Trust,Owner Rights,Employee Rights and Firm Performance

This is a study of variations in trust relationships according to institutional setting. A wide body of comparative institutional literature within economics and finance engages with trust. However, as most of this literature uses macro‐level data and/or stylistic ideal types, it normally neglects intra‐firm trust. This paper redresses this lacuna by using both macro‐level data and comparative firm‐level evidence. We found that both country trust and firm trust increase firm performance, but that there is a trade‐off between the two as high levels of both reduce performance. Finally, both employee rights and investor rights are negatively correlated with country trust.     

Assessing the potential impact of Microfinance with agent-based modeling

Recent empirical results have cast doubt on the value of Microfinance as a tool for reducing poverty. But, the difficulty lies in evaluating the impact of Microfinance in a world where the Microfinance institutions, their borrowers, and government policies are constantly changing. There is a need for a comprehensive and transparent framework to develop the theoretical grounds for believing in (or against) the efficacy of Microfinance, which can at the same time be used as a testing ground for policymakers. This paper presents a first attempt to develop an agent-based modeling (ABM) framework for pre-policy-implementation testing of the effects of Microfinance. Under the ABM paradigm, a set of behaviors for individual agents in the economy is used to construct a simulation whereby random interaction allows agents to change their state over time. Simulation of the model in different scenarios supported all our intuitions about Microfinance; in particular, there was positive impact of Microfinance on the wealth level of the poor. It was found that increase of available funds, easy access for producers and lower interest rates increase the effectiveness of Microfinance.     

CO-MOVEMENTS IN INTERNATIONAL EQUITY MARKETS

We examine the co-movements of equity returns in four major international markets by characterizing the time-varying cross-country covariances and correlations. Using a generalized positive definite multivariate GARCH model, we find that the Japanese and U.S. stock markets have significant transitory covariance, but zero permanent covariance. The other pairs of markets examined display significant permanent and transitory covariance. We also find that, while conditional correlations between returns are generally small, they change considerably over time. An event analysis suggests that basing diversification strategies on these conditional correlations is potentially beneficial.     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.     

Can fiat currencies really hedge Bitcoin? Evidence from dynamic short-term perspective

Decisions in Economics and Finance - Whereas much research has largely investigated the safe haven, diversifier and hedge proprieties of cryptocurrency, very few papers have analyzed the hedging...