Inflation targeting in Brazil

The purpose of this paper is to examine the Inflation Targeting (IT) framework as it is applied in the case of Brazil since its adoption in June 1999. For this purpose we first summarize the macroeconometric model utilized by the Central Bank of Brazil (BCB) in its pursuit of the IT framework. While the focus of this paper is on Brazil, we also examine the experience of other countries with IT (in particular, the BRIC countries: Brazil, Russia, India, and China), both for comparative purposes and for evidence of the extent of success of this ‘new’ economic policy pursued by other IT countries. In addition, we compare the experience of Brazil with IT and with that of non‐IT countries. In the context of non‐IT countries, we ask the question of whether it makes a difference in the fight against inflation whether a country has adopted IT or not. Finally, we examine some features of the Brazilian experience with IT regime.     

Trends of the human resources management model in Brazilian companies: a forecast according to opinion leaders from the area

This paper is the result of research carried out to identify trends of the human resources management model in Brazilian organizations. In order to do this, we employed a research methodology known as Delphi, which consists of gradually adjusting the perceptions of specialists. Respondents were selected using criteria that characterized them as opinion leaders in human resource management.

Having consulted more than 160 people, we observed that themes such as the management of competences, corporate education and encouragement for organizational learning will be gradually assimilated by companies. One of our main conclusions is that the change process in human resources has ceased to be reactive. Data relating to changes that occurred during the 1980s and 1990s showed that the function concentrated on internal restructuring, on reducing the number of specialist employees, on encouraging the outsourcing of operational processes and on establishing a new model for looking after in-house customers.     

Adolph Wagner’s economic thought in Brazil: money and public finance in the turn of the 20th century

This paper addresses the dissemination of German economist Adolph Wagner’s ideas in Brazil. It shows that his monetary thinking, inspired by Thomas Tooke and the Banking School, as well as his ideas on public finance, particularly the law of increasing state spending, were assimilated by two Brazilian economic thinkers: Rui Barbosa and Vieira Souto. The way these policymakers incorporated Wagner’s ideas into their texts and the arguments with which they chose to engage show the kind of economic discourse they were shaping in Brazil. This analysis reveals the different functions that the original ideas could perform in another national context.     

Implied volatility sentiment: a tale of two tails

We propose a sentiment measure jointly derived from out-of-the-money index puts and single stock calls: implied volatility (IV-) sentiment. In contrast to implied correlations, our measure uses information from the tails of the risk-neutral densities from these two markets rather than across their entire moneyness structures. We find that IV-sentiment measure adds value over and above traditional factors in predicting the equity risk premium out-of-sample. Forecasting results are superior when constrained ensemble models are used vis-à-vis unregularized machine learning techniques. In a mean-reversion strategy, our IV-sentiment measure delivers economically significant results, with limited exposure to a set of cross-sectional equity factors, including Fama and French's five factors, the momentum factor and the low-volatility factor, and seems valuable in preventing momentum crashes. Our novel measure reflects overweight of tail events, which we interpret as a behavioral bias. However, we cannot rule out a risk-compensation rationale.     

Can sustainable investments outperform traditional benchmarks? Evidence from global stock markets

To contribute to overcoming global sustainability challenges, investors have been increasingly interested in making sustainable investments and incorporating environmental, social and governance (ESG) criteria into their portfolio selection decisions and managerial activities. However, these investors and other agents interested in sustainable investment need updated and robust information to support their decision making. We analyzed the performance of several Dow Jones Sustainability Indices (DJSIs) and compared them with their respective market benchmarks from 2013 to 2018. The indices comprise the following regions and countries: the world, the Asia‐Pacific, Europe, emerging markets and the US. The analysis was conducted based on both classic and modern portfolio metrics. The results suggest that sustainable investment performance is still heterogeneous worldwide, but there is a promising opportunity for investors to obtain superior risk‐adjusted returns in certain regions while incorporating sustainable investment practices. The findings are of utmost importance to financial market practitioners, business managers, academics and other stakeholders interested in promoting investments, corporate practices and scientific knowledge to achieve the Sustainable Development Goals (SDGs).     

The Impact of Distance (External) and Organizational Factors (Internal) on the Knowledge Chain of Multinational Corporations: South Africa as a Host Country

The geographic dispersion of multinational corporations (MNCs) implies that while it gives them access to new and different knowledge from diverse localities, it also adds to the costs and complexities of managing that knowledge and its effective dispersal across geographies. The purpose of this article is to examine how knowledge is transferred within MNCs and provide a framework for this process, particularly focusing on the role that distance (external) and organizational (internal) factors plays therein. A qualitative study is utilized, focusing on two technology companies from different cultural home countries and the technology transfer process with their South African subsidiaries. We find that the standardization of knowledge impacts the creation and diffusion of knowledge; expatriates impact on the creation, diffusion, and adoption; and, finally, relevance and localization impact on the adoption and utilization of knowledge. We present a conceptual framework around trust and rationalization as regards transferring knowledge within MNCs and find some evidence of the impact of distance, particularly cultural, on the methods employed in this transfer. The article illustrates the practical ways in which MNCs organize their internal resources and overcome various dimensions of distance in ensuring knowledge transfers. By choosing companies from such divergent home countries (one industrialized and one newly industrialized, with very different cultural settings) and examining their knowledge transfers with their South African subsidiaries, we are able to unpack various dimensions of distance and how organizational mechanisms affect this process. © 2017 Wiley Periodicals, Inc.     

Improving time series forecasting: An approach combining bootstrap aggregation,clusters and exponential smoothing

Some recent papers have demonstrated that combining bagging (bootstrap aggregating) with exponential smoothing methods can produce highly accurate forecasts and improve the forecast accuracy relative to traditional methods. We therefore propose a new approach that combines the bagging, exponential smoothing and clustering methods. The existing methods use bagging to generate and aggregate groups of forecasts in order to reduce the variance. However, none of them consider the effect of covariance among the group of forecasts, even though it could have a dramatic impact on the variance of the group, and therefore on the forecast accuracy. The proposed approach, referred to here as Bagged.Cluster.ETS, aims to reduce the covariance effect by using partitioning around medoids (PAM) to produce clusters of similar forecasts, then selecting several forecasts from each cluster to create a group with a reduced variance. This approach was tested on various different time series sets from the M3 and CIF 2016 competitions. The empirical results have shown a substantial reduction in the forecast error, considering sMAPE and MASE.     

Crypto-assets portfolio optimization under the omega measure

Abstract

Crypto-currencies, or crypto-assets, represent a new class of investment assets. The traditional portfolio analysis approach of Markowitz is not appropriate for use with portfolios containing crypto-assets, as the model requires that the investor have a quadratic utility function or that the returns be normally distributed, which isn’t the case for crypto-assets. We develop a portfolio optimization model based on the Omega measure which is more comprehensive than the Markowitz model, and apply this to four crypto-asset investment portfolios by means of a numerical application. The results indicate that these portfolios should favor traditional market assets over crypto-assets. In the case of portfolios formed only by crypto-assets, there is no clear preference in favor of any crypto-asset in particular.