Investment Decisions Research Articles

PENGARUH TAX AVOIDANCE, DIVIDEND POLICY, DAN FAKTOR LAINNYA TERHADAP NILAI PERUSAHAAN

The increasing level of business competition in Indonesia causes every company to try to develop its firm value. One of which is through capital which can be obtained by selling shares. Firm value is an important influencing factor for making investment decisions because investors will pay attention to how well the company's performance is. This research was conducted with the aim of obtaining empirical evidence regarding the influence of the independent variables dividend policy, investment decision, leverage, profitability, firm size, liquidity, institutional ownership, and tax avoidance. The object of this research uses a population of all consumers non-cyclicals and consumer cyclicals sector companies listed on the Indonesia Stock Exchange (BEI) from 2020 to 2022. Sample selection was carried out using a purposive sampling method. Data analysis uses multiple linear regression. The data that passed the sample selection criteria was 33 company data out of a total of 99 company data. The testing results indicate that profitability and leverage influence firm value. A high level of leverage makes the company able to maximize its operational activities to obtain high profits so that it becomes a positive signal for investors and increases firm value. A high profitability serves as a signal that the company has good growth prospects in the future so that investors will be more interested in investing and contributing to an increase in the firm value. However, dividend policy, investment decision, firm size, liquidity, institutional ownership, and tax avoidance show no significant influence on firm value

Building a framework to drive government systems' adoption of cloud computing through IT knowledge

abstractMany governments in developing countries are finding it challenging to integrate cloud computing into their e-government systems because of the limited effective identification and evaluation of important factors. Cloud computing usage in government organizations is still restricted due to concerns over security, regulatory challenges, cultural resistance to new technologies, infrastructure limitations, and other factors (Abied et al. in Sustainability 14:15590, 2022). This research suggests a model that analyses the factors influencing the adoption of cloud computing in e-government systems within Jordanian government organizations by decision-makers. The survey was distributed online to a purposive sample consisting of 373 IT professionals and top-level managers in government organizations in Jordan. The study demonstrates that the technological factors which include complexity, compatibility, relative advantage, and security, the organisational factors which consist of top management support and cost, and the environmental factors which include industry competition and government rules and regulations, had a significant and positive impact on the adoption of cloud-based services in government systems. Furthermore, IT knowledge held by decision makers was recognized as a key factor in the adoption of cloud computing for e-government systems. This research is anticipated to provide significant insights for any organizations making investment decisions about the use of cloud-based services. This research considers the assessment of multiple aspects to encourage top management to analyse the consequences of implementing cloud services for e-government systems. This case study might not be applied broadly to all types of organizations in Jordan. Future research should employ large-scale samples to ensure the study's representativeness and could also broaden the scope by including samples from similar countries to validate the model or its implementation across different cultures, IT infrastructure and geographical settings. In addition, it is recommended that upcoming research consider using the level of experience and education as moderators.

BEHAVIOUR BIAS IN INVESTMENT DECISIONS: EMPIRICAL STUDY OF INVESTOR PSYCHOLOGY IN INDONESIA

Investing involves putting money into a variety of asset classes for a predetermined amount of time in the hopes of increasing total value and producing more income. The goals of investments and economics, such as value creation, profit generation, and other related goals, are strongly correlated. It is critical to recognize that investments may include additional financial, social, and spiritual goals. This research aims to assess investor behavior from internal and external perspectives concerning investing decisions. This study employs the Structural Equation Model (SEM) methodology utilizing the Smart-PLS software. This study's findings demonstrate that qualities including anger, anxiety, overconfidence, and self-monitoring positively influence investment decisions. Herding mitigates the adverse impacts of anger, anxiety, overconfidence, and self-monitoring on investment decisions. This research's consequences can assist investors in circumventing irrational risks and yield more objective findings grounded in thorough study. This tendency can result in inefficient market transactions, asset bubbles, and significant volatility. This study arose as a counter to the conventional understanding of exchange markets predicated on the premise of trader rationality. This research identifies that emotional emotions, risk perception, and cognitive biases frequently affect investment decisions by comprehending human nature.

