Fixed Asset Revaluation:

2017 ◽  
Author(s):  
Jeremia Pradnya ◽  
Handiani Suciati
Keyword(s):  
Fixed Asset

A neural computing-based cash price prediction using multi-layer perceptron (MLP) and feature selection for health economics

2019 ◽  
Vol 14 ◽  
Author(s):  
Zinat Ansari
Keyword(s):  
Feature Selection ◽  
Health Economics ◽  
Cash Flow ◽  
Multi Layer Perceptron ◽  
Asset Sales ◽  
Fixed Asset ◽  
Academic Fields ◽  
Investment Returns ◽  
Short And Long Term

Background: The present study proceeds to incorporate feature selection as a means for selecting the most relevant features affecting the prediction of cash prices in Iran in terms of health economics. Health economics are between academic fields that can aid in ameliorating conditions so as to perform better decisions in regards to the economy such as determining cash prices. Methods: Accordingly, a series of search algorithms, namely the Best-First, Greedy-Stepwise, and Ranker methods, are deployed in order to extract the most relevant features from among a 500 data samples. The validity of the methods was evaluated via the LMT procedure. The corresponding dataset used for this study constitutes a variety of features including net cash flow, dividends, revenue from short and long-term deposits, cash flow from investment returns, income tax, fixed asset purchases, fixed asset sales, long-term investment purchases, long-term investment sales, total cash flow from investment activities, financial facilities, and repayment of financial facilities. Results: The results were indicative of the superiority of the Ranker model using the RelieF-Attribute-Eval tool in Weka over the remaining classification methods. Ergo, the LMT approach could be employed to remove data redundancies and thereby accelerate the estimation process, while saving time and money. The results of the multi-layer perceptron (MLP) further confirmed the high accuracy of the proposed method in estimating cash prices. Conclusions: The present research attempted to reduce the volume of data required for predicting end cash by means of employing a feature selection so as to save both precious money and time.

Influencing Factors and Decoupling Analysis of Carbon Emissions in China's Manufacturing Industry

2021 ◽  
Author(s):  
baoling jin ◽  
ying Han
Keyword(s):  
Carbon Emissions ◽  
Manufacturing Industry ◽  
Energy Intensity ◽  
Value Added ◽  
Carbon Intensity ◽  
Energy Structure ◽  
Investment Efficiency ◽  
Fixed Asset ◽  
Carbon Productivity ◽  
Fixed Asset Investment

Abstract The manufacturing industry directly reflects national productivity, and it is also an industry with serious carbon emissions, which has attracted wide attention. This study decomposes the influential factors on carbon emissions in China’s manufacturing industry from 1995 to 2018 into industry value added (IVA), energy consumption (E), fixed asset investment (FAI), carbon productivity (CP), energy structure (EC), energy intensity (EI), investment carbon intensity (ICI) and investment efficiency (IE) by Generalized Divisia Index Model (GDIM). The decoupling analysis is carried out to investigate the decoupling states of the manufacturing industry under the pressure of "low carbon" and "economy.” Considering the technological heterogeneity, we study the influential factors and decoupling status of the light industry and the heavy industry. The results show that: (1) Carbon emissions of the manufacturing industry present an upward trend, and the heavy industry is the main contributor. (2) Fixed asset investment (FAI), industry value added (IVA) are the driving forces of carbon emissions. Investment carbon intensity (ICI), carbon productivity (CP), investment efficiency (IE), and energy intensity (EI) have inhibitory effects. The impact of the energy consumption (E) and energy structure (EC) are fluctuating. (3) The decoupling state of the manufacturing industry has improved. Fixed asset investment (FAI), industry value added (IVA) hinder the decoupling; carbon productivity (CP), investment carbon intensity (ICI), investment efficiency (IE), and energy intensity (EI) promote the decoupling.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI REVALUASI ASET TETAP PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DIBURSA EFEK INDONESIA

CASH ◽  
2020 ◽  
Vol 3 (01) ◽  
pp. 21-28
Author(s):  
Meidi Yanto
Keyword(s):  
Fixed Asset

Abstrak: Tujuan penelitian ini untuk mengetahui pengaruh Laverage, Arus Kas Operasi, Ukuran Perusahaan, Fixed Asset Intensity dan Market to Book Ratio pada Perusahaan Manufaktur yang Terdaftar di Bursa Efek Indonesia. Hasil penelitian ini menunjukkan bahwa ukuran perusahaan berpengaruh signifikan terhadap keputusan melakukan revaluasi. Laverage, arus kas kas operasi, fixed asset intensity dan market to book ratio tidak berpengaruh terhadap keputusan melakukan revaluasi aset tetap. Kesimpulan penelitian ini adalah perusahaan yang memiliki ukuran perusahaan yang tinggi cenderung untuk melakukan revaluasi aset tetap.

scholarly journals PENGARUH KINERJA KEUANGAN TERHADAP INVESTASI AKTIVA TETAP PT. SEPATU BATA, Tbk DI BURSA EFEK INDONESIA

2018 ◽  
Author(s):  
STIM Sukma
Keyword(s):  
Financial Performance ◽  
Hypothesis Test ◽  
Data Use ◽  
Coefficient Of Determination ◽  
Return On Assets ◽  
Fixed Asset ◽  
Processing Data ◽  
Regression Test ◽  
Simple Regression ◽  
Fixed Asset Investment

The purpose of this research is to determine impact of the financial performance of fixed asset investment. The data analyzedby simple regression test with hypothesis test, using test the coefficient of determination (R2), partial test (t test), while processing data use SPSS. The results showed that the coefficient of determination (R2) variable Return On Assets (ROA) able to explain the variations that occur in fixed asset investment, but it is partially noeffect and significant fixed asset investment.

scholarly journals Operating Cash Flow, Earning Response Coefficient, and Fixed Asset Revaluation: Study on Manufacturing Company

ETIKONOMI ◽  
2017 ◽  
Vol 16 (1) ◽  
pp. 93-102
Author(s):  
Andison Andison ◽  
Etty M Nasser
Keyword(s):  
Multiple Regression ◽  
Cash Flow ◽  
Abnormal Return ◽  
Fixed Asset ◽  
Response Coefficient ◽  
Manufacturing Company ◽  
Operating Cash Flow

The purpose of this study to determine the effect of operating cash flow to the abnormal return and the effect of operating cash flow to the abnormal return of companies that conduct the revaluation is higher than that of non revaluation which adopted SFAS No. 16 (2012). The analysis used in this study are multiple regression, for the period 2012-2015. The results showed that operating cash flow has no effect on non-sampled companies revaluation, while the sample of firms that perform revaluation proves that operating cash flow has a positive and significant impact on the abnormal return. Moreover, the effect of revaluation policy can strengthen the influence between operating cash flow to the firm abnormal return than non revaluation.DOI: 10.15408/etk.v16i1.4820

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