Sadam Alwadi

• Professor of Statistics, Department of Finance, Collage of Business, at University of Jordan

This research examines irregularities and the rounding behavior of financial accounting figures (revenue and net income) within the context of 169 Jordanian firms listed in Amman Stock Exchange. The investigation employs Benford's Law to analyze the distribution patterns of the first and second digits of these financial metrics over the period span...

Data imputation strategies are necessary to address the prevalent difficulty of missing values in data observation and recording operations. This work utilizes diverse imputation methods to forecast and complete absent values inside a financial time-series dataset, specifically the daily prices of gold. The predictive accuracy of imputed data is as...

This empirical research endeavor seeks to enhance the accuracy of forecasting time series data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied on daily closed price index data, spanning from October 2014 to December 2022, encompassing a total of 2048 observations. To attain statistically significant res...

We aim to detect outliers in the daily stock price indices from the Saudi Arabia stock exchange (Tadawul) with 2026 observations from October 2011 to December 2019 provided by the Saudi Authority for Statistics and the Saudi Central Bank. We apply the Multi-Layer Perceptron (MLP) algorithm for detecting outliers in stock returns. We select the infl...

This study uses historical data and modern statistical models to forecast future Gross Domestic Product (GDP) in Jordan. The Wavelet Transformation model (WT) and Autoregressive Integrated Moving Average (ARIMA) model were applied to the time series data and yielded a best-fitting result of (2,1,1) for estimating GDP between 2022-2031. The study co...

We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the model’s parameters based on the...

Mortality studies are essential in determining the health status and demographic composition of a population. The Lee–Carter (LC) modelling framework is extended to incorporate the macroeconomic variables that affect mortality, especially in forecasting. This paper makes several major contributions. First, a new model (LC-WT-ANFIS) employing the ad...

This research employs the gradient descent learning (FIR.DM) approach as a learning process in a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) to improve volatility prediction of daily stock market prices using Saudi Arabia’s stock exchange (Tadawul) data. The MODWT comprises five mathematical functions and fuzz...

In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tada-wul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The pr...

This study explores the impact of electronic payment systems on Saudi Arabia’s customer satisfaction during the COVID-19 pandemic. Descriptive analytical approach of a sample of 1,025 people living in Saudi Arabia was used to answer the study questions and test its hypotheses. Then, a new hybrid fuzzy inference system (HyFIS) is proposed to predict...

In this study, we estimated the performance efficiency of the Jordanian mining and extracting sector based on Data Envelopment Analysis (DEA). The utilized dataset includes 6 out of 15 corporations that reflect around 90% of the total market capitalization under the mining and extracting sector in the Amman Stock Exchange (ASE). The sample consists...

This study aims to increase revenue forecasting accuracy by modeling a time series of monthly revenue data obtained from Aqaba Company for Ports Operations and Managements-Jordan from January 2011 to December 2020. Numerous mathematical functions are utilized in this investigation, including the non-linear spectral model, the maximal overlapping di...

We employ a Boxplot method for detecting and analyzing outlying daily returns of 14 international stock market indices sampled from around the world. The main objective of the paper is to provide an extensive analysis of the main characteristics, features and effects of the detected outlier returns. The results show that from about 4–10% of observa...

We introduce a new wavelet based procedure for detecting outliers in financial discrete time series. The procedure focuses on the analysis of residuals obtained from a model fit, and applied to the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) like model, but not limited to these models. We apply the Maximal-Overlap Discrete Wav...

This study implements various, maximum overlap, discrete wavelet transform filters to model and forecast the time-dependent mortality index of the Lee-Carter model. The choice of appropriate wavelet filters is essential in effectively capturing the dynamics in a period. This cannot be accomplished by using the ARIMA model alone. In this paper, the...

This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functi...

Given that the predictability of financial assets is indispensable to optimize the allocation of the investors' portfolio, a large literature review was dedicated to the question of predictability. Indeed, different studies have examined the relationship between the expected returns and the financial and macroeconomic variables to determine the mos...

