Successful Investment Research Articles

The Application of Mathematical Analysis in Investment Planning

Investment serves as a means to cultivate assets and secure long-term profits. However, inadequate investment planning poses a substantial risk of significant losses. Consequently, precise analysis becomes imperative for making intelligent investment decisions. One effective analytical approach involves the application of mathematical concepts in investment planning. This article conducts a comprehensive review of existing literature pertaining to the utilization of mathematical analysis in investment planning. Common mathematical concepts employed in investment analysis encompass probability theory, statistics, portfolio theory, and options theory. Probability theory and statistics aid investors in comprehending and forecasting potential gains and losses from specific investments. The primary objective of investment planning is profit maximization through effective risk management, necessitating a mature and structured analysis. Mathematical analysis emerges as a valuable method, and its application in investment planning yields more accurate and efficient results. Techniques such as statistical analysis, portfolio theory, and probability analysis contribute to risk minimization and return maximization by considering factors like asset correlation and risk levels. Thus, the incorporation of mathematical analysis proves crucial for well-structured and successful investment planning. The fifth entry in the bibliography emphasizes the importance of careful and planned analysis in making investment decisions. Economic and financial factors affecting market and investment performance are explored, along with portfolio risk diversification techniques and investment control. The article underscores the significance of understanding investment objectives and investor profiles while discussing various investment instruments such as stocks, bonds, and mutual funds. Overall, the article provides insights into managing investment portfolios effectively for optimal profitability.

Can Perturbations Help Reduce Investment Risks? Risk-aware Stock Recommendation via Split Variational Adversarial Training

In the stock market, a successful investment requires a good balance between profits and risks. Based on the learning to rank paradigm, stock recommendation has been widely studied in quantitative finance to recommend stocks with higher return ratios for investors. Despite the efforts to make profits, many existing recommendation approaches still have some limitations in risk control, which may lead to intolerable paper losses in practical stock investing. To effectively reduce risks, we draw inspiration from adversarial learning and propose a novel Split Variational Adversarial Training (SVAT) method for risk-aware stock recommendation. Essentially, SVAT encourages the stock model to be sensitive to adversarial perturbations of risky stock examples and enhances the model’s risk awareness by learning from perturbations. To generate representative adversarial examples as risk indicators, we devise a variational perturbation generator to model diverse risk factors. Particularly, the variational architecture enables our method to provide a rough risk quantification for investors, showing an additional advantage of interpretability. Experiments on several real-world stock market datasets demonstrate the superiority of our SVAT method. By lowering the volatility of the stock-recommendation model, SVAT effectively reduces investment risks and outperforms state-of-the-art baselines by more than 30% in terms of risk-adjusted profits. All the experimental data and source code are available at https://drive.google.com/drive/folders/14AdM7WENEvIp5x5bV3zV_i4Aev21C9g6?usp=sharing .

Factor Affecting Investment Success of Individual Investors at NEPSE

This study examined the relationships between investment success and three key variables: investment education, portfolio management, and risk management. The data were analyzed using descriptive statistics, correlations, and regression analysis. The findings revealed that investment education had a significant positive impact on investment success, suggesting that individuals with higher levels of investment education were more likely to achieve successful investment outcomes.

Compulsory pension insurance investment policy in Georgia

An important factor of economic development in Georgia is the reform of mandatory pension insurance and its successful investment policy, which started in the country five years ago and is being successfully implemented to this day. Many countries were not able to implement the said reform voluntarily, and Georgia is no exception. The reform has already been able to maximize the number of participants in the pension scheme, create a significant pension assets and multi-billion dollar base of investment capital, and have yielded practical results, which are reflected in the number of beneficiaries, disbursed funds and investment returns. The investment policy reform of the mandatory pension system has a great social impact and is characterized by high public interest. But the challenge is that the implemented large-scale reform requires further deep analysis and the development of mechanisms for sustainable development, which is manifested by the risk management of pension insurance assets, investment policies and obtaining high returns on the pension assets of the participants of investment activities through diversified and financially safe, transparent and reliable investments, Considering the investment and liquidity objectives of the pension system and the reputational requirements for adherence to investment principles.

