Suchergebnisse

PurposeThis study is motivated in part by the fact that the unfolding 2022 bear market, which has reached the −25% drawdown, has not been preceded by the inverted 10Y-3 m spread or an inverted near-term forward spread.Design/methodology/approachThe authors develop a three-factor probit model to predict/explain the deep stock market drawdowns, which the authors define as the drawdowns in excess of 20%.FindingsThe study results show that (1) the rising credit risk predicts a deep drawdown about a year in advance and (2) the monetary policy easing precedes an imminent drawdown below the 20% threshold.Originality/valueThis study three-factor probit model shows adaptability beyond the typical recessionary bear market and predicts/explains the liquidity-based selloffs, like the 2022 and possibly the 1987 deep drawdowns.

The Emergency Economic Stabilization Act of 2008 was the response of the Federal government to the economic crisis of 2007-2009. Within this act, the Troubled Asset Relief Program (TARP) was the mechanism to attempt to stabilize the financial market through the injection of liquidity into troubled firms. This paper examines the effect of TARP bailouts on stock market volatility and investor fear. Using an event study methodology, we find evidence of a significant decrease in stock-market volatility on the day of bailouts, and the day after. Additionally, findings show that the VIX, a proxy of investor fear, significantly declines on the second day subsequent to bailouts. The results suggest that government intervention, in the form of bailouts, is successful in stabilizing financial markets and reducing investor anxiety in the short-run.

IntroductionFrailty is a geriatric syndrome that is predictive of heightened vulnerability for disability, hospitalization, and mortality. Annually an estimated 250,000 frail Canadians die, and this estimate is expected to double in the next 40 years, as Canadians grow older. Currently there is no single accepted clinical definition of frailty.
Objectives and ApproachThe objective of this study was to develop an operational definition of frailty using machine learning that can be applied to a primary care electronic medical record (EMR) database.
The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a pan-Canadian network of primary care practices that collect de-identified patient information (such as encounter diagnoses, health conditions, and laboratory data) from EMRs.
780 patients from CPCSSN have were randomly selected and assessed by physicians using the Rockwood Clinical Frailty Scale (as frail or not frail), and their clinical characteristics from CPCSSN used to develop the definition using machine-learning.
ResultsA total of 8,044 clinical features were extracted from these tables: billing, problem list, encounter diagnosis, labs, medications and referrals. A chi-squared automatic interaction detector (CHAID) approach was selected as the best approach. The bootstrapping process used a cost matrix that prioritized high sensitivity and positive predictive value. 10-fold cross validation was used for validity measures. Key features factored into the algorithm included: diagnosis of dementia (ICD-9 code 290), medications furosemide and vitamins, and use of key word "obstruction" within the billing table. The validation measures with 95% confidence intervals are as follows: sensitivity of 28% (95% CI: 21% to 36%), specificity of 94% (95% CI: 93% to 96%), positive predictive value of 53% (95% CI: 42% to 64%), negative predictive value of 86% (95% CI: 83% to 88%).
Conclusion/ImplicationsNo other primary care specific frailty screening tools have sufficient validity. These results suggest heterogeneous diseases require clearly defined features and potentially more sophisticated algorithms to account for heterogeneity. Further research utilizing continuous features and continuous frailty scores may be more suitable in the creation of a case detection algorithm.