Adoption des Politiques de Protection de l'Environnement et performance des Entreprises Camerounaises

The objective of this paper is to evaluate the effect of the environmental protection policies of by Cameroonian firms on their performance. It uses the endogenous switching regression technique and propensity scores applied to micro-data from 639 firms in Cameroon. The results show that only 17% of firms adopt these measures, while on average 85% of firms produce solid, gaseous or liquid waste. The results also indicate that the adoption of these environmental protection policies increases operating costs while significantly improving the turnover and the performance of the productive capacity of the company. These increases are 39.11%, 58.6%, and 38.63% for operating costs, turnover and return on productive capacity of the company, respectively. However, firms can also suffer significant losses resulting from the non-adoption of environmental policies. In fact, firms that do not adopt environmental protection policies have their performance reduced by an average of 1.625 percentage points.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Do Islamic Stock Returns Hedge Against Inflation? A Wavelet Approach

This article makes an initial attempt to study the hedging effectiveness of Islamic stock returns against inflation for different investment horizons. We applied the wavelet analysis to measure the cross-correlations between the time series as a function of time-scales using data ranging from 2007 to early 2015. The main results tend to indicate the following: First, that for investment horizons not exceeding 3 years, the FTSE Bursa Malaysia Emas Shariah Index constituent returns may potentially hedge against inflation. Additionally, the hedging ability of stock returns was absent from 2008 to 2009 following the global financial crisis. Finally, a buy-and-hold strategy exceeding 3 years may erode investments. The results are plausible and have strong policy implications.     

Testing vertical price transmission for Vietnam's Robusta coffee

Vietnam has undergone market reforms over the last three decades; and as a consequence, the coffee sector has become increasingly market‐driven. The success of the government's liberalisation policies in terms of market efficiency is investigated by examining the transmission of both positive and negative price changes for Robusta coffee between export and farmgate prices. We used a threshold vector error correction model and high‐frequency daily data. The primary result here is that of a symmetric price transmission between export and farm‐level prices. This result holds when tested with weekly price data, derived from the daily data. Farmgate prices respond faster to decreases than increases in export prices when the long‐run deviation exceeds a certain threshold. These price changes are transmitted within several days. This research also confirms the importance of transaction costs, and other price frictions mostly ignored in prior analyses for coffee.     

Higher Intensity,Higher Profit? Empirical Evidence from Dairy Farming in New Zealand

Over the last two decades there have been significant and well‐documented changes in the nature and structure of New Zealand dairy production. One particular feature has been a marked shift in the ‘input intensity’ of dairy farming systems through increased use of supplementary feed. These changes have generated debate about the impact of dairy farm intensity on the performance of farm businesses and the competitiveness of the New Zealand dairy sector. Using a novel econometric approach, we assess statistically the impact of three types of dairy farming systems on milk production and financial performance, using farm business data provided by DairyBase^®. Our empirical results show that higher input systems perform significantly better physically than lower input systems, but not financially. The disaggregated analysis suggests that the average treatment effects differ by region and performance quantiles.     

Efficiency,economy and effectiveness—but what about ethics? Supreme audit institutions at a critical juncture

Ethics is increasingly recognized as one of several important dimensions of performance. Yet, performance audit, or value-for-money audit, as conducted by supreme audit institutions (SAIs), does not typically include this dimension. Instead, the focus is on the classical ‘three Es’ (efficiency, economy and effectiveness). The reluctance to address issues of ethical misconduct has taken the audit practice of SAIs to a critical juncture, where the legitimacy of these audits ultimately is at stake. This paper explains why SAIs need to add a fourth ‘E’— ethical audit. Possible implications are discussed.     

China and international market integration: Evidence from the law of one price in the Middle East and Africa

We model market integration in the Middle East and Africa by analyzing price dispersion and testing the law of one price (LOP) on highly-comparable actual local retail prices of 135 goods and services across 23 countries in the region over the period of 1990–2016. Second-generation panel estimators are applied to four price benchmarks: Regional average, South Africa, China, and US prices. Cross-regional price dispersion diminishes considerably over time up to 2008, particularly for non-tradeables around China price. The test of LOP indicates the percentage of convergent prices is highest in China price benchmark, followed by US, South Africa, and regional average benchmarks. Direct estimation of the convergence speed confirms this order. Overall, the results show evidence of increasing market integration in Middle East and Africa but it appears to be driven by global forces and, especially, the rise of China as a new economic power. The results show that some emerging market economies, such as China, can step up and promote integration while traditional economic powerhouses, such as the USA and UK, disengage from international economic relations.     

The major determinants of logistic performance in a global perspective: evidence from panel data analysis

The global logistics industry has grown significantly and logistics has become an important sector of the business economic system and a major global economic activity in recent years. Logistics activities accelerate economic and productivity growth. Efficient logistics is also important to a country’s competitiveness and source of employment. The purpose of this paper is to uncover and understand the major determinants of logistics performance (LP) to further lift the LP of countries. Using unbalanced panel data of 93 selected countries from 2007 to 2014, the present research attempts to critically investigate the major determinants of LP. In estimating the model, this study prefers to use static panel data approach owing to limited data. The findings of the present study reveal that (a) countries with low level of corruption and stable political environment are likely to yield a high level of LP; (b) improvement in resources supply such as infrastructure, technology, labour, and education also have a significant positive effect on LP. Therefore, institutional reforms and upgrading resources will effectively accelerate LP.     

我国大麦价格波动特征及其影响因素分析

[目的]大麦价格剧烈波动会直接影响大麦种植户的生产积极性和大麦产业的平稳发展,研究大麦价格波动特征及其影响因素,有助于提升大麦产业链相关主体识别和应对市场风险的能力,促进大麦产业的健康发展。[方法]文章先采用HP滤波法和ARCH类模型分析了2011年4月至2017年2月我国大麦价格波动特征,然后采用脉冲响应函数分析了我国大麦价格波动影响因素。[结果]我国大麦价格波动存在明显的季节性和周期性,样本期内总体上呈现逐渐下降趋势;我国大麦价格具有显著的波动集聚性,我国大麦价格具有显著的不对称性;在该文选择的影响因素中,大麦进口量和国际大麦价格是影响我国大麦价格波动的主要因素。[结论]该文提出必须保障并提高国内大麦合理产能、完善大麦价格监测预警体系、加强国内大麦进口企业整合和推动大麦进口来源多元化的政策建议。     

Positive Feedback Trading and Investor Sentiment

This article examines how investor sentiment affects positive feedback trading behavior. By analyzing the daily closing total return of CSI 300 index and its individual returns of stocks, we find that relatively high or low sentiment induces active positive feedback trading. With a specific indicator of sentiment, we explain the microstructure setting of the relationship between positive feedback trading and sentiment. We adopt the classical feedback model from Sentana and Wadhwani (1992) to measure positive feedback trading behavior. By adding sentiment factor to the model, we successfully explain how sentiment influences the behavior of both feedback traders and rational investors. The empirical findings suggest that positive feedback traders are more likely to trade when the prices of most securities move forward together. When the sentiment of feedback traders is at an intermediate level, the feedback trading behavior is insignificant.