Search results for: analysts' earnings forecasts

Authors: Mujeeb Saif Mohsen Al-Absy, Ku Nor Izah Ku Ismail, Sitraselvi Chandren

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This study examines whether corporate governance (CG) mechanisms in firms that have a whistle-blowing policy (WHBLP) are more effective in constraining earnings management (EM), than those without. A sample of 288 Malaysian firms for the years 2013 to 2015, amounting to 864 firm-years were grouped into firms with and without WHBLP. Results show that for firms without WHBLP, the board chairman tenure would minimize EM activities. Meanwhile, for firms with WHBLP, board chairman independence, board chairman tenure, audit committee size, audit committee meeting and women in the audit committees are found to be associated with less EM activities. Further, it is found that ownership concentration and Big 4 auditing firms help to reduce EM activities in firms with WHBLP, while not in firms without WHBLP. Hence, functional and effective governance can be achieved by having a WHBLP, which is in line with agency and resource dependent theories. Therefore, this study suggests that firms should have a WHBLP in place, and policymakers should come up with enhanced criteria to strengthen the mechanisms of WHBLP.

Keywords: corporate governance, earnings management, whistle-blowing policy, audit committee, board of directors

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Authors: Yousef Abu Nahleh, Arun Kumar, Fugen Daver, Reham Al-Hindawi

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Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability of disaster for each country in the world by using decision tree modeling. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.

Keywords: decision tree modeling, forecasting, humanitarian relief, emergency supply chain

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Authors: Ramin Vafadary, Maryam Khanbaghi

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Forecasting electricity load is important for various purposes like planning, operation, and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet, and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria, namely, the mean absolute error and root mean square error. The National Renewable Energy Laboratory (NREL) residential energy consumption data is used to train the models. The results of this study show that the SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts, we can improve the robustness of the models for 24 hours ahead of electricity load forecasting.

Keywords: bagging, Fbprophet, Holt-Winters, LSTM, load forecast, SARIMA, TensorFlow probability, time series

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Authors: Ugbede Onalo, Mohd Lizam, Ahmad Kaseri, Otache Innocent

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Giving the paucity of studies on IFRS adoption and quality of banks accounting quality, particularly in emerging economies, this study is motivated to investigate whether the Nigeria decision to adopt IFRS beginning from 1 January 2012 is associated with high quality accounting measures. Consistent with prior literatures, this study measure quality of financial statement information using earnings measurement, timeliness of loss recognition and value relevance. A total of twenty Nigeria banks covering a period of six years (2008-2013) divided equally into three years each (2008, 2009, 2010) pre adoption period and (2011, 2012, 2013) post adoption period were investigated. Following prior studies eight models were in all employed to investigate earnings management, timeliness of loss recognition and value relevance of Nigeria bank accounting quality for the different reporting regimes. Results suggest that IFRS adoption is associated with minimal earnings management, timely recognition of losses and high value relevance of accounting information. Summarily, IFRS adoption engenders higher quality of banks financial statement information compared to local GAAP. Hence, this study recommends the global adoption of IFRS and that Nigeria banks should embrace good corporate governance practices.

Keywords: IFRS, SAS, quality of accounting information, earnings measurement, discretionary accruals, non-discretionary accruals, total accruals, Jones model, timeliness of loss recognition, value relevance

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Authors: Daniil Karzanov

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This paper studies the effect of quarterly earnings reports on the stock price. The profitability of the stock is modeled by geometric Brownian diffusion and the Constant Elasticity of Variance model. We fit several variations of stochastic differential equations to the pre-and after-report period using the Maximum Likelihood Estimation and Grid Search of parameters method. By examining the change in the model parameters after reports’ publication, the study reveals that the reports have enough evidence to be a structural breakpoint, meaning that all the forecast models exploited are not applicable for forecasting and should be refitted shortly.

Keywords: stock market, earnings reports, financial time series, structural breaks, stochastic differential equations

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Authors: Hashem Dehghanniri

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Crime scripting is a simple and effective crime modeling technique that aims to improve understanding of security analysts about security and crime incidents. Low-quality scripts provide a wrong, incomplete, or sophisticated understanding of the crime commission process, which oppose the purpose of their application, e.g., identifying effective and cost-efficient situational crime prevention (SCP) measures. One important and overlooked factor in generating quality scripts is the crime scripting method. This study investigates the problems within the existing crime scripting practices and proposes a crime scripting approach that contributes to generating quality crime scripts. It was validated by experienced crime scripters. This framework helps analysts develop better crime scripts and contributes to their effective application, e.g., SCP measures identification or policy-making.

