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This study attempts to provide a systematic theoretical analysis of the portfolio selection approach to the determination of inter‐regional and international capital flows, and to identify the implications of this analysis for the appropriate specification of short‐run econometric models of the foreign exchange market.

We examine the determinants of a current migrant's location choice emphasizing the relative importance and interaction of migrant stocks and flows. We show that both stocks and flow have significant impacts on the migrant's decision of where to locate. The significance and size of the effects vary according to legal status and whether the migrant is a “new” or a “repeat” migrant.

Utilising a database that distinctly classifies firm-level ESG (environmental, social and governance) news sentiment as positive or negative, the authors examine the information flow between the two types of ESG news sentiment and stock returns for 20 companies listed on the Johannesburg Stock Exchange between 2015 and 2021.

The authors use Shannonian transfer entropy to examine whether information significantly flows from ESG news sentiment to stock returns and a modified event study analysis to establish how stock prices react to changes in the two types of ESG sentiment.

Using Shannonian transfer entropy, the authors find that for the majority of the companies studied, information flows from the positive ESG news sentiment to stock returns while only a minority of the companies exhibit significant information flow from negative ESG news sentiment to returns. Furthermore, the study’s findings show significantly positive (negative) abnormal returns on the event date and beyond for both upgrades and downgrades in positive ESG news sentiment.

This study is among the first in an African context to investigate the impact of ESG news sentiment on stock market returns at high frequencies.

The purpose of this paper is to study the determinants of aggregate fund flows to both equity and hybrid mutual funds. The authors test three hypotheses that help explaining the relationship between mutual fund flows and stock market returns, namely; the feedback-trader hypothesis, the price-pressure hypothesis, and the information-response hypothesis.

The study relies on Swedish quarterly data on mutual fund flows over the period 1998-2013. The methodology is twofold; through the structural models (AR(1)) the authors can say something regarding the relationship between mutual fund flows and financial macro variables. The analysis is further strengthened by utilizing a vector autoregressive model to test for Granger causality in order to determine the order of events.

Similar to both Warther (1995) and Jank (2012), the authors only find support for the information-response hypothesis. Additionally, the authors find new financial variables that have predictive power in determining mutual fund flows, namely; market fear (VIX), exchange rate, households’ expectation regarding inflation as well as outflows from mutual bond funds.

The study contributes to the body of literature in three ways. First, it complements recent findings on determinants of mutual fund flows but the authors also add to the knowledge by included new macro financial variables describing the real economy. Second, the authors include a few additional variables. Third, the vast majority of previous studies have used US data, the authors add to that a deeper understanding of determinants of mutual fund flows in smaller economies by using Swedish data.

Nationally, the welfare caseload declined by more than 50% between 1994 and 2000. Considerable research has been devoted to understanding what caused this decline. Much of the literature examining these changes has modeled the total caseload (the stock) directly. Klerman and Haider (forthcoming) model the underlying flows and show analytically and empirically that previous methods are likely to be biased because they ignore important dynamics. However, due to their focus on the bias of the stock models, they present only limited results concerning the robustness of their findings and utilize only a single measure of economic conditions, the unemployment rate. This paper examines the robustness of the basic stock-flow model developed in Klerman and Haider (forthcoming), considering both richer dynamic specifications and richer measures of economic condition. We find that more complex dynamic specifications do not change the substantive conclusions, but richer measures of the economy do. While a model that only includes the unemployment rate attributes about half of the California caseload decline between 1995 and 1998 to the economy, models that incorporate richer measures of the economy attribute more than 90% of the decline to the economy.

Strategy researchers have become fascinated with the possibilities for developing theoretical perspectives rooted in knowledge and intellectual assets as drivers of superior performance. However, there have been many different schools of thought, each with its own conceptualization lenses and operationalization approaches. In this chapter, we focus on three schools of thought: (1) knowledge as stocks; (2) knowledge as flow; and (3) knowledge as a driver of an organizational capability. We use them to: (a) lay out the distinct approaches to conceptualization and operationalization of strategy-related concepts; and (b) identify specific ways to enhance theory-method correspondence. We believe that considerable progress could be made towards developing a knowledge-based view of strategy but only when accompanied by serious attention to measurement and methodological issues.

This paper aims to investigate the drivers of foreign direct divestment (FDD), how it relates to foreign direct investment (FDI) flows and stocks and its implications for developing countries. While divestment occurs for various reasons, it can be explained by reversing the propositions implied by FDI theories.

The authors combine FDI data and FDI theories to provide theoretical explanations for FDD and what it means for developing countries. FDI stock and flow data are used to derive inferences on trends in FDD and examine the implications of FDI theories on FDD.

Changes in the modes of global production and the rise of COVID-19 have reinforced the trend of stagnant or diminishing FDI flows observed since the global financial crisis, with implications for FDD. The authors demonstrate how the various FDI theories can be used to explain FDD, except for the currency areas hypothesis. By reviewing the costs and benefits of FDI, it is concluded that shrinking FDI flows and stocks may not be as detrimental for developing economies as it is typically portrayed.

The paper uses two original approaches to measure and explain the motives for FDD. The first is a reassessment of FDI theories in a way that makes them valid theories for FDD. The second original approach is to interpret data on FDI flows and stocks to imply the trends governing FDD, which is useful, as data on foreign divestment are not available on a country or regional basis.

This paper analyzes how organizations may use different knowledge management styles according to higher or lower emphasis on (1) techno‐structural initiatives for information processing, and (2) behavioral solutions for knowledge sharing by organizational members. As a consequence, the effects of these styles on learning capacity are also different. The empirical analysis of the present study found that knowledge management practices can be categorized into four styles: (1) passive, (2) behavioral, (3) techno‐structural, and (4) active. The active style, which implies superior management of both techno‐structural and behavioral tools of knowledge management has been the most effective in the development of learning capacity. In contrast, the passive style, which implies weak management of both kinds of knowledge management initiatives, results in lower learning capacity. Hence, this work focuses on suggesting and empirically testing a characteristic framework for how a set of knowledge management initiatives interact and influence learning capacity in organizations.

The purpose of this paper is to propose a new temporal disaggregation method for time series based on the accumulated and inverse accumulated generating operations in grey modeling and the interpolation method.

This disaggregation method includes three main steps, including accumulation, interpolation, and differentiation (AID). First, a low frequency flow series is transformed to the corresponding stock series through accumulated generating operation. Then, values of the stock series at unobserved time is estimated through appropriate interpolation method. And finally, the disaggregated stock series is transformed back to high frequency flow series through inverse accumulated generating operation.

The AID method is tested with a sales series. Results shows that the disaggregated sales data are satisfactory and reliable compared with the original data and disaggregated data using a time series model. The AID method is applicable to both long time series and grey series with insufficient information.

The AID method can be easily used to disaggregate low frequency flow series.

The AID method is a combination of grey modeling technique and interpolation method. Compared with other disaggregation methods, the AID method is simple, and does not require auxiliary information or plausible minimizing criterion required by other disaggregation methods.