Investment Decisions Research Articles

Analysis of Financial Fraud Based on the GONE TheoryA Case Study of Hangzhou Century Co., Ltd.

Convertible bonds allow creditors to convert their claims into company shares under certain conditions. Because of this debt-equity conversion feature, the issuance conditions, pricing and conversion value of convertible bonds are highly susceptible to financial information. Increasing the market value of a company's stock by exaggerating earnings or concealing debt may lead to convertible bond investment decisions based on misinformation, which in turn affects investors and the healthy operation of the market. Based on the GONE theory, this paper analyses the motivation of financial fraud in convertible bond documents and proposes corresponding preventive measures for its characteristics. Through the research and analysis of this type of financial forgery, the preventive governance of financial forgery can be further strengthened, which not only helps to protect the company's reputation and ensure the accuracy and reliability of the financial report, but also helps to attract and maintain the trust of investors, avoid possible legal risks and economic losses, and create a transparent and honest financial environment for the market to promote the efficiency of the market and the fair competition of enterprises.

Research on Quantitative Investment Strategies of Securities Investment Funds

This paper investigates the quantitative investment strategies of securities investment funds, aiming to explore their applications in financial markets and their impact on investment decision-making. Through a review of relevant literature and empirical data, the paper first outlines the basic concepts of quantitative investing and its importance in securities investment funds. Then, it discusses various quantitative strategies, including multi-factor models and machine learning algorithms, and validates their effectiveness through case studies. The research finds that quantitative investing can enhance the scientific and accurate nature of investment decisions while effectively reducing investment risks. Finally, the paper provides suggestions for optimizing quantitative strategies and discusses future trends, offering practical references for the investment industry.

A Study on Carbon Emission in Logistics Industry: Creating Awareness to Reduce

Objective: Climate change is a reality we cannot ignore and now more than ever we need visible action irrespective of our geographic location or economic status. This study explores the multifaceted hurdles impeding scope 3 emission reduction initiatives within the logistic sector, challenges that create barriers in their investment decisions, regulatory pressures, unavailability of technology and monitoring support, etc. Theoretical Framework: Globally, alerts are being created for climate actions that are lacking and resulting in huge gaps between the target set and covered. Bangladesh is leading the way towards green initiatives scope 1 (Direct emission) and 2 (Indirect emission) but not visible driving scope 3 emission reduction (All indirect emissions in a company’s value chain, including downstream transportation). Method: Employing a qualitative approach, data was gathered through interviews and meetings with 500 or more green factories in Bangladesh. Due to less time and political barriers, could not continue the research and accumulate more data. Results and Discussion: The findings highlight revealing key barriers such as unavailability of technology, overly expensive options or huge investment for intensive technology, limited access to those technologies, lack of skilled resources, monitoring and reporting challenges, lack of collaboration, etc. that are creating obstacles for climate actions all around the world. Research Implications: Awareness and collaboration could bring a positive impact to reduce the significant challenges that the logistics industry confronts in reducing Scope 3 emissions, obliging collaborative efforts among service providers, manufacturers end users. Originality/Value: Policy interventions and technological upgrades can emphasize the significance of unified action in transforming the logistics landscape towards sustainability which would impact positively and make a difference.

Applying trade mechanisms to quantify dynamic GHG emissions of electricity consumption in an open economy - The case of Switzerland

Quantifying greenhouse gas (GHG) emissions from electricity consumption presents a significant challenge due to the variable nature of the generation mix throughout the year and the complex dynamics of cross-border electricity trade. In this paper, we introduce an innovative methodology designed to accurately capture the hourly GHG emissions linked to electricity imports, and thus electricity consumption. Contrary to conventional approaches, our methodology identifies the relevant generation technologies in exporting countries which are activated for export. This selection is based on (i) the classification of the technologies according to their dispatch cost (merit order), and (ii) considers trade mechanisms from economic theory.We apply this methodology to Switzerland, a country characterized by a low-carbon electricity generation mix, which however heavily relies on imports from neighbouring countries, particularly during the winter months. Over the period from 2017 to 2021, we find that 88 % of Switzerland's domestic electricity demand is met by its own generation—primarily hydro and nuclear—while the remaining 12 % is imported, mainly from fossil-fuel-based sources. Consequently, the average annual GHG emission factor for electricity consumption in Switzerland is 98 g-CO2eq/kWh, with 29 % attributed to domestic generation and 71 % to imports. This figure is 40 % higher than the 70 g-CO2eq/kWh estimated using a conventional approach, which typically underestimates the specific impacts of electricity imports.The disparity between these approaches becomes even more pronounced when considering the seasonal and hourly variations in the electricity mix and the resulting emission factors. These insights are particularly crucial in the context of emerging electricity demands, such as heat pumps and electric vehicles, where accurate GHG accounting can significantly influence policy and investment decisions.