Keywords: attack modelling, crime commission process, crime script, situational crime prevention

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Authors: Jian Xu, Xiaochun Yun, Yongzheng Zhang, Yafei Sang, Zhenyu Cheng

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Network attack prevention is a critical research area of information security. Network attack would be oppressed if attribution techniques are capable to trace back to the attackers after the hacking event. Therefore attributing these attacks to a particular identification becomes one of the important tasks when analysts attempt to differentiate and profile the attacker behind a piece of attack trace. To assist analysts in expose attackers behind the scenes, this paper researches on the connections between attribution traces and proposes probabilistic relevance based attribution patterns. This method facilitates the evaluation of the plausibility relevance between different traceable identifications. Furthermore, through analyzing the connections among traces, it could confirm the existence probability of a certain organization as well as discover its affinitive partners by the means of drawing relevance matrix from attribution traces.

Keywords: attribution trace, probabilistic relevance, network attack, attacker identification

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Authors: I. Martinez-Conesa, E. Garcia-Meca, M. Barradas-Quiroz

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The objective of this study is to assess the impact on earnings management of latest two Hofstede cultural dimensions: long-term orientation and indulgence. Long-term orientation represents the alignment of a society towards the future and indulgence expresses the extent to which a society exhibits willingness, or restrain, to realise their impulses. Additionally, this paper tests if there are relevant differences by testing the moderating role of the legal tradition, Continental versus Anglo-Saxon. Our sample comprises 15 countries: Belgium, Canada, Germany, Spain, France, Great Britain, Hong Kong, India, Japan, Korea, Netherlands, Philippines, Portugal, Sweden, and Thailand, with a total of 12,936 observations from 2003 to 2013. Our results show that managers in countries with high levels of long-term orientation reduce their levels of discretionary accruals. The findings do not confirm the effect of indulgence on earnings management. In addition, our results confirm previous literature regarding the effect of individualism, noting that firms in countries with high levels of collectivism might be more inclined to use earnings discretion to protect the welfare of the collective group of firm stakeholders. Uncertainty avoidance results in downwards earnings management as well as high disclosure, suggesting that less manipulation takes place when transparency is higher. Indulgence is the cultural dimension that confronts wellbeing versus survival; dimension is formulated including happiness, the perception of live control and the importance of leisure. Indulgence shows a weak negative correlation with power distance indicating a slight tendency for more hierarchical societies to be less indulgent. Anglo-Saxon countries are a positive effect of individualism and a negative effect of masculinity, uncertainty avoidance, and disclosure. With respect to continental countries, we can see a significant and positive effect of individualism and a significant and negative effect of masculinity, long-term orientation, and indulgence. Therefore, we observe the negative effect on earnings management provoked by higher disclosure and uncertainty avoidance only happens in Anglo-Saxon countries. Meanwhile, the improvement in reporting quality motivated by higher long-term orientation and higher indulgence is dominant in Continental countries. Our results confirm that there is a moderating effect of the legal system in the association between culture and earnings management. This effect is especially relevant in the dimensions related to uncertainty avoidance, long term orientation, indulgence, and disclosure. The negative effect of long-term orientation on earnings management only happens in those countries set in continental legal systems because of the Anglo-Saxon legal systems is supported by the decisions of the courts and the traditions, so it already has long-term orientation. That does not occur in continental systems, depending mainly of contend of the law. Sensitivity analysis used with Jones modified CP model, Jones Standard model and Jones Standard CP model confirm the robustness of these results. This paper collaborates towards a better understanding on how earnings management, culture and legal systems relate to each other, and contribute to previous literature by examining the influence of the two latest Hofstede’s dimensions not previously studied in papers.

Keywords: Hofstede, long-term-orientation, earnings management, indulgence

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Authors: Pradyumna Mohanty

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The paper aims to explore whether accounting information of Indian companies in the Steel sector are value relevant or not. Ohlson’s model which usually takes into consideration book value per share (BV) and earnings per share (EARN) has been used and the same has been expanded to include two more variables such as cash flow from operations (CFO) and return on equity (ROE). The data were collected from CMIE-Prowess data base in respect of BSE-listed steel companies and the time frame spans from 2010 to 2014. OLS regression has been used to test the value relevance of these accounting numbers. Results indicate that both CFO and BV are having significant influence on the stock price in two out of five years of study. But, BV is emerging as the most significant and highly value relevant of all the four variables during the entire period of study.

