Selected Aspects of Carbon Stock Assessment in Aboveground Biomass

Given the significance of national carbon inventories, the importance of large-scale estimates of carbon stocks is increasing. Accurate biomass estimates are essential for tracking changes in the carbon stock through repeated assessment of carbon stock, widely used for both vegetation and soil, to estimate carbon sequestration. Objectives: The aim of our study was to determine the variability of several aspects of the carbon stock value when the input matrix was (1) expressed either as a vector or as a raster; (2) expressed as in local (1:10,000) or regional (1:100,000) scale data; and (3) rasterized with different pixel sizes of 1, 10, 100, and 1000 m. Method: The look-up table method, where expert carbon content values are attached to the mapped landscape matrix. Results: Different formats of input matrix did not show fundamental differences with exceptions of the biggest raster of size 1000 m for the local level. At the regional level, no differences were notable. Conclusions: The results contribute to the specification of best practices for the evaluation of carbon storage as a mitigation measure, as well as the implementation of national carbon inventories.

Pengaruh Biaya Modal dan Profitabilitas Terhadap Nilai Saham Bank BTPN Syariah Periode 2018-2020

Operations and investments of Islamic banks certainly require capital as the foundation and start of the bank's business, so estimating the cost of capital required is an important process and good corporate profits will provide a good image for the company. This study aims to determine the effect of capital with the Weighted Average Cost Of Capital (WACC) method on stock values. The effect of profitability with the ratio of Return On Equity (ROE) on stock value. And the effect of the cost of capital and profitability on the value of the stock. This study uses a descriptive quantitative research method. By using secondary data in the form of monthly financial statements of Bank BTPN Syariah for the 2018-2020 period. All of these data are materials for estimating and calculating the cost of capital and profitability of Bank BTPN Syariah. The results showed that partially the cost of capital had a significant positive effect on the stock value, profitability had a significant negative effect on the stock value. Meanwhile, simultaneously the cost of capital and profitability affect the value of the stock.

Stock Price Prediction Methods based on FCM and DNN Algorithms

With the rapid economic development and the continuous expansion of investment scale, the stock market has produced increasing amounts of transaction data and market public opinion information, making it further difficult for investors to distinguish effective investment information. With the continuous enrichment of artificial intelligence achievements, the status and influence of artificial intelligence researchers in academia and society have been greatly improved. Expert system, as an important part of artificial intelligence, has made breakthrough progress at this stage. Expert system is based on a large amount of professional knowledge and experience for a specific field. Computers of this system can be used to simulate the decision-making process of experts to provide a decision-making basis for solving some complex problems. This research mainly discusses stock price prediction methods on the basis of artificial intelligence (AI) algorithms. Fuzzy clustering is a data mining tool that has been developed in recent years and is widely used. Using this method to process super large-scale databases with various data attributes has the characteristics of high efficiency and small amount of information loss. Theoretically speaking, the use of fuzzy clustering technology and related index method can effectively reduce the massive financial fundamentals of listed companies. By analyzing the influencing factors of stock value investment, we specifically select from the financial statements of listed companies the five aspects that can reflect their profitability, development ability, shareholder profitability, solvency, and operating ability. The full text runs through a variety of AI methods that is the characteristic of the research method used in this article, which pays special attention to verifying the theoretical method model. Doing so ensures its effectiveness in practical applications. In stock value portfolio research, a portfolio optimization model, which integrates the dual objectives of portfolio risk and returns into the risk-adjusted return of capital single objective constraints and solves the portfolio, is established. The accuracy and recall of the FCM model are relatively stable, with accuracies of 0.884 and 0.001, respectively. This research can help improve the number and quality of listed companies.

The Influence of Corporate Governance Perception Index, Profitability, and Company Size on Stock Value on Go Public Companies at CGPI Listed on the Indonesia Stock Exchange

This research aims to determine the impact of corporate governance perception index, profitability, and company size on the value of CGPI-listed companies listed on the Indonesian Stock Exchange for three years. The data used in this study are second-hand data. The data for this research comes from the company's 2017-2019 annual report for the observation period. Using purposeful sampling methods, samples were obtained from as many as 16 companies using data collection techniques. The data analysis methods used in this study are multiple linear regression analysis, classical hypothesis testing, and hypothesis testing. The results show that the impact of corporate governance perception index, profitability, and company size also affect stock value. The influence of the independent variable on the dependent variable is 37.7%, and the remaining 62.3% are affected by variables other than this study. For partial results, the influence of the Corporate Governance Perception Index and Profitability and has a significant positive effect on Share Value, while the size of the company has no significant positive effect on the value of the share.

Structural Credit Risk Model with Jumps Based on Uncertainty Theory

Traditional finance studies of credit risk structured models are based on the assumption that the price of the underlying asset obeys a stochastic differential equation. However, according to behavioral finance, the price of the underlying asset is not entirely stochastic, and the credibility of financial investors also plays a very important role in asset prices. In this paper we introduce uncertainty theory to describe these credibility of investors and propose a new credit risk structured model with jumps based on the assumption that the underlying asset is described by an uncertain differential equation with jumps. The company default belief degree formula, zero coupon bond value and stock value formula are formulated. Company bond credit spread and credit default swap (CDS) pricing are studied as applications of the proposed model in uncertain markets.

Ensemble Stock Market Prediction using SVM,LSTM, and Linear Regression

<div>Abstract: Stock forecasting is challenging because of stock volatility and dependability on external factors, such as economic, social, and political factors. This motivates investors to seek tools to identify stock trends to reap profits.</div><div><div>In this research, we compared several heterogeneous ensembles for financial forecasting, including averaging, weighted, stacking, and blending ensembles. In addition, we used a random forest regressor as the baseline.<br></div><div>Regression was used to predict the next day’s closing stock price. We used classification to label closing stock value as HIGH or LOW by comparing with the opening stock value of a particular company. We used Long Short Term Memory (LSTM) models, Linear Regression, and Support Vector Machines (SVM) as individual models. Further, we analyzed 10 years of historical data of the most active 20 companies of the NASDAQ stock exchange for implementing ensemble models.<br></div><div>In conclusion, experimental results depict blending ensembles perform the best out of compared ensembles in financial forecasting. Further, they reveal SVM is under-performing, LSTM outputs are satisfactory, while linear regression produced promising results.<br></div></div><div>Data: Data for this research was gathered from online available sources from the NASDAQ American stock exchange.</div><div>We gathered data for most active 20 companies and 10 years of historical data from 21st September 2019 backwards. We used 40044 data points in total.</div>

THE DEVELOPMENT OF RESEARCH FRAMEWORK FOR THE DECISION TO PARTICIPATE IN STOCK MARKET: A SYSTEMATIC REVIEW OF LITERATURES FROM 2011-2021

The purpose of this paper is to understand stock market participation among individual investors. This paper has established a descriptive systematic-analysis of the empirical literature from journal indexed by Scopus, ISI ad ERA with an emphasis on the conceptualization and antecedents of stock market participation. Based on the review of past literature, this paper suggested social alignment, fundamental stock literacy, trust on institution and stock value expectation that would relate to stock market participation. The recommended framework may serve as a practical guide for stock broking firm to understand the behaviour of individual investors, encourage participation in the stock market and aid in the development of marketing strategies in competitive market.