IMPACT ASSESSMENT OF INFORMATION TECHNOLOGY ON INVESTMENT CHOICE DECISIONS OF ACADEMIC PROFESSIONALS

The substantial impact of information technology on investment decision-making is explored in this study, and strong empirical support is provided through a significant regression model. Understanding the extent to which technology influences investment choices is of utmost relevance in a time of fast technological advancement. The research makes use of a varied dataset that includes a variety of economic and market characteristics, employing robust statistical analysis to evaluate the relationship between technology and investment decisions. Our findings show that information technology and investing choices are statistically significantly correlated. The regression model illustrates how technology is increasingly being used into investment strategies and how it influences investors' decisions. This study emphasizes the importance of incorporating technical expertise into investment plans, with a focus on data analytics, artificial intelligence, and long-term tech investments. Furthermore, the ease with which mutual funds can be invested in, with alternatives for both novice and experienced investors, has made them a practical and accessible option as the study points mutual funds as the most preferred option for investment. Their liquidity and transparency also resonate with investors who appreciate the ability to monitor their investments easily and access their funds when needed. The results of the study highlight how crucial it is to comprehend investor preferences for financial goods. The popularity of mutual funds not only demonstrates their benefits but also emphasizes the necessity for financial institutions and advisors to customise their services and products in order to meet investor needs. KEYWORDS: Information Technology, Investment Choice Decisions, Mutual Funds

ANALYSIS OF PORTFOLIO FORMATION ON THE LQ45 STOCKS INDEX, USING THE MARKOWITZ AND SINGLE INDEX MODELS

In an investment, there will always be a return and risk, especially in the capital market in the form of stocks. The risk in an investment can be minimized by diversifying assets into several stocks to form a portfolio formation. Several models, such as the Single Index and Markowitz, can evaluate optimal portfolio formation. In this study we provide additional information and discourse on capital market studies and as the input for investors in making investment decisions in the form of stocks. The study shows that based on 25 companies, the Markowitz model gives 12 companies as the optimal portfolio with the largest proportion of funds owned by PT Bank Central Asia Tbk (BBCA), 82.22%. The portfolio of those 12 stocks can provide an expected return of 44.8% where its risk is about 13.77%. The Single Index model provides a formation based on 9 companies as the optimal portfolio with the largest proportion of funds owned by -again- PT Bank Central Asia Tbk (BBCA) which is 66.23%. The portfolio of these nine stocks can provide the expected return of 1.68% and its risk is 0.43%. The ratio of risk and return from each model justifies that the Single Index model gives better portfolio formation. This result should be further compared with other stock indexes, nationally and globally, and also needs to be compared with the period after the pandemic.

An Empirical Study on Asset Pricing Models Based on The Chinese A-Share Market

Based on the analysis of the A-share markets in Shanghai and Shenzhen Stock Exchange from 2000 to 2023, this paper systematically evaluates the applicability and explanatory power of the Capital Asset Pricing Model (CAPM), the Fama-French Three-Factor Model (FF3) and the Fama-French Five-Factor Model (FF5) in the Chinese market. The study finds that although CAPM has some explanatory power in the early stage, the three-factor and five-factor models can better explain the cross-sectional changes in stock returns as the market develops. In the descriptive statistics, each factor's volatility and correlation reveal the Chinese market's unique characteristics, such as the relative strength of small-cap and growth stocks. In the regression analysis, the results of the Fama-MacBeth regression showed that the significance and explanatory power of the market factor, size factor and value factor were different in different models. Further robustness analysis reveals the COVID-19's impact on the model's performance through the sub-period analysis. This paper provides a new perspective on understanding the asset pricing of China's A-share market and a theoretical basis for investors' investment decisions. It also points out the limitations of existing models in explaining emerging market equity returns. It suggests that future research can deeply explore the performance of more factors in different market environments.