Keywords: value relevance, accounting information, book value per share, earnings per share

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Authors: Fatemeh Noughani, Seyd Mehdi Sadat

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Medical tourism or travel therapy is travelling from one country to another to be under medical treatment, utilizing the health factors of natural sector like mineral water springs and so on. From 1990s medical tourism around the world developed and grew because of different factors like globalization and free trade in the fields of health services, changes in exchange rates in the world economy (which caused the desirability of Asian countries as a medical tourist attraction) in a way that currently there is a close competition in this field among famous countries in medical services to make them find a desirable place in medical tourism market of the world as a complicated and growing industry in a short time. Perhaps tourism is an attractive industry and a good support for the economy of Iran, if we try to merge oil earnings and tourism industry it would be better and more constructive than putting them in front of each other. Moving from oil toward tourism economy especially medical tourism, must be one of the prospects of Iran's government for the oil industry to provide a few percent of the yearly earnings of the country. Among the achievements in medical tourism we can name the prevention of brain drain to other countries and an increase in employment rate for healthcare staff, increase in foreign exchange earnings of the country because of the tourists' staying and followed by increasing the quality of life and cultural transmission as well as empowering the medical human resources.

Keywords: developing countries, health tourism, quality of life, cultural transmission

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Authors: Etienne Gaborit, Dorothy Durnford, Daniel Deacu, Marco Carrera, Nathalie Gauthier, Camille Garnaud, Vincent Fortin

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A new experimental streamflow forecasting system was recently implemented at the Environment and Climate Change Canada’s (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP). It relies on CaLDAS (Canadian Land Data Assimilation System) for the assimilation of surface variables, and on a surface prediction system that feeds a routing component. The surface energy and water budgets are simulated with the SVS (Soil, Vegetation, and Snow) Land-Surface Scheme (LSS) at 2.5-km grid spacing over Canada. The routing component is based on the Watroute routing scheme at 1-km grid spacing for the Great Lakes and Nelson River watersheds. The system is run in two distinct phases: an analysis part and a forecast part. During the analysis part, CaLDAS outputs are used to force the routing system, which performs streamflow assimilation. In forecast mode, the surface component is forced with the Canadian GEM atmospheric forecasts and is initialized with a CaLDAS analysis. Streamflow performances of this new system are presented over 2019. Performances are compared to the current ECCC’s operational streamflow forecasting system, which is different from the new experimental system in many aspects. These new streamflow forecasts are also compared to persistence. Overall, the new streamflow forecasting system presents promising results, highlighting the need for an elaborated assimilation phase before performing the forecasts. However, the system is still experimental and is continuously being improved. Some major recent improvements are presented here and include, for example, the assimilation of snow cover data from remote sensing, a backward propagation of assimilated flow observations, a new numerical scheme for the routing component, and a new reservoir model.

Keywords: assimilation system, distributed physical model, offline hydro-meteorological chain, short-term streamflow forecasts

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Authors: Shay Kee Tan, Kok Haur Ng, Jennifer So-Kuen Chan

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This paper extends the conditional autoregressive range (CARR) model to multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain in-sample estimates and forecasts of volatilities for these individual and pairwise sum series. Then covariances are calculated to construct the fitted variance-covariance matrix of returns which are imputed into the stage-two return model to capture the heteroskedasticity of assets’ returns. We investigate different choices of mean functions to describe the volatility dynamics. Empirical applications are based on the Standard and Poor 500, Dow Jones Industrial Average and Dow Jones United States Financial Service Indices. Results show that the stage-one MCARR models using asymmetric mean functions give better in-sample model fits than those based on symmetric mean functions. They also provide better out-of-sample volatility forecasts than those using CARR models based on two robust loss functions with the scaled realised open-to-close volatility measure as the proxy for the unobserved true volatility. We also find that the stage-two return models with constant means and multivariate Student-t errors give better in-sample fits than the Baba, Engle, Kraft, and Kroner type of generalized autoregressive conditional heteroskedasticity (BEKK-GARCH) models. The estimates and forecasts of value-at-risk (VaR) and conditional VaR based on the best MCARR-return models for each asset are provided and tested using Kupiec test to confirm the accuracy of the VaR forecasts.

