What do we know about the dividend puzzle? – A literature survey

Purpose: The purpose of this paper is to shed light on the ongoing debate of dividend policy, which is considered one of the most controversial topics in corporate finance literature. Design/methodology/approach: The paper provides a survey of literature; it, first, outlines the main theoretical arguments of dividend policy and then critically discusses the most important and influential previous empirical studies in the dividend literature. Findings: The analysis of literature review detects that no general consensus has yet been reached after many decades of investigation, despite extensive debate and countless research. Consequently, the main motivation for paying dividends is still unsolved and thus remains as a puzzle. In addition, there is no doubt that carrying the dividend debate into the context of emerging markets attaches more pieces to this puzzle. Originality/value: This paper offers an updated and more comprehensive survey of literature by examining the relationship between theory and practice from both developed and emerging markets

A geospatial solution using a TOPSIS approach for prioritizing urban projects in Libya

© 2018 Proceedings - 39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018 The world population is growing rapidly; consequently, urbanization has been in an increasing trend in many developing cities around the globe. This rapid growth in population and urbanization have also led to infrastructural development such as transportation systems, sewer, power utilities and many others. One major problem with rapid urbanization in developing/third-world countries is that developments in mega cities are hindered by ineffective planning before construction projects are initiated and mostly developments are random. Libya faces similar problems associated with rapid urbanization. To resolve this, an automating process via effective decision making tools is needed for development in Libyan cities. This study develops a geospatial solution based on GIS and TOPSIS for automating the process of selecting a city or a group of cities for development in Libya. To achieve this goal, fifteen GIS factors were prepared from various data sources including Landsat, MODIS, and ASTER. These factors are categorized into six groups of topography, land use and infrastructure, vegetation, demography, climate, and air quality. The suitability map produced based on the proposed methodology showed that the northern part of the study area, especially the areas surrounding Benghazi city and northern parts of Al Marj and Al Jabal al Akhdar cities, are most suitable. Support Vector Machine (SVM) model accurately classified 1178 samples which is equal to 78.5% of the total samples. The results produced Kappa statistic of 0.67 and average success rate of 0.861. Validation results revealed that the average prediction rate is 0.719. Based on the closeness coefficient statistics, Benghazi, Al Jabal al Akhdar, Al Marj, Darnah, Al Hizam Al Akhdar, and Al Qubbah cities are ranked in that order of suitability. The outputs of this study provide solution to subjective decision making in prioritizing cities for development

Determinants and value relevance of UK CEO pay slice

This paper studies the CEO pay slice (CPS) of UK listed firms during the period 2003 to 2009. We investigate the determinants of CPS. We study the links between CPS and measures of firm performance. We find that firms with higher levels of corporate governance ratings and those with more independent boards tend to have higher CPS. In addition, we find that CEOs are more likely to receive lower compensation when they chair the board and when they work in firms with large board size. We also find that higher CPS is positively associated with firm performance after controlling for the firm-specific characteristics and corporate governance variables. We get compatible results when we examine the association between equity-based CPS and firm performance. Our results remain robust to alternative accounting measures of firm performance. Our results suggest that high UK CPS levels do indeed reflect top managerial talent rather than managerial power

Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms

© 2019 SPIE. Landslides are type of natural geohazard interfering with many economical and social activities and causing serious damages on human life. It is ranked as a great disaster, threatening life, property and environment. Therefore, early prediction of landslide prone areas is vital. Variety of causative factors such as glaciers melting, excessive raining, mining, volcanic activities, active faults, earthquake, logging, erosion, urbanization, construction, and other human activities can trigger landslide occurrence. Then, identification of factors that directly influences the slide events is highly in demand. Some topographical, geological, and hydrological datasets (e.g., slope, aspect, geology, terrain roughness, vegetation index, distance to stream, distance to road, distance to fault, land use, precipitation, profile curvature, plan curvature) are considered to be effective conditioning factors. However, the importance of each factor differs from one study to another. This study investigates the effectiveness of four sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into four groups G1, G2, G3, and G4. Three machine learning algorithms namely, Random Forest (RF), Naive Bayes (NB), and Boosted Logistic Regression (LogitBoost) were constructed based on each dataset in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 227 landslide inventory datasets of the study area were used where 70% was used for training and 30% for testing. To this end, in the present research, the two main objectives were: 1) Investigation on effectiveness of 14 landslides conditioning factors (altitude, slope, aspect, total curvature, profile curvature, plan curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), distance to fault, distance to road, distance to stream, land use, and geology) by analyzing and determining the most important factors using variance-inflated factor (VIF), Pearson's correlation and Chi-square techniques. Consequently, 4 categories of datasets were defined; first dataset included all 14 conditioning factors, second dataset included Digital Elevation Models (DEM) derivatives (morphometrice factors), third dataset was only based on 5 factors namely lithology, land use, distance to stream, distance to road, and distance to fault, and last dataset was included 8 factors selected using factor analysis and optimization. 2) Evaluate the sensitivity of each modeling technique (NB, RF and LogitBoost) to different conditioning factors using the area under curve (AUC). Eventually, RF technique using optimized variables (G4) performed well with AUC of 0.940 followed by LogitBoost (0.898) and NB (0.864)