Decrypting the specifics of professional football club investments: Why? What? How? An integrative review

PurposeSince the beginning of the 2000s, investors have more frequently invested into professional football clubs, thereby radically changing the industry landscape. This review's purpose is to analyze and synthesize the state of research to understand motives, roles and implications of football club investors, and to provide recommendations for further research.Design/methodology/approachThe paper presents an integrative literature review by identifying relevant English articles based on the search terms investor, owner, investment, ownership, shareholder and stakeholder in combination with soccer or football. Around 2,431 articles were reviewed. A total of 129 relevant articles was analyzed and synthesized within eight subject areas.FindingsInvestors in professional club football is a young research stream with a clear European focus. Investor motives and roles are diverse and implications are multidimensional. Investors mostly aim for indirect returns rather than pure profit- or win-maximization.Research limitations/implicationsFootball clubs comprise an own investment class for which the identified, unique specifics must be considered to develop a financially successful investment model. Thorough academic research of investors' inherent characteristics, investor-club pairings and the pillars of long-term strategies for successful investor-club liaisons are avenues of future research. Furthermore, the results illustrate the need for research outside of Europe.Originality/valueThe paper is the first systematic, integrative review of existing literature in the domain of equity investments into professional club football. The findings genuinely show that, depending on the investor type and ownership structure, investors have a wide impact in professional club football.

Review Paper- Study of Investment Strategy in WINWIN PLUS

Abstract -This study delves into the investment strategy of WinWin Plus Company, aiming to provide a comprehensive understanding of its approach to financial management and wealth creation. WinWin Plus is a fictional company used as a case study for the purpose of this research. The research encompasses an analysis of WinWin Plus Company's investment philosophy, risk tolerance, and diversification strategies. It explores the company's historical financial performance and its impact on investment decision-making. Additionally, the study investigates the key factors influencing investment choices, such as market conditions, industry trends, and macroeconomic variables. Furthermore, the research scrutinizes the role of technology and innovation in WinWin Plus Company's investment strategy. In an era of rapid technological advancements, understanding how the company leverages innovation for competitive advantage and sustained growth is crucial. The study evaluates the integration of digital tools, artificial intelligence, and data analytics in decision-making processes related to investments. In the context of sustainable investing, the research examines WinWin Plus Company's commitment to environmental, social, and governance (ESG) criteria. The study analyzes the company's adherence to responsible investment practices, ethical considerations, and its impact on long-term financial performance. The findings of this research are based on a combination of quantitative and qualitative analyses, incorporating financial statements, market data, and interviews with key stakeholders within WinWin Plus Company. The results aim to provide insights into the effectiveness of the company's investment strategy, offering valuable information for investors, financial analysts, and researchers interested in understanding successful investment approaches in the corporate landscape. Keywords :- Investment Strategy, Financial Management, Wealth Creation, Risk Tolerance,Diversification Strategy, Market Conditions,Industry Trends,Macroeconomic Variables

Impact of Technological Capability on Competitive Advantage of Selected Industrial Firms in South-East, Nigeria

The study focused on the impact of technological capability on competitive advantage of selected industrial firms in South-East, Nigeria. The objective of the study was to examine the effect of technological capability on competitive advantage of selected industrial firms in South-East, Nigeria:. The study adopted survey research design. The population of the study was 10420 selected industrial firms in South-East, Nigeria. A sample size of 385 (Three hundred and eighty-five) was drawn from the population using Taro yamani statistical formula. One hypothesis was formulated and tested with Simple Linear Regression model.. The major findings in the study showed that: there is a significant effect of technological capability on competitive advantage of selective industrial firms in South-East, Nigeria;. Hence, we concluded that new knowledge and new technology generated from R&D activities increase competitive advantage, not only at the firm’s level, but also at the industry and national levels and that successful investments in R&D result in innovative products and services that enable the company to improve its sales revenue. We, therefore, recommended that for firms in the clusters to be strong collaborators and boost marketing alliances, there is a need to upgrade their activities relating to cost efficiency, quality, variety/diversification, responsiveness, acceptance of entrepreneurial risks, and a positive attitude towards change and innovation. These factors alone do not give a competitive advantage, but represent prerequisites for surviving in a globalized market., among others

Investing in nature: analysis of the relationship between environmental capital and economic growth

This paper analyzes an important and urgent problem that is the relationship between investments in nature, represented by environmental capital and economic growth. In the context of sustainable development and environmental conservation, interest in this topic is becoming increasingly significant. The paper examines the main theoretical concepts related to the impact of environmental capital on the economy, as well as presents the results of empirical studies confirming or refuting hypotheses about the positive impact of investments in nature on economic growth. This analysis allows us to identify key aspects of the interaction between environmental capital and economic development, which can serve as a basis for the development of effective strategies and policies in the field of sustainable development. The article also analyzes various approaches to measuring environmental capital and its impact on economic growth, taking into account the specifics of different regions and countries. Examples of successful investment strategies in nature are discussed, as well as obstacles and challenges facing the adoption of such measures.

