Search results

The purpose of this paper is to examine the impact of earnings smoothing on the underpricing of seasoned equity offerings (SEOs). It aims to investigate whether earnings smoothing can add value to firms by reducing the degree of SEO underpricing.

The sample of US common stock seasoned equity offerings (SEOs) by non‐regulated firms during 1989‐2009 was used to conduct various cross‐section, univariate, and multivariate tests, using several proxies for earnings smoothing, in order to confirm the impact of earnings smoothing on the degree of SEO underpricing. Three‐stage least square estimation was used to address the possible endogeneity of pricing and earnings smoothing.

Smooth earnings performance resulting from discretionary accruals is negatively related to SEO underpricing and improves earnings informativeness. Consistent with risk management and signaling theories, managers' efforts to produce smooth earning reports may add value to their firms. Based on the mean values for SEOs, such smoothing reduces underpricing by $0.33 per share offered and increases the value of the average offering by $1.65 million. Smoothed earnings also conveys information about the firms' future performance, as firms with a long historical pattern of smooth earnings prior to SEOs significantly outperform, for at least three years after the SEO, those with more volatile earnings, with respect to stock returns and operating performance.

The paper contributes specifically to the current literature on earnings smoothing by demonstrating that high quality firms that expect larger quantity of cash flows in the near future are more likely to actively smooth earnings via discretionary accruals before SEOs to reduce underpricing. The paper contributes generally by showing that firms can signal their quality to outside investors by showing smooth earnings over a long period of time and such firms are more likely to experience a lower degree of underpricing through SEO episodes.

When modeling heat propagation in biological bodies, a non-negligible relaxation time (typically between 15-30 s) is required for the thermal waves to accumulate and transfer, i.e. thermal waves propagate at a finite velocity. To accommodate for this feature that is characteristic to heat transfer in biological bodies, the classical Fourier's law has to be modified resulting in the thermal-wave model of bio-heat transfer. The purpose of the paper is to retrieve the space-dependent blood perfusion coefficient in such a thermal-wave model of bio-heat transfer from final time temperature measurements.

The non-linear and ill-posed blood perfusion coefficient identification problem is reformulated as a non-linear minimization problem of a Tikhonov regularization functional subject to lower and upper simple bounds on the unknown coefficient. For the numerical discretization, an unconditionally stable direct solver based on the Crank–Nicolson finite difference scheme is developed. The Tikhonov regularization functional is minimized iteratively by the built-in routine lsqnonlin from the MATLAB optimization toolbox. Both exact and numerically simulated noisy input data are inverted.

The reconstruction of the unknown blood perfusion coefficient for three benchmark numerical examples is illustrated and discussed to verify the proposed numerical procedure. Moreover, the proposed algorithm is tested on a physical example which consists of identifying the blood perfusion rate of a biological tissue subjected to an external source of laser irradiation. The numerical results demonstrate that accurate and stable solutions are obtained.

Although previous studies estimated the important thermo-physical blood perfusion coefficient, they neglected the wave-like nature of heat conduction present in biological tissues that are captured by the more accurate thermal-wave model of bio-heat transfer. The originalities of the present paper are to account for such a more accurate thermal-wave bio-heat model and to investigate the possibility of determining its space-dependent blood perfusion coefficient from temperature measurements at the final time.

This article considers that one way to help the small‐ and medium‐sized enterprise (SME) to survive is to offer it a robust but simple monitoring and control technique that would help it manage the business effectively and this, in turn, should help to increase its chances of survival. This technique should also be of interest to all people involved with monitoring or advising a large number of small enterprises or business units within a larger organization. For example, a bank manager or a small business consultant responsible for a portfolio of firms. The authors utilize process control techniques more often used in production and inventory control systems to demonstrate how one might monitor the marketing “health” of small firms.

This paper aims to propose a new linear parameter varying (LPV) controller for the robot tracking control problem. Using the identification of the robot dynamics in different work space points about modeling trajectory based on the least square of error algorithm, an LPV model for the robotic arm is extracted.

Parameter set mapping based on parameter component analysis results in a reduced polytopic LPV model that reduces the complexity of the implementation. An approximation of the required torque is computed based on the reduced LPV models. The state-feedback gain of each zone is computed by solving some linear matrix inequalities (LMIs) to sufficiently decrease the time derivative of a Lyapunov function. A novel smoothing method is used for the proposed controller to switch properly in the borders of the zones.

The polytopic set of the resulting gains creates the smooth switching polytopic LPV (SS-LPV) controller which is applied to the trajectory tracking problem of the six-degree-of-freedom PUMA 560 robotic arm. A sufficient condition ensures that the proposed controller stabilizes the polytopic LPV system against the torque estimation error.

Smoothing of the switching LPV controller is performed by defining some tolerances and creating some quasi-zones in the borders of the main zones leading to the compressed main zones. The proposed torque estimation is not a model-based technique; so the model variation and other disturbances cannot destroy the performance of the suggested controller. The proposed control scheme does not have any considerable computational load, because the control gains are obtained offline by solving some LMIs, and the torque computation is done online by a simple polytopic-based equation.

