The distribution of extreme share return in different Malaysian economic circumstances

This paper presents a study on the performance of probability distribution in various financial periods by investigating the effect of economic cycle on extreme stock return activity. Malaysian stock price KLCI data from 1994-2008 were split into three economy periods corresponding to the growth, financial crisis, and recovery. Four prevalent distributions, specifically generalized lambda distribution (GLD), generalized extreme value (GEV), generalized logistic (GLO), and generalized pareto (GPA) had been employed to model weekly and monthly maximum and minimum share returns of Kuala Lumpur Composite Index (KLCI). L-moment approach had been used to estimate the parameter, while k-sample Anderson darling (k-ad) test had been applied to measure the goodness of fit estimation. In conclusion, GLD is the most appropriate distribution to represent weekly maximum and minimum returns for overall three economic scenarios in Malaysia

Two stages fitting techniques using generalized lambda distribution: application on Malaysian financial return

The underline distribution assumption used in the analysis of share market returns is crucial in risk management. An important aspect related to stock return modelling is to obtain accurate prediction. This paper presents an innovative fitting method called two stages (TS) method for modelling daily stock returns. The proposed approach by first establishing trend in the series, and then separately performing L-moment estimation on the generalized lambda distribution (GLD) parameter. The performance of the TS-GLD models had been evaluated using Monte Carlo simulation and Malaysian Kuala Lumpur Composite Index (KLCI) returns from year 2001 to 2015. Based on k-sample Anderson darling goodness of fit test, the two stages GLD model in location parameter (GLD.1) performed well in all studied cases. The GLD.1 model benefits risk management by providing effective distribution fitting

Streamflow estimation at ungauged basin using modified group method of data handling

Among the foremost frequent and vital tasks for hydrologist is to deliver a high accuracy estimation on the hydrological variable, which is reliable. It is essential for flood risk evaluation project, hydropower development and for developing efficient water resource management. Presently, the approach of the Group Method of Data Handling (GMDH) has been widely applied in the hydrological modelling sector. Yet, comparatively, the same tool is not vastly used for the hydrological estimation at ungauged basins. In this study, a modified GMDH (MGMDH) model was developed to ameliorate the GMDH model performance on estimating hydrological variable at ungauged sites. The MGMDH model consists of four transfer functions that include polynomial, hyperbolic tangent, sigmoid and radial basis for hydrological estimation at ungauged basins; as well as; it incorporates the Principal Component Analysis (PCA) in the GMDH model. The purpose of PCA is to lessen the complexity of the GMDH model; meanwhile, the implementation of four transfer functions is to enhance the estimation performance of the GMDH model. In evaluating the effectiveness of the proposed model, 70 selected basins were adopted from the locations throughout Peninsular Malaysia. A comparative study on the performance was done between the MGMDH and GMDH model as well as with other extensively used models in the area of flood quantile estimation at ungauged basins known as Linear Regression (LR), Nonlinear Regression (NLR) and Artificial Neural Network (ANN). The results acquired demonstrated that the MGMDH model possessed the best estimation with the highest accuracy comparatively among all models tested. Thus, it can be deduced that MGMDH model is a robust and efficient instrument for flood quantiles estimation at ungauged basins

Examine generalized lambda distribution fitting performance: An application to extreme share return in Malaysia

Understand the extreme volatility in the market is important for the investor to make a correct prediction. This paper evaluated the performance of generalized lambda distribution (GLD) by comparing with the popular probability distribution namely generalized extreme value (GEV), Generalized logistic (GLO), generalized Pareto (GPA), and Pearson (PE3) using Kuala Lumpur composite index stock return data. The parameter for each distribution estimated using the L-moment method. Based on k-sample Anderson darling goodness of fit test, GLD performs well in weekly maximum and minimum period. Evidence for preferring GLD as an alternative to extreme value theory distribution also described

Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm

Linear regression is widely used in flood quantile study that consists of meteorological and physiographical variables. However, linear regression does not capture the complex nonlinear relationship between predictor and target variables. It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. GMDH model is known to be an effective model in complying with a nonlinear relationship. Therefore, in this paper, we enhance the GMDH model by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. Then, ensemble averaging combines the output from those various transfer functions and becomes the new ensemble GMDH model coupled with the ABC algorithm (EGMDH-ABC) model. The results show that this method significantly improves the prediction performance of the GMDH model. The EGMDH-ABC model satisfies the nonlinearity in data to produce a better estimation. Also, it provides more robust, accurate, and efficient results

