Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model

The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.

Optimization of physical characteristics of bioplastics from agricultural waste using response surface methodology (RSM)

Rapid technological advances are in line with the increasing use of plastics. The problem faced is that plastic waste is not easily degraded by microorganisms in the soil. Bioplastics are made from renewable natural materials so that they have properties that can be broken down by microorganisms. In this study, bioplastics were made from agricultural waste containing starch, namely from jackfruit seed starch plus corncob starch. Optimization using the response surface methodology (RSM) was carried out to see the optimum conditions of the variables and also the response to the physical properties of the resulting bioplastic. The research variables used were ZnO consisting of 3, 6, and 9 percent (%) and the glycerol variable consisted of 2, 5, and 8 mL. From the test results using RSM obtained the optimum conditions of ZnO 7.42% and glycerol 4.96 mL with a residual value of 81.3612%; water resistance 86.1333%; and heat resistance of 96.888°C with a desirability value of 0.725. The combination of the basic ingredients of agricultural waste in the form of jackfruit seeds and corn cobs is able to produce bioplastics with good physical properties.

Excel’s Calculation of Basic Assets Amortisation Values

For Excel’s calculation of basic (fixed) assets amortisation values, the investigated economic and mathematical foundation with required values and their relations were used. The investigated and introduced theory is adapted to Excel calculations of fixed assets amortisation based on today’s needs. All values for Excel’s calculations are sorted into input and output values, and input to main and nested calculations. Two methods for calculating fixed assets amortisation were introduced using Excel. The first method is based on a linear decreasing function, G(t) = G0 (1-pt), which presents the simple interest calculation of the reduced equities for equal periodic amortisation values. The second method is based on the exponential degrading function, G(t) = G0qt, which presents a complex interest calculation of the reduced equities for periodic amortisation amounts in a descending sequence. The continuity of the introduced functions results from the continuity of: the life of the fixed asset, periodic amortisation, accumulated amortisation and non-amortized amount (residual value) of the fixed asset. It is particularly important to introduce dates with the exact time, for the beginning and the end of each amortisation period of the fixed asset. The theory for Excel’s calculation of the fixed asset output values for an arbitrary (planned or unplanned) term has also been explored and introduced. Such calculations relate mainly to terms of alienation, permanent damage, permanent loss of the process function of a fixed asset and periodic accounting reporting.

Valuating hydrocarbon pipeline facility service beyond 20–25 year economic life: Accounting for residual value

The 20–25 years economic life for hydrocarbon pipelines in the investment decision model is at wide variance with historical statistical records of more than 90-percent world-wide. Opinions diverge, from service type to the product quality, and materials resilience as basis for this premise. While, financial experts consider time to fully depreciate a capital investment, irrespective of the rate of returns, engineers consider operational availability and reliability duration. The risk is that actual residue values of pipelines worldwide are erroneously omitted in every project’s economics Cash-flow computation, thus eroding the investment decision quality. Statistics showed that more than 60-percent of pipelines worldwide have already exceeded the 25 years economic life, while more than 40-percent have operated more than 30-years and above. This theoretical appraisal identified a gap in the economic model in handling multi-criteria risk management uncertainties like hedging, weighting, etc., and highlighted the exigency to craft and assign numeric residue values for pipelines in the investment Cash-flow models.

Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: a management decision support model

PurposeHeavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.Design/methodology/approachBased on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.FindingsThe developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.Originality/valueThe proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

Comparing the Cost of Rigid and Flexible Aircraft Pavements Using a Parametric Whole of Life Cost Analysis

The construction and maintenance costs, as well as the residual value, were calculated for structurally equivalent rigid and flexible airfield pavements, for a range of typical commercial aircraft, as well as a range for typical subgrade conditions. Whole of life cost analysis was performed for a range of analysis periods, from 40 years to 100 years. For the standard 40-year analysis period and a residual value based on rigid pavement reconstruction, the rigid pavements had a 40% to 105% higher whole of life cost than equivalent flexible pavements, although this comparison is limited to the pavement compositions and material cost rates adopted. However, longer analysis periods had a significant impact on the relative whole of life cost, although the rigid pavements always had a higher cost than the flexible pavements. The assumed condition of the rigid pavement at the end of the design life was the most influential factor, with a 60-year service life resulting in the rigid pavements having a lower whole of life cost than the flexible pavements, but assuming a requirement for expedient rigid pavement reconstruction resulted in the rigid pavements costing approximately 4–6 times the cost of the flexible pavements over the 40-year analysis period.

Theoretical and Methodological Approach to the Study of Residual Value

The purpose of this article is to expand the understanding of the negative environmental load from production activities. It is based on the theoretical foundations relating to residual value. It also makes use of environmental and economic accounting introduced for deep analyses of hidden residual values, in particular in discussing concluding terminal costs and remedial costs in the context of dynamics of environmental sustainability. A complex understanding of the hidden residual value is suggested on macroeconomic in the capital concept and global levels. The macroeconomic measures (indicators) are considered in accounting the hidden environmental impact resulting from production activity.