Informed Investment Decisions Research Articles

Financial technology, social media influencers, and experience of cryptocurrency investment decisions: Financial literacy’s role

This study evaluates the impact of financial technology usage, social media influencer presence, and investment experience on cryptocurrency investment decisions, considering financial literacy as a moderating factor. Financial technology refers to using technology in financial systems to create new service products, technologies, and business models. Social media influencers have gained fame and exert significant influence over their followers on social media platforms. Meanwhile, investment experience encompasses the duration of an investor's experience in trading transactions. Data for the study were obtained through a Google Form questionnaire distributed via social media to cryptocurrency investors aged 20 to 30 in the Jabodetabek area. Convenience sampling technique was employed to obtain a sample of 192 respondents. Data analysis was conducted using the SEM-PLS method with SmartPLS software. The results indicate that using financial technology and social media influencers positively influences cryptocurrency investment decisions, while investment experience does not significantly impact. However, financial literacy does not strengthen the influence of financial technology usage, social media influencers, and investment experience on cryptocurrency investment decisions. The research findings indicate that enhancing financial literacy among young investors should be prioritized. This can be achieved through formal education programs and financial literacy campaigns. Practitioners in the financial industry also need to provide financial education to their clients and offer educational content on cryptocurrency. These measures can help mitigate uncontrolled investment risks and promote informed investment decisions, thereby contributing to the stability of the financial market and the financial protection of the wider community.

Predictive Three-Dimensional Printing of Spare Parts with Internet of Things

Industry 4.0 integrates digital and physical technologies to transform work management, where two core enablers are the internet of things (IoT) and three-dimensional printing (3DP). IoT monitors complex systems in real time, whereas 3DP enables agile manufacturing that can respond to real-time information. However, the details of how these two can be integrated are not yet clear. To gain insights, we consider a scenario where a three-dimensional (3D) printer supplies a critical part to multiple machines that are embedded with sensors and connected through IoT. Although the public perception indicates that this integration would enable on-demand printing, our research suggests that this is not necessarily the case. Instead, the true benefit is the ability to print predictively. In particular, it is typically more effective for the 3D printer to predictively print to stock based on a threshold that depends on the system’s status. We also identify a printing mode called predictive print on demand that allows for minimal inventory, and we find the speed of 3DP to be the primary factor that influences its optimality. Furthermore, we assess the value of IoT in cost reductions by separately analyzing the impact of advance information from embedded sensors and the real-time information fusion through IoT. We find that IoT provides significant value in general. However, the conventional wisdom that IoT’s value scales up for larger systems is suitable only when the expansion is paired with appropriate 3DP capacity. Our framework can help inform investment decisions regarding IoT/embedded sensors and support the development of scheduling tools for predictive 3DP. This paper was accepted by David Simchi-Levi, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00978 .

The Impact of Tax Planning, Profitability, and Leverage on the Value of Listed Logistics Companies

The COVID-19 pandemic significantly impacted businesses worldwide including in Indonesia. The COVID-19 pandemic's unprecedented impact on businesses has prompted a surge in research examining its effects on firm value. This study aims to analyse the value of the company after before and after the COVID-19 period and analyse the impact of tax planning, profitability and leverage. The logistics sector has an important role in business activities, including the delivery of health products during the pandemic. There are 36 transport and logistics companies sampled using purposive sampling method in this study with the research years 2018 to 2022. Using multiple linear regression analysis, the results of this study prove the effect of tax planning on firm value, while profitability and leverage cannot be proven. These results offer valuable insights for investors in the transportation and logistics sector. By incorporating tax planning information alongside traditional financial metrics, investors can make more informed investment decisions. Kata Kunci : Perencanaan Pajak, Profitabilitas, Leverage, Nilai Perusahaan

Tác động của Thông tư 200 đến mô hình xếp hạng tín dụng sử dụng phương pháp trí tuệ nhân tạo

The study investigates the impact of Circular 200 on the credit rating model for small, medium, and large enterprises in Vietnam during the period 2008-2018, using Artificial Neural Network (ANN) methodology. The research compares credit rating models before and after the implementation of Circular 200 between 2008-2014 and 2015-2018, using data from 39,162 businesses in Vietnam obtained from the Orbis database. Results indicate that NITA, ROE, liquidity, and current payment ratio are important independent variables with significant differences in credit rating models before and after Circular 200. The research method proves useful for investors in analyzing investment risks for informed investment decisions. Vietnamese financial institutions can apply the model to identify specific credit rating issues for borrowers, enabling them to formulate appropriate credit policies and set different interest rates for varying risk levels. Future research directions include (1) enhancing data and variables, (2) improving data analysis methods, and (3) enhancing performance metrics.