In this paper, a new SS-LPV controller is addressed for the trajectory tracking problem of robotic arms. Robot workspace is zoned into some main zones in such a way that the number of models in each zone is almost equal. Data obtained from the modeling trajectory is used to design the state-feedback control gain.

This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.

The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.

The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.

To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

The purpose of this paper is to explore the dynamics of the relationships between sales and internal and external environmental drivers in a retail company using a systems perspective in order to support retail management decisions with nonlinear methods.

The research and results are presented in two parts: the collection and explorative analysis of the data; and discussion of the managerial implications following a systems perspective. The exploratory analysis is conducted using a statistical comparison of linear and nonlinear models of sales data from a retail company. The data, which comprise two data sets, come from 45 retail stores located in different regions of the USA.

Specifically, nonlinear models provided a better explanation of variation in retail activity (R²=46 per cent) than linear models (R²=16 per cent). In such a situation, the nonlinear analysis captures the influence of internal and external environmental drivers on retail sales.

With a limited variety of external and internal drivers, the exploratory analysis aims to describe a general situation in which retailers are managing activities in complex environments as opposed to reflect on a particular retail chain.

The systems perspective is used to interpret the managerial implications of the nonlinear analysis fits, particularly in cases where retail decision-makers are adapting, transforming and restructuring sources of competitive advantage in complex environments.

The paper provides an alternative perspective (the systemic one) of how retailers could interpret the relationships between internal and external variables in the dynamic environment of the retail chains with nonlinear models.

This study aims to trace the impact of corporate governance and its mechanisms in preventing companies from turning to fraudulent financial reporting.

For this purpose, using the systematic elimination pattern, the information of 187 listed companies on the Tehran Stock Exchange over six years from 2013 to 2019 were collected, and the hypotheses were examined using a linear regression model. To measure fraudulent financial reporting, the adjusted model of Beneish (1999) was used to evaluate corporate governance. Its mechanisms based on nine corporate governance mechanisms, including board independence, board remuneration, CEO financial expertise, expertise in CEO industry, board financial expertise, board industry expertise, board effort, CEO duality and managerial ownership, have been examined. These mechanisms are calculated as a combined index of corporate governance.

The findings indicate that robust corporate governance significantly reduces companies’ intention toward fraudulent financial reporting. In the same way, a negative and significant relationship was observed between each of the nine corporate governance mechanisms, except for board compensation and fraudulent financial reporting.

This study’s findings provide valuable insight into the importance of strengthening companies to prevent companies’ managers from engaging in fraudulent financial reporting activities. Hence, it is suggested that professional references bodies more seriously follow the rules to dictate to companies for using and empowering their corporate governance.

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

This study aims to develop a high-quality impairment loss allowance model in conformity with Indonesian Financial Accounting Standards 71 (PSAK 71) that has significant contribution to national interests and the banking industry.

The determination of the impairment loss allowance model is settled through 7 stages, using integration of some statistical methods such as Markov chain, exponential smoothing, time series analysis of behavioral inherent trends of probability of default, tail conditional expectation and Monte Carlo simulation.

The model which is developed by the authors is proven to be a high-quality and reliable model. By using the model, it can be shown that the implementation of the expected credit losses model on Indonesian Financial Accounting Standards 71 is more prudent than the implementation of the incurred loss model on Indonesian Financial Accounting Standards 55.

Determination of defaults was based on days past due, and the analysis in this study did not touch the aspects of hedge accounting in general.

This developed model will contribute significantly to national interests as a source of reference for other banks operating in Indonesia in calculating impairment loss allowance (CKPN) and can be used by the Financial Services Authority of Indonesia (OJK) as a guideline in assessing the formation of impairment loss allowance for banks operating in Indonesia.

As so far there is not yet an available standardized model for calculating impairment loss allowance on the basis of Indonesian Financial Accounting Standards 71, the model developed by the authors will be a new breakthrough in Indonesia.

The purpose of this paper is to investigate whether income smoothing does indeed improve the informativeness of stock prices about firms' future earnings and cash flows. Also an approach to studying the effects of income smoothing is presented.

This study uses data from 1992‐2006 and runs regressions on each of the 560 industry‐year cross‐sections. The data compiled from the financial statements of firms were collected for each year available from the Tehran Stock Exchange database. Income smoothing is defined as the management of accruals to reduce time‐series variation in income, and uses a cross‐sectional version of the Jones model, modified by Kothari, Leone and Wasley. Smoothing is measured as the variation of net income relative to the variation in CFO, or the correlation between changes in accruals and changes in CFO. Informativeness is measured as the coefficient on future earnings (cash flows) in a regression of current stock return against current and future earnings (cash flows and accruals).

The findings suggest that income smoothing enhances the information content of the effect of stock price on future earnings, thus improving the ability of market participants to make informed decisions about the allocation of capital resources.

Although previous research on the subject of income smoothing in an emerging market has been documented, its effect on stock prices efficiency is largely unknown. Thus, this paper presents an approach to studying the effects of income smoothing and the knowledge that the ability to manage earnings could improve stock prices efficiency could be useful for academics and policymakers in this market.