Given that the predictability of financial assets is indispensable to optimize the allocation of the investors’ portfolio, a large literature review was dedicated to the question of predictability. Indeed, different studies have examined the relationship between the expected returns and the financial and macroeconomic variables to determine the mos...

Artificial intelligence (AI) based business process optimization has a significant impact on a country’s economic development. We argue that the use of artificial neural networks in business processes will help optimize these processes ensuring the necessary level in the functioning and compliance with the foundations of sustainable development. In...

Statistical sciences specially operations research is related with different fields as mathematics, statistics, economics, psychology, engineering such as (Alsaraireh, et al., 2018; AL Wadi, et al., 2018). Also operation research used to make a new decision. Recently operation research became a professional science with respect to other science. Th...

This study focuses on intellectual capital and its effect on marketing Per�formance through an economy based on knowledge. This study aims to determine the role of intellectual capital and its different dimensions (human capital ; structural capital and technological capital ) in the company of our study. A questionnaire was developed to ensure the...

This study aims to discuss a special structure that can be exploited in the construction of efficient solution. The advantage of their methods usually has been the need to solve larger problems than otherwise would be possible to solve with computer technology. Two methods are discussed in this paper to determine a best method to solve these proble...

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathemat...

Online Food Delivery Platforms (OFDPs) has witnessed phenomenal growth in the past few years, especially this year due to the COVID-19 pandemic. This Pandemic has forced many governments across the world to give momentum to OFD services and make their presence among the customers. The Presence of several multinational and national companies in this...

This study aims to implement a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) to predict the stock market. The methodology of this article summarizes as: First, the stock market data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Secondly, all components are forecasted by HW...

This study aims to survey and summarize the studies that introduced forecasting time series method based on EMD, providing references for researchers relating to this topic. We highlight results that have published during 1998 − 2017 (since presented the EMD technique). In this survey, we also present some studies that improved EMD methodology to o...

This study aims to discuss a different way to solve a linear programming problems. Two methods are discussed in this paper to determine a suitable method to solve these problems, and to determine which one is the easiest. We used : Simplex method and Lagrange method. Two methods were applied in general system to evaluate the result and compare betw...

Nowadays, the volatility of stock market data have contributed an essential section in risk study. Volatility is measured by standard deviation of the return. This study explores volatility event for the insurance time series data from Amman Stock Exchange (ASE). Wavelet models (WT) have used in order to study the events in the volatility data that...

This study aims to discuss a special structure that can be exploited in the construction of efficient solution. The advantage of their methods usually has been the need to solve larger problems than otherwise would be possible to solve with computer technology. Two methods are discussed in this paper to determine a best method to solve these proble...

This study focuses on intellectual capital and its effect on marketing Performance through an economy based on knowledge. This study aims to determine the role of intellectual capital and its different dimensions (human capital ; structural capital and technological capital) in the company of our study. A questionnaire was developed to ensure the r...

The study aims to estimate and forecast earnings of the firms listed in Amman Stock exchange (ASE) using a time series data of earning per share (EPS) for the period from 1978 till 2016. The data has been extracted from firms' annual reports. A wavelet Transform (WT) decomposes the data and detects the fluctuations and outlay values. The parameters...

Forecasting time series recently has attracted considerable attention in the field of analyzing financial time series data specifically stock market index. This considerable attention confined itself in the need of transparent change in the governmental policies whether attracting foreign investment or/and economical advancements. In this study, a...

Forecasting time series recently has attracted considerable attention in the field of analyz-ing financial time series data specifically stock market index. This considerable attention confined itself in the need of transparent change in the governmental policies whether attracting foreign investment or/and economical advancements. In this study, a...