Empirical Analysis of Factors Affecting Stock Liquidity --Taking Ping An Bank as an Example

With the continuous development of the Chinese economy, the stock market has become one of the important choices for investors, and the liquidity of the stock market has always been the focus of investors. Ping An Bank is one of China's well-known commercial banks and occupies an important position in the A-share market. The goal of this paper is to study the relationship between stock liquidity and return volatility, risk factors, circulation market value and cross-period of risk factors. Through ordinary least squares (OLS) regression analysis, it can be found that earnings volatility and circulating market value have an impact on stock liquidity significantly, that is, the higher the earnings volatility and circulating market value, the higher the stock liquidity. Risk factors and cross-terms of risk factors have no significant influence on stock liquidity. These findings have important implications for investors and market regulators to help them more fully assess stock value and risk and make more informed investment decisions.

DAO Dynamics: Treasury and Market Cap Interaction

This study examines the dynamics between treasury and market capitalization in two Decentralized Autonomous Organization (DAO) projects: OlympusDAO and KlimaDAO. This research examines the relationship between market capitalization and treasuries in these projects using vector autoregression (VAR), Granger causality, and Vector Error Correction models (VECM), incorporating an exogenous variable to account for the comovement of decentralized finance assets. Additionally, a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is employed to assess the impact of carbon offset tokens on KlimaDAO’s market capitalization returns’ conditional variance. The findings suggest a connection between market capitalization and treasuries in the analyzed projects, underscoring the importance of the treasury and carbon offset tokens in impacting a DAO’s market capitalization and variance. Additionally, the results suggest significant implications for predictive modeling, highlighting the distinct behaviors observed in OlympusDAO and KlimaDAO. Investors and policymakers can leverage these results to refine investment strategies and adjust treasury allocation strategies to align with market trends. Furthermore, this study addresses the importance of responsible investing, advocating for including sustainable investment assets alongside a foundational framework for informed investment decisions and future studies in the field, offering novel insights into decentralized finance dynamics and tokenized assets’ role within the crypto-asset ecosystem.

A Comparative Study on Relation Between ROE and Standard Deviation of Public Sector Company National Stock Exchange

This study investigates the relationship between Return on Equity (ROE) and Standard Deviation (SD) of public sector companies listed on the National Stock Exchange (NSE). ROE serves as a measure of profitability, reflecting a company's ability to generate earnings from shareholders' equity, while SD represents the volatility or risk associated with the stock returns of a company. Understanding the interplay between these two metrics is crucial for investors seeking to optimize their investment portfolios by balancing risk and return. Over a five-year period, this comparative study analyzes the ROE and SD of public sector companies on the NSE, aiming to uncover any significant correlations between these metrics. By conducting statistical analysis, including correlation and regression analysis, we seek to identify patterns and trends that can provide valuable insights into the financial performance and risk profiles of public sector companies. The study is motivated by the need to offer empirical evidence and insights that can assist investors in making informed decisions regarding their investments in public sector companies on the NSE. By examining the relationship between ROE and SD, we aim to provide investors with a deeper understanding of the risk-return trade off inherent in these companies, thus enabling them to better manage their investment strategies and portfolios. Through the findings of this study, investors can gain insights into the financial health and risk characteristics of public sector companies on the NSE, facilitating more informed investment decisions. Additionally, the study contributes to the existing body of research on financial performance and risk management in the context of government-owned enterprises, offering valuable implications for both investors and policymakers.

Stock Price Prediction and Pattern Detection Using Deep Learning

Accurately predicting stock market movements remains a significant challenge, yet one that continues to attract intense interest. This project leverages data mining and warehousing techniques to explore historical stock price data, aiming to uncover recurring patterns, forecast potential future trends, and evaluate the consistency of a specific stock's behaviour relative to its identified patterns. By employing various data mining algorithms and implementing effective warehousing strategies, the project seeks to extract valuable insights that can inform investment decisions and potentially contribute to superior market performance. But among these goals are some serious obstacles. Accurate prediction is hampered by the noise and uncertainties that are present in stock market data. Furthermore, fine-tuning and experimentation are necessary to achieve optimal results when optimizing the hyperparameters of long short-term memory networks (LSTM networks). Moreover, strong preprocessing methods are required to handle datasets that contain inconsistent or missing data. Lastly, proficient data visualization and user interface design abilities are required when creating an educational and engaging dashboard with Plotly Dash.