Determinants on Successful Investment of Venture Capitalist

Venture capital (VC) has transcended its Silicon Valley origins to become a global force shaping the innovation and economic landscape. It is imperative to recognize the critical role of VC and improve its success rate. Employing the “jockey-horse-meadow” model, this paper identifies three crucial determinants influencing VC success: the entrepreneurial team, strategic approach and business model, and the market context. Delving into specific sub-points, the paper underscores the unique significance of each determinant, substantiating their pivotal roles in shaping VC outcomes. This research not only contributes insights into the success dynamics of VCs but also serves as a valuable reference for startups seeking financing and development. Additionally, it offers guidance to investors navigating the selection process, aiding them in identifying startups with genuine potential for impactful investment. By shedding light on these fundamental factors, this paper enriches the discourse surrounding VC success, fostering a more informed and strategic approach in the dynamic world of venture capital.

Can exponential smoothing do better than seasonal random walk for earnings per share forecasting in Poland?

The accurate prediction of listed companies’ earnings plays a critical role in successful investing. This piece of research contrasts estimation errors of the seasonal random walk model and exponential smoothing models employed in the earnings per share (EPS) data for Polish listed businesses from the timespan between 2008–2009. The models are compared using the mean arctangent absolute percentage error (MAAPE) metric. The best model across all quarters and years is the seasonal random walk (SRW) model, when contrasted with the other models studied regardless of the analysed time spans and error metrics. Contrary to the results obtained from the US market, the more intricate exponential smoothing model, comprising a seasonal and a trend component, does not suitably explain the behaviour of Polish companies. This could be attributable to the simpler demeanour of the Polish market and the absence of a trend in the EPS data.

The nationalization of the large-scale copper mines in Chile: successful investment or financial failure?

AbstractBetween 1967 and 1971, the Chilean government nationalized its large-scale copper mining industry. While this process is widely considered successful given the demonstrated long-term state capacity to profitably operate the assets, the discussion on its net financial convenience has usually been disregarded. Aiming to improve the understanding of this case of resource nationalism, this paper follows two complementary approaches. First, we provide a systematic review of the historical background, development, and short-term impact of the Chilean nationalization, consolidating at the same time the available financial data on fiscal inflows and outflows as part of this process. Secondly, we perform an ex-post NPV assessment using a 10% discount rate over the annual cash flows that the government has perceived from the nationalized mining operations from 1967 to 2022 and compare it with a hypothetical counterfactual scenario in which these mines would have remained privately owned. This yields a higher NPV of the Nationalization compared to the counterfactual. Considering that discount rates above 11.1% or operational cost reductions by 4.5% indicate a net financial loss, the results suggest that, purely from a financial perspective, the nationalization process was marginally positive for the government. We expect that this analysis provides support for a thoughtful mineral policy that contributes to sustainable human and economic development in resource-rich countries.

Abnormal Returns: Takeover Prediction Modelling - Shanghai Stock Exchange

Around the Merge and Acquisition (M&A) announcement date, the targets will experience dramatic increases in their share price. The movement of share pricesbrings investment opportunities. To successfully predict the future targets and make an investment strategy, investors can obtain the abnormal return from the increment of the share price. However, many articles of research show that even successfully predicted large numbers of targets, within the investment portfolio, are still unable to show the abnormal return. To make a successful investment strategy, researchers summarized the potential negative factors of the portfolios' return. For example, the investment timing, the distressed firms which share the common characteristics with predicted targets. This paper motivated by the potential improvement space for target portfolio's return, attempts to rationalised a successful investment portfolio. Through selecting the determinate characteristics of targets, building a prediction model, studying its abnormal return and analysing the influence of a distressed firm on the abnormal return, this paper determines that the targets can be distinguished from non-targets with the characteristics of inefficient management, misevaluation, high sales growth, low liquidity and larger free cash flow; consistent with determinant characters, the prediction model has a good predictability; however, good predictability could not produce the abnormal return; furthermore, even the empirical result shows that the target portfolio actually involves a certain percentage of the distressed firms which have significant differences from no-distressed firms, the portfolio can still not obtain abnormal return by screening the distressed firms using its characteristics.

