Risk of Adverse Financial Events in Patients With Cancer: Evidence From a Novel Linkage Between Cancer Registry and Credit Records

PURPOSE Although financial toxicity is a growing cancer survivorship issue, no studies have used credit data to estimate the relative risk of financial hardship in patients with cancer versus individuals without cancer. We conducted a population-based retrospective matched cohort study using credit reports to investigate the impact of a cancer diagnosis on the risk of adverse financial events (AFEs). METHODS Western Washington SEER cancer registry (cases) and voter registry (controls) records from 2013 to 2018 were linked to quarterly credit records from TransUnion. Controls were age-, sex-, and zip code–matched to cancer cases and assigned an index date corresponding to the case's diagnosis date. Cases and controls experiencing past-due credit card payments and any of the following AFEs at 24 months from diagnosis or index were compared, using two-sample z tests: third-party collections, charge-offs, tax liens, delinquent mortgage payments, foreclosures, and repossessions. Multivariate logistic regression models were used to evaluate the association of cancer diagnosis with AFEs and past-due credit payments. RESULTS A total of 190,722 individuals (63,574 cases and 127,148 controls, mean age 66 years) were included. AFEs (4.3% v 2.4%, P < .0001) and past-due credit payments (2.6% v 1.9%, P < .0001) were more common in cases than in controls. After adjusting for age, sex, average baseline credit line, area deprivation index, and index/diagnosis year, patients with cancer had a higher risk of AFEs (odds ratio 1.71; 95% CI, 1.61 to 1.81; P < .0001) and past-due credit payments (odds ratio 1.28; 95% CI, 1.19 to 1.37; P < .0001) than controls. CONCLUSION Patients with cancer were at significantly increased risk of experiencing AFEs and past-due credit card payments relative to controls. Studies are needed to investigate the impact of these events on treatment decisions, quality of life, and clinical outcomes.

Investigating the Financial Crisis in 2008 from the Perspective of Banking Systems

The financial crisis of 2008 precipitated by credit issues in the US housing market is probably one of the most profound financial events in recorded history. Its shockwaves have significantly affected almost every market centre as well as country in the world. The aim of this report is accordingly to investigate major reasons behind the crisis from a special angle of banking systems. In particular, problems hidden in regulations, mechanisms and systems in the wake of the financial crisis are focused specifically in this report.

Role of Islamic Banking during COVID-19 on Political and Financial Events: Application of Impulse Indicator Saturation

This paper attempts to detect the unavoidable impacts of COVID-19 on geopolitical and financial events related to Islamic banking and the finance sector in Pakistan. It considers only those major events that triggered imbalances in the equity prices of selected Islamic banks. Employed here is the GARCH model, used to predict the volatility series using daily data from January 2007 to July 2020. The Impulse Indicator Saturation (IIS) helps to identify the structural breaks due to COVID-19, as well as the effects of political and financial events on the returns and volatility series of Islamic banks. The results indicate that all the events due to COVID-19 are significant. While 19 out of 21 political and financial events impacted the returns and volatility series, there were only 2 political events out of 18 that showed no significant effect on the returns and the volatility series. The state’s and Islamic banks’ policymakers can use these results to build an effective and sustainable financial policy regarding Islamic finance and the banking sector.

Cancer diagnosis and adverse financial events: Evidence from credit reports.