Keywords: range-based volatility, correlation, multivariate CARR-return model, value-at-risk, conditional value-at-risk

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Authors: Ilaria Lucrezia Amerise

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Electricity markets throughout the world have undergone substantial changes. Accurate, reliable, clear and comprehensible modeling and forecasting of different variables (loads and prices in the first instance) have achieved increasing importance. In this paper, we describe the actual state of the art focusing on reg-SARMA methods, which have proven to be flexible enough to accommodate the electricity price/load behavior satisfactory. More specifically, we will discuss: 1) The dichotomy between point and interval forecasts; 2) The difficult choice between stochastic (e.g. climatic variation) and non-deterministic predictors (e.g. calendar variables); 3) The confrontation between modelling a single aggregate time series or creating separated and potentially different models of sub-series. The noteworthy point that we would like to make it emerge is that prices and loads require different approaches that appear irreconcilable even though must be made reconcilable for the interests and activities of energy companies.

Keywords: interval forecasts, time series, electricity prices, reg-SARIMA methods

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Authors: I. Diaz Sanchez, I. M. Martinez-Conesa, M. Illueca

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Accounting conservatism is a desirable quality of earnings that is positively associated with the stridency of regulatory and supervisory regimen and high market discipline. But how these three pillars interact each other is the main research question that is not empirically solved. We analyze how regulatory and supervisory regimes interact with the market discipline measures, such as listing status, ownership and market concentration using a sample of 14,651 bank-year observations covering 54 countries over the period 1997-2009. We evidence that regulation a supervision and extend on which they are enforcement is a strong mechanism to achieved accounting conservatism in those countries or situations where the market discipline fails. Generally, the supervisory power reinforces the effect of listing status, ownership and concentration on conservatism, while capital regulatory mitigates the effect of market discipline on conservatism. This paper may contribute to debate about the mechanism introduced by Basel III that strongly increases the regulation, his enforcement, and the supervisory power after long deregulation period. Although Market discipline is relevant to achieve the financial stability, strong Pillar I and II can ensure the quality of the accounting earnings to prevent bank failures.

Keywords: accounting conservatism, bank regulation, bank supervision, loan loss recognition, market discipline

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Authors: Jamila Garba Audu, Shehu Usman Hassan

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The widespread failure in the financial information quality has created the need to improve the financial information quality and to strengthen the control of managers by setting up good firms structures. Published accounting information in financial statements is required to provide various users - shareholders, employees, suppliers, creditors, financial analysts, stockbrokers and government agencies – with timely and reliable information useful for making prudent, effective and efficient decisions. The relationship between corporate governance and performance to financial reporting quality is imperative; this is because despite rapid researches in this area the findings obtained from these studies are constantly inconclusive. Data for the study were extracted from the firms’ annual reports and accounts. After running the OLS regression, a robustness test was conducted for the validity of statistical inferences; the data was empirically tested. A multiple regression was employed to test the model as a technique for data analysis. The results from the analysis revealed a negative association between all the regressors and financial reporting quality except the performance of listed manufacturing firms in Nigeria. This indicates that corporate governance plays a significant role in mitigating earnings management and improving financial reporting quality while performance does not. The study recommended among others that the composition of audit committee should be made in accordance with the provision for code of corporate governance which is not more than six (6) members with at least one (1) financial expert.

Keywords: corporate governance, financial reporting quality, manufacturing firms, Nigeria, performance

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Authors: Giriraj Achari, Malay Bhattacharyya

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In this paper, the effectiveness of Copula Markov Switching Multifractal (MSM) models at forecasting Value-at-Risk of a two-stock portfolio is studied. The innovations are allowed to be drawn from distributions that can capture skewness and leptokurtosis, which are well documented empirical characteristics observed in financial returns. The candidate distributions considered for this purpose are Johnson-SU, Pearson Type-IV and α-Stable distributions. The two univariate marginal distributions are combined using the Student-t copula. The estimation of all parameters is performed by Maximum Likelihood Estimation. Finally, the models are compared in terms of accurate Value-at-Risk (VaR) forecasts using tests of unconditional coverage and independence. It is found that Copula-MSM-models with leptokurtic innovation distributions perform slightly better than Copula-MSM model with Normal innovations. Copula-MSM models, in general, produce better VaR forecasts as compared to traditional methods like Historical Simulation method, Variance-Covariance approach and Copula-Generalized Autoregressive Conditional Heteroscedasticity (Copula-GARCH) models.

