This paper evaluates the predictive accuracy of neural networks in forecasting exchange rate. The multilayer perceptron (MLP) and radial basis function (RBF) networks with different architectures are used to forecast five exchange rate time series. The results of each prediction are evaluated and compared according to the networks and architectures used. It is found that neural networks can be effectively used in forecasting exchange rate and hence in designing trading strategies. RBF networks performed better than MLP networks in our simulation experiment. This experiment suggests that it is possible to extract information hidden in the exchange rate and predict it into future.
Purpose
Certified emission reduction (CER) survey studies in the literature are quite restrictive in scope. These studies are based on convenience sampling and, therefore, cannot be relied upon. The current study comprehensively surveys the strengths, weaknesses and suggestive measures for clean development mechanism (CDM). This paper systematically aims to conduct the survey on top 50 companies in terms of CER volume.
Design/methodology/approach
The survey is aimed to target top 50 companies which account for 55 per cent of total number of CERs of all the Indian projects. The online survey link was sent to all 50 companies, and the finance managers were followed up regularly over a period of one year. Finally, 37 responses (a response rate of 72 per cent) have been received.
Findings
“CER is cheaper than EUA for Emission Compliance” is rated as topmost strength and “Methodology of Financial Additionality is Subjective” is rated as topmost weakness of CER mechanism. Removal of Quantitative Restrictions on CERs is rated as the topmost suggestive measure for stabilization of CER. Companies overwhelmingly favored continuation of banking and inclusion of carbon emission cost as one of the internal cost of business.
Practical implications
The current study throws light on future policy interventions for minimization of carbon footprint and efficient energy management.
Social implications
This study gives vital reflections for stabilization of CDM. This will help sustainable development, generation of green energy, mitigation of carbon emission at the least cost and employment generation in developing countries because of CDM project development.
Originality/value
This study differs from earlier studies because it comprehensively surveys the pertinent issues relating to strengths, weaknesses and suggestive measures for CDM. It also differs from them because it is not based on convenience sampling. It conducts the survey systematically on top 50 companies in terms of CER volume. Therefore, unlike previous studies of questionable validities, the findings of this study can be safely considered for policy interventions
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