Novel logic mining incorporating log linear approach

Mining the best logical rule from the data is a challenging task because not all attribute of the dataset will contribute towards the optimal logical representation. Even if the correct attributes were selected, wrong logical connection in the logical formula will lead to suboptimal logical representation of the datasets. These two factors must be carefully considered in creating more robust logic mining method. In this paper, we proposed a novel logic mining by introducing log-linear analysis to select the best attributes which formulate the logical rule that will be embedded into the energy-based ANN named Discrete Hopfield Neural Network (DHNN). In log-linear phase, the test of the association for each attributes will be carried out where the attributes that have a significant level less than α will be selected before proceeding to the logic mining phase. By using DHNN, the selected attributes via log-linear will be learned and retrieved the optimal induced logic with classification ability. The proposed hybrid model has been tested using various real-life datasets and was compared with several established logic mining methods. Based on the findings, several winning points for the proposed model where the proposed model dominates 3 metrics out of 5 in the average rank. The metrics that achieve the highest average rank are Accuracy (1.800), Precision (3.500), and Mathews Correlation Coefficient (2.700). In accordance with the experimental result obtained, the proposed model has achieved optimal performance with a statistically significant p-value. Hence, these findings lead to an advancement of the existing logic mining via the statistical method

Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

Online shopping is a multi-billion-dollar industry worldwide. However, several challenges related to purchase intention can impact the sales of e-commerce. For example, e-commerce platforms are unable to identify which factors contribute to the high sales of a product. Besides, online sellers have difficulty finding products that align with customers’ preferences. Therefore, this work will utilize an artificial neural network to provide knowledge extraction for the online shopping industry or e-commerce platforms that might improve their sales and services. There are limited attempts to propose knowledge extraction with neural network models in the online shopping field, especially research revolving around online shoppers’ purchasing intentions. In this study, 2-satisfiability logic was used to represent the shopping attribute and a special recurrent artificial neural network named Hopfield neural network was employed. In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. The performance of the genetic algorithm with 2-satisfiability-based reverse analysis was measured according to the selected performance evaluation metrics. The simulation suggested that the proposed model outperformed the existing model in doing logic mining for the online shoppers dataset

Non-stationary in extreme share return: World indices application

This paper investigates the behaviour of the extreme share return for the 26 different major indices shares by exploring their stationarity. Extreme return for weekly and monthly series is generated by using block maxima method. Four-employed test permits us to spot non-stationarity in extreme movement. The Augmented Dickey-Fuller and Kwiatkowski Phillips Schmidt Shin (KPSS) test scanned the unit root and the stationarity, and Mann-Kendall and Spearman's test inspected the trend and correlation in the series. Our approach approximates global stock returns for weekly and monthly series market activity. We find most of the extreme stock to be active in shift movement, and we have confirmed that the movement of extreme share return for the majority of the stock indices in the weekly and monthly interval is non-stationary. This testified statistical property in the series can be used as the first crucial appraisal when scrutinizing extreme share return for future research

Two stages fitting techniques using generalized lambda distribution: application on Malaysian financial return

The underline distribution assumption used in the analysis of share market returns is crucial in risk management. An important aspect related to stock return modelling is to obtain accurate prediction. This paper presents an innovative fitting method called two stages (TS) method for modelling daily stock returns. The proposed approach by first establishing trend in the series, and then separately performing L-moment estimation on the generalized lambda distribution (GLD) parameter. The performance of the TS-GLD models had been evaluated using Monte Carlo simulation and Malaysian Kuala Lumpur Composite Index (KLCI) returns from year 2001 to 2015. Based on k-sample Anderson darling goodness of fit test, the two stages GLD model in location parameter (GLD.1) performed well in all studied cases. The GLD.1 model benefits risk management by providing effective distribution fitting

Streamflow estimation at ungauged basin using modified group method of data handling

Among the foremost frequent and vital tasks for hydrologist is to deliver a high accuracy estimation on the hydrological variable, which is reliable. It is essential for flood risk evaluation project, hydropower development and for developing efficient water resource management. Presently, the approach of the Group Method of Data Handling (GMDH) has been widely applied in the hydrological modelling sector. Yet, comparatively, the same tool is not vastly used for the hydrological estimation at ungauged basins. In this study, a modified GMDH (MGMDH) model was developed to ameliorate the GMDH model performance on estimating hydrological variable at ungauged sites. The MGMDH model consists of four transfer functions that include polynomial, hyperbolic tangent, sigmoid and radial basis for hydrological estimation at ungauged basins; as well as; it incorporates the Principal Component Analysis (PCA) in the GMDH model. The purpose of PCA is to lessen the complexity of the GMDH model; meanwhile, the implementation of four transfer functions is to enhance the estimation performance of the GMDH model. In evaluating the effectiveness of the proposed model, 70 selected basins were adopted from the locations throughout Peninsular Malaysia. A comparative study on the performance was done between the MGMDH and GMDH model as well as with other extensively used models in the area of flood quantile estimation at ungauged basins known as Linear Regression (LR), Nonlinear Regression (NLR) and Artificial Neural Network (ANN). The results acquired demonstrated that the MGMDH model possessed the best estimation with the highest accuracy comparatively among all models tested. Thus, it can be deduced that MGMDH model is a robust and efficient instrument for flood quantiles estimation at ungauged basins