Analysis of Solow Growth Model in Sharia Hotel Industry In Pandemic Era, Case Study: Indonesia and Malaysia

2021 ◽  
Vol 1 (1) ◽  
pp. 65-82
Author(s):  
Aries Sudarmawan ◽  
◽  
Sri Harnani
Keyword(s):  
Stock Exchange ◽  
Moving Average ◽  
Hotel Industry ◽  
Theory Of The Firm ◽  
Arma Model ◽  
Research Literature ◽  
Solow Model ◽  
Great Chance ◽  
Absolute Income ◽  
Swot Matrix

This journal aims to determine the business prospects for the Islamic hotel industry in the digital era and post-pandemic. We carry out simulations based on the estimated growth of internet users, tourism arrival, and the performance of sharia and non-sharia hotels using data from the annual report of all hotels listed on the stock exchange both in Indonesia and Malaysia every year from 2000 to 2019. In making hotel performance forecasting, This study uses the Autoregressive moving average (ARMA) model. The estimation results are used to compare the performance of the sharia and non-sharia hotel industries in Indonesia and Malaysia from 2000 to 2019. We find that massive internet inclusion makes it easier for consumers to make decisions in choosing accommodation both sharia hotels, non-sharia hotels, and non-hotel accommodations. To understand the resilience of Islamic hotels in facing the Covid-19 pandemic and the opportunities and threats aimed at Islamic hotels, the SWOT Matrix is used through a qualitative research literature approach to strengthening analysis based on ARMA forecasting and to understand the competitive conditions of Islamic hotels both in Indonesia and in Malaysia. We find that the key factor in increasing the competitive power of Islamic hotels is the use of internet inclusion for campaigns that encourage the perspective of prospective Islamic hotel customers in taking purchase actions or encourage prospective customers' perspectives towards creating demand for Islamic hotel services. Based on the results of analysis and forecasting that combines a quantitative approach based on the theory of the firm and absolute income theory, adopting the Cobb-Douglas function and the Solow model with a qualitative approach to research literature tested in the SWOT Matrix, Islamic hotels have a competitive advantage with clear segmentation and positioning as well as has a great chance to rise and recover the financial performance of the organization after the Covid-19 pandemic ends

scholarly journals Helsinki Stock Exchange Index during World War One: Statistical Test of Hypotheses on the Basis of Counter-Factual Modeling

2020 ◽  
pp. 55-65
Author(s):  
Daria Anisimova
Keyword(s):  
Stock Exchange ◽  
Moving Average ◽  
Arma Model ◽  
Stationary Time Series ◽  
Economic Factors ◽  
Software Environment ◽  
Autoregressive Moving Average Model ◽  
Test Of Hypotheses ◽  
Improved Model ◽  
Helsinki Stock Exchange

The article proposes an improved model of St. Petersburg Stock Exchange index dynamics and constructs a similar model of Helsinki Stock Exchange index on the basis of published results of a counterfactual model predicting the hypothetical dynamics of St. Petersburg Stock Exchange index after July 1914 under the assumption that there is no war. The author hypothesizes that internal economic factors that determined the downward trend of St. Petersburg Stock Exchange index also influenced the dynamics of Helsinki Stock Exchange index under the assumption that there was no war. To test this hypothesis the author has constructed (in the R software environment) the ARIMA statistical model that is an integrated autoregressive-moving average model which extends the ARMA model for non-stationary time series. The constructed counterfactual models proved that while the influence of pre-war factors remained, the dynamics of both indices did not show similar trends thus suggesting that the Finnish stock market was developing without any noticeable look at St. Petersburg Stock Exchange and inner economic factors of the Russian Empire.

The Potential of The Hotel Industry In Pandemic Era Based On Finance Performance Point Of View, Case Study: Indonesia

2021 ◽  
Vol 1 (1) ◽  
pp. 111-123
Author(s):  
Anton Effendi ◽  
◽  
Bambang Hadi Prabowo
Keyword(s):  
Hospitality Industry ◽  
Stock Exchange ◽  
Hotel Industry ◽  
Secondary Data ◽  
Reliable Data ◽  
Point Of View ◽  
Threshold Autoregressive ◽  
Performance Point ◽  
Occupancy Rates