Pentingnya Struktur Modal Untuk Meningkatkan Kinerja Keuangan Perusahaan Sub-Sektor Teknologi

This research aims to determine the impact of investment decision, structure capital, and tax aggressiveness on financial performance.. This type of research is quantitative research that uses secondary data in 2018-2022 periods. The sample used was 44 technology subsector companies listed on the Indonesia Stock Exchange (BEI). This research uses purposive sampling method with certain criteria. There were 63 data obtained which were used in this research. The hypotheses in this research were analysis uses multiple linear regression. The results of this research show that 1) investment decisions have no significant effect on the company's financial performance, 2) capital structure has a positive significant effect on the company's financial performance, 3) tax aggressiveness have no significant effect on the company's financial performance

Performance-Based Efficiency and Effectiveness Analysis of Technology Companies Listed on Borsa Istanbul Using Data Envelopment and Logistic Regression Analysis

This study analyses the performance-based efficiency and effectiveness of technology companies listed on Borsa Istanbul using Data Envelopment Analysis (DEA) and Logistic Regression (LR). The research focuses on evaluating the relative efficiency of selected technology firms over the period 2015-2023. DEA, a non-parametric technique, is used to measure the efficiency of decision-making units, while logistic regression helps identify the factors influencing efficiency. The study aims to assess the impact of financial ratios, such as current ratio, net profit margin, accounts receivable turnover, and others, on the firms' technical efficiency. The results indicate that Net Profit Margin (NPM) and Accounts Receivable Turnover (ADH) have the most significant positive effects on efficiency, while the Fixed Assets to Equity ratio (MDVOZK) negatively impacts efficiency. This analysis provides insights into the financial health and operational performance of technology firms, offering actionable recommendations for improving efficiency and guiding investment decisions. Key Words: Data Envelopment, Logistic Regression, BİST JEL: C45, C88, G21

The Ripple Effects of Financial Fraud: Investor Trust and Risk Appetite Post-Kangmei Pharmaceutical

The financial fraud at Kangmei Pharmaceutical (stock code: 600518) has had profound implications for retail investors. This paper, through data analysis, explores the correlation between information asymmetry and investment outcomes for retail investors. Information asymmetry often leaves retail investors unable to access the true financial state and operational conditions of companies, leading them to make speculative investments. Post-exposure, as information transparency increased, retail investors became aware of the company's financial issues and rushed to sell, causing a stock plunge. This highlights how information asymmetry not only affects investment decisions but also amplifies market volatility. To mitigate risks from information asymmetry, regulators must strengthen oversight and penalties, improve disclosure transparency and authenticity, while investors need to enhance self-protection awareness and information gathering and analysis capabilities. Only then can we minimize the impact of information asymmetry on retail investors and uphold fairness and stability in financial markets. This paper suggests that by implementing a robust regulatory framework and encouraging proactive investor education, we can better safeguard retail investors from the detrimental effects of information asymmetry, thereby fostering a more resilient and equitable financial market ecosystem.

Hospitality 2.0: Applying the UTAUT model to understand guest perspectives on personalised technologies in hotels

ABSTRACT This study evaluates hotel guest perspectives on adopting personalised technology-enabled hospitality services by using a quantitative survey methodology. Applying unified theory of acceptance and use of technology (UTAUT) model constructs, the questionnaire gauges 200 guest views on performance benefits, usage effort and conditions influencing adoption of artificial intelligence (AI), the internet of things (IoT) and mobility. Descriptive and correlational analyses highlight innovation by capability and reveal barriers around complexity, privacy and reliability. Findings can inform hotel technology investment decisions and strategic planning to effectively leverage data-driven customisation capabilities across the entire guest journey — from pre-arrival trip planning to post-stay loyalty building. It will also help towards the Hospitality 2.0 vision of balancing digitisation with human connections. This empirical assessment aims to advance sector transformation and next-generation experience elevation through prudent prioritisation guided by evidence-based user willingness going beyond speculative innovation.

Why do we argue about risk? The invisibility of worldviews in marine decision-making

The paper asks “why are we always arguing about risk?” It explores what drives risk perception in decision-making processes in Aotearoa New Zealand’s marine environment. The research’s holistic framing shows that the prevailing concern with risk and uncertainty (RU) issues in government and managerial settings deflects attention from and hides the background influences of worldviews. After introducing the invisibilities around RU and outlining worldview-based perceptions of risk, the paper turns to an empirical investigation focusing on deep-seabed mining investment in the context of Aotearoa’s Exclusive Economic Zone and Continental Shelf Act (Environmental Effects) 2012. Enacted as part of neoliberal reforms, the Act seeks to reduce environmental degradation in tandem with economic growth. A situated methodology examines a consenting process within this Act as a strategic site to establish expressions of RU and shaping invisibilities. Three key influences are revealed: the positionality of decision-makers, their disciplinary training, and the largely unknown filtering of environmental and economic options that come from 3 worldviews held in Aotearoa (dominant social paradigm, new environmental paradigm, Te Ao Māori). We contend hidden worldviews impact on economy–environment relations and ensuing economic and environmental practices and outcomes. This arises because they underpin and fuel debates around what RU are acceptable and how benefits should be distributed (public vs. private benefits investment decisions, and economy vs. environment). Unless we recognize and understand these hidden influences, we simply cannot work with them. This insight has major implications for how RU ideas might be framed and navigated in emerging policy settings.

