Price Modeling of Eucalyptus Wood under Different Silvicultural Management for Real Options Approach

Munis, Rafaele Almeida; Camargo, Diego Aparecido; Silva, Richardson Barbosa Gomes da; Tsunemi, Miriam Harumi; Ibrahim, Siti Nur Iqmal; Simões, Danilo

doi:10.3390/f13030478

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…This method amplifies model categorization by accentuating distinctions among individual models [ 13 ]. In order to assess and fortify the model’s resistance to transformations or enhance its robustness, the Random Forest adopts the pocket-out-of-bag approach [ 14 ]. Given that each variable can train a substantial number of samples, the random forest’s multi-decision tree structure ensures the utilization of multiple variables in tax assessment.…”

Section: Random Forest Model In the Identification Of Income Tax Risk...mentioning

confidence: 99%

Random forest model in tax risk identification of real estate enterprise income tax

Xu,

Kong

2024

PLoS ONE

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The text describes improvements made to the random forest model to enhance its distinctiveness in addressing tax risks within the real estate industry, thereby tackling issues related to tax losses. Firstly, the paper introduces the potential application of the random forest model in identifying tax risks. Subsequently, the experimental analysis focuses on the selection of indicators for tax risk. Finally, the paper develops and utilizes actual taxpayer data to test a risk identification model, confirming its effectiveness. The experimental results indicate that the model’s output report includes basic taxpayer information, a summary of tax compliance risks, value-added tax refund situations, directions of suspicious items, and detailed information on common indicators. This paper comprehensively presents detailed taxpayer data, providing an intuitive understanding of tax-related risks. Additionally, the paper reveals the level of enterprise risk registration assessment, risk probability, risk value, and risk assessment ranking. Further analysis shows that enterprise risk points primarily exist in operating income, selling expenses, financial expenses, and total profit. Additionally, the results indicate significant differences between the model’s judgment values and declared values, especially in the high-risk probability of total operating income and profit. This implies a significant underreporting issue concerning corporate income tax for real estate enterprises. Therefore, this paper contributes to enhancing the identification of tax risks for real estate enterprises. Using the optimized random forest model makes it possible to accurately assess enterprises’ tax compliance risks and identify specific risk points.

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Section: Random Forest Model In the Identification Of Income Tax Risk...mentioning

confidence: 99%

Random forest model in tax risk identification of real estate enterprise income tax

Xu,

Kong

2024

PLoS ONE

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“…The reward of the shared participants is uncertain and fluctuates randomly. Since geometric Brownian motion is a common way to describe random variables [37], we use geometric Brownian motion to describe the reward fluctuations of the shared participants. We assume that the average reward b i of shared participants satisfies the following Formula (12):…”

Section: Design Of Maintenance Mechanism Based On Prospect Theorymentioning

confidence: 99%

A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory

Liu,

Xu,

Deng

et al. 2023

Mathematics

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Crowdsensing uses the participants’ smart devices to form a new perception network. The coverage of crowdsensing’s tasks determines the quality of services. Under the constraint of budget and the number of participants, the platform needs to increase the participation duration of participants through incentive mechanisms to increase the coverage of tasks. There are two problems with the existing incentive mechanisms: (1) many incentives ignore the participants’ characteristics, and using a single incentive mechanism for different participants will make the incentive effect not reach the expectation; (2) many incentives will affect the effectiveness because of the decision problem caused by asymmetric information. Inspired by ensemble learning and prospect theory, this paper proposes the Incentive Mechanism based on Ensemble Learning and Prospect Theory (IMELPT). First, we propose the Deep-Stacking-Generation algorithm based on Dropout (DSGD), to predict the participants and distinguish whether they are long-term or short-term participants. If the participants are short-term, we incentivize them through the Short-term Participant Incentive Mechanism based on Prospect Theory (SPIMPT). We increase the participation duration by transforming the change in reward into asymmetric information that aligns the participant’s goal with the platform. If the participants are long-term participants, we motivate them through the Long-term Participant Incentive Mechanism (LPIM), to maintain the participation rate of participants by maximizing their utility. Theoretical analysis and experiments on real datasets demonstrated that IMELPT can reliably improve the coverage of crowdsensing tasks.

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“…As a result, even after a brief amount of time, the investors will make a larger profit. Some studies that applied GBM for forecasting or simulating prices are [1], [2], and [6], while some applied GFBM, such as [13]. In reference [1], they forecast the stock close price for several small companies registered in Malaysia stock exchange.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in [6], the study uses GBM to simulate the prices of rubber of various grades. Reference [13] compares both GBM and GFBM for modeling Eucalyptus wood prices.…”

Section: Introductionmentioning

confidence: 99%

Comparison of GBM, GFBM and MJD Models in Malaysian Rubber Prices Forecasting

Ibrahim

Muda²

2023

Mal. J. Fund. Appl. Sci.

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This research studies three mathematical models, namely geometric Brownian motion (GBM), geometric fractional Brownian motion (GFBM) model which was developed by adding the Hurst parameter to GBM to characterize the long-memory phenomenon, and Merton jump-diffusion (MJD) model which captures shocks via GBM. This study sets out to forecast Malaysia rubber prices for the six months period beginning in January 2022 and ending in June 2022, which involves four main steps; calculating the logarithmic return of rubber prices; estimating the parameters for forecasting the rubber prices using the three models; simulating the rubber prices using the GBM, GFBM and MJD models via Monte Carlo simulation; and computing the mean absolute percentage errors (MAPE) and forecast accuracy. Simulation results show that the MJD model is the most accurate model in forecasting the rubber prices.

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Price Modeling of Eucalyptus Wood under Different Silvicultural Management for Real Options Approach

Cited by 6 publications

References 42 publications

Random forest model in tax risk identification of real estate enterprise income tax

Random forest model in tax risk identification of real estate enterprise income tax

A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory

Comparison of GBM, GFBM and MJD Models in Malaysian Rubber Prices Forecasting

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