Investment Decisions Research Articles

Framework for Cyber Risk Loss Distribution of Client‐Server Networks: A Bond Percolation Model and Industry Specific Case Studies

ABSTRACTCyber risk has emerged as a significant threat to businesses that have increasingly relied on new and existing information technologies (IT). Across various businesses in different industries and sectors, a distinct pattern of IT network architectures, such as the client‐server network architecture, may, in principle, expose those businesses, which share it, to similar cyber risks. That is why in this article, we propose a probabilistic structural framework for loss assessments of cyber risks on the class of client‐server network architectures with different client types. To our knowledge, there exist no theoretical models of an aggregate loss distribution for cyber risk in this setting. With this structural framework via the exact mean and variance of losses, we demonstrate how the changing cybersecurity environment of a business's IT network impacts the loss distribution. Furthermore, our framework provides insights into better investment strategies for cybersecurity protection on the client‐server network. Motivated by cyberattacks across industries, we apply our framework to four case studies that utilize the client‐server network architecture. Our first application is implantable medical devices in healthcare. Our second application is the smart buildings domain. Third, we present an application for ride‐sharing services such as Uber and Lyft. The fourth is the application of vehicle‐to‐vehicle cooperation in traffic management. The results are corresponding exact means and variances of cyber risk loss distributions parameterized by various cybersecurity parameters allowing for liability assessments and decisions in cybersecurity protection investments.

Cracking the Code: Hidden Choices and Visible Impacts Pattern Recognition in Corporate Finance

AbstractResearch in corporate finance suffers from bounded rationality due to static modeling. Adopting factor analysis, an unsupervised machine learning approach, and balance sheet information (accounts) over time, we find underlying dynamic latent corporate finance decisions. Our study identifies three latent corporate finance decisions adopted by executives in Pakistan, (1) long-term capital investment, (2) short-term debt credit, and (3) financial flexibility. The order of the decisions and the empirical tests highlight agency problems rooted in familial ownership concentration. We find that long-term capital investment and short-term debt credit decisions significantly reduce the firms’ present and future performance. Conversely, managers do not embrace financial flexibility, despite its ability to improve performance. The study highlights a contradiction, firms bounded by ownership concentration prefer control over performance and do not make decisions to optimize and protect minority shareholders’ wealth, depicting a moral hazard problem.

Influence of Overconfidence and Loss Aversion Biases on Investment Decision: The Mediating Effect of Risk Tolerance

This study aims to examine the effects of overconfidence and loss aversion on Investing behavior with the mediating role of risk tolerance. Employing a quantitative methodology, data was collected using a structured questionnaire featuring multiple choice and Likert scale questions. Convenience sampling was used to gather responses, and the data was analyzed through multiple regression techniques. The mediating effect of risk tolerance was measured using Andrew F. Hayes’ Process V4.2 Macro. The study found that risk tolerance partially mediates the relationship between overconfidence and investment decision-making behavior, with both direct and indirect effects being statistically significant. Similarly, the study found that loss aversion has a statistically insignificant direct effect on investment decisions, while its indirect effect through risk tolerance is statistically significant. The study discloses that risk tolerance partially mediates the relationship between overconfidence and investment decision behavior, while it fully mediates the relationship between loss aversion and investment decision making behavior. Risk tolerance significantly influences investment decisions, influencing both overconfidence and loss aversion, while loss aversion’s influence is partially explained by risk tolerance.

Factors Affecting Investment Decisions of Individuals: A Comparative Study Between Developed and Emerging Markets (The Case of Jordan)

This study aimed to analyze the factors influencing individuals' investment decisions in Jordan, comparing emerging markets (such as Jordan) and developed markets (such as Dubai). The sample included 100 individual investors (50 from Jordan and 50 from Dubai), and a 30-item questionnaire covering economic, psychological, social, and technical factors was used. The results showed that economic factors such as inflation and growth significantly influence investment decisions in both markets, but without significant statistical differences, as the T-statistic value for economic factors was 0.8665 and the P-value was 0.3883. On the other hand, psychological factors were more influential in developed markets, as the T-Statistic showed a value of -4.3778 and the P-value was 0.00003, indicating that Dubai investors bear more risks than Jordan. Technical factors significantly impacted developed markets, as the F-statistic value was 9.623 and the P-value was 0.0025. In contrast, the study showed that investors in emerging markets face more significant challenges compared to developed markets, with a T-Statistic of 6.6154 and a P-Value of 0.0000000019. The study recommends increasing government support to enhance political and economic stability in emerging markets to mitigate investor challenges. It also recommends enhancing the use of technology in investing in emerging markets, and improving financial education to encourage informed risk-taking. In addition, it is suggested that regulators in developed markets adopt strategies that support greater transparency and clarity of investment opportunities to facilitate decision-making.

