Sahar Charfi, Salah BenHamad and Afif Masmoudi
The purpose of this paper is to analyze how monetary fundamentals affect exchange rate movements.
Abstract
Purpose
The purpose of this paper is to analyze how monetary fundamentals affect exchange rate movements.
Design/methodology/approach
To develop this paper, a Bayesian Network modeling is applied to explore the causal interactions between monetary fundamentals and exchange rate fluctuations. Subsequently, a sensitivity analysis is performed to asses and estimate exchange rate behavior with uncertain monetary fundamentals. Furthermore, a Granger Causality test is used as suggested in the Econometric literature to determine the causality direction among factors.
Findings
The empirical findings show that money supply and interest rate have a significant positive effect on exchange rate, whereas inflation rate has a considerable negative effect on exchange rate. In addition, the authors deduce that real income has an indirect impact on exchange rate and a direct impact on inflation rate, interest rate and money supply. Furthermore, the sensitivity analysis shows that monetary uncertainty has a considerable effect on exchange rate fluctuations. Moreover, the Granger Causality test reveals that there is a unique unidirectional causality running from money supply to exchange rate.
Practical implications
The model can be considered as a vital management tool for international investors and financial analysts to explore the effect of monetary fundamentals on exchange rate behavior. It allows estimating exchange rate fluctuations with uncertain monetary factors.
Originality/value
This study is the first one which applied a Bayesian Network modeling to examine the exchange rate determination problem. Results of this research are presented under a clear graphical representation that can be easily useful by monetary policymakers and international traders to determine the influential monetary factors on exchange rate behavior. Also, the model will help them in estimating the effect of monetary uncertainty on exchange rate fluctuations.
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Yousra Trichilli, Mouna Boujelbène Abbes and Afif Masmoudi
The purpose of this paper is to evaluate the capability of the hidden Markov model using Googling investors’ sentiments to predict the dynamics of Islamic indexes’ returns in the…
Abstract
Purpose
The purpose of this paper is to evaluate the capability of the hidden Markov model using Googling investors’ sentiments to predict the dynamics of Islamic indexes’ returns in the Middle East and North Africa (MENA) financial markets from 2004 to 2018.
Design/methodology/approach
The authors propose a hidden Markov model based on the transition matrix to apprehend the relationship between investor’s sentiment and Islamic index returns. The proposed model facilitates capturing the uncertainties in Islamic market indexes and the possible effects of the dynamics of Islamic market on the persistence of these regimes or States.
Findings
The bearish state is the most persistent sentiment with the longest duration for all the MENA Islamic markets except for Jordan, Morocco and Qatar. In addition, the obtained results indicate that the effect of sentiment on predicting the future Islamic index returns is conditional on the MENA States. Besides, the estimated mean returns for each state indicates that the bullish and calm states are ideal for investing in Islamic indexes of Bahrain, Oman, Morocco, Kuwait, Saudi Arabia and United Arab Emirates. However, only the bullish state is ideal for investing Islamic indexes of Jordan, Egypt and Qatar.
Research limitations/implications
This paper has used data at a monthly frequency that can explain only short-term dynamics between Googling investor’s sentiment and the MENA Islamic stock market returns. Moreover, this work can be done on the stock markets while taking into account the specificity of each activity sector.
Practical implications
In fact, the findings of this paper are helpful for academics, analysts and practitioners, and more specifically for the Islamic MENA financial investors. Moreover, this study provides useful insights not only into the duration of the relationship between the indexes’ returns and the investors’ sentiments in the five states but also into the transition probabilities which have implications for how investors could be guided in their choice of future investment in a portfolio with Islamic indexes. Findings of this paper are important and valuable for policy-makers and investors. Thus, predicting the effect of Googling investors’ sentiment on the MENA Islamic stock market dynamics is important for portfolio diversification by domestic and international investors. Moreover, the results of this paper gave new insights into financial analysts about the dynamic relationship between Googling investors’ sentiment and Islamic stock market returns across market regimes. Therefore, the findings of this study might be useful for investors as they help them capture the unobservable dynamics of the changes in the investors’ sentiment regimes in the MENA financial markets to make successful investment decisions.