Gross Domestic Product (GDP) is the market value of the all goods and services that are produced within the country's national borders in a year, Our study aims to estimate and predict Jordanian's GDP using a time series data for the period from 1978 till 2017, the data has been taken from Jordanian's department of statistics, Minitab and Matlab st...

The study aims to estimate and forecast earnings of the firms listed in Amman Stock exchange (ASE) using a time series data of earning per share (EPS) for the period from 1978 till 2016. The data has been extracted from firms' annual reports. A wavelet Transform (WT) decomposes the data and detects the fluctuations and outlay values. The parameters...

structure break is a famous features in stock market data that gain consideration from many kind of researchers. Generally, it occurs because of unexpected variations in the strategy of the government. Recently, wavelet method (WT) is more popular in the stock market data analysis since it has significant benefits than the other traditional methods...

Closed price forecasting plays a main rule in finance and economics which has encouraged the researchers to introduce a fit model in forecasting accuracy. The autoregressive integrated moving average (ARIMA) model has developed and implemented in many applications. Therefore, in this article the researchers utilize ARIMA model in predicting the clo...

Banking time series forecasting gains a main rule in finance and economics which has encouraged the researchers to introduce a fit models in forecasting accuracy. In this paper, the researchers present the advantages of the autoregressive integrated moving average (ARIMA) model forecasting accuracy. Banking data from Amman stock market (ASE) in Jor...

This study aims to determine the most effective method in operation research regarding the potential to reduce the cost in a minimum time and achieving more profit. In this study, three methods were used : Simplex method, Simplex method and transportation problems and Simplex method, transportation problems, and critical path method. A sample of 10 t...

Abstract. Since the industrial data plays significant element in any economic growth and these data have many factors that effect on its behavior. Therefore, in this article events of productivity of the Extractive Industry in Jordan will be forecasted using some of traditional model which is (ARIMA model) compound with Orthogonal wavelet transform (...

This study aims to determine the most effective method in operation research regarding the potential to reduce the cost in a minimum time and achieving more pro�t. In this study, three methods were used : Simplex method, Simplex method and transportation problems and Simplex method, transportation problems, and critical path method. A sample of 10...

Since the industrial data plays signi�cant element in any economic growth and these data have many factors that e�ect on its behavior. Therefore, in this article events of productivity of the Extractive Industry in Jordan will be forecasted using some of traditional model which is (ARIMA model) compound with Orthogonal wavelet transform (OWT) in or...

This study aims to determine the most effective method in operation research regarding the potential to reduce the cost in a minimum time and achieving more pro�t. In this study, three methods were used : Simplex method, Simplex method and transportation problems and Simplex method, transportation problems, and critical path method. A sample of 10...

Many researchers documented that the stock market data are nonstationary and nonlinear time series data. In this study, we use EMD-HW bagging method for nonstationary and nonlinear time series forecasting. The EMD-HW bagging method is based on the empirical mode decomposition (EMD), the moving block bootstrap and the Holt-Winter. The stock market t...

Displays the actual data for Australia stock market from 2010.02.09 to 2016.01.07 with the its IMFs and residues. (CSV)

Displays the actual data for Malaysia stock market from 2010.02.09 to 2016.01.07 with the its IMFs and residues. (CSV)

Displays the actual data for Sri Lanka stock market from 2010.02.09 to 2016.01.07 with the its IMFs and residues. (CSV)

Displays the actual data for France stock market from 2010.02.09 to 2016.01.07 with the its IMFs and residues. (CSV)

Displays the actual data for USSP500 stock market from 2010.02.09 to 2016.01.07 with the its IMFs and residues. (CSV)

Displays the actual data for Netherlands stock market from 2010.02.09 to 2016.01.07 with the its IMFs and residues. (CSV)

It is well known that the simplest way of estimation of statistical parameters is the method of least squares using linear functions. However, the problem with this method is in how to find out a linear observations. estimation accuracy is very important concept in many field such that; medicine, humanities, engineering, industry, economics and oth...