Carbon Taxation and Electricity Price Dynamics: Empirical Evidence from the Australian Market

AbstractIn this paper, we study the change of Australian electricity price dynamics that was observed before, during and after the two-year period in which a Carbon Pricing Mechanism was in force. We fit a two-states Markov Switching Model, representing a high- and a low-volatility state of the world. To avoid the interference due to periodic patterns, a deseasonalization process accounting for short- and long-term seasonality is carried out prior to the study of volatility. Estimation results highlight that, during the period when the carbon tax applies, the volatility level is lower for both the states of the world. Furthermore, the persistence in the low-volatility state is increased in the presence of the carbon tax. This conclusion is particularly relevant for macroeconomic and investment considerations because the increased uncertainty in electricity prices can significantly influence firms’ investment decisions and shape inflation expectations.

Financial Literacy and Retirement Readiness:A Case Study of the Namibian Police Services

Retirement preparedness is a priceless process during one’s tenure of employment because no individual can continue working during his or her lifetime. However, the low levels of financial literacy among retired Namibian police officers compromises sound retirement planning. Financial stress upon retirement is particularly problematic for police officers in Namibia, and this study examined the nexus between financial literacy and retirement planning. The research was qualitative, allowing for an in-depth exploration of the personal experiences and challenges faced by retirees. There were interviews with 26 participants, purposively selected from retired Namibian police officers. In addition, five key informants in the human resources department of the Namibian Police services then corroborated information collected. Purposive-sampling enabled the research to target individuals with direct experience of retirement from the Namibian Police Service. Thematic analysis indicated that upon retirement, police officers found themselves in dire poverty, with no sustainable income sources. The process of determining pension amounts was opaque resulting in the police officers failing to determine the income gap they would face after retirement. There were no financial literacy training programmes for the police officers resulting in their failure to make sound investments during employment period. Therefore, the study recommended that the Namibian Police Services should implement financial literacy programmes, encourage officers to make sound investment decisions, and ensure that retirement becomes an attractive part of one’s lifetime

The Influence of Economic Variables on Stock Prices of Telecommunications Companies

The general background fluctuation of stock prices is a critical concern for investors, particularly in dynamic economic environments where various indicators can significantly impact financial performance. The specific background of this study focuses on the telecommunications sector, which has been increasingly influenced by economic variables such as the Inflation Rate, SBI Interest Rate, Earnings Per Share, Rupiah Exchange Rate, and Company Fundamentals. Despite existing research, there is a knowledge gap regarding how these factors collectively influence stock prices in publicly listed telecommunications companies. This study aims to investigate the effects of these economic variables on stock prices from 2020 to 2023, utilizing a quantitative approach with secondary data obtained from annual financial reports of 16 companies. The results demonstrate that all examined variables—Inflation Rate, SBI Interest Rate, Earnings Per Share, Rupiah Exchange Rate, and Company Fundamentals—significantly affect stock prices. The novelty of this research lies in its comprehensive analysis of the interplay between these economic indicators specifically within the telecommunications sector. The implications of these findings suggest that investors and policymakers should consider these economic factors when making investment decisions, thereby enhancing their financial strategies and contributing to a more robust understanding of stock market dynamics in the telecommunications industry. Highlights: All examined economic variables significantly affect stock prices. The study analyzes data from 16 publicly listed telecommunications companies. Findings enhance investment strategies and understanding of market dynamics. Keywords: Inflation, Interest Rate, Earnings Per Share, Exchange Rate, Stock Price

Technological Innovation, Trade Openness, Natural Resources, and Environmental Sustainability in Egypt and Turkey: Evidence from Load Capacity Factor and Inverted Load Capacity Factor with Fourier Functions