Examining the Random Walk Hypothesis: An investigation of the Indian stock market

Using the Efficient Market Hypothesis (EMH) as the theoretical foundation, this research study explores the Random Walk Hypothesis in the context of the Indian stock market. By using the Runs Test to analyse the daily closing prices of eight well-known Indian companies between January 1, 2018, and June 30, 2021, the study finds that patterns in stock prices in the Indian stock market show weak-form efficiency and are consistent with the Random Walk Hypothesis. The analysis indicates that stock price movements are largely unpredictable and devoid of discernible patterns, aligning with the notions posited by proponents of the Efficient Market Hypothesis. The research underscores significant implications, validating the presence of weak-form efficiency and emphasizing the need for alternative analytical approaches in investment strategies. Despite inherent limitations, this study contributes to ongoing discourse on market efficiency and financial dynamics, advocating for interdisciplinary approaches and global perspectives to refine understanding and facilitate more informed investment decisions in the Indian capital market.

Study on Implementation of Concepts of Project Management Theories in Delivery of Projects in Ghana

Objective: The purpose of this study is to investigate on the project management theories on project delivery. Project Management Theories has been underlined as one of the key diagnosis among businesses and economic growth with project delivery.
 Methods: A comprehensive review was carried out with the aid of online research journal websites as well as other in-context articles. While conducting this study, the key words in the search query were directed towards project management theories and project delivery in Ghana. Areas noted in relation to this study was use of project management theories among businesses and Ghana’s economic growth. Therefore, there was linkage of papers pointing out the effects on project management theories on project delivery.
 Results: The findings indicate that corporations have little to no say over project performance metrics and are unable to take on responsibilities outside of their core competencies. Managers' decision-making improves when they have access to reliable data. Having clear project goals in mind is crucial for making informed investment decisions. 
 Conclusion: This comprehensive review provides an in-depth understanding of project management theories on the project management deliveries among businesses and the growth of the economy in Ghana.

Improvement of financial reporting of organisations in the conditions of digital economy development

The purpose of the study is to investigate the current trends in the development of financial reporting of entities in the context of digitalisation of the economy both at the domestic and global level. The prospects of using XBRL (eXtensible Business Reporting Language) as the main digital format for presenting business reporting were analysed.Methodology. In the work on this article were applied such scientific methods as analysis, collection and processing of primary data, statistical observation and comparison, comparative methods.Originality / value of the study - The study is of value and originality in the context of formation and presentation of financial statements in the Republic of Kazakhstan. The author's approach to this process is unique and has the potential to improve the efficiency and transparency of financial reporting in the country.Results of the study - The results of the study provide valuable information that can be used by various financial market stakeholders, namely national financial regulators in making decisions to develop and improve policies and regulations; to help investors make informed investment decisions; international financial market participants, forecasting currency fluctuations and making strategic decisions; professional associations.

An empirical evaluation of the performance of Nigerian pension fund managers

PurposeThis paper evaluates the risk-adjusted returns, selectivity, market timing skills and persistence of the performance of Nigerian pension funds.Design/methodology/approachAnnual return data of 23 pension funds that operated in Nigeria between 2018 and 2022 were obtained from the National Pension Commission (PenCom). Risk-adjusted return was appraised using the Treynor ratio, Sharpe ratio and Jensen alpha, while the Treynor–Mazuy and Henriksson–Merton multiple regression models were applied to decompose selective and timing skills. Performance persistence was assessed using the contingency table and rank correlation models.FindingsEvidence shows that pension funds deliver excess risk-adjusted returns and exhibit selective skills. However, the evidence does not support the presence of timing skills, and there is overwhelming evidence that good (bad) performance does not repeat.Practical implicationsAn evaluation of the investment performance of pension funds is crucial for ensuring the financial stability of retirees, maintaining economic stability and making informed investment decisions. It serves the interests of pensioners, pension fund managers, regulators and the broader economy. Our evidence that pension funds generate positive excess returns is a departure from most of the literature on managed funds. We recommend that more Nigerians should leverage the pension fund industry to grow their wealth and prepare for retirement.Originality/valueThis study, to our knowledge, is the first to appraise all the key facets of the investment performance of pension funds in the Nigerian context.