Keywords: Copula, Markov Switching, multifractal, value-at-risk

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Authors: Farizy Yunaz, Catur Sasongko

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This research aims to provide the empirical evidence regarding cost stickiness behavior and its determinants on listed manufacturing companies. Hypothesis testing is performed using pooled least square method. The result concludes that there is cost stickiness behavior in selling, general and administrative costs. In term of determinants, firm-specific adjustment costs measured by asset intensity and employee intensity have significant positive impact on the level of cost stickiness. Meanwhile, earnings target and leverage have significant negative impact on the level of cost stickiness. However, the management empire building incentives measured by free cash flow has no significant positive impact.

Keywords: adjustment cost, cost behavior, cost stickiness, earnings target, leverage, management empire building incentive

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Authors: Selin Guney, Andres Riquelme

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Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

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Authors: András Medve, Katalin Szabad, István Patkó

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According to the International Energy Association the previous principles of the energy sector should be reassessed, in which renewable energy sources have a significant role. We might witness the exchange of roles of countries from importer to exporter, which look for the main resources of market needs. According to the World Energy Outlook 2013, the duration of high oil prices is exceptionally long in the history of the energy market. Forecasts also point at the expected great differences between the regional prices of gas and electric energy. The energy need of the world will grow by its third. two thirds of which will appear in China, India, and South-East Asia, while only 4 per cent of which will be related to OECD countries. Current trends also forecast the growth of the price of energy sources and the emission of glasshouse gases. As a reflection of these forecasts alternative energy sources will gain value, of which geothermic energy is one of the cheapest and most economical. Hungary possesses outstanding resources of geothermic energy. The aim of the study is to research the environmental effects of geothermic energy and the opportunities of its exploitation in Hungary, related to „Horizon 2020” project.

Keywords: sustainable energy, renewable energy, development of geothermic energy in Hungary

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Authors: Kevin Smith

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Publicly traded company in the US are legally obligated to host earnings calls that discuss their most recent financial reports. During these calls, investors are able to ask these companies questions about these financial reports and on the future direction of the company. This paper examines whether common institutional owners use these calls as a way to indirectly signal to companies in their portfolio to not take actions that could hurt the common owner's interests. This paper uses transcripts taken from the earnings calls of the six largest health insurance companies in the US from 2014 to 2019. This data is analyzed using text analysis and sentiment analysis to look for patterns in the statements made by common owners. The analysis found that common owners where more likely to recommend against direct price competition and instead redirect the insurance companies towards more passive actions, like investing in new technologies. This result indicates a mechanism that common owners use to reduce competition in the health insurance market.

Keywords: common ownership, text analysis, sentiment analysis, machine learning

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Wilmer Osvaldo Martinez, Manuel Dario Hernandez, Juan Manuel Julio

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We propose to assess the performance of k forecast procedures by exploring the distributions of forecast errors and error losses. We argue that non systematic forecast errors minimize when their distributions are symmetric and unimodal, and that forecast accuracy should be assessed through stochastic loss order rather than expected loss order, which is the way it is customarily performed in previous work. Moreover, since forecast performance evaluation can be understood as a one way analysis of variance, we propose to explore loss distributions under two circumstances; when a strict (but unknown) joint stochastic order exists among the losses of all forecast alternatives, and when such order happens among subsets of alternative procedures. In spite of the fact that loss stochastic order is stronger than loss moment order, our proposals are at least as powerful as competing tests, and are robust to the correlation, autocorrelation and heteroskedasticity settings they consider. In addition, since our proposals do not require samples of the same size, their scope is also wider, and provided that they test the whole loss distribution instead of just loss moments, they can also be used to study forecast distributions as well. We illustrate the usefulness of our proposals by evaluating a set of real world forecasts.

