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This research proposes new estimations of the Fama–French three‐ and five‐factor models via a machine learning approach. Specifically, it uses a Bayesian optimization‐support vector regression (BSVR) approach to obtain predictions of portfolio returns. On data from five industries' portfolio returns in the United States over the period July 1926 to January 2019, the BSVR models perform well. Specifically, our new model, called the Fama–French BSVR three‐factor model, outperformed the Fama–French BSVR five‐factor model. More precisely, the Fama–French BSVR three‐factor estimations attain out‐of‐sample (testing dataset) correlation coefficients of 94% for portfolio returns for the consumption and manufacturing industries. A correlation of 92% between the predicted and experimental values of portfolio returns was found for the high‐tech industry; 91% was found for the mining, construction, transportation, hotels, entertainment, and finance industries. However, for the Fama–French BSVR five‐factor model, the correlation coefficients lie between 48% (health industry) and 89% (high‐tech industry). [ABSTRACT FROM AUTHOR]

This study investigated the reliability and predictive validity of the Sense of Coherence (SOC) scale for 299 American survivors of gender-based violence (GBV). First, confirmatory factor analysis examined the construct validity of the SOC-13. Then, relationships between SOC and clinical scales were examined. Confirmatory factor analysis supported the 3-factor solution, and Cronbach's alpha reliability values for comprehensibility, manageability, and meaningfulness subscales and total score of SOC-13 were:.62,.53,.65, and.81, respectively. Multiple regression revealed that age, posttraumatic growth, depression, and posttraumatic stress scores explained 53% of the variance of SOC scores. We interpret this to suggest that SOC is a protective factor in GBV, especially in younger women. Clinical implications are suggested. [ABSTRACT FROM AUTHOR]

Patients diagnosed with pancreatic cancer who have 5-year survival rates of ~5% are typically in the advanced stage. Pancreatic cancer has become the third leading cause of cancer-related death in the United States and there is still a lack of effective treatments to improve patient survival rate. Hence, the purpose of the present retrospective study was to assess the potential clinical impact of repeated high-intensity focused ultrasound (HIFU) combined with iodine-125 (125I) interstitial brachytherapy for the treatment of patients with advanced pancreatic cancer who were ineligible for or declined surgery and chemotherapy. A total of 52 patients diagnosed with advanced pancreatic cancer were included in the study. At least one course of HIFU therapy combined with percutaneous ultrasound-guided 125I seed implantation was administered to each patient. The clinical assessment included an evaluation of Karnofsky Performance Scale (KPS) score at baseline, and at 1 and 2 months after combined therapy. Pain intensity was additionally evaluated with the numerical rating score (NRS). Overall survival (OS) times and survival rates at 3, 6, 9 and 12 months after combined treatment were evaluated. Adverse events commonly associated with HIFU and 125I seed implantation were recorded, and the severity of adverse events was graded according to the Common Terminology Criteria for Adverse Events, version 4. All 52 patients received successful repeated HIFU treatment combined with 125I seed implantation and were included in the analysis of efficacy and safety. The median OS time of patients was estimated to be 13.1 months (95% CI, 11.3–14.8). The survival rates at 3, 6, 9 and 12 months were 100.0, 86.5, 61.5 and 53.8%, respectively. The mean KPS score was 62.7±6.3 at baseline, 73.7±7.9 at 1 month and 68.8±6.5 at 2 months after combined treatment. KPS score increased significantly after combined therapy. The mean NRS score was 6.7±1.6 at baseline, and 4.7±1.7 and 5.4±1.5 at 1 and 2 months after combined treatment, respectively. The number of patients with severe pain and the NRS score were both significantly lower at 1 and 2 months after 125I seed implantation compared with those at baseline. No serious complications were detected during the follow-up period. In conclusion, the present study demonstrated the survival benefit and improvement in quality of life of patients with advanced pancreatic cancer receiving repeated HIFU treatment combined with 125I interstitial brachytherapy, which may provide new ideas and methods for the treatment of pancreatic cancer. [ABSTRACT FROM AUTHOR]

