Pattern recognition of financial innovation life cycle for renewable energy investments with integer code series and multiple technology S-curves based on Q-ROF DEMATEL

Life insurance companies, as equity stakeholders in policyholders’ lives, have incentives to mitigate their health risks. I introduce a framework that enables life insurers to evaluate the financial viability of developing and implementing health engagement programs. By leveraging a proprietary big database of health and mortality information from a large U.S. life insurer, I use machine learning techniques to quantify the benefits and use a rational addiction model to calculate the costs associated with these programs. The estimated net benefit available to the life insurer from the smoking cessation program is USD 87 million and the aggregate benefit from including other chronic conditions is USD 872 million. I explore the broader application of this framework in a general health policy context.

The financial benefits of health engagement programs to life insurers

Lee

2024

Financial ambiguity and oil prices

Ayoub,

Qadan

2024

Recent theoretical developments in economics distinguish between risk and ambiguity (Knightian uncertainty). Using state-of-the-art methods with intraday stock market data from February 1993 to February 2021, we derive financial ambiguity and empirically examine the effect of shocks to it on the price and volatility of crude oil. We provide evidence that ambiguity carries important information about future oil returns and volatility perceived by investors. We validate these results using Granger causality and in-sample and out-of-sample forecasting tests. Our findings reveal that financial ambiguity is a possible factor that explains future drops in oil prices and their increased variability. Our findings will benefit scholars and investors interested in how financial ambiguity shapes short-term oil prices.

Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data

Marchese,

Martínez-Miranda,

Nielsen

et al. 2024

The availability of many variables with predictive power makes their selection in a regression context difficult. This study considers robust and understandable low-dimensional estimators as building blocks to improve overall predictive power by optimally combining these building blocks. Our new algorithm is based on generalized cross-validation and builds a predictive model step-by-step from a simple mean to more complex predictive combinations. Empirical applications to annual financial returns and actuarial telematics data show its usefulness in the financial and insurance industries.