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We explore the link between uncertainty in economic policy, firm-level FDI, and firm hedging behavior – building upon a newspaper-based index of economic policy uncertainty (EPU). We find that the relative difference in EPU between home and host country has a significant relationship with FDI. Firms increase their FDI level in countries, which have a low level of EPU relative to their home country. In addition, firms use derivatives more intensively in response to an increase in EPU. Interestingly, the link between EPU and corporate derivatives use varies according to the type of firm. Domestic MNCs make the most effective use of derivatives to hedge against EPU exposure.

In this review, we introduce a conducting polymer called poly(3,4-ethylenedioxythiophene) (PEDOT), due to its interesting features such as satisfactory conductivity, good transparency, easy processability, low price, small redox potential and good electrochromic properties. We review the synthesis routes, conductivity enhancement methods and applications of PEDOT in industrial and biomedical fields. We also discuss the challenges to use PEDOT such as difficulty in deposition on the electrode surface and conductivity enhancement. PEDOT is the most promising derivative of polythiophene in the cases of physical and chemical stability, conductivity, biocompatibility and transparency. PEDOT is usually paired with polystyrene sulfonate (PSS) to enhance its molecular weight. PEDOT:PSS is an electronically conducting polymer, which is water-soluble causing easy processing.

Motivated by a class of applied problems arising from physical layer based security in a digital communication system, in particular, by a secrecy sum-rate maximization problem, this paper studies a nonsmooth, difference-of-convex (dc) minimization problem. The contributions of this paper are (i) clarify several kinds of stationary solutions and their relations; (ii) develop and establish the convergence of a novel algorithm for computing a d-stationary solution of a problem with a convex feasible set that is arguably the sharpest kind among the various stationary solutions; (iii) extend the algorithm in several directions including a randomized choice of the subproblems that could help the practical convergence of the algorithm, a distributed penalty approach for problems whose objective functions are sums of dc functions, and problems with a specially structured (nonconvex) dc constraint. For the latter class of problems, a pointwise Slater constraint qualification is introduced that facilitates the verification and computation of a B(ouligand)-stationary point.

The bacterial infections have always a serious problem to public health. Scientists are developing new antibacterial materials to overcome this problem. Polysaccharides are promising biopolymers due to their diverse biological functions, low toxicity, and high biodegradability. Chitin and chitosan have antibacterial properties due to their cationic nature, while cellulose/bacterial cellulose does not possess any antibacterial activity. Moreover, the insolubility of chitin in common solvents, the poor solubility of chitosan in water, and the low mechanical properties of chitosan have restricted their biomedical applications. In order to solve these problems, chemical modifications such as quaternization, carboxymethylation, cationization, or surface modification of these polymers with different antimicrobial agents, including metal and metal oxide nanoparticles, are carried out to obtain new materials with improved physiochemical and biological properties. This mini review describes the recent progress in such derivatives and composites with potential antibacterial applications.

We present evidence on the term structure of the equity premium. We recover prices of dividend strips, which are short-term assets that pay dividends on the stock index every period up to period T and nothing thereafter. It is short-term relative to the index because the index pays dividends in perpetuity. We find that expected returns, Sharpe ratios, and volatilities on short-term assets are higher than on the index, while their CAPM betas are below one. Short-term assets are more volatile than their realizations, leading to excess volatility and return predictability. Our findings are inconsistent with many leading theories.

We document the causal effects of single-name options trading on the absolute level of information content of prices (stock price informativeness) by exploiting the Penny Pilot Program as an exogenous shock to options trading volume. We find that options trading increases underlying stock price informativeness and information acquisition by both option and stock investors, consistent with the framework of Goldstein and Yang (2015). The findings are driven by firms for which options are more important sources of information and firms with more efficiently priced options. Options market introduction in a sample of 25 other economies also leads to higher price informativeness.

We examine the accuracy and contribution of the Merton distance to default (DD) model, which is based on Merton's (1974) bond pricing model. We compare the model to a \"naive\" alternative, which uses the functional form suggested by the Merton model but does not solve the model for an implied probability of default. We find that the naive predictor performs slightly better in hazard models and in out-of-sample forecasts than both the Merton DD model and a reduced-form model that uses the same inputs. Several other forecasting variables are also important predictors, and fitted values from an expanded hazard model outperform Merton DD default probabilities out of sample. Implied default probabilities from credit default swaps and corporate bond yield spreads are only weakly correlated with Merton DD probabilities after adjusting for agency ratings and bond characteristics. We conclude that while the Merton DD model does not produce a sufficient statistic for the probability of default, its functional form is useful for forecasting defaults.

The generation of random bit sequences based on non-deterministic physical mechanisms is of paramount importance for cryptography and secure communications. High data rates also require extremely fast generation rates and robustness to external perturbations. Physical generators based on stochastic noise sources have been limited in bandwidth to ∼100 Mbit s −1 generation rates. We present a physical random bit generator, based on a chaotic semiconductor laser, having time-delayed self-feedback, which operates reliably at rates up to 300 Gbit s −1 . The method uses a high derivative of the digitized chaotic laser intensity and generates the random sequence by retaining a number of the least significant bits of the high derivative value. The method is insensitive to laser operational parameters and eliminates the necessity for all external constraints such as incommensurate sampling rates and laser external cavity round trip time. The randomness of long bit strings is verified by standard statistical tests. The generation of random bit sequences at a data rate of up to 300 Gbit s −1 — a rate many orders of magnitude faster than previously achieved — is realized by exploiting the output of a chaotic semiconductor laser. The randomness of the generated bits is verified by standard statistical tests.