Originality/value
To the best of the authors’ knowledge, this paper is the first to use the hidden Markov model to examine changes in the Islamic index return dynamics across five market sentiment states, namely the depressed sentiment (S1), the bullish sentiment (S2), the bearish sentiment (S3), the calm sentiment (S4) and the bubble sentiment (S5).
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Yousra Trichilli, Sahbi Gaadane, Mouna Boujelbène Abbes and Afif Masmoudi
In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies…
Abstract
Purpose
In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies, commodities and stock markets before and during COVID-19 periods.
Design/methodology/approach
The authors investigate the impact of the confirmation bias on the estimated returns and the expectations of optimistic and pessimistic traders by employing the financial stochastic model with confirmation bias. Indeed, the authors compute the optimal portfolio weights, the optimal hedge ratios and the hedging effectiveness.
Findings
The authors find that without confirmation bias, during the two sub periods, the expectations of optimistic and pessimistic trader’s seem to convergence toward zero. However, when confirmation bias is particularly strong, the average distance between these two expectations are farer. The authors further show that, with and without confirmation bias, the optimal weights (the optimal hedge ratios) are found to be lower (higher) for all pairs of financial market during the COVID-19 period as compared to the pre-COVID-19 period. The authors also document that the stronger the confirmation bias is, the lower the optimal weight and the higher the optimal hedge ratio. Moreover, results reveal that the values of the optimal hedge ratio for optimistic and pessimistic traders affected or not by the confirmation bias are higher during the COVID-19 period compared to the estimates for the pre-COVID period and inversely for the optimal hedge ratios and the hedging effectiveness index. Indeed, either for optimists or pessimists, the presence of confirmation bias leads to higher optimal hedge ratio, higher optimal weights and higher hedging effectiveness index.
Practical implications
The findings of the study provided additional evidence for investors, portfolio managers and financial analysts to exploit confirmation bias to make an optimal portfolio allocation especially during COVID-19 and non-COVID-19 periods. Moreover, the findings of this study might be useful for investors as they help them to make successful investment decision in potential hedging strategies.
Originality/value
First, this is the first scientific work that conducts a stochastic analysis about the impact of emotional biases on the estimated returns and the expectations of optimists and pessimists in cryptocurrency and commodity markets. Second, the originality of this study stems from the fact that the authors make a comparative analysis of hedging behavior across different markets and different periods with and without the impact of confirmation bias. Third, this paper pays attention to the impact of confirmation bias on the expectations and hedging behavior in cryptocurrencies and commodities markets in extremely stressful periods such as the recent COVID-19 pandemic.
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Reshma Dnyandev Vartak Koli and Avinash Sharma
This study aims to compare traffic sign (TS) and obstacle detection for autonomous vehicles using different methods. The review will be performed based on the various methods, and…
Abstract
Purpose
This study aims to compare traffic sign (TS) and obstacle detection for autonomous vehicles using different methods. The review will be performed based on the various methods, and the analysis will be done based on the metrics and datasets.
Design/methodology/approach
In this study, different papers were analyzed about the issues of obstacle detection (OD) and sign detection. This survey reviewed the information from different journals, along with their advantages and disadvantages and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of autonomous vehicles and is obliged to accurately identify various TS.
Findings
The review of different approaches based on deep learning (DL), machine learning (ML) and other hybrid models that are utilized in the modern era. Datasets in the review are described clearly, and cited references are detailed in the tabulation. For dataset and model analysis, the information search process utilized datasets, performance measures and achievements based on reviewed papers in this survey.
Originality/value
Various techniques, search procedures, used databases and achievement metrics are surveyed and characterized below for traffic signal detection and obstacle avoidance.