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Notes that macro‐economic policy faces the same challenges in both developing and post‐socialist economies: to reduce inflation while achieving or maintaining stable economic growth. Moreover, there are developing countries like Argentina which succeeded in overcoming a type of institutional chaos which comes quite close to what is to be observed in post‐socialist countries. By looking at the theoretical concept and by its implementation in Argentina, examines the main advantages and flaws of an exchange rate anchor. Shows that this is a high risk strategy for post‐socialist countries which needs radical complementary reforms in order to be effective and to minimize risks. Suggests that credibility cannot be imported.

This study aims to deal with the problem of trajectory tracking control for the quadrotor under external environmental disturbance and variable payloads.

In the field of unmanned aerial vehicle (UAV) control, external environmental disturbance and internal variable payloads as two major interference factors lead to control performance degradation or even instability, thus a trajectory tracking controller which innovatively combines sliding mode control technology and model-free control technique is proposed. The proposed controller is constructed with a learning rate-based sliding mode controller and an ultra-local model. Based on the proposed controller, the nonlinear system model of variable load quadrotor is locally estimated and the system’s uncertainties and disturbances can be compensated.

The simulation and actual test results demonstrate the satisfactory control performance and the robustness of the proposed controller compared with the PID and Backstepping controller under external environmental disturbance and variable payloads. Moreover, the proposed controller solves the trajectory tracking control problem not only when payloads change at the center of gravity but also when the position of load variation deviates from the center of gravity.

In both military and civilian domains, the quadrotor may encounter such situations that the payloads change, such as transporting goods, aerial refueling and so on. As a large internal interference factor, variable load tends to lead to unstable control. The research results provide theoretical guidance and technical support for trajectory tracking control of quadrotor under variable payloads.

The proposed controller combines learning rate-based sliding mode controller and model-free control technique to achieve a more efficient and accurate trajectory control of the quadrotor when considering system uncertainties and the load variation that happens in the unknown location.

Pharmaceutical companies are faced with identifying development compounds for their Drug Development Processes (DDPs) that will not only gain approval for sale by the regulatory agencies, such as the Food and Drug Administration (FDA), but also establish a sustainable and profitable market presence. This identification of compounds for the DDP includes projection of objective criteria, such as ability to generate revenue and profitability (Financial) and safety and efficacy (Clinical), as well as more subjective criteria, such as determination of insurance coverage by payers, such as the Centers for Medicare and Medicaid Services and pricing (Reimbursement), ability to produce a product of consistent quality (Manufacturing), and attain approval for sale in a timely manner (Registration). The Analytical Hierarchy Process (AHP) is a multi-criteria decision model that can integrate both objective and subjective information. This study applies the AHP methodology to the identification of compounds resulting in a dynamic application of the model that can be used by pharmaceutical companies to determine the best compounds to put in the DDP, at a time when the cost of conducting clinical evaluations for development compounds is very high and global market conditions are evolving.

Despite all the attempts developed so far to measure corporate social performance in the last decades, a standard metric for it is still missing. In this work, the author tries to understand why is this the case. To do so, the author has reviewed 69 relevant metrics developed in the literature since the 1970s until today, covering approaches based on social, reputational, and environmental ratings, as well as several others constructed ad hoc by reputated scholars. The author analyzes each of them through a double optics, checking if they meet the minimum requirements to be considered standard and truly social. The research reveals that the main factor that prevents such a standard is the lack of truly social orientation of the existing metrics.

The research for this paper focuses on the implied expected exchange rates between the U.S. dollar and the Deutsche Mark. We use one year debt yield and one year inflation forecasts to derive expected exchange rates based on uncovered interest arbitrage and on the purchasing power parity relationship. We also explore the explanatory power of combinations of these two alternative expected exchange rates including what they might reveal regarding exchange rate premiums. Our results indicate that a combination of the two approaches which models a risk premium may be beneficial. We indicate some further work to be done along this line and provide a summary and conclusion based on the work reported here.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

The aim of this research is to theorize and demonstrate that analyzing consumers’ text product reviews using text mining can enhance the explanatory power of a product sales model, particularly for hedonic products, which tend to generate emotional and subjective product evaluations. Previous research in this area has been more focused on utilitarian products.