Unveiling investor behavior: An investigation into India’s pharmaceutical sector during COVID-19

This study examines the factors influencing retail investors’ decision to invest into the Indian pharmaceutical sector amidst the COVID-19 global health crisis. Confronting the obstacles introduced by the pandemic has helped create a robust foundation for the Indian pharma sector to grow rapidly in the future. As the industry demonstrated signs of considerable future growth, investors favored pharmaceutical investments during COVID-19. Retail investors were mainly the ones who went on a shopping spree during the pandemic. This backdrop provides a unique context to explore the retail investors’ behavior in the Indian pharmaceutical landscape. Leveraging the Theory of Planned Behavior (TPB), this study marks the first attempt to introduce behavior toward market fundamentals as a mediating factor into the TPB framework. To conduct the study, data were collected from 305 retail investors located in Karnataka’s South Canara region, who invested into the Indian pharmaceutical industry during the pandemic, using snowball sampling. The e-survey was employed for data collection. The results reveal that attitudes toward pharmaceutical investments, subjective norms, Perceived Behavioral Control (PBC), and behavior toward market fundamentals have significantly influenced investor decisions. Notably, behavior toward market fundamentals has partially mediated the relationship between attitude toward pharma investment and investment decision and PBC and investment decision. However, the mediation effect of behavior toward market fundamentals was not evident between the subject norm and the investment decision. The study holds pivotal implications for policymakers, government bodies, and industry stakeholders to promote pharmaceutical investments.

The Role of Nudges and Behavioral Interventions in Financial Decision Making

Financial decision-making, both at individual and institutional levels, plays a critical role in determining economic outcomes. Traditional economic theory posits that individuals are rational agents who make decisions based on complete information and a consistent set of preferences. However, behavioral economics has challenged this notion, highlighting cognitive biases, heuristics, and social influences that can drive suboptimal decisions. Nudges and behavioral interventions have emerged as tools to guide individuals toward better financial choices without restricting their freedom of choice. The purpose of this research is to present a descriptive investigation of behavioral interventions and nudges in financial decision-making. The main cognitive biases influencing financial behavior are first described, and then several nudge kinds and their applicability in diverse financial contexts— retirement savings, debt management, spending control, and investment decisions—are explored. Along with reviewing empirical data on nudges' efficacy, the paper addresses the ethical issues surrounding their application and pinpoints the variables that affect their effectiveness. This study aims to educate consumers, financial institutions, and policymakers about the potential advantages and constraints of behavioral interventions by providing a thorough overview of how nudges might impact financial decision-making. Keywords: Nudges, Behavioral Intervention, Financial Decision-Making, Cognitive Biases, Financial Literacy.

LOSS AVERSION: THE UNSEEN FORCE SHAPING INVESTMENT DECISIONS

This research paper aims to investigate the profound impact of loss aversion on individual investment decision-making, with a specific focus on its influence on risk preferences. Loss aversion, a key concept in behavioral economics, posits that individuals experience a greater psychological impact from losses than equivalent gains, thereby shaping their decision-making processes. The study employs an empirical approach, utilizing a diverse sample of individual investors to analyze how loss aversion interacts with risk preferences and influences investment choices. The research methodology involves a combination of surveys and data analysis to explore the intricate relationship between loss aversion and risk preferences. By examining real-world investment decisions in various market conditions, the study seeks to identify patterns and behavioral tendencies that emerge when individuals confront potential losses in their investment portfolios. The research design also considers demographic factors, financial literacy, and other relevant variables to provide a comprehensive understanding of the factors influencing investment choices. Ultimately, this study aims to enhance our comprehension of the psychological factors driving individual investment decisions and to inform the development of strategies that mitigate the negative impacts of loss aversion on financial outcomes.

Mastering Market Movements: Technical Indicators in IDX30 Stock Selection

This study aims to analyze the role of trading volume, foreign money flows, and technical chart patterns in shaping investment decisions for IDX30 companies. Utilizing descriptive quantitative research, data was collected from Stockbit and IPOT, focusing on stock price charts, trading volume, and broker summaries from January 1, 2020, to December 31, 2022. The findings reveal that purchase decisions were made when stock prices broke out of a chart pattern, accompanied by increased trading volume and foreign accumulation. Conversely, sell decisions occurred when stock prices broke down from a pattern, followed by increased volume and foreign distribution. These results underscore the importance of technical analysis in guiding informed investment decisions, helping investors avoid irrational practices and potential losses. The study highlights the practical need for investors to integrate technical indicators, such as volume and foreign flows, into their decision-making processes to enhance investment outcomes.