Keywords: forecast evaluation, stochastic order, multiple comparison, non parametric test

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Authors: Chiara De Amicis, Sonia Falconieri, Mesut Tastan

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In this study, we analyze the language employed by Chief Executive Officers (CEOs) and Chief Financial Officers (CFOs) during earnings conference calls from a gender perspective. We find evidences that conference calls held by female CEOs and/or CFOs exhibit a higher level of optimism compared to conference calls held by male CEOs and/or CFOs. Moreover, female managers tend to present and discuss firm performances with less vagueness as compared to their male colleagues. We then observe the market reaction around each earnings conference call: while manager optimism is perceived as a good signal by investors, manager vagueness significantly dampens the market reaction around the call. Whether the gender of the CEO and/or the CFO delivering the conference call affects investors’ perceptions about the firm performance is still an open question. Some evidences show that the language employed by female managers conveys more valuable information for market participants as compared to the language employed by their male counterparts. This study contributes to a growing literature in finance and accounting that uses textual analysis to assess the informativeness of corporate disclosure. To our knowledge, this is the first paper that aims at answering the question whether the gender of firm’s top managers does matter when it comes to assess the informativeness of corporate spoken communication. We believe that our results will be of relevance for future research in the field. Moreover, our evidence may be used in support of the debate if a larger participation by women in the management of companies should be encouraged or not.

Keywords: conference calls, even study, gender, market reaction, textual analysis

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Authors: Abisola Akinola, Akinsulere Femi

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The stock market has continually been a source of economic development in most developing countries. This study examined the relationship between investors’ ratio analysis and profitability of quoted companies in Nigeria using secondary data obtained from the annual reports of forty-two (42) companies. The study employed the multiple regression technique to analyze the relationship between investors’ ratio analysis (measured by dividend per share and earning per share) and profitability (measured by the return on equity). The results from the analysis show that investors’ ratio analysis, when measured by earnings per share, have a positive and significant impact on profitability. However, the study noted that investors’ ratio analysis, when measured by dividend per share, tend to have a positive impact on profitability but it is statistically insignificant. By implication, investors and other stakeholders that are interested in investing in stocks can predict the earning capacity of listed firms in the stock market.

Keywords: dividend per share, earnings per share, profitability, return on equity

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Authors: Watchareeporn Chaimongkol

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In this paper, we compare the statistical measures accuracy of composite forecasting model to estimate automobile customer demand in Thailand. A modified simple exponential smoothing and autoregressive integrate moving average (ARIMA) forecasting model is built to estimate customer demand of passenger cars, instead of using information of historical sales data. Our model takes into account special characteristic of the Thai automobile market such as sales promotion, advertising and publicity, petrol price, and interest rate for loan. We evaluate our forecasting model by comparing forecasts with actual data using six accuracy measurements, mean absolute percentage error (MAPE), geometric mean absolute error (GMAE), symmetric mean absolute percentage error (sMAPE), mean absolute scaled error (MASE), median relative absolute error (MdRAE), and geometric mean relative absolute error (GMRAE).

Keywords: composite forecasting, simple exponential smoothing model, autoregressive integrate moving average model selection, accuracy measurements

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Authors: Gabriel Woldu, Maria Sassi

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Coffee performs a pivotal role in Ethiopia's GDP, revenue, employment, domestic demand, and export earnings. Ethiopia's coffee production and exports show high variability in the amount of production and export earnings. Despite being the continent's fifth-largest coffee producer, Ethiopia has not developed its ability to shine as a major exporter in the globe's green coffee exports. Ethiopian coffee exports were not stable and had high volume and earnings fluctuations. The main aim of this study was to analyze the dynamics of the export of coffee variation to different importing nations using a first-order Markov Chain model. 14 years of time-series data has been used to examine the direction and structural change in the export of coffee. A compound annual growth rate (CAGR) was used to determine the annual growth rate in the coffee export quantity, value, and per-unit price over the study period. The major export markets for Ethiopian coffee were Germany, Japan, and the USA, which were more stable, while countries such as France, Italy, Belgium, and Saudi Arabia were less stable and had low retention rates for Ethiopian coffee. The study, therefore, recommends that Ethiopia should again revitalize its market to France, Italy, Belgium, and Saudi Arabia, as these countries are the major coffee-consuming countries in the world to boost its export stake to the global coffee markets in the future. In order to further enhance export stability, the Ethiopian Government and other stakeholders in the coffee sector should have to work on reducing the volatility of coffee output and exports in order to improve production and quality efficiency, so that stabilize markets as well as to make the product attractive and price competitive in the importing countries.

