The Macroeconomic Management of Increased Aid: Policy Lessons from Recent Experience

The authors investigate the macroeconomic challenges created by a surge in aid inflows. They develop an analytical framework for examining possible policy responses to increased aid, in terms of absorption and spending of aid—where the central bank controls absorption, through monetary policy and the sale of foreign exchange, and where the fiscal authority controls spending. Different combinations of absorption and spending lead to different macroeconomic consequences. Evidence from five countries that recently experienced an aid surge (Ethiopia, Ghana, Tanzania, Mozambique, and Uganda) shows no support for aid-related real exchange rate appreciation in these countries, but it does indicate that the fear of Dutch disease played an important part in the policy reaction to aid surges. Fiscal and monetary authorities should coordinate their responses to an aid surge, because an uncoordinated response—typically when the fiscal authority wants to spend aid while the central bank wants to avoid exchange rate appreciation—can have serious negative macroeconomic consequences.     

Paradox strategic cognition and ECSR in China: a three-tripod perspective

Literature rooted in institutional theory and stakeholder theory offers conflicting views on the relationship between environmental corporate social responsibility (ECSR) and financial performance. However, both failed to explain why firms interpret this relationship variously which is subjective to a firm’s strategic cognition. How firms interpret this relationship affects the ECSR decision and implementation. Drawing on the paradox theory, four hypotheses were proposed and examined using data from 170 manufacturing firms in China. This study found paradox strategic cognition positively affects ECSR. Legal enforcement inefficiency, competitive intensity and industrial power weaken the effect of paradox strategic cognition on ECSR.     

Pricing of time-varying liquidity risk in Finnish stock market: new evidence

Using two recently developed illiquidity measures, we estimate a conditional version of liquidity-adjusted capital asset pricing model (LCAPM), which allows for a time-varying decomposition of the total illiquidity premium into a level component and three risk components. The total estimated annualized illiquidity premium for the Finnish equities during 1997–2015 is 1.13–1.90% depending on the illiquidity measure. Of the three systematic liquidity risk components, risk arising from hedging of wealth shocks is the most important followed by commonality in liquidity risk, whereas flight to liquidity risk is not significantly priced in the Finnish stock market. Our results show that the liquidity risk is time varying, therefore the models estimating the risk-return relationship should address the issue of conditionality.     

Long-Term Synthetic Puts

We present the possibility of replicating the performance of a long-term put, which is not available in the financial markets, by a set of other traded financial assets. First, a benchmark portfolio is formed out of one share of stock and one put on the stock with a certain exercise price and a long time until maturity. The general form of a portfolio, consisting of shares of stock, bonds, and options on the stock, is discussed, which is expected to perform like the benchmark portfolio. Then a class of these synthetic puts is examined to determine which type of synthetic put may dominate the others.     

Evaluating interpretable machine learning predictions for cryptocurrencies

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.     

Mutual fund flows and investors’ expectations in BRICS economies: Implications for international diversification

This paper empirically examines the relationship between different classes of mutual funds, measures of investors’ expectations and business cycle movements in the BRICS markets over the 1996Q1-2017Q3 period. Applying the Panel Vector Autoregressive (PVAR) model in a Generalized Method of Moments (GMM) setting, the results suggest a strong causal relationship between mutual fund flows and measures of investors’ future expectations. In particular, fund flows are forward-looking and assist in forecasting real economic conditions. Moreover, investors choose to invest in riskier funds when economic conditions are good, while they prefer safer options in poor economic situations. These findings have important implications for international diversification.     

Influence of Experiences on Memories,Satisfaction and Behavioral Intentions: A Study of Creative Tourism

To date, few empirical studies have focused on the constituents of creative tourists’ experience and its consequences. This study aims to bridge this research gap by examining the effect of creative tourists’ experience on their memories, satisfaction, and behavioral intentions. Data were collected from 296 creative tourists, selected through purposive sampling, at selected resort hotels in the Malaysian states of Terengganu and Kedah. The results show that creative-tourist experience is a second-order factor with five dimensions, namely escape and recognition, peace of mind, unique involvement, interactivity, and learning. The results of structural equation modeling show that creative tourists’ experience is a good predictor of their memories, satisfaction, and behavioral intentions. The proposed model and findings can greatly help researchers and practitioners understand the concept of creative-tourist experience and its complex relationships with their memories, satisfaction, and behavioral intentions.     

Dynamic spillover effects among tourism,economic growth and macro-finance risk factors

Portuguese Economic Journal - This paper examines spillover effects among international tourism growth, economic growth and a group of major macroeconomic and financial variables in the US. The...