stock Exchange. A multivariate regression model was used to test the hypotheses. The research hypotheses were tested using a sample of 210 observations of the listed Iraqi firms from 2013 to 2018 and a multiple regression model based on the random-effects model’s panel data technique. The findings indicate a negative and significant relationship between the accounting quality, audit, auditor industry specialization and SPCRs. Results also conveyed a meaningful and positive association between stock price crash risk (SPCR) and audit fees. The results did not confirm the relationship between corporate governance and audit concentration with SPCR. The primary research model was tested with additional methods (t + 1, fixed effects, ordinary least squares). Since this is the first study addressing this issue in the emerging markets, it provides users, analysts, and legal entities with helpful information about audit attributes that significantly affect SPCR. These results also contribute to developing science and knowledge in this field and fill the literature gap.

“…A frequently used procedure relies on the analysis of a mean excess plot, which represents the mean of the excesses of the threshold u. This method was applied in Aboura (2014), Cifter (2011), Gilli and Këllezi (2006), Łuczak and Just (2020) and Omari et al (2017). The change in the pattern for a very high threshold is observed in this plot; therefore, the choice of a threshold is ambiguous.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Tail Selection in Risk Measurement

Just

Echaust

2021

The appropriate choice of a threshold level, which separates the tails of the probability distribution of a random variable from its middle part, is considered to be a very complex and challenging task. This paper provides an empirical study on various methods of the optimal tail selection in risk measurement. The results indicate which method may be useful in practice for investors and financial and regulatory institutions. Some methods that perform well in simulation studies, based on theoretical distributions, may not perform well when real data are in use. We analyze twelve methods with different parameters for forty-eight world indices using returns from the period of 2000–Q1 2020 and four sub-periods. The research objective is to compare the methods and to identify those which can be recognized as useful in risk measurement. The results suggest that only four tail selection methods, i.e., the Path Stability algorithm, the minimization of the Asymptotic Mean Squared Error approach, the automated Eyeball method with carefully selected tuning parameters and the Hall single bootstrap procedure may be useful in practical applications.