A new integrated approach for landslide data balancing and spatial prediction based on generative adversarial networks (GAN)

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory / data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality landslide inventory map, including a complete record of historical data, is difficult or expensive to collect. As such, this can considerably affect one’s ability to obtain a sufficient inventory or representative samples. This research developed a new approach based on generative adversarial networks (GAN) to correct imbalanced landslide datasets. The proposed method was tested at Chukha Dzongkhag, Bhutan, one of the most frequent landslide prone areas in the Himalayan region. The proposed approach was then compared with the standard methods such as the synthetic minority oversampling technique (SMOTE), dense imbalanced sampling, and sparse sampling (i.e., producing non-landslide samples as many as landslide samples). The comparisons were based on five machine learning models, including artificial neural networks (ANN), random forests (RF), decision trees (DT), k-nearest neighbours (kNN), and the support vector machine (SVM). The model evaluation was carried out based on overall accuracy (OA), Kappa Index, F1-score, and area under receiver operating characteristic curves (AUROC). The spatial database was established with a total of 269 landslides and 10 conditioning factors, including altitude, slope, aspect, total curvature, slope length, lithology, distance from the road, distance from the stream, topographic wetness index (TWI), and sediment transport index (STI). The findings of this study have shown that both GAN and SMOTE data balancing approaches have helped to improve the accuracy of machine learning models. According to AUROC, the GAN method was able to boost the models by reaching the maximum accuracy of ANN (0.918), RF (0.933), DT (0.927), kNN (0.878), and SVM (0.907) when default parameters used. With the optimum parameters, all models performed best with GAN at their highest accuracy of ANN (0.927), RF (0.943), DT (0.923) and kNN (0.889), except SVM obtained the highest accuracy of (0.906) with SMOTE. Our finding suggests that RF balanced with GAN can provide the most reasonable criterion for landslide prediction. This research indicates that landslide data balancing may substantially affect the predictive capabilities of machine learning models. Therefore, the issue of DI in the spatial prediction of landslides should not be ignored. Future studies could explore other generative models for landslide data balancing. By using state-of-the-art GAN, the proposed model can be considered in the areas where the data are limited or imbalanced

Landslide Detection Using a Saliency Feature Enhancement Technique from LiDAR-Derived DEM and Orthophotos

© 2013 IEEE. This study proposes a new landslide detection technique that is semi-automated and based on a saliency enhancement approach. Unlike most of the landslide detection techniques, the approach presented in this paper is simple yet effective and does not require landslide inventory data for training purposes. It comprises several steps. First, it enhances potential landslide pixels. Then, it removes the image background using slope information derived from a very high-resolution LiDAR-based (light detection and ranging) digital elevation model (DEM). After that, morphological analysis was applied to remove small objects, separate landslide objects from each other, and fill the gaps between large bare soil objects and urban objects. Finally, landslide scars were detected using the Fuzzy C-means (FCM) clustering algorithm. The proposed method was developed based on datasets acquired over the Kinta Valley area in Malaysia and tested on another area with a different environment and topography (i.e., Cameron Highlands). The results showed that the proposed landslide detection technique could detect landslides in the training area with a Prediction Accuracy, Kappa index, and Mean Intersection-Over-Union (mIOU) of 71.12%, 0.81, and 68.52%, respectively. The Prediction Accuracy, Kappa index, and mIOU of the method based on the test dataset were 65.78%, 0.68, and 56.14%, respectively. These results show that the proposed method can be used for landslide inventory mapping and risk assessments

Landslide susceptibility modeling: An integrated novel method based on machine learning feature transformation

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement)

A meta-learning approach of optimisation for spatial prediction of landslides

Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an objective function, i.e., k-fold cross-validation accuracy. However, the overhead of hyperparameter optimisation can be prohibitive, especially for computationally expensive algorithms. This paper introduces an optimisation approach based on meta-learning for the spatial prediction of landslides. The proposed approach is tested in a dense tropical forested area of Cameron Highlands, Malaysia. Instead of optimising prediction models with a large number of hyperparameter configurations, the proposed approach begins with promising configurations based on several basic and statistical meta-features. The proposed meta-learning approach was tested based on Bayesian optimisation as a hyperparameter tuning algorithm and random forest (RF) as a prediction model. The spatial database was established with a total of 63 historical landslides and 15 conditioning factors. Three RF models were constructed based on (1) default parameters as suggested by the sklearn library, (2) parameters suggested by the Bayesian optimisation (BO), and (3) parameters suggested by the proposed meta-learning approach (BO-ML). Based on five-fold cross-validation accuracy, the Bayesian method achieved the best performance for both the training (0.810) and test (0.802) datasets. The meta-learning approach achieved slightly lower accuracies than the Bayesian method for the training (0.769) and test (0.800) datasets. Similarly, based on F1-score and area under the receiving operating characteristic curves (AUROC), the models with optimised parameters either by the Bayesian or meta-learning methods produced more accurate landslide susceptibility assessment than the model with the default parameters. In the present approach, instead of learning from scratch, the meta-learning would begin with hyperparameter configurations optimal for the most similar previous datasets, which can be considerably helpful and time-saving for landslide modelings