We provide new empirical evidence for the way in which financial markets process information. Our results rely critically on high-frequency intraday price and volume data for the S&P 500 equity portfolio and U.S. Treasury bonds, along with new econometric techniques, for making inference on the relationship between trading intensity and spot volatility around public news announcements. Consistent with the predictions derived from a theoretical model in which investors agree to disagree, our estimates for the intraday volume-volatility elasticity around important news announcements are systematically below unity. Our elasticity estimates also decrease significantly with measures of disagreements in beliefs, economic uncertainty, and textual-based sentiment, further highlighting the key role played by differences-of-opinion. [ABSTRACT FROM AUTHOR]

In the present study, the effect of physical and operational uncertainties on the hydrodynamic and hemocompatibility characteristics of a centrifugal blood pump designed by the U.S. food and drug administration is investigated. Physical uncertainties include the randomness in the blood density and viscosity, while the operational uncertainties are composed of the pump rotational speed, mass flow rate, and turbulence intensity. The non-intrusive polynomial chaos expansion has been employed to conduct the uncertainty quantification analysis. Additionally, to assess each stochastic parameter's influence on the quantities of interest, the sensitivity analysis is utilized through the Sobol' indices. For numerical simulation of the pump's blood flow, the SST [Formula: see text] turbulence model and a power-law model of hemolysis were employed. The pump's velocity field is profoundly affected by the rotational speed in the bladed regions and the mass flow rate in other zones. Furthermore, the hemolysis index is dominantly sensitive to blood viscosity. According to the results, pump hydraulic characteristics (i.e., head and efficiency) show a more robust behavior than the hemocompatibility characteristics (i.e., hemolysis index) regarding the operational and physical uncertainties. Finally, it was found that the probability distribution function of the hemolysis index covers the experimental measurements., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

We consider an approach to analyze big data of time series. Big dependent data are first transformed into functional time series of densities via nonparametric density estimation. We then discuss some tools for exploratory data analysis of the resulting functional time series. The tools employed include K-means cluster analysis and tree-based classification. For modeling, we propose a threshold approximate-factor model and a Hellinger distance autoregressive model for functional time series of continuous densities. The latent factors of factor models are estimated by functional principal component analysis. Cross-validation and Hellinger distance are used to select the number of principal component functions. For prediction of high-dimensional time series, we use the results of cluster analysis to obtain parsimonious models. We demonstrate the proposed analysis by considering the demand of electricity, the behavior of daily U.S. stock returns, and U.S. income distributions. [ABSTRACT FROM PUBLISHER]

Motivated by the need for a positive-semidefinite estimator of multivariate realized covariance matrices, we model noisy and asynchronous ultra-high-frequency asset prices in a state-space framework with missing data. We then estimate the covariance matrix of the latent states through a Kalman smoother and expectation maximization (KEM) algorithm. Iterating between the two EM steps, we obtain a covariance matrix estimate which is robust to both asynchronicity and microstructure noise, and positive-semidefinite by construction. We show the performance of the KEM estimator using extensive Monte Carlo simulations that mimic the liquidity and market microstructure characteristics of the S&P 500 universe as well as in a high-dimensional application on US stocks. KEM provides very accurate covariance matrix estimates and significantly outperforms alternative approaches recently introduced in the literature. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We quantify the effect of uncertainty in the volatility parameter σ on the Black–Scholes price of the European and American put. We apply probabilistic uncertainty analysis to the Black–Scholes model and compare the results with those of the Uncertain Volatility model. From historical data, we calibrate a probability distribution for the volatility. We then use Monte Carlo (MC) and a surrogate Polynomial Chaos (PC)/MC method to compute uncertainty bounds. The calibrated probability distribution is not one related to a standard orthogonal basis, so a basis is constructed numerically for the PC approximation. We show how to construct one stably from the probability distribution. We show that both methods give the same results, and quantify the relative speedup of the surrogate method. Finally, we investigate the effect of the parametric uncertainty, and show, for example, that the presence of uncertainty smoothes out the optimal exercise boundary of the American put. [ABSTRACT FROM PUBLISHER]

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks. [ABSTRACT FROM AUTHOR]