The Effect of Green Accounting, Sales Growth, and Firm Size on Stock Prices in Agricultural Companies Listed on the Indonesia Stock Exchange in 2021-2023

In a effort to increase Stock Price, management, as representatives of the company’s owners, develops and implements various strategies to manage the company's business operations. However, sometimes the strategies and actions taken by management to maximize profit do not always align with legal values, social norms, or environmental sustainability. This can lead to social and environmental gaps, and even legal violations. A stronger focus by management on economic and business aspects rather than social aspects often draws attention to the company. This study aims to examine and analyze the impact of Green Accounting, Sales Growth, and Firm Size on stock price. The sampling technique used is purposive sampling, and based on the established criteria, 14 agricultural companies listed on the IDX from 2021 to 2023 were selected. This research uses a quantitative approach with data analysis methods including descriptive statistical analysis and panel data regression analysis. The results show that Green Accounting has a positive and significant effect on stock price. Sales Growth has a positive effect on stock price. Firm Size does not have an effect on stock price. This study is expected to be learning material as well as a more in-depth and reference and be able to contribute to further research related to stock price, for companies it can be used as consideration and evaluation material in taking a policy that can pose a risk to the stock price in the future, as well a providing information related to making investment decisions more effectively and efficiently for stakeholders.

The Risk Management And Usage Of The Net Present Value-At-Risk To Evaluate Indonesian Toll Road

This study evaluates the financial feasibility and risk management of toll road infrastructure so as characteristics of toll road projects prone to risks and uncertainties. The study was conducted using case study and qualitative methods. The parameters used to determine the fair value of the project through parameters such as NPV, IRR, Payback Period, NPV at risk, and Risk Adjusted Return on Capital (RAROC) for financial and risk-based feasibility evaluation. This study also conducted a risk assessment in risk management analysis. The study provides various illustrations of possible outputs (NPV) and risk variability so that it can provide a more communicative and varied offer and recommendation, especially for investors as a consideration in making investment decisions. The results of the risk assesment process showed a low to the very high-risk level that requires different treatment. This study contributes understanding the investment in Indonesia toll road project

Beliefs about the Stock Market and Investment Choices: Evidence from a Survey and a Field Experiment

Abstract We survey retail investors at an online bank to study how beliefs about the autocorrelation of aggregate stock returns shape investment decisions measured in administrative account data. Individuals’ beliefs exhibit substantial heterogeneity and predict trading responses to market movements. We inform half of our respondents that, historically, the autocorrelation was close to zero, which causes them to update their perceived current autocorrelation and return expectations. The treatment shifts respondents’ equity purchases during the COVID-19 crash months later in the direction implied by the intervention. Our results provide causal evidence about the drivers of disagreement and trade in asset markets. (JEL D83, D84, G11, G12, G41, G50)

Forecasting ETF Performance: A Comparative Study of Deep Learning Models and the Fama-French Three-Factor Model

The global financial landscape has witnessed a significant shift towards Exchange-Traded Funds (ETFs), with their market capitalization surpassing USD 10 trillion in 2023, due to advantages such as low management fees, high liquidity, and broad market exposure. As ETFs become increasingly central to investment strategies, accurately forecasting their performance has become crucial. This study addresses this need by comparing the efficacy of deep learning models against the traditional Fama-French three-factor model in predicting daily ETF returns. The methodology employs eight artificial neural network architectures, including ANN, LSTM, GRU, CNN, and their variants, implemented in Python and applied to data ranging from 2010 to 2020, while also exploring the impact of additional factors on forecast accuracy. Empirical results reveal that LSTM and the Fama-French three-factor model exhibit a superior performance in ETF return prediction. This study contributes to the literature on financial forecasting and offers practical insights into investment decision making. By leveraging advanced artificial intelligence techniques, this study aims to enhance the toolkit available for ETF performance analysis, potentially improving investment strategies in this dynamic market segment.

