Mortality Compression and Longevity Risk

Abstract

Financial statements contain quantitative information and manager’s subjective evalua-tion of firm’s financial status. Using information released in U.S. 10-K filings. Both qualita-tive and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, we built tagging models based on the conditional ran-dom field (CRF) techniques, considering a variety of combinations of linguistic factors in-cluding morphology, orthography, predicate-argument structure, syntax, and simple seman-tics. Our results show that the CRF models are reasonably effective to find opinion holders in experiments when we adopted the popular MPQA corpus for training and testing. The contribution of our paper is to identify opinion patterns in multiword expressions (MWEs) forms rather than in single word forms. We find that the managers of corporations attempt to use more optimistic words to ob-fuscate negative financial performance and to accentuate the positive financial performance. Our results also show that decreasing earnings were often accompanied by ambiguous and mild statements in the reporting year and that increasing earnings were stated in assertive and positive way.

Mortality improvements, especially of the elderly, have been a common phenomenon in many countries since the end of World War II, and many believe life expectancy will continue to increase. As a result, longevity risk becomes an essential component in designing annuity products, as overestimating mortality rates may result in financial insolvency for a life insurance company. Stochastic mortality models have been a popular choice for addressing this risk. However, the longevity predictions of these models are based on historical data and the predictions behave somewhat like extrapolation. But there are no guarantees that future longevity will follow historical trends.