Data science for energy applications: A Bibliometric Analysis

Global digitalization is altering the energy sector, demanding the adoption of data science applications to improve efficiency and innovation, despite the industry's existing data analytics. Data science is revolutionizing the energy and utilities industries, enabling efficient, sustainable, and innovative decision-making through data analysis and smart grid optimization. In the energy industry, organizations are turning to data science to reduce waste, optimize energy usage, and provide alternative energy sources. With the different parts of Africa facing energy crises, different applications are needed to provide a solution. Data science has the potential to provide good information and knowledge that could be used to contribute to energy solutions. To address these concerns, data science models enable utilities to accurately forecast energy demand, enabling efficient generation, distribution, waste reduction, and informed investment decisions by leveraging historical consumption data, weather patterns, and economic indicators. This article aims to explore data science for energy applications. The findings show tools and techniques that can be utilized to provide energy efficiency and energy sustainability through data science applications.

An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique

As it knows prediction is always a challenging task in all terms. The goal of any stock prediction method is to develop a robust method for predicting trading price that can be used to improve investment decisions and accurate models. The paper proposes a hybrid model that combines the strengths of deep learning models CNN and DNN, and to develop a comprehensive methodology for the prediction of stock/index prices on Banknifty (NSE Bank), a highly volatile Indian sectorial Index that represents 12 major banks of the country. The hybrid model consists of two main components a CNN for feature extraction and a DNN for regression or classification tasks. In the context of stock price prediction, CNN layers can be used to extract features from input data (such as stock prices and indicators) related to an estimated future value. DNN layer can be used to combine features learned from the CNN layers. Model performance will be evaluated using various metrics including Accuracy, Precision, Recall, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The method also analyses the impact of various factors on stock prices, including market volatility, economic indicators, and geopolitical events. The achieved accuracy of 97.48% indicates that the model was successful in accurately predicting the stock prices of Bank Nifty. The proposed method is expected to provide investors and financial analysts with a valuable tool for making informed investment decisions.

Return and Risk in Dynamic Stocks

This research paper titled “Exploring Risk and Return Dynamics in Equity Stocks of Chosen 10 Companies: A Five-Year Investigation (2019-2023)” delves into the complex interplay between risk and return within equity markets. Over a five-year period, the study meticulously examines the performance of equity stocks from ten selected companies, offering valuable insights into the correlation between risk and return. Leveraging a diverse methodological approach, including historical data analysis and quantitative techniques, the research provides empirical evidence and fresh perspectives to aid investors and financial analysts in making informed investment decisions. The findings underscore the importance of understanding the risk-return tradeoff and the necessity of diversification to construct well-balanced investment portfolios. By offering actionable insights and recommendations, this study contributes to the broader understanding of risk and return analysis in financial markets, empowering stakeholders to navigate dynamic market conditions effectively.

Exploring Risk and Return Dynamics in Equity Stocks of Chosen 10 Companies: A Five-Year Investigation (2019-2023)

This research paper titled “Exploring Risk and Return Dynamics in Equity Stocks of Chosen 10 Companies: A Five-Year Investigation (2019-2023)” delves into the complex interplay between risk and return within equity markets. Over a five-year period, the study meticulously examines the performance of equity stocks from ten selected companies, offering valuable insights into the correlation between risk and return. Leveraging a diverse methodological approach, including historical data analysis and quantitative techniques, the research provides empirical evidence and fresh perspectives to aid investors and financial analysts in making informed investment decisions. The findings underscore the importance of understanding the risk-return tradeoff and the necessity of diversification to construct well-balanced investment portfolios. By offering actionable insights and recommendations, this study contributes to the broader understanding of risk and return analysis in financial markets, empowering stakeholders to navigate dynamic market conditions effectively.