Keywords: coffee, CAGR, Markov chain, direction of trade, Ethiopia

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Authors: Christos Spandonidis, Stefanos Tsantilas, Elias Sedikos, Nektarios Galiatsatos, Fotios Giannopoulos, Panagiotis Papadopoulos, Nikolaos Demagos, Dimitrios Reppas, Christos Giordamlis

Abstract:

In the present paper, a low cost, compact and modular Internet of Things (IoT) platform for air quality monitoring in urban areas is presented. This platform comprises of dedicated low cost, low power hardware and the associated embedded software that enable measurement of particles (PM2.5 and PM10), NO, CO, CO₂ and O₃ concentration in the air, along with relative temperature and humidity. This integrated platform acts as part of a greater air pollution data collecting wireless network that is able to monitor the air quality in various regions and neighborhoods of an urban area, by providing sensor measurements at a high rate that reaches up to one sample per second. It is therefore suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction. The first real world test for the developed platform took place in Thessaloniki, Greece, where 16 devices were installed in various buildings in the city. In the near future, many more of these devices are going to be installed in the greater Thessaloniki area, giving a detailed air quality map of the city.

Keywords: distributed sensor system, environmental monitoring, Internet of Things, smart cities

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Authors: A B M Enamol Hassan

Abstract:

The forest storing up in Bangladesh is of deep concern to policy analysts because of increasing encroachment that results in deforestation and degradation of the ecosystem. To address these problems, forest-dependent people, as responsible for encroachment, could be involved in the co-management process along with other local stakeholders through a participatory approach. On the basis of this premise, this paper conceptualizes and empirically assesses the integration of all stakeholders in the co-management process through two lenses such as participation and collaboration. The study also analyzed the issues of sustainability in local communities along with examining constraints that limit the processes of integration. The study used a qualitative research method, which included face-to-face interviews with semi-structured questionnaires and field notes following the purposive sampling technique focusing on Comilla Sadar South Upazila (CSSU), Bangladesh. The findings of this paper reveal beneficiaries, Bangladesh Forest Department (BFD) and Union Parishad (UP), come together as leading actors, while NGOs and business entrepreneurs are ignored in the co-management process of social forestry. However, integrated management contributes to the strength of community sustainability, although it has some major limitations causing the matter of concerns among the local communities and policy analysts.

Keywords: integration, participation, collaboration, stakeholders, community sustainability

Procedia PDF Downloads 151

Authors: M. Bartal

Abstract:

A large empirical evidence points to sizable lifetime earnings losses associated with the displacement of tenured workers. The causes of these losses are still not well-understood. Existing explanations are heavily based on human capital depreciation during non-employment spells. In this paper, a new avenue is explored. Evidence on the role of household liquidity constraints in accounting for the persistence of post-displacement earning losses is provided based on SIPP data. Then, a directed search and matching model with endogenous human capital and wealth accumulation is introduced. The model is computationally tractable thanks to its block-recursive structure and highlights a non-trivial, yet intuitive, interaction between wealth and human capital. Constrained workers tend to accept jobs with low firm-sponsored training because the latter are (endogenously) easier to find. This new channel provides a plausible explanation for why young (highly constrained) workers suffer persistent scars after displacement. Finally, the model is calibrated on US data to show that the interplay between wealth and human capital is crucial to replicate the observed lifecycle pattern of earning losses. JEL— E21, E24, J24, J63.

Keywords: directed search, human capital accumulation, job displacement, wealth accumulation

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Authors: Nina Miklavčič, Urška Kugovnik, Natalia Galkina, Primož Ribarič, Rudi Vončina

Abstract:

Today, the need for weather predictions is deeply rooted in the everyday life of people as well as it is in industry. The forecasts influence final decision-making processes in multiple areas, from agriculture and prevention of natural disasters to air traffic regulations and solutions on a national level for health, security, and economic problems. Namely, in Slovenia, alongside other existing forms of application, weather forecasts are adopted for the prognosis of electrical current transmission through powerlines. Meteorological parameters are one of the key factors which need to be considered in estimations of the reliable supply of electrical energy to consumers. And like for any other measured value, the knowledge about measurement uncertainty is also critical for the secure and reliable supply of energy. The estimation of measurement uncertainty grants us a more accurate interpretation of data, a better quality of the end results, and even a possibility of improvement of weather forecast models. In the article, we focused on the estimation of measurement uncertainty of the diagnostic microscale meteorological model CALMET. For the purposes of our research, we used a network of meteorological stations spread in the area of our interest, which enables a side-by-side comparison of measured meteorological values with the values calculated with the help of CALMET and the measurement uncertainty estimation as a final result.

Keywords: uncertancy, meteorological model, meteorological measurment, CALMET

Procedia PDF Downloads 51