Recently,thevolatilityoffinancialmarketshascontributedanecessaryparttoriskmanagement.Volatilityriskischaracterizedas thestandarddeviationoftheconstantlycompoundreturnperday. ThispaperpresentsforecastingofvolatilityfortheJordanian industrysectorafterthecrisisin2009.ARIMAandARIMA-WaveletTransform(WT)havebeenconductedinordertoselectthe best forecastin...

Nowadays, stock market data forecasting has drawn a high attention in the ffield of nonstationary and nonlinear time series data with a high heteroscedasticity, since improving the forecasting accuracy is a hot topic for the researchers. Therefore, in this article the authors are proposed a new methodology via combining Empirical Mode decomposition...

In recent years, the instability and unpredictability of financial markets have played an essential part in risk management. Volatility risk is characterized as the standard deviation of the constantly compound return per day. This paper shows forecasting of volatility for the Jordanian banking sector after 2006 crisis. Parameters p, d, and q are e...

The main goal of this unprecedented study was forecasting handled cargo in Aqaba port in 2030. The results of forecasting were built on a data collected over 64 years period’. Auto Regressive Integrated Moving Average (ARIMA) revealed a forecasted amount of 36M tons in 2030 (with a 95 % high confidence level of 56M tons). This jump from 27.7M tons...

In this study, we present a new technique for the bootstrap aggregation (bagging) for nonlinear and nonstationary financial time series, which results in significant improvements in the forecasts. This technique is based on empirical mode decomposition (EMD), quantile regression (QR) and Holt-Winter model (HW). The bagging uses an EMD with QR to sepa...

Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Moving A...

The study aimed to measure the degree of labor market regulation across business sectors in Jordan through identifying the mechanisms of wage growth determination for the period 1976-2009. Wage determinants in financial sector was less depending on output growth since to take considerable time for wages to adjust to output change unlike other secto...

In 2008, Ahmad Mahir and Al-Khazaleh [11] proposed a new method to estimate the missing data by using the filtering process. However, in this article, we make use of the previous techniques in order to forecast two groups of financial time series data with missing data and without missing data. Real closed price data were collected from Amman Stock...

The study aimed to measure the degree of labor market regulation across business sectors in Jordan through identifying the mechanisms of wage growth determination for the period 1976-2009. Wage determinants in financial sector was less depending on output growth since to take considerable time for wages to adjust to output change unlike other secto...

One of the main problems in large datasets is outlier detection, the outliers are detected using Z-score, box plot method, statistical measures and asymmetric Winsorized mean. This paper has a novel method for detecting the outlier values by combining the asymmetric Winsorized mean with the famous spectral analysis function which is wavelet transfo...

During the current financial crisis, the insurance enterprise received more attention. As a result, this paper displays forecasting methods based on filtering insurance time series data set. There are two types of Wavelet Transform (WT) used in this study; discrete wavelets transform (DWT) and maximum overlapping discrete wavelet transform (MODWT)....

Outlier detection is one of the major problems of large datasets. Outliers have been detected using several methods such as the use of asymmetric winsorized mean. Al-Khazaleh et al. (2015) has proposed new methods of detecting the outlier values. This is achieved by combining the asymmetric winsorized mean with the famous spectral analysis function...

Selection approaches are used to identify the best system from a finite set of alternative systems. If it involves a small number of alternatives, then Ranking and Selection is the right procedures to be used in order to select the best system. Nevertheless, for a case of a large number of alternatives we need to change our concern from finding the...

During the current financial crisis, the insurance enterprise received more attention. As a result, this paper displays forecasting methods based on filtering insurance time series data set using Wavelet Transform (WT) such as discrete wavelet transform (DWT) and maximum overlapping discrete wavelet transform (MODWT) and ARIMA model. The insurance...

During the last decades, no researches have conducted in order to prove some properties of the of the multivariate power series distribution, as results of the present study proved that any multivariate power series distribution is determined uniquely from the mean –function of any marginal random variable. Furthermore these results indicated also...