Testing Stock Market Anomalies in Nifty 50 Stocks: An Empirical Analysis in the Indian Context By IJISRT

Stock market anomalies are persistent patterns in stock returns that traditional financial models cannot explain. Anomalies challenge the Efficient Market Hypothesis (EMH), which states that all information is reflected in stock prices. This research aims to test the stock market anomalies in Nifty 50 stocks in India. The study will use various statistical methods to examine a range of calendar anomalies. The findings of this study will have important implications for investors and policymakers. If anomalies are found to exist, investors may be able to generate abnormal returns by exploiting them. Policymakers may also be interested in understanding the causes of anomalies and their impact on the overall market. This process involves examining a large dataset of stocks within the Nifty 50 index to identify any patterns or trends that may be considered abnormal or unexpected. By conducting this analysis, investors and financial analysts can gain deeper insights into the behaviour of the stock market in India and make more informed investment decisions. This study is significant for several reasons: it is one of the few studies to examine stock market anomalies in the Indian market; the study uses comprehensive statistical methods to test for anomalies. The study is expected to find evidence of some stock market anomalies in Nifty 50 stocks. The study's conclusions will have an impact on investors and policymakers.

Prediction of bitcoin stock price using feature subset optimization

In light of recent cryptocurrency value fluctuations, Bitcoin is gradually gaining recognition as an investment vehicle. Given the market's inherent volatility, accurate forecasting becomes crucial for making informed investment decisions. Notably, previous research has utilized machine learning methods to enhance the accuracy of Bitcoin price predictions. However, few studies have explored the potential of employing diverse modeling methods for sampling with varying data formats and dimensional characteristics. This study aims to identify the internal feature subset that yields the highest returns in forecasting Bitcoin's price. Specifically, Bitcoin's internal features were categorized into four groups: currency data, block details, mining information, and network difficulty. Subsequently, a long short-term memory (LSTM) artificial neural network was employed to predict the next day's Bitcoin closing price, utilizing various categorizations of feature subsets. The model underwent training using two and a half years of historical data for each feature. The findings revealed a mean absolute error rate of 6.38% when modeling with the block details category features. This enhanced performance primarily stemmed from the positive relationship between Bitcoin price and this data subset's low ambiguity. Experimental results underscored that, compared to other investigated feature subsets, the categorization of block detail features provided the most accurate Bitcoin price predictions, laying the foundation for future research in this domain.

Exploring Risk and Return Dynamics in Equity Stocks of Chosen 10 Companies: A Five-Year Investigation

This research paper titled “Exploring Risk and Return Dynamics in Equity Stocks of Chosen 10 Companies: A Five-Year Investigation (2019-2023)” delves into the complex interplay between risk and return within equity markets. Over a five-year period, the study meticulously examines the performance of equity stocks from ten selected companies, offering valuable insights into the correlation between risk and return. Leveraging a diverse methodological approach, including historical data analysis and quantitative techniques, the research provides empirical evidence and fresh perspectives to aid investors and financial analysts in making informed investment decisions. The findings underscore the importance of understanding the risk-return tradeoff and the necessity of diversification to construct well-balanced investment portfolios. By offering actionable insights and recommendations, this study contributes to the broader understanding of risk and return analysis in financial markets, empowering stakeholders to navigate dynamic market conditions effectively.

Capital market effects of integrated reporting quality: evidence from South African context

Purpose The purpose of this study is to examine the interplay between integrated reporting quality (IRQ) and capital markets. More specifically, the authors test the impact of IRQ on stock liquidity, cost of capital and analyst forecast accuracy. Design/methodology/approach The sample consists of listed firms on the Johannesburg Stock Exchange in South Africa, covering the period from 2012 to 2020. The IRQ measure used in this study is based on data from Ernst and Young. To test the proposed hypotheses, the authors conducted a generalized least squares regression analysis. Findings The empirical results evince a positive relationship between IRQ and stock liquidity. However, the authors did not find a significant effect of IRQ on the cost of capital and financial analysts’ forecast accuracy. In robustness tests, it was shown that firms with a higher IRQ score exhibit higher liquidity and improved analyst forecast accuracy. Additional analysis indicates a negative association between IRQ and the cost of capital, as well as a positive association between IRQ and financial analyst forecast accuracy for firms with higher IRQ scores (TOP ten, Excellent, Good). Originality/value The study stands as one of the initial endeavors to investigate the impact of IRQ on the capital market. It provides valuable insights for managers and policymakers who are interested in enhancing disclosure practices within the financial market. Furthermore, these findings are significant for investors as they make informed investment decisions.