Information system of analysis for segmentation of the securities portfolio of the product market

The investment market is a complex competitive environment where speed and accuracy of decision-making play a crucial role. Understanding sentiment and asset behavior is key to forecasting and successful investment portfolio management. The results of information analysis confirm the hypothesis of the effectiveness of food market assets, and this in turn proves the possibility of increasing the portfolio's profitability by including food sector assets in it. Creation of an information analysis system for the purpose of segmentation of the contained portfolio is an urgent task, which will allow obtaining real and objective information about the potential risk and profitability of various assets. The paper provides an overview of specialized applied software products that are available on the Internet and are used to analyze financial data. An overview of modern information technologies, which allow convenient processing, visualization of data and forecasting of asset price movements, was carried out. An alternative version of the own information system of analysis is proposed, which focuses on the assets of the food sector. The main task of the created information system is to provide convenient tools for market data analysis, visualization and forecasting of price movements. The main functionalities of the created system are described, including a visual comparison of historical data, price movement forecasting using machine learning, and recommendations on the optimal content of the investment portfolio. The conducted example of the analysis demonstrates how the received information will help to reduce risks and increase the efficiency of decision-making, when managing a portfolio that contains assets of the food sector. The results of the work show that in the case of a small set of assets and, accordingly, restrictions on the diversification of capital investments, the use of such information systems in the field of food products is effective and profitable. The presented technique also demonstrates that an investor, even with non-professional knowledge in the field of programming and information and communication technologies, can effectively use them to create specialized systems for data analysis, visualization and forecasting.

Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models

The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.

Hierarchizing the Criteria of Construction Equipment Procurement Decision Using the AHP Method

Successful investment in construction equipment (CE) is a complex process as it involves the selection and prioritization of many criteria. Fleet managers, contractors, and technical companies search for maximum equipment profitability during the economic life of a machine. Decisions on when to replace a piece of equipment are characterized by many uncertain factors that are difficult to quantify. A literature review and a scientometric analysis are conducted, revealing a gap in the global research, concerning the hierarchy of the criteria in decision making, when coming to procure a CE. The purpose of this article is to address that void in the extant literature by proposing a holistic equipment procurement decision model. A scientometric analysis of the last 25 years is performed to investigate the use of analytical hierarchy process (AHP) in the thematic area. A modified decision model based on the AHP is developed to prioritize factors that determine decisions on equipment procurements. Unstructured interviews with academia, experts, original equipment manufacturers, and practitioners in the field are conducted in conjunction with an extensive literature review to determine the critical “soft” and “cost” factors incorporated in the model. It seems that this is the first integrated attempt to prioritize the “key” factors that determine decisions of equipment procurement.

Reproductive success, fruit removal and local distribution patterns in the early‐flowering shrub Daphne mezereum

In insect‐pollinated, bird‐dispersed plants, both investment in reproduction and reproductive success involve interactions between plants and their pollinators and dispersers. The outcome of these plant–animal interactions may be affected by the number of flowers and fruits, as well as by the plants' local environment and by spatial associations among plants. In this study we mapped the spatial distribution of individuals in a population of the early flowering, fleshy‐fruited shrub Daphne mezereum, in a forest in boreo‐nemoral Sweden. For all mapped individuals we collected data on numbers of flowers and fruits and fruit removal, for three consecutive years. We analysed spatial associations among individuals, and the effects on reproductive performance and fruit removal of plant height, numbers of flowers and fruits, distance to forest edge, and neighbouring flower and fruit density. Our results show that the density of D. mezereum increases with increasing proximity to forest edge. The number of flowers produced, as well as fruit set and fruit removal, show the same positive relationship with increasing proximity to forest edges. We further show that individuals are aggregated up to distances of about 10 m. The flower production of neighbouring conspecific individuals within 10 m is negatively related to fruit set whereas the fruit production of neighbours is positively related to fruit removal. Our main conclusion is that the spatial distribution of D. mezereum affects reproductive success and fruit removal, which in turn has the potential to feed back to the spatial distribution pattern. Combining studies of reproduction with spatial analyses is important to advance our understanding of the dynamics of plant populations.