Recently, maximum overlap discrete wavelet transform (MODWT) has gained very high attention in many fields and applications such as finance, engineering, signal processing, [13]. Amman stock exchange (ASE) from Jordan was selected as a tool to show the ability of MODWT in detecting the fluctuations in the banking sector in ASE. Finally, the claim i...

Recently, Wavelet Transform (WT) has gained very high attention in many fields and applications such as physics, engineering, signal processing, applied mathematics and statistics. In this paper, we present the advantages of WT in analyzing and improving the forecasting accuracy financial time series data. Amman stock market (Jordan) was selected a...

In this paper, we present the advantages of Maximum overlapping Discrete Wavelet Transform (MODWT) in improving the forecasting accuracy financial time series data. Amman stock market (ASE) in Jordan was selected as a tool to show the ability of MODWT in forecasting financial time series, using Banking sector. Experimentally, this article suggests...

One of the main features in financial and economic time series data that trigger attention from researchers is regime shifts or structure breaks. Usually structure breaks occur because of abrupt of change in the government policy, financial and economic crisis and political instability. In recent years wavelet transform becomes more popular in the...

In recent years the study of regime shifts or structural breaks behaviour in time series has gained much attention. This is due to realization that many economic time series undergo episodes in which the behaviour of the series change quite dramatically as a result of financial crises or abrupt changes in the government policy. The paper use two di...

Recently, wavelet transforms have gained very high attention in many fields and applications such as physics, engineering, signal processing, applied mathematics and statistics. In this paper, we present the advantage of wavelet transforms in forecasting financial time series data. Amman stock market (Jordan) was selected as a tool to show the abil...

This article suggests a novel technique for forecasting the volatility data based on Wavelet transforms and ARIMA model. The volatility data are decomposed via Wavelet transforms. Then, the future observations of this series are forecasted using a suitable and best fitted ARIMA model. Daily prices from Amman Stocks Market (Jordan) from 1993 until 2...

this article suggests a novel technique for forecasting the financial time series data based on Wavelet transforms and neural network model. The financial data are decomposed via Haar Wavelet transforms. Then, the future observations of this series are forecasts using a suitable and best fitted neural network model. Daily prices from Amman Stocks M...

Recently, regime shifts or structure breaks had acquired very high attention in analyzing financial time series data. Abrupt of changes in the government policy, financial crises and many of challenges lead to change in the behavior of the financial time series data. In addition, wavelet transform also becomes very famous in the financial sector an...

Recently, the Fast Fourier Transforms (FFT) and the Discrete Wavelet Transforms (DWT) are two time series filtering methods that are used to represent the fluctuations of stocks market. In general the basic wavelet function, Haar wavelet transform is a mathematical function that cut off the data into different frequency components, satisfies some o...

It is well known that during the developments in the economic sector and through the financial crises occur everywhere in the whole world, volatility measurement is the most important concept in financial time series. Therefore in this paper we discuss the volatility for Amman stocks market (Jordan) for certain period of time. Since wavelet transfo...

During the current financial crisis, the insurance enterprise received more attention. As a result, this paper displays two types of filtering time series representing the Wavelet analysis and the Fourier analysis. We use the insurance income time series data taken from the Amman Stocks Market (Jordan) for a certain period of time. First, according...

This article suggests a novel technique for forecasting the financial time series data based on Wavelet transforms and ARIMA model. The financial data are decomposed via Haar Wavelet transforms. Then, the future observations of this series are forecasts using a suitable and best fitted ARIMA model. Daily prices from Amman Stocks Market (Jordan) fro...

Regime shifts or structure breaks acquire very high attention in analyzing financial time series data. Abrupt of changes in the government policy, financial crises and many of challenges lead to change the behavior of the financial time series data. In recent years wavelet transform becomes more popular in the financial time series analysis and it...