How Power Influences Decision-Makers’ Investment Behavior in the Domains of Loss and Gain

We present a study (N = 645) investigating how power alters people’s propensity to take investment risks in a changing decision context of gains and losses and the intensity of their reactions to this experience. The results indicate that people in a state of power made more risky investment decisions than the control group regardless of prior gain or loss outcome, whereas people lacking power took less investment risk than the control group, regardless of previous outcomes. Moreover, people with power and those lacking power differed in their reactions to gains and losses, with the former reacting more to gains and the latter to losses.

PERAMALAN VOLATILITAS DAN ESTIMASI VALUE AT RISK (VaR) SAHAM BLUE CHIP PADA SEKTOR PERBANKAN

Investment is an activity to invest an asset to obtain a greater profit. The investment there's in great demand by investors are stock investments. Based on market capitalization, stocks are classified into first-tier, second-tier, and third-tier stocks. Stocks that have the highest market capitalization are first-tier or blue-chip stocks. Blue-chip stocks are stocks that are classified as main shares on the listing board on the IDX. Before investing, it's important to know the level of investment risk in order to make the right investment decisions. The purpose of this study is to determine the risk of investing in blue-chip stocks namely BRI, BCA, and Bank Mandiri through volatility forecasting using the GARCH, EGARCH, or TGARCH models. The data used is the daily closing price of shares for the period of 25 May 2005 to 21 May 2021 which was obtained through the Yahoo Finance website. Based on the research results, it's known that Bank Mandiri has the highest investment risk and BCA has the lowest investment risk. Based on these results, it can be suggested that investors who like risk can choose to invest in Bank Mandiri shares, and those who don't like risk can invest in BCA shares.

EFFECT OF FINANCIAL LITERACY AND RISK ATTITUDE ON INVESTOR BEHAVIOR

This research aims to examine the related effects of financial literacy and risk attitudes towards investor behavior in the Indonesian capital market with the motive of saving as a mediating variable. This study uses a quantitative approach and partial least squares- structural equation modelling (PLS-SEM) to test hypotheses. The research data was obtained from 110 questionnaires distributed to capital market investors in Indonesia using the purposive sampling method. The results of this study indicate that financial literacy, risk attitude and saving motives have positive and significant effects on investor behavior in the Indonesian capital market. The influence of financial literacy and risk attitude also has a positive and significant effect on saving motives. However, the motive for saving money cannot mediate the effect of financial literacy and risk attitude on investor behavior. Theoretically, the implications of the results of this study are the level of financial literacy, risk attitude, and saving motives can directly influence investor behavior. The higher the financial literacy, the better the attitude in facing investment risk and the greater the motive for saving, the better the investor's behavior in making investment decisions. Whereas in practical terms, this implication is used as input for investors to further increase financial literacy, pay attention to the level of risk of selected investments, and enlarge the motives for saving so that the purpose of investing can be achieved well.

Adjustable Security Proportions in the Fuzzy Portfolio Selection under Guaranteed Return Rates

Based on the concept of high returns as the preference to low returns, this study discusses the adjustable security proportion for excess investment and shortage investment based on the selected guaranteed return rates in a fuzzy environment, in which the return rates for selected securities are characterized by fuzzy variables. We suppose some securities are for excess investment because their return rates are higher than the guaranteed return rates, and the other securities whose return rates are lower than the guaranteed return rates are considered for shortage investment. Then, we solve the proposed expected fuzzy returns by the concept of possibility theory, where fuzzy returns are quantified by possibilistic mean and risks are measured by possibilistic variance, and then we use linear programming model to maximize the expected value of a portfolio’s return under investment risk constraints. Finally, we illustrate two numerical examples to show that the expected return rate under a lower guaranteed return rate is better than a higher guaranteed return rates in different levels of investment risks. In shortage investments, the investment proportion for the selected securities are almost zero under higher investment risks, whereas the portfolio is constructed from those securities in excess investments.

Economic Model and Risk Analysis of Energy Investments Based on Cogeneration Systems and Renewable Energy Sources

Today, distributed energy production is a key activity supporting energy systems in many countries around the world. Applicable regulations, fees and subsidies encourage entrepreneurs to look for solutions that will reduce operating costs and limit their negative impact on the natural environment. In the article, it was decided to carry out a technical and economic analysis and investment risk analysis for the distributed production of electricity and heat based on natural gas. Six scenarios were taken into account, depending on the number of gas engines, the use of the photovoltaic installation and the Organic Rankine Cycle (ORC) system. It has been shown that the most advantageous of the presented solution is the use of a system adjusted to the power of an industrial plant (return on investment in 4th year). The least beneficial for the investor are solutions aimed at the use and resale of energy supplemented with photovoltaic panels and an ORC system. An investment risk analysis and a sensitivity analysis were also performed. It shows how changes in electricity and gas prices and the environmental fee affect the profitability of investments. It has been shown that solutions with variable power are characterised by the lowest investment risk. The summary indicates the possible activities leading to greater economic efficiency. Such actions will be forced in the future by the market, political and environmental situation. Analyses such as these will allow entrepreneurs to thoroughly prepare for the European Union energy modernization process.

Factors and Economic Evaluation of Transnational Investment Risks

Transnational investment is featured by its large scale and high risks. During transnational investment, the frequent risks often bring a huge loss to the investors. Therefore, the risk factors should be included in the economic evaluation of transnational investment projects. However, the existing evaluation models and systems are rather complex, lacking a unified framework. Besides, there are few practical applications of these models or systems. To solve the problem, this paper explores the factors and economic evaluation of transnational investment risks. Firstly, an economic evaluation system was constructed for transnational investment projects, and the economic evaluation result was depicted in two aspects: economic income factors and investment risk factors. Then, the applicability and economic meanings of common indices were clarified one after another. After that, the economic evaluation flow was designed for risk-based transnational investment projects. Finally, an economic evaluation was performed on actual projects, which verifies the feasibility of the proposed evaluation method.

An improved density-based approach to risk assessment on railway investment

PurposeDensity-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.Design/methodology/approachFirst, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.FindingsThe IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.Originality/valueThis study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

Macro-Financial Parameters Influencing Bitcoin Prices: Evidence from Symmetric and Asymmetric ARDL Models

Bitcoins are evolving as a modern class of investment assets and it is crucial for investors to manage their investment risk. This paper examines the impact of macroeconomic-financial indicators on Bitcoin price using symmetric and asymmetric version of autoregressive distributed lag (ARDL) models with structural breaks. The asymmetric long-run association ascertained between Bitcoin prices and the macroeconomic-financial indicators is evident. Our empirical results indicate that the Bitcoin cannot be used to hedge against the inflation, Federal funds rate, stock markets and commodity markets. We further find that Bitcoin can be regarded as a hedging device for the oil prices. Our findings have significant implications for market participants who consider including alternate investment assets in their portfolios.