A Mononuclear Fe(III) Single Molecule Magnet with a 3/2↔5/2 Spin Crossover

The air stable complex [(PNP)FeCl(2)] (1) (PNP = N[2-P(CHMe(2))(2)-4-methylphenyl](2)(−)), prepared from one-electron oxidation of [(PNP)FeCl] with ClCPh(3), displays an unusual S = 3/2 to S = 5/2 transition above 80 K as inferred by the dc SQUID magnetic susceptibility measurement. The ac SQUID magnetization data, at zero field and between frequencies 10 and 1042 Hz, clearly reveals complex 1 to undergo a frequency dependent of the out-of-phase signal and thus be a single molecular magnet with a thermally activated barrier of U(eff) = 32-36 cm(−1) (47 - 52 K). Variable temperature Mössbauer data also corroborate a significant temperature dependence in δ and ΔE(Q) values for 1, which is in agreement with the system undergoing a change in spin state. Likewise, variable temperature X-band EPR spectra of 1 reveals the S = 3/2 to be likely the ground state with the S = 5/2 being close in energy. Multi-edge XAS absorption spectra suggest the electronic structure of 1 to be highly covalent with an effective iron oxidation state that is more reduced than the typical ferric complexes due to the significant interaction of the phosphine groups in PNP and Cl ligands with iron. A variable temperature single crystal X-ray diffraction study of 1 collected between 30-300 K also reveals elongation of the Fe–P bond lengths and increment in the Cl–Fe–Cl angle as the S = 5/2 state is populated. Theoretical studies show overall similar orbital pictures except for the d(z(2)) orbital which is the most sensitivity to change in the geometry and bonding where the quartet ((4)B) and the sextet ((6)A) states are close in energy

A Closed-Form Solution of the Multi-Period Portfolio Choice Problem for a Quadratic Utility Function

In the present paper, we derive a closed-form solution of the multi-period portfolio choice problem for a quadratic utility function with and without a riskless asset. All results are derived under weak conditions on the asset returns. No assumption on the correlation structure between different time points is needed and no assumption on the distribution is imposed. All expressions are presented in terms of the conditional mean vectors and the conditional covariance matrices. If the multivariate process of the asset returns is independent it is shown that in the case without a riskless asset the solution is presented as a sequence of optimal portfolio weights obtained by solving the single-period Markowitz optimization problem. The process dynamics are included only in the shape parameter of the utility function. If a riskless asset is present then the multi-period optimal portfolio weights are proportional to the single-period solutions multiplied by time-varying constants which are depending on the process dynamics. Remarkably, in the case of a portfolio selection with the tangency portfolio the multi-period solution coincides with the sequence of the simple-period solutions. Finally, we compare the suggested strategies with existing multi-period portfolio allocation methods for real data.Comment: 38 pages, 9 figures, 3 tables, changes: VAR(1)-CCC-GARCH(1,1) process dynamics and the analysis of increasing horizon are included in the simulation study, under revision in Annals of Operations Researc

Mental accounting, access motives, and overinsurance

People exercising mental accounting have an additional motive for buying insurance. They perceive a risk of having insufficient funds available to self-insure. In this way insurance protects the consumption value of the insured asset beyond the expenditure to acquire/replace it. This complements previous approaches based on probability weighting and loss aversion to explain the high profitability of warranties and an aversion toward deductibles. It helps to account for why the value of a warranty is found to be positively related to the value of the product and why there is seemingly contradictory empirical evidence on how household income affects demand for warranties. The adapted model rationalizes a strong aversion to deductibles, and explains the observed sensitivity of this aversion to the insurance context. Finally, it predicts a strong impact of how an insurer pays out benefits on the value and cost of insurance. This can explain both the evidence on strong deductible aversion for flood insurance and the lack of such evidence for long-term care insurance

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

Affective Reactions Influence Investment Decisions: Evidence from a Laboratory Experiment With Taxation

We investigate the effect of taxation on gains and losses on the investment behavior of investors. Based on the insights of both economic research on the influence of taxation on investment behavior and psychological concepts dealing with the descriptive decision behavior of investors we expect investors to react to taxation of investment alternatives they face with behavioral and affective changes. By conducting a laboratory experiment with a total of 72 participants based on the experimental design of Fochmann, Kiesewetter, and Sadrieh (2012) that allows to quantify the reactions of investors to taxation on gains and loss deduction independent of their individual risk preferences and additionally measuring the affective reactions of our participants, we explore the role of affect in the relation of taxation and decision making. Hence, we are able to show that affective reactions to the taxation situations, in particular the perceived valence of these situations, influence the change in behavior of investors when confronted with taxation on gains and limited loss deduction