The Mediating Role of Financial Self-Efficacy in the Relationship between Financial Inclusion, Literacy, and Investment Behavior among Banking Employees in Jakarta

The aim of this study is to identify and analyse the impact of Financial Inclusion, Financial Literacy on investment decisions through Financial Self-Efficacy on employees working in the banking sector of Jakarta region. This research uses a quantitative approach, the type of data in this study uses primary data obtained from the spread of the questionnaire. The population in this study is employees who work in the banking sector in the area of Jakarta, this study uses the Probability Sampling Technique that is Simple Random sampling so that the sample used is equal to 100 respondents. Data processing in this study uses SmartPLS. The results of this research show that the Financial Inclusion has an effect on Financial Self-Efficacy, Financial Literacy has no effect on Financial Self-Efficacy, Financial Self-Efficacy has no effect on Investment Decisions, Financial Inclusion has no effect on Investment Decisions, Financial Literacy has no effect on Investment Decisions, Financial Inclusion has no effect on Investment Decisions through Financial Self-Efficacy and Financial Literacy has no effect on Investment Decisions through Financial Self-Efficacy.

The impact of intellectual capital and market capitalization on corporate investment decisions: exploring the mediating and moderating effect of knowledge sharing and the COVID-19 pandemic

PurposeThis paper examines the effect of intellectual capital (IC) and market capitalization (MC) on corporate investment decisions (ID) through the mediating and moderating effects of knowledge/information sharing (KS) and the Covid-19 pandemic.Design/methodology/approachWith the use of SPSS 26 and SmartPLS version 3.0, the partial least square structural equation modelling (PLS-SEM) technique is employed with 1,036 observations to examine the effects.FindingsOur findings show that IC and social interactions (SI) have a positive effect on KS. KS affects positively both MC and ID. KS has the mediating and moderating effects while the Covid-19 has the moderating impact on ID.Practical implicationsThis research suggests that company leaders should understand the important role of IC and MC in enhancing ID through KS. They should pay attention to MC to enhance their investment and SI among employees, partners, consumers and authorities should be encouraged.Originality/valueThis research contributes to the existing literature by employing the perceptual scale to examine the effect of IC and MC, the mediating and moderating effects of KS, and analyze the moderating role of the Covid-19 on ID. It also expands the current models by including the Covid-19 and MC to clarify the ID determinants. New measurements of MC and the Covid-19 constructed are also another contribution.

Behavioral Biases and their Influence on Financial Decisions: An Analysis of Social Entrepreneurship in a Medium-Sized City

In recent years, entrepreneurship has been an alternative economic livelihood that independently seeks to generate employment and in turn the economic growth of countries. Social entrepreneurship is a topic of great importance in recent research, however, there is still not enough empirical evidence to analyze the rationality of decision making. Given the social factor they pursue, it is possible that they may be affected by behavioral distortions that influence their rationality, especially in three financial aspects: investment, savings and indebtedness. Therefore, the objective of this work is to determine the existing influence between behavioral biases and financial decision making of social enterprises in the canton of Cuenca. The study is a descriptive-correlational cross-sectional approach, using structural equations from Partial Least Squares (PLS). The sample obtained is 162 social enterprises. The results show a significant positive influence of overconfidence, procrastination, status quo, and negative influence of over optimism on investment decisions. Also, procrastination and status quo directly influence savings decisions, but the gambler's fallacy indirectly. On the other hand, the risk aversion, overconfidence, over optimism and status quo biases were significantly influenced on borrowing decisions

The Effect of Leverage, Corporate Governance, and Profitability on Firm Value

This research analyzes the influence of debt on firm value with corporate governance as a variable presumed to moderate the relationship between debt and firm value. The research sample was selected using a purposive sampling method, resulting in 44 non-cyclical consumer sector companies listed on the Indonesia Stock Exchange (IDX) from 2018 to 2022. The study results indicate that corporate governance and profitability positively affect firm value, and firm size affects firm value. Corporate governance as a moderating variable can negatively moderate the relationship between debt and firm value. The findings of this study provide insights into institutional ownership, profitability, and firm size that affect firm value. Investors can use this information to conduct risk analysis and inform their investment decisions. Investors should choose companies with strong corporate governance and high profitability, considering institutional ownership in the corporate governance variable. Investors should preferably select companies with high institutional ownership and high profitability to ensure high firm value but choose companies with smaller sizes where the company uses total assets productively.