Investment Tool Research Articles

A Study on Performance Evaluation of Equity Fund Scheme of Aditya Birla Mutual Fund, Kotak Mahindra Mutual Fund ICICI Mutual Fund

In India the various investment avenues are provided by the capital market to the investors, to support them to invest in different investment tools and to make assured the profitable return. Mutual fund is subject to market risk and the mutual fund vary from fund to fund. In this context, the performance evaluations of mutual funds has become essential. Therefore, one of the main important issue is choosing profitable mutual fund for investment. This study, deals with the equity mutual funds that are offered by the various fund houses in India for investment. In the terms of risk return relationship this study predominantly selected mutual fund schemes is the main objective of this study

School District Housing Buyers and Educational Investment Returns: A Case Study of X District in Shanghai

As a dual carrier of educational resources and economic assets, school district housing has become an important tool for family educational investment and social class reproduction. This paper takes Shanghai's X District as an example to explore, from a sociological perspective, the purchasing behaviors of different groups regarding school district housing and their educational investment returns. It reveals the key role of school district housing in the accumulation of cultural and social capital. The study finds that high-income families, the middle class, and the working class in X District exhibit different demand tendencies when purchasing school district housing, reflecting goals of obtaining high-quality educational resources, expanding social capital, and achieving upward social mobility, respectively. The phenomenon of school district housing exacerbates the imbalance in the distribution of educational resources and limits social mobility.

Feature selection with annealing for forecasting financial time series

Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability. However, obtaining accurate and reliable predictions is challenging, noting that accuracy does not equate to reliability, especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies. To mitigate this complexity, this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning (ML) models. During the prediction process, selecting the relevant indicators is vital to obtaining the desired results. In the financial field, limited attention has been paid to this problem with ML solutions. We investigate the use of feature selection with annealing (FSA) for the first time in this field, and we apply the least absolute shrinkage and selection operator (Lasso) method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags. Boruta (BOR) feature selection, a wrapper method, is used as a baseline for comparison. Logistic regression (LR), extreme gradient boosting (XGBoost), and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks. The dependent variables consisted of daily logarithmic returns and trends. The mean-squared error for regression, area under the receiver operating characteristic curve, and classification accuracy were used to evaluate model performance, and the statistical significance of the forecasting results was tested using paired t-tests. Experiments indicate that the FSA algorithm increased the performance of ML models, regardless of problem type. The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification. FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification. None of the hybrid BOR models outperformed the hybrid FSA models. Lasso-based models, excluding the LR type, were comparable to the best models for six of the 10 datasets for classification. Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately, providing the field with a useful tool for investors.

An algorithmic approach to portfolio construction: A Turkish stock market case

The goal of building a diversified portfolio is to mitigate risks and manage risk-reward tradeoffs. Diversification is also crucial for ensuring the long-term success of a portfolio. Although passive investment has been on the rise in most developed markets, it has not gained wider acceptance in some emerging markets, such as Türkiye. This study proposes an approach to construct a diversified portfolio that serves as a passive investment tool in the context of the Turkish stock market. Despite the long history of equity markets in Türkiye, almost all available passive investment alternatives are prohibitively expensive, unlike those in developed markets. We design a simple methodology that addresses the essential components of building a successful portfolio while avoiding excessive fees. Additionally, we propose a dynamic balancing strategy that algorithmically adjusts the weight of each stock in the portfolio. We test this approach with historical data, demonstrating that it can achieve reasonable returns with minimal effort. Even when its performance does not beat the benchmark, the percentage deviation is still below the management fees charged for alternative investments.

Analysis of Federal Reserve Policy Rates and Bitcoin Prices: Pre and Post COVID-19 Differentiations

This study aims to conduct a comparative analysis of the relationship between policy interest rates declared by the Federal Reserve and Bitcoin prices, taking into account both pre and post-COVID-19 periods. Employing a robust analytical framework based on Vector Error Correction Model, scrutinizing each phase individually. In the pre-COVID-19 period, a notable VECM coefficient of -0.03 for Bitcoin variable implies shock stabilization after approximately 33 weeks, while the FED Policy Rate variable lacks significance in the return-to-balance mechanism. Conversely, the post-COVID-19 period unveils a substantial -0.08 VECM coefficient for Bitcoin, signaling a shock returning to balance in around 12.5 weeks. Furthermore, the FED Policy Rate exhibits a noteworthy -0.13 VECM coefficient, indicating shock stabilization after about 7.7 weeks. These findings suggest a growing acceptance of Bitcoin and cryptocurrencies as conventional investment tools, propelled by the circumstances of the COVID-19 period.

A Study on Mutual Fund Investors Awareness Regarding Factsheet of the Scheme

Mutual fund is an investment tool, getting popular day by day. Indian Mutual fund industry manage Rs. 64.71 trillion in July 2024 an increase of about 40% from July 2023. Individual investors hold 61.0% of overall Assets under Management in July 2024. Fact sheet of scheme depict performance of mutual fund scheme. Awareness of individual investors regarding contents of fact sheets is highly desirable and this awareness level is found out in a small group for getting information and suggesting appropriate action. Key Words: Mutual fund, Investors, Awareness, Factsheet

Estimating and planning hospital costs of public hospitals in Brazil.