This article aims to investigate and analyze the potential of the hospitality industry by comparing the potential occupancy rates and hotel revenues of foreign and domestic tourists. This investigation uses an investigation of company data obtained from reports from hotel companies throughout Indonesia which are listed on the Indonesia Stock Exchange and secondary data obtained from world banks and other reliable data. This study uses behavioral data analysis using Threshold Autoregressive from 2000 to 2019. It was found that domestic tourists are a new hope that needs to be considered in surviving and restoring the hospitality industry after being exposed to the COVID-19 pandemic which has led hotel companies. temporarily closed operations and part of the hotel went bankrupt. Optimization of domestic tourists allowed the hotel industry to develop rapidly after the Covid-19 pandemic ended.

scholarly journals Financial Ratio Analysis to Assess Financial Performance of the Hotel Industry

2021 ◽  
Vol 5 (3) ◽  
pp. 346
Author(s):  
Tjeng Gloria Santoso ◽  
Supatmi Supatmi
Keyword(s):  
Financial Performance ◽  
Activity Ratio ◽  
Stock Exchange ◽  
Research Result ◽  
Hotel Industry ◽  
Analysis Tool ◽  
Current Ratio ◽  
Financial Ratio ◽  
Leverage Ratio ◽  
Hotel Performance

Analysis of financial performance can be implemented to all companies, including to hotel industries. For the last few years, there are many issues that income of hotel industry increases because of the soaring numbers of foreign and domestic tourists in Indonesia.  This study aims to analyze financial ratio to assess the financial performance of hotel industry in 2015-2018. Research sample were 12 companies of 35 hotel industries that were listed in Indonesian Stock Exchange in 2015-2018. Analysis tool used in this research were liquidity ratio, profitability ratio, activity ratio, leverage ratio, and operational ratio. Research result showed good ratios; they were liquidity ratio that was indicated by current ratio, profitability ratio that was pointed by net profit margin, return on asset, and return on equity, also paid occupancy percentage on activity ratio. While the not good ratio, which was activity ratio was pointed by total asset turnover, then leverage ratio by equity multiplier, debt to asset ratio, and debt to equity ratio, also operational ratio which was showed through average room rate and food and beverage cost. Research result of hotel performance in 2015-2018 which is based on financial ratio, they are liquidity, activity, profitability, leverage, and operational, describes that hotel performance is fluctuated. Generally seen, a good ratio in this study is the liquidity ratio that is pointed by current ratio, then profitability ratio that are demonstrated by NPM, ROA, and ROE, also paid occupancy percentage in activity ratio.

scholarly journals Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems

2021 ◽  
Author(s):  
Ines Sansa ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Cloud Cover ◽  
Moving Average ◽  
Arma Model ◽  
Weather Conditions ◽  
Pv Systems ◽  
Auto Regressive ◽  
Micro Grid ◽  
One Year

Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.

scholarly journals An Improved Deep Learning Algorithm for Risk Prediction of Corporate Internet Reporting

2020 ◽  
Vol 34 (4) ◽  
pp. 437-444
Author(s):  
Lingyan Ou ◽  
Ling Chen
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Moving Average ◽  
Arma Model ◽  
Evaluation Index System ◽  
Online Information ◽  
Deep Learning Algorithm ◽  
Research Findings

Corporate internet reporting (CIR) has such advantages as the strong timeliness, large amount, and wide coverage of financial information. However, the CIR, like any other online information, faces various risks. With the aid of the increasingly sophisticated artificial intelligence (AI) technology, this paper proposes an improved deep learning algorithm for the prediction of CIR risks, aiming to improve the accuracy of CIR risk prediction. After building a reasonable evaluation index system (EIS) for CIR risks, the data involved in risk rating and the prediction of risk transmission effect (RTE) were subject to structured feature extraction and time series construction. Next, a combinatory CIR risk prediction model was established by combining the autoregressive moving average (ARMA) model with long short-term memory (LSTM). The former is good at depicting linear series, and the latter excels in describing nonlinear series. Experimental results demonstrate the effectiveness of the ARMA-LSTM model. The research findings provide a good reference for applying AI technology in risk prediction of other areas.

scholarly journals Peramalan Tingkat Penghunian Tempat Tidur Hotel Bintang Tiga Kota Surakarta Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA)

2019 ◽  
Vol 2 (1) ◽  
pp. 53
Author(s):  
Shindy Dwi Pratiwi
Keyword(s):  
Moving Average ◽  
Hotel Industry ◽  
Occupancy Rate ◽  
Autoregressive Integrated Moving Average ◽  
Occupancy Rates ◽  
The City