To What Extent Have Economic Policies Affected Small Businesses in the UK in the Past 10 Years?

The research studied and investigated the economic policies in the UK in the past ten years and the economy of small businesses in the UK. The dissertation is trying to answer whether the economic policies in UK affect the small businesses, whether these effects are positive, and the extent of these effects brought by economic policies. The significance of this research topic is that economic policies will affect small enterprises and businesses from many aspects, including operating mode, costs, market competitiveness, and investment decisions, etc. Therefore, in-depth research on how economic policies affect small businesses can guarantee their development and help them make correct economic decisions. Two effective methods have been used to reach the conclusion. The first one is to investigate the subject of the research and to obtain a more comprehensive understanding of the relationship between economic policy and small business by collecting materials about the current or historical situation of the research objects in a purposeful, planned and systematic way. Another method used is to obtain data by researching the literature and essays, so as to comprehensively, correctly understand, and grasp the economic policy and the development of small businesses in the UK. The conclusion is that economic policies have a great impact on small businesses, these effects are positive or negative depending on the policies. However, most economic policies have a positive impact on small businesses. These policies are also key to the success of many small businesses.

Corporate resilience during crises and the role of ESG factors: Evidence from the COVID-19 pandemic

This study explores the relationship between environmental, social, and governance (ESG) factors and investor behavior during the COVID-19 crisis, utilizing a sample of S&P 500 companies. Findings suggest that these factors may not have had a significant influence on investment decisions during this period. Despite a notable overall market response to major COVID-19 related events, our findings reveal that ESG factors provide minimal explanatory power for individual stock price returns. This observation invites further investigation into the conditions under which ESG considerations are prioritized by investors.

Diversification strategies for indirect real estate. Intersection of business, economics, and society in shanghai mixed-use developments

This study examines the impact of large mixed-use developments on the financial performance of publicly listed real estate firms in Shanghai from 2009 to 2019. The research uses a Multi-Criteria Decision Making (MCDM) approach, considering 14 key performance indicators (KPIs) across three categories: Corporate and Financial Information, Strategy Indicators, and Specifics of Mixed-Use Development. The article analyses the performance of large, listed investors on the Hong Kong, Shanghai, and Singapore Stock Exchanges, providing insights into their evolution over the past decade. The literature review highlights the relationship between companies’ performance and corporate finance, capital structure, and financial ratios. It identifies an existing gap in research related to mixed-use assets and indirect real estate. By using the MCDM framework, the study evaluates these assets and provides actionable insights for global investors, asset managers, and developers, helping them make informed decisions in indirect real estate investment. The findings contribute to understanding investment strategy formation in dynamic real estate markets and support informed diversification and urban policy development. This paper offers a useful appraisal framework for future MCDM risk assessments, assisting stakeholders in navigating the complexities of the real estate landscape in one of the world’s foremost metropolitan hubs. It also offers recommendations and insights on best practices for global listed firms while providing a framework for future research in the field of property investment.

EXPRESS: The Last Hurrah Effect: End-of-Period Temporal Landmarks Increase Optimism and Financial Risk-Taking

Understanding what drives risk-taking is fundamental for the study of choice under uncertainty. One widely discussed question is when and why people engage in risk-taking. Prior work finds evidence for an ending effect, where risk-taking increases on the last gamble in a series when outcomes are immediately realized. We test whether socially ubiquitous endof-period temporal landmarks (e.g., last day of the work week, month, year) alter financial risk-taking even when outcomes are not immediate. Using data from a large peer-to-peer investment platform in the United States, we show that investors make riskier financial investment decisions on Fridays relative to those made earlier in the week. Consistent with a broader end-of-period effect, risk-taking also increases on the last day of the month, last day of the year, and on weekdays prior to a long weekend. Follow-up lab experiments identify a novel mechanism driving ending effects: As people near the end of a temporal period, they feel more optimistic that their financial risks will pay off, driving greater financial risk-taking. The shift in risk-taking is not without consequence: In our specific peer-to-peer context, we show that end-of-period investments perform worse over time, losing money relative to investments made on other days.

Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

PurposeThe Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.Design/methodology/approachHybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.FindingsOur study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.Originality/valueTo the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Understanding Behavioural Biases Driving Equity Investors in India: A Factor Analysis Approach

In the dynamic realm of equity investment decisions, this study challenges the traditional assumption of investor impartiality and rationality, arguing that investors’ choices are often shaped more by beliefs and psychological inclinations than by objective reasoning alone. Drawing from behavioural finance theory, which contests the adequacy of the standard finance model in capturing the nuances of decision-making, this research delves into the psychological forces influencing private investors. Recognizing the prevalent biases coloring their decisions, the study investigates common prejudices impacting investor choices. Leveraging survey data from 375 equity investors, the research employs exploratory factor analysis and regression models to disentangle the effects of cognitive biases, such as the gambler’s fallacy, overconfidence, aversion to ambiguity, lack of representativeness, aversion to loss, and anchoring. The findings challenge the assumption of investor rationality, offering valuable insights into the complexities of financial decision-making. Notably, the study underscores a robust and positive association between these biases and equity investment decisions, shedding light on the intricate interplay between psychological factors and investment behaviour.

Dynamic evolution of causal relationships among cryptocurrencies: an analysis via Bayesian networks

AbstractUnderstanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.

Investor Perception of Corporate Social Irresponsibility (CSiR) and Investing Decisions

The study examines a behavioral finance paradigm, where investors incorporate emotional/expressive and financial factors. The impact of emotional ties to the firm’s products and an expressive factor such as corporate social irresponsibility (CSiR) are examined to determine whether their investment decisions are based on them. We find that investors make decisions that are wealth maximizing as well as emotional/expressive. Moreover, we find that our respondents believe corporate profits should not come from using the cheapest labor nor from engaging in CSiR behavior, and they would be less likely to invest in such a firm.

A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes.

Quantifying benefits of renewable investments for German residential Prosumers in times of volatile energy markets

The COVID-19 pandemic and the Russian invasion of Ukraine have led to unseen disruptions in the global energy markets since the end of 2021. Residential renewable investments like photovoltaic systems, battery home storage systems, and heat pumps are therefore gaining traction. However, the benefits of those technologies during the energy crisis and beyond have not been fully quantified yet. Therefore, in this study, we benchmark renewable investments for a broad variety of single-family homes by evaluating potential cost savings and emission reductions. In addition, the study considers the influence of recent political incentives and subsidies. The results show that photovoltaic systems are a no-regret investment decision, both economically and environmentally. At the climax of the energy crisis, a typical German household with a heat pump could save 1850 € and reduce equivalent CO2 emissions by 250 g/kWh annually. Politically introduced price breaks on electricity and natural gas do not reverse this advantage. Furthermore, when owning an electric vehicle renewable investments are often more beneficial.

Behavioral determinants of investment decisions: evidence from Indian retail equity investors in the wake of COVID-19 induced financial risks

Purpose The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks. Design/methodology/approach The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks. Findings The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies. Research limitations/implications The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes. Originality/value Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.

The Influence of Financial Literacy on Investment Decision In The Millennial Generation Post The Covid-19 Pandemic

The Influence of Financial Literacy on Investment Decision In The Millennial Generation Post The Covid-19 Pandemic

Pengaruh Literasi Keuangan dan Perilaku Keuangan terhadap Keputusan Investasi Mahasiswa Universitas Nusa Nipa

This research aims to analyze the influence of financial literacy and financial behavior on investment decisions of students of the Accounting Study Program, Faculty of Economics and Business, Nusa Nipa University. This type of research is quantitative research with an associative approach. The sampling technique uses probability sampling, a stratified random sampling method, where The sampling was carried out and determined randomly by dividing the population into several sub-classes and semesters with a sample size of 93 people. The data collection technique used a questionnaire (goole from) which was distributed to 93 respondents. Data analysis used multiple linear regression analysis using multiple linear regression analysis. multiple linear regression using the Statistical Package For the Social Sciences (SPSS) version 25. The results of this research show that partially the sig value of the financial literacy value Sig. is 0.013 smaller than 0.05, with a calculated t of 2.535 greater than the t table of 1.66159, and the financial behavior value of Sig. is 0.059 greater than the alpha value. 0.05. To avoid bias in the results from the Sig value. then a comparison is made between the calculated t and t table values, where the calculated t is 1.911 which is greater than the t table 1.66159 and simultaneously the Sig value. is 0.001 smaller than 0.05, with a calculated F of 7.926 which is greater than the F table of 3.945, so it can be concluded that partially or simultaneously Financial Literacy and Financial Behavior have a significant influence on investment decisions.