6504 Background: Increasing evidence shows that cancer patients (pts) experience financial hardships after diagnosis. Few studies, however, have used objective financial data to estimate the relative risk of adverse financial events (AFEs) in cancer pts versus individuals without cancer. Using a retrospective case-control design, we investigated whether cancer pts are at increased risk of new AFEs, as measured by their credit reports. Methods: Western Washington Surveillance Epidemiology and End Results (SEER) cancer registry (cases) and voter registry (controls) records from 2013 to 2018 were linked to quarterly credit records from TransUnion (2012-2020), one of the 3 largest national credit agencies. Controls were age and sex matched to cases and assigned an index date corresponding to the diagnosis (dx) date of the matched case. Individuals with evidence of any AFE in the credit report closest to index/dx date or did not survive to 24 months were excluded. Cases and controls experiencing any of the following AFEs within 24 months were compared, using two-sample z tests: severe (3rd party collections, charge-offs), more severe (tax liens, delinquent mortgage payments), and most severe (foreclosures, repossessions). Multivariate logistic regression models were used to evaluate the association between cancer dx and AFE, adjusting for age, sex, dx year, and available credit 6 months before the index/dx date. Results: A total of 332,825 individuals (84,185 cases and 248,640 controls, mean age 66 (SD 13), 52.7% female) were included. The mean available line of credit in the year before index/dx date was $12,303. AFEs were more common in cases versus controls (Table). After adjusting for age, sex, available credit above or below $12,303, and dx year, cancer dx was significantly associated with any AFE (OR 1.77, 95% CI 1.7-1.85, p<0.0001), severe AFEs (OR 1.94, 95% CI 1.85-2.03, p<0.0001), more severe AFEs (OR 1.23, 95% CI 1.12-1.36, p<0.0001), and most severe AFEs (OR 1.46, 95% CI 1.16-1.86, p=0.0016). Age >65 and higher available baseline credit were associated with decreased risk of any and each category of AFE. Conclusions: Within 24 months from dx, significantly higher proportions of cancer pts experienced AFEs relative to controls. Such events on credit reports have serious and long-lasting consequences on financial status. Studies that link clinical and financial data to investigate the impacts of these events on treatment decisions, quality of life, and clinical outcomes are needed.[Table: see text]

Disability and Distress: The Effect of Disability Programs on Financial Outcomes

What is the relationship between disability programs and financial distress? We provide the first evidence on this relationship using several markers of financial distress: bankruptcy, foreclosure, eviction, and home sale. Rates of these adverse financial events peak around the time of disability application. Using variation induced by an age-based eligibility rule, we find that disability allowance reduces the likelihood of bankruptcy by 20 percent, foreclosure by 33 percent, and home sale by 15 percent. We present evidence that these changes reflect true reductions in financial distress. Considering these extreme events increases the optimal disability benefit amount and suggests a shorter optimal waiting time between application and benefit receipt. (JEL G51, H55, J14)

TDJEE: A Document-Level Joint Model for Financial Event Extraction

Extracting financial events from numerous financial announcements is very important for investors to make right decisions. However, it is still challenging that event arguments always scatter in multiple sentences in a financial announcement, while most existing event extraction models only work in sentence-level scenarios. To address this problem, this paper proposes a relation-aware Transformer-based Document-level Joint Event Extraction model (TDJEE), which encodes relations between words into the context and leverages modified Transformer to capture document-level information to fill event arguments. Meanwhile, the absence of labeled data in financial domain could lead models be unstable in extraction results, which is known as the cold start problem. Furthermore, a Fonduer-based knowledge base combined with the distant supervision method is proposed to simplify the event labeling and provide high quality labeled training corpus for model training and evaluating. Experimental results on real-world Chinese financial announcement show that, compared with other models, TDJEE achieves competitive results and can effectively extract event arguments across multiple sentences.

Political Bias in the Media's Coverage of Firms' Earnings Announcements

This study examines whether firms' political activism induces bias in the media's coverage of earnings announcements and how such coverage impacts markets. We infer firm political ideology based on employee political contributions, and identify firm and manager characteristics associated with distinct ideologies. We find that media outlets negatively slant their coverage of earnings announcements when the political leanings of the outlet are incongruent with the political ideology of the firm. Consistent with slanted coverage affecting market outcomes, we provide evidence that the price reaction to good (bad) earnings news is decreasing (increasing) in the percent of incongruent media outlets covering the earnings announcement. In addition, trading volume and returns volatility are decreasing for good earnings news with the percent of incongruent media outlets. Our results suggest that the prevalent bias across some media outlets in their coverage of political news also affects their coverage of corporate financial events.