Global Liquidity,Current Account Deficit,and Exchange Rate Balance Sheet Effects in Turkey

This study investigates the current account deficit (CAD) of Turkey from the perspective of its capital account. We discuss how global liquidity conditions and monetary policies in Turkey have contributed to higher deficits through real exchange rate appreciations. We analyze the impact and consequences of exchange rate (ER) changes on the investments of non-financial firms. In the case of real ER depreciations, we find that the magnitude of the contractionary effect through balance sheets of firms with dollarized liabilities is significantly higher than the expansionary effect through trade competitiveness. We also analyze the “soft-landing” policies aimed at reducing the CAD in Turkey and estimate the rate of economic growth that must be foregone for a percentage reduction in CAD.     

The place attachment experience regarding the disabled people: the typology of coffee shops

This study inquires the possible creation of a sense of a place in coffee houses for the disabled people as public places. The physical characteristics of a space are the main factors allowing all people including the disabled ones as well to perceive that place properly and within the “person, place and interaction process” framework. The place attachment, which can be defined as the main emotional bond between space and the users, is heavily influenced by the individualistic experiences and reactions whether they are ordinary or differently abled. In order to initiate a place attachment, spaces should be designed in a way that they respect the occupant’s perception, feelings, memories, senses and their physical abilities to enable them to develop a sense of belonging and ownership. Therefore, creating designs which foster people’s construction process of sense of a place that comprises the concept of identity and sense of belonging, is among the essential issues for realizing place attachment in architecture and design of public spaces. In our contemporary world, there are settings that possess these qualities; contrariwise there are more architectural and urban spaces which ignore these qualities and their benefits for people, especially for the disabled ones. Relatedly, a building should offer people enough information for perceiving it and with its physical features should also support the experiences that occur in it. There is a strong link between memorable events and the space where those events occur. An environment which provides outstanding and meaningful ambiance by its architectural characteristics can establish a sense of a place. Disabled people often have less opportunities to experience the spaces properly compared to others because their limitations are not taken into consideration when designing the public spaces. Therefore, the design process of public spaces should involve the basic needs of disabled people. In order to propose a typology for traditional coffee houses, this research aims to define and discuss the place attachment experience of disabled people in public spaces. The study begins with an overview of identifying different types of disabilities and special needs, and an introductory discussion of how space attachment can help to meet those needs. Therefore, the physical, social and typological aspects of traditional coffee houses were evaluated in terms of location, space organization and functions by considering the profiles of disabled users. Methodologically, this is a case study, which has a mixed research design including qualitative and quantitative research instruments.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

Online donation behavior in nonprofit organizations: The role of congruent processing styles

With the advent of technology, donors are increasingly donating via online channels. This shift deems it necessary for nonprofit organizations to better understand the online donor behavior. As such, the purpose of this paper is twofold. First, we investigate if nonprofit organizations are perceived differently in terms of their cognitive and affective natures. Then, we examine how different consumer processing styles, i.e. cognitive and affective, interact with consumer perceptions of nonprofit cognitive/affective orientations in influencing their donation intentions. Our results indicate that consumers with a high need for cognition are more willing to donate to predominantly cognitive nonprofit organizations, while those with high need for emotion are more willing to donate to predominantly affective nonprofit organizations. Based on these results, we suggest that nonprofit organizations can garner more donations if they request funds from donors whose processing styles are congruent with the organization. Additional recommendations for future research are provided.     

Adverse selection in cryptocurrency markets

In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse-selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse-selection costs for major cryptocurrencies. Also, our results suggest that adverse-selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse-selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse-selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.