Investment Feasibility Analysis of Container Depot for Sustainable Development: A Case Study on PT. Pelabuhan Indonesia (Persero)

This research aims to analyze the feasibility of container depot investment as a sustainable development objective on the independent variables for the revenue of PT. Pelabuhan Indonesia (Persero) as the dependent variable. The study utilizes secondary data obtained from the company. A descriptive quantitative method and business feasibility analysis technique are employed, considering legal, market and marketing, economic and environmental, technical, management, and financial aspects. The analysis includes Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PP), and Profitability Index (PI). The research findings conclude that the feasibility analysis of the container depot contributes to the revenue of PT. Pelabuhan Indonesia (Persero) Regional Jawa. However, revenue sharing has minimal impact on the income of the Property Division and Miscellaneous Business Division compared to lease cooperation. The study suggests that the investment allocation through revenue sharing is more optimal and effective, provided the throughput remains stable. It highlights the importance of thorough analysis and stable market conditions for successful investment decisions in the container depot industry.
 Highlight:
 
 The feasibility analysis of container depot investment contributes to the revenue of PT. Pelabuhan Indonesia (Persero) Regional Jawa.
 Revenue sharing has minimal impact on the income of the Property Division and Miscellaneous Business Division compared to lease cooperation.
 The allocation of investment in the container depot can be more optimal and effective through revenue sharing, provided that the throughput conditions remain stable.
 
 Keyword:
 Investment, Container Depot, PT Pelabuhan Indonesia, Feasibility Analysis, Revenue Sharing.

Investment Strategies in the North American Market in the Post-Epidemic Era

This paper delves into the intricacies of investing in the North American market, particularly in the aftermath of the Covid-19 pandemic. While the pandemic has caused significant disruption and uncertainty across the global economy, it has also created new opportunities for investors in North America. The paper highlights sectors such as healthcare biotechnology, digital services, and e-commerce as offering significant growth potential due to increased adoption of digital technologies and heightened awareness of ESG factors. However, it also acknowledges the risks posed by geopolitical tensions, trade disputes, and industry chain issues. To succeed in the North American market, investors must adopt successful investment strategies that take into account these risks and opportunities. This may involve staying informed about political influences and economic conditions, building partnerships with local businesses and industry associations, and incorporating an assessment of social responsibility into investment decisions. Ultimately, the key to achieving sustainable growth in the North American market is to have a thorough understanding of market trends and challenges and to implement effective investment strategies that mitigate risks and capitalize on opportunities. By doing so, investors can achieve their investment objectives and position themselves for long-term success in a rapidly evolving economic landscape.

Evaluation of Various Machine Learning Methods to Predict Istanbul’s Freshwater Consumption

Planning, organizing, and managing water resources is crucial for urban areas and metropolitans. Istanbul is one of the largest megacities, with a population of over 15 million. The large volume of water demand and increasing scarcity of clean water resources make long-term planning necessary for this city, as sustained water supply requires large-scale investment projects. Successful investment plans require accurate projections and forecasting for freshwater demand. This study considers different machine learning methods for freshwater demand forecasting for Istanbul. Using monthly consumption data provided by the municipality since 2009, we compare forecasting accuracies of ARIMA, Holt-Winters, Artificial Neural Networks, Recursive Neural Networks, Long-Short Term Memory, and Simple Recurrent Neural Network models. We find that the monthly freshwater demand of Istanbul is best predicted by Multi-Layer Perceptron and Seasonal ARIMA. From the predictive modeling perspective, this result is another indication of the combined usage of conventional forecasting models and novel machine learning techniques to achieve the highest forecasting accuracy.

Impacts of startup founders’ personal and business networks on fundraising success by mediating fundraising opportunities: Moderating role of firm age

This study examines the effects of personal and business networks on fundraising successes through fundraising opportunities and analyze the differences in the effects of personal and business networks vary depending on the firm’s age groups. The first finding of this study is that both the startup founder's personal and business networks increase the funding opportunities for startups. Second, the number of successful investments increases as the number of fundraising opportunities from business networks increases. Third, the total effect of business networks is greater than the total effect of personal networks. Fourth, the difference in the effect of a founder's business and personal network on investment success in an old or mature startup firm is greater than the difference in effect in a new and nascent startup firm. This result means that young start-ups that do not yet have sufficiently developed business networks rely on private networks to attract investment, and that the two types of networks have similar effects on investment attraction success in the early stages of the startup business. This study contributes to the literature by showing that the effectiveness of fundraising through business networks is greater than those of personal networks and the impacts of social networks on fundraising successes vary depending on the age or growth stage of startups.