<p>Surakarta is a cultural city that is now starting to attract domestic and foreign tourists. This makes many tourists visit the city of Surakarta so that it affects the occupancy rate of hotels in Surakarta. The occupancy rate of hotels in Surakarta has fluctuations from each year. The uncertainty of hotel occupancy rates in Surakarta will certainly affect investors to choose policies in the hotel industry so that hotel occupancy rates in Surakarta City need to be estimated for the next year. In this study, the Autoregressive Integrated Moving Average (ARIMA) method was used to forecast hotel occupancy rates in Surakarta from January to May 2018. By using the best model IMA (1.1), it was concluded that the occupancy rate of three-star Surakarta hotels increased every the month.</p><p><strong>Keywords</strong><strong> : </strong>occupancy rate of hotel, forecasting, ARIMA.</p>

Research on Reliability Assessment of Power System with Wind Farms

2012 ◽  
Vol 608-609 ◽  
pp. 588-591
Author(s):  
Wen Jiang ◽  
Ye Xia Cheng ◽  
Ye Jian Cheng
Keyword(s):  
Power System ◽  
Wind Farm ◽  
Sequential Monte Carlo ◽  
Moving Average ◽  
Reliability Assessment ◽  
Arma Model ◽  
Wind Farms ◽  
Test System ◽  
Wind Speeds ◽  
State Models

Due to randomness and fluctuation of wind speed, reliability of power system will be affected severely with increasing wind energy injected into power grid. In order to evaluate the effect on reliability of power system with wind farms, the author considers feature of time-sequential and self-correlation of wind speeds and builds an auto-regressive and moving average (ARMA) model to forecast wind speeds. Combining with state models of conventional generating units, transmission lines and transformers, a time-sequential Monte Carlo simulation reliability model is proposed to do reliability assessment of composite generation and transmission system with wind farm. IEEE-RTS test system is introduced to prove the proposed model. Analysis and comparison of results show that reliability can be improved clearly after integration of wind farm.

Method for Determining System Eigenvalues From FRF for Noise Contaminated Subsystems

1993 ◽  
Author(s):  
H. C. Chen ◽  
Eric K. Lee ◽  
Y. G. Tsuei
Keyword(s):  
Frequency Response Function ◽  
Numerical Study ◽  
Moving Average ◽  
Arma Model ◽  
Frequency Range ◽  
Laplace Domain ◽  
Force Method ◽  
Modal Force ◽  
Auto Regressive ◽  
Inverse Operation

Abstract A method for determining the eigenvalues of a synthesized system from the Frequency Response Function (FRF) for noise contaminated subsystems is presented. This method first uses matrix Auto-Regressive Moving-Average (ARMA) model in the Laplace domain to describe each subsystem. Then a modal force method by ARMA model can be established. Only the FRF at the connecting joints is needed in the analysis to form a matrix named Modal Force Matrix. From this matrix, both synthesized system modes and substructure modes can be extracted simultaneously. Since the inverse operation is not required to form Modal Force Matrix, the computation is reduced drastically. The eigensolution of the system in any frequency range can be determined independently. Numerical study suggests that good results can be achieved by this method.

An Optimal Band for Prediction of Buy and Sell Signals and Forecasting of States

2015 ◽  
Vol 2 (2) ◽  
pp. 33-53
Author(s):  
Vivek Vijay ◽  
Parmod Kumar Paul
Keyword(s):  
Time Series ◽  
Linear Function ◽  
Markov Models ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Moving Average ◽  
Linear Optimization ◽  
Optimization Technique ◽  
Price Series ◽  
Security Price

A trading band, based on historical movements of a security price, suggests buy or sell pattern. Bollinger band is one of the most famous bands based on moving average and volatility of the security. The authors define a new trading band, namely Optimal Band, to forecast the buy or sell signals. This optimal band uses a linear function of local and absolute extrema of a given financial time series. The parameters of this linear function are then estimated by simple linear optimization technique. The authors then define different states using various upper and lower values of Bollinger band and the optimal band. The approach of Markov and Hidden Markov Models are used to forecast the future states of given time series. The authors apply all the techniques on the closing price of Bombay stock exchange and intra-day price series of crude oil and Nifty stock exchange.

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