Search results

As Australians experience more fierce and frequent natural disasters, there are urgent calls for businesses to meaningfully respond to climate change. Australian financial and professional services employees occupy an ambiguous space as climate mitigation measures have different economic implications for their clients. The purpose of this paper is to investigate how Australian professionals experience climate change and respond to the issue within their workplace.

This mixed methods study applies a systems thinking framework to investigate: how do professionals’ experiences of the issue of climate change and the workplace influence their cognitions, emotions and behaviour? And in particular, what psychosocial antecedents precede voicing climate concern?

Firstly, a survey of professionals (N = 206) found social norms, perceived behavioural control and biospheric values, but not attitudes, significantly predicted prohibitive green voice. Middle managers were significantly likely to voice climate concern, whereas senior managers were significantly likely to express climate scepticism. Ten professionals were then interviewed to gain a contextualised understanding of these trends. Interpretive phenomenological analysis identified five interrelated themes: (1) active identity management, (2) understanding climate change is escalating, (3) workplace shapes climate change response, (4) frustration and alienation and (5) belief that corporations prioritise profit.

Findings are discussed in relation to how employees may both embody and adapt their organisations. These results have implications for understandings of workplace meaningfulness and organisational risk governance.

This case invites students to assess the impact that Brexit, the withdrawal of the United Kingdom from the European Union, might have on a New York–based hedge fund's portfolio and, specifically, its UK assets. The case is designed to prompt students to make market assumptions and investment hypotheses based on a combination of numerical data and qualitative information. It requires no numerical computations; instead, it asks the student to interpret both markets' short-term reactions to the Brexit vote and strategy shifts from UK and European business leaders in order to evaluate longer-term implications for the economies of the United Kingdom, Europe, and the world.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

This study empirically demonstrates a contradiction between pillar 3 of Basel norms III and the designation of Systemically Important Banks (SIBs), also known as Too Big to Fail (TBTF). The objective of this study is threefold, which has been approached in a phased manner. The first is to determine the systemic importance of the banks under study; second, to examine if market discipline exists at different levels of systemic importance of banks and lastly, to examine if the strength of market discipline varies at different levels of systemic importance.

This study is based on all the public and private sector banks operating in the Indian banking sector. The Gaussian Mixture Model algorithm has been utilized to classify banks into distinct levels of systemic importance. Thereafter, market discipline has been observed by analyzing depositors' sentiments toward banks' risk (CAMEL indicators). The analysis has been performed by employing the system Generalized Method of Moments (GMM) to estimate models with different dependent variables.

The findings affirm the existence of market discipline across all levels of systemic importance. However, the strength of market discipline varies with the systemic importance of the banks, with weak market discipline being a negative externality of the SIBs designation.

By employing the Gaussian Mixture Model algorithm to develop a framework for categorizing banks on the basis of their systemic importance, this study is the first to go beyond the conventional method as outlined by the Reserve Bank of India (RBI).

The case examines the development and launch of an exchange-traded fund (ETF) based on JUST Capital's socially responsible corporate ranking methodologies. The case provides a market overview of Environment, Social, and Corporate Governance (ESG) and socially responsible investing (SRI), what has driven growth in those areas worldwide, and several best-practice investment approaches. Following the overview, the case describes the founding and development of JUST Capital, explores JUST Capital's ranking methodologies, and presents the decision point faced by the CEO: requisite selection of one of three strategies in order for JUST Capital to generate “self-sustaining” revenue.

The case describes a hypothetical hedge fund manager who is examining whether to invest in bitcoin. The case discusses potential risks and rewards of investing in bitcoin, the role of bitcoin and digital currencies more broadly, and financial innovation in the space, such as ICOs. It can be taught as part of a second-year MBA elective course in investments, financial institutions/capital markets, or fintech.