Our text clustering-based procedure segments text reviews into multiple clusters in association with consumers’ numeric ratings to address consumer heterogeneity in taste preferences and quality valuations and the J-distribution of numeric product ratings. This approach is novel in terms of combining text clustering with numeric product ratings to address consumers’ subjective product evaluations.

Using the movie industry as our empirical application, we find that our approach of making use of product text reviews can improve the explanatory power and predictive validity of the box-office sales model.

Marketing scholars have actively investigated the impact of consumers’ online product reviews on product sales, primarily focusing on consumers’ numeric product ratings. Recently, studies have also examined user-generated content. Similarly, this study looks into users’ textual product reviews to explain product sales. It remains to be seen how generalizable our empirical results are beyond our movie application.

Whereas numeric ratings can indicate how much viewers liked products, consumers’ reviews can convey why viewers liked or disliked them. Therefore, our review analysis can help marketers understand what factors make new products succeed or fail.

Primarily our approach is suitable to products subjectively evaluated, mostly, hedonic products. In doing so, we consider consumer heterogeneity contained in reviews through our review clusters based on their divergent impacts on sales.

This paper explores the possibilities for policy coordination in the Southern African Development Community (SADC) as well as real effective exchange rate (REER) stability as a prerequisite towards sensible monetary integration. The underlying hypothesis goes with the assertion that countries meeting optimum currency area conditions face more stable exchange rates.

The quantitative analysis encompasses 12 SADC member states over the period 1995-2012. Correlation matrixes, dynamic pooled mean group (PMG) and mean group (MG) estimators and real effective exchange rate (REER) and real exchange rate (RER) equilibrium and misalignment analysis are carried out to arrive at the conclusions.

The study finds that the structural variables used in the PMG model show that there are common fiscal and monetary policy variables that determine REER/RER in the region. However, the exchange rate equilibrium misalignment analysis reveals that SADC economies are characterised by persistent overvaluation at least in the short term. This calls for further sustained policy coordination in the region.

The findings in this paper have important policy implications for economic stability and for the attempt of policy coordination in SADC region for the proposed monetary integration to proceed.

This study is the first attempt that relates the exchange rate as a policy coordination instrument among SADC economies.

Employs discounted cash flow analysis as a valid method for valuing future cash flows from property investments. The method has regard to the market participants’ investment behaviour and thus is able to encapsulate the economics of the investment decisions made; particularly by institutional investors. Observes, however, that there has always been serious concern about discounting property investment returns using discount rates that predicate long‐term bond yields. Reports on a comparative study between Australian ten‐year bond and the prime Sydney and Melbourne office yields which has been carried out to find out if there is a correlation. The relationship between the two yields for period March 1980‐March 1995 shows an inverse relationship, i.e. a negative correlation between the two yields. The result is consistent for both studies. The actual f‐value of 89 for Sydney office yields versus bond yields and 110 for Melbourne office yields versus bond yields, respectively, when compared with the table f‐value of 7.08 for the data set, indicate that the negative relationship is significant. Argues, in addition, that the achieved R² of 60 and 65, respectively, indicates that the explanatory power of the model is acceptable. So a discount rate based on bond yields, in the rising bond market, will indicate a higher future return from the property investment. Concludes that the result is totally flawed, given the inverse relationship between bond and property yields, because the actual future return will be lower; and that, as a result, in the rising bond market the prospective property investors who ought to achieve higher future return will actually end up achieving a lower return. A converse flawed result will be achieved in the falling bond market. In this market the vendors using this method to calculate the exit value of their investments are actually accepting an incorrect lower price.