The environmental degradation in the Middle East and North Africa (MENA) region leads to significant challenges regarding economic sustainability and the attainment of sustainable development goals (SDGs). The extensive use of fossil fuels in the region, as well as rapid urbanization and economic growth, has led to significant carbon emissions, together with unprecedented ecological footprints compromising environmental sustainability. The study aims to elucidate the influence exerted by technological innovation, trade openness, and natural resources on environmental sustainability in Turkey and Egypt for the period 1990–2022. In assessing the empirical relations, the study employed the Fourier function incorporate estimation techniques, that is, Fourier ADF for unit root test, Fourier ARDL, and Fourier NARDL for long-run and short-run elasticities of technological innovation (TI), trade openness (TO,) and natural resources rent (NRR) on load capacity factor (LCF) and inverted LCF (ILCF); finally, the directional causality evaluate through Fourier TY causality test. The results revealed that both Turkey and Egypt have severe environmental problems due to their high carbon emissions and ecological footprints. Technological change and international trade separately negatively affect environmental sustainability; however, these negative impacts have mixed character. On the one hand, technology can improve efficiency and reduce ecological footprints by obviating the use of high-impact processes or allowing cleaner production systems. In the same vein, trade openness helps transfer green technologies more quickly, but it can also lead to unsustainable resource extraction and pollution. The findings of the paper propose that in order to move forward, Turkey and Egypt need strategic policy shifts to ensure environmental sustainability, including transitioning towards renewable energy from fossil fuels while bolstering their capacity for energy efficiency. Policymakers must balance economic development with environmental conservation to reduce the harmful effects of climate degradation and help safeguard continued economic survival in the face of increasing climatic instability. This research helps to inform policy and investment decisions about how the SDGs can be achieved and how they are relevant for sustainable development in the MENA region.

A deep learning-based model for stock price prediction under consideration of financial news publishers

Stock price prediction with financial news is beneficial for making correct investment decisions. Recent researches mainly focus on extracting sentiments from news. The publishers as a crucial part of news, which can reflect the authority and reliability, but are usually neglected. In this paper, we propose a novel hybrid deep learning model for stock price prediction that considers the financial news publishers. Specifically, a novel feature about financial news publishers is constructed by classifying financial news publishers. Furthermore, a novel hybrid model named 2d-CNN-AM-LSTM is proposed by combining Convolutional Neural Network (CNN), Attention Mechanism (AM) and Long Short-term Memory, so that it can fuse textual and numerical data in-depth. To verify the superiority of the proposed model, numerous baselines are employed to conduct experiments with representative stock datasets. The experimental analysis demonstrates that the proposed model considering financial news publishers surpasses other benchmarks and achieves outstanding prediction performance.

Corporate bond coupon prediction based on deep learning

As the second largest bond market in the world, China's bond market has attracted extensive attention in recent years. Given its importance in facilitating financing arrangements and informing investment decisions, accurate bond coupon prediction is valuable. This paper proposes an ensemble model combining TabNet, DeepFM, and XGBoost for predicting the coupons of investment-grade corporate bonds. Specifically, to optimize the hyperparameters of the proposed model, an improved butterfly optimization algorithm incorporating the concepts of good point sets, refraction opposition-based learning, switching probability adjustment, and Solis & Wets search strategies is developed. Extensive experiments using data on China's investment-grade corporate bonds demonstrate the superior performance of the proposed model in the accuracy of bond coupon predictions. Additionally, the importance of various features has been discussed. The results show that the base interest rate for valuation and term to maturity are important to bond coupon predictions obtained by the proposed model.

An Extra-High Voltage Test System for Transmission Expansion Planning Studies Considering Single Contingency Conditions

This paper presents an extra-high voltage synthetic test system that consists of 500 kV and 765 kV voltage levels, specifically designed for transmission expansion planning (TEP) studies. The test network includes long transmission lines whose series impedance and shunt admittance are calculated using the equivalent π circuit model, accurately reflecting the distributed nature of the line parameters. The proposed test system offers technically feasible steady-state operation under normal and all single contingency conditions. By incorporating accurate modeling for long transmission lines and EHV voltage levels, the test system provides a realistic platform for validating models and theories prior to their application in actual power systems. It supports testing new algorithms, control strategies, and grid management techniques, aids in transmission expansion planning and investment decisions, and facilitates comprehensive grid evaluations. Moreover, a TEP study is conducted on this test system and various scenarios are evaluated and compared economically.

Impact of Islamic Financial Literacy on Saving and Investment Decision: The Individual Attitude Effect

Impact of Islamic Financial Literacy on Saving and Investment Decision: The Individual Attitude Effect

The pension system of Kazakhstan: savings analysis, review of investment portfolio and prospects for profitability increase

The article considers the pension system of the Republic of Kazakhstan as the most important element of the system of social protection of the population and a major investment player in the market, which is responsible for the safety of pension savings of the population. Consequently, it is necessary to constantly monitor the quality of pension provision and financial sustainability of the pension system in order to ensure the adequacy of pension payments to maintain a decent standard of living for pensioners, taking into account the inflation rate in the country. Thus, it is necessary to carefully assess the possible consequences of investment decisions in order to prevent the depreciation of pension system participants' savings. The pension system is an important element for ensuring the stability of the country's population. It forms the foundation of social security for citizens, creating financial security for them during the period of disability or retirement. Due to demographic changes in the country, the pension system is being reformed, which in turn affects financial stability in the Republic of Kazakhstan. The article presents a number of key roles and functions within the pension system, analyzes the volume of pension savings and the number of individual pension accounts in the cities of national importance, presents the structure of the investment portfolio of the PAs of the Unified Accumulative Pension Fund (UAPF), which are under the trust management of the NBRK, in the context of financial instruments. Proposals on how to increase investment income have been developed.

The advantages of CBOE credit VIXs for corporate bond investors in North America: A sectoral analysis

This paper examines the safe-haven role of the recently introduced CBOE credit VIXs for investment-grade and high-yield corporate bonds, at both aggregate and sectoral levels. Using a time-varying quantile-based framework and daily data from June 5, 2014 to December 10, 2023, the safe-haven role of credit VIX is confirmed irrespective of bond sector. The safe-haven property of credit VIX is pronounced for high-yield bonds which embed a high credit risk-premium. This result stands when taking into account interest rate volatility, as measured by the MOVE index. A time-varying analysis shows the persistence of credit VIX as a safe-haven for all bond sectors after the COVID-19 pandemic and during a high US interest-rate regime. Corporate bond investors and traders can use these findings to refine their investment and trading decisions and offset credit risk during both normal and turbulent periods.

Cross-Market Volatility Dynamics Among Crude Oil and South Asian Emerging Markets During the COVID-19 Pandemic.

The COVID-19 pandemic had profound impacts on the global economy, initially triggering a severe economic downturn due to widespread lockdowns, this outbreak generated various economic shocks, a major one being the oil price crash. A brief understanding of how this pandemic is affecting the financial markets, empirical analysis is done on three financial markets. Three stock exchange markets are selected from emerging markets in South Asia (Pakistan, China, and India). The world crude oil market is selected as a reference, to how it transmits volatility to South Asian markets during the COVID-19 period. We employ a bivariate BEKK-GARCH model for analyzing the volatility spillover from November 19, 2019, to March 31, 2021. All markets show negative coefficients with the oil market, indicating that the previous day’s shocks in oil prices reduce today's stock market volatility, with significant values for India and Pakistan. China has a negative coefficient while the rest of the two markets show a positive and significant coefficient means past volatilities in the stock market decrease today’s oil market volatility, likely indicating a reactive or adaptive market behavior to stock volatility. Generally, oil price changes have a significant impact on these markets, with both immediate effects (via ARCH terms) and persistent effects (via GARCH terms). The study's findings offer valuable insights for policymakers, as they help identify the direction of spillovers among interconnected economies. Additionally, portfolio investors can use this information to effectively allocate weights to different asset classes when making investment decisions.

Economic policy uncertainty and the predictability of stock returns: impact of China in Asia

PurposeMotivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.Design/methodology/approachWe estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.FindingsApplying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.Research limitations/implicationsOur analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.Originality/valueThe findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

Impact of behavioral factors on the investor’s intention in peer-to-peer lending

PurposePeer-to-peer lending has emerged as a promising alternative investment avenue globally. This study explores the variables influencing investors' intention in peer-to-peer lending. Studying these factors is crucial for inspiring investor motivation, which leads to the growth of peer-to-peer lending platforms in India.Design/methodology/approachThe research utilizes information gathered from 293 investors of the Rang de platform through a questionnaire. It employs ordinal logistic regression to assess how demographic characteristics, investor experience, social projects and investment factors influence the study’s outcomes.FindingsThe research indicates that the education qualification and income level of the investors are significant demographic variables in their investment decisions. Moreover, social projects, the experience of the investors and investment factors affect their intent to invest more in peer-to-peer lending projects.Research limitations/implicationsIt helps stakeholders and borrowers of crowdfunding platforms think about these aspects when creating plans to bring in and keep investors. This approach also enhances the openness and dependability of lending processes.Originality/valueThis research sheds light on Indian investors' behavior toward peer-to-peer lending platforms and includes new investment factors like project categories and impact partners that other researchers did not identify.

A Comprehensive Study of Techno Economic Viability (TEV) Report of Various Projects

This paper provides a comprehensive analysis of the TEV process, focusing on its application to both greenfield and brownfield projects across various sectors, including manufacturing, infrastructure, energy, and services. The study aims to guide decision-makers by bridging technical assessments with financial analysis to establish project profitability and sustainability. A Techno-Economic Viability (TEV) Study serves as a crucial tool in the decision- making process for both lenders and investors. It offers a detailed assessment of a project's technical feasibility, economic viability, and overall sustainability. It delves into the dual aspects of technical feasibility and economic viability, emphasizing their roles in influencing investment decisions across a wide spectrum of industries, including manufacturing, infrastructure, energy, and services. The findings from this research also illustrate real-world applications, highlighting two case studies with an emphasis on key financial indicators such as the Debt Service Coverage Ratio (DSCR) and Internal Rate of Return (IRR), which are used to measure a project’s ability to meet debt obligations and generate profits. Keywords: Techno Economic Study, Financial Viability, Technical Feasibility, Financial Modelling, Risk Analysis, Investment Decision

Strategic configurations: exploring MNEs’ commitment reduction in an emerging economy

AbstractCommitment reduction in the foreign investment process is a critical strategic decision for multinational enterprises (MNEs). Recognizing the complex nexus of drivers for MNE commitment reduction in an emerging economy, this study investigates the configurations of resource, industrial, and institutional factors that collectively influence the strategic decision of commitment reduction. We incorporated the strategic tripod framework to reveal the interactive patterns among various sets of factors influencing the investment reduction decisions of MNEs. Using fuzzy-set qualitative comparative analysis (fsQCA), we analyzed foreign investment reduction by MNEs operating across nineteen industries in China between 2013 and 2019. Our findings identify configurational solutions leading to four propositions, applying the strategic tripod to explain MNE commitment reduction decisions. This study concludes with important theoretical and practical implications, suggesting ideas for pushing the research landscape further.

Does prior success influence risk‐taking in foreign location decisions? A prospect theory perspective

AbstractResearch SummaryResearch suggests that foreign direct investment decisions can be biased and deviate from economic theory. This study deploys prospect theory to analyze the impact of the success of previous investments on risk‐taking in subsequent investment decisions. Our theorizing suggests that prior success influences foreign investors' decisions to take host‐country risk, and that this moderating influence on the risk‐investment relation is specific to the type of risk. The results of an event‐history study of 1259 location decisions support the notion that the success of previous investments encourages investors to enter host countries with high negative or positive risk, whereas it discourages them from entering locations with high mixed risk. The effects are stronger for investment locations that are similar than for those that are dissimilar.Managerial SummaryRegarding the varying risks that investors face in foreign countries, investment decisions may sometimes appear rather confident and sometimes rather cautious. Our study relates these decisions to the success that investors had with similar investments in the past, since research suggests that experiencing gains or losses can influence decision behavior. The results suggest that prior success affects risk‐taking in foreign direct investment decisions and comes in contrary forms, depending on the type of risk: prior success leads to overconfident investment decisions in the case of negative and positive risk, whereas it induces overcautious investment decisions in the case of mixed risk. Being aware of these behavioral tendencies can prevent managers from making biased investment decisions.