While the estimate of hospital costs concerns the past, its planning focuses on the future. However, in many low and middle-income countries, public hospitals do not have robust accounting health systems to evaluate and project their expenses. In Brazil, public hospitals are funded based on government estimates of available hospital infrastructure, historical expenditures and population needs. However, these pieces of information are not always readily available for all hospitals. To solve this challenge, we propose a flexible simulation-based optimisation algorithm that integrates this dual task: estimating and planning hospital costs. The method was applied to a network of 17 public hospitals in Brazil to produce the estimates. Setting the model parameters for population needs and future hospital infrastructure can be used as a cost-projection tool for divestment, maintenance, or investment. Results show that the method can aid health managers in hospitals' global budgeting and policymakers in improving fairness in hospitals' financing.

Research on Influencing Factors and Strategies of Art Collection Investment Decision

The realm of art collection holds an irreplaceable position in the long-standing cultural heritage, showcasing the taste and status of the holders from ancient nobility to modern high-net-worth individuals. However, with the development of economic globalization, art collection is not merely a symbol of identity, but also a hot investment target sought after by numerous investors. In recent years, the market value of the global art industry has rapidly climbed, making art gradually an essential component of diversified investment portfolios for investors. The development of the art market has sparked widespread attention from various sectors, prompting scholars and companies to explore, through scientific methods and data analysis, how to maximize the investment returns of art collection. However, art differs significantly from other investment tools in the financial market, involving not only the intrinsic value judgment of the art itself but also complex market environments and individual judgment and psychological expectations of investors. Its unpredictability and unique value assessment system make art collection investment more challenging and research-worthy compared to other types of investments.

Cryptocurrency: A new player or a new crisis in financial markets? —— Evolutionary analysis of association and risk spillover based on network science

In recent years, cryptocurrency has attracted widespread research attention. Against the backdrop of financial globalization, the high volatility of cryptocurrency prices may pose a threat to the stability of the entire financial system. To explore the potential association and transmission of financial risks between cryptocurrency and traditional financial assets, this paper constructs a complex network using the Granger causality method and the DY spillover method. Based on data from 24 categories of assets or index returns from different markets, networks of association and risk spillover between cryptocurrency market and traditional financial markets are established under different time periods, the study covers the period from January 2015 to November 2023. Analyzing the mutual influence between cryptocurrency and traditional financial markets from the perspective of complex networks, and evaluating the market positioning, risk characteristics, and investment potential of cryptocurrency. Research findings indicate that cryptocurrency possesses not only monetary characteristics but also functions as an investment tool, significantly impacting the stability of traditional financial markets. Furthermore, different types of cryptocurrencies vary in market functions, risk characteristics, and investment potentials. Future research could further explore the nonlinear and dynamic associations between cryptocurrency and traditional financial markets, as well as the micro mechanisms and behavioral factors within cryptocurrency markets.

Green Bonds as A New Investment Tool For Sustainable Development In India

The present paper conceptualizes the meaning, need and importance of green bonds as an initiative towards investment tool and sustainable development in India. A green bond is a type of investment that provides financial support for particular projects related to sustainability issue of climate change and control. Indian Companies have incorporated investment in green bonds as a part of climate finance strategy. The ICMA issued principles of green bonds investment and voluntary process guidelines for issuing green bonds in 2021. The four principles are: use of proceeds, process for project evaluation and selection, management of proceeds and reporting. The SEBI has issued several rules for green bonds securities in India including disclosure requirements, monitoring and third-party review. The private bank of India i.e. Yes bank was the first issuer of green bonds in India in the year 2015. Indian green bond issuance has grasped a total of $21 billion as on February 2023. The scenario of green bond market in India for the year 2021 shows that the Utilities sector has more share of green bonds followed by corporates, Renewable Energy sector and Financials. The least share is with local government in India.

"Analyzing Infrastructure REITs in China: Development, Regulations, and Financial Innovation"

Infrastructure Real Estate Investment Trusts (REITs) in China have become a vital investment tool, reflecting the country's fast-growing infrastructure sector. Since the first batch of 9 public infrastructure REITs in 2021, the market has expanded to 29 REITs, valued at nearly RMB 1,000 billion. Supported by the government, REITs in China offer investors a chance to engage in real estate without owning physical properties, providing opportunities to diversify risks and achieve market liquidity. This paper analyzes the development, market dynamics, regulation, and financial innovation of REITs in China, offering insights for various stakeholders. It emphasizes REITs' potential in China's economic growth and suggests strategies for sustainable development in the infrastructure sector. This paper conducts an analysis of the development, market dynamics, regulation, operating structures, and financial innovation of REITs in China. By examining the global acceptance and influence of REITs, the paper suggests strategies for China to harness this investment tool for sustained development in the infrastructure sector.

ANALISIS HUBUNGAN HARGA EMAS DAN PASAR SAHAM MENGGUNAKAN MIXED-COPULAS

Gold is considered as a reliable investment tool for long-term savings and/or investment portfolios. Investors who do not like high risks trust gold to be a safe haven commodity that can mitigate the impact of any financial crisis. Gold and stocks are often used as substitutes for each other, where the two have an inverse relationship. Copula is used to capture the dependence relationship between world gold prices and the stock indexes. The data used are the stock index data for the JKSE Indonesia), PSE (Phillipines), Nikkei 225 (Japan), HSI (Hong Kong), and world gold prices (XAU) from January 1, 2014 to December 31, 2019. From the data, the ARMA-GARCH model is made to solve the problem of autocorrelation and heteroscedasticity. Then, the correlation between assets is calculated using the rank correlation. Furthermore, four pairs of data are made from each stock index with the price of gold. Next, the best copula and the estimated Value-at-Risk (VaR) are sought for each portfolio. From the results of the selecting of the best copula for each pair of data, it is found that gold can be a safe haven asset in the Hong Kong's stock market. The VaR results show that the biggest loss is in the Japanese market.

The Level of Awareness of Non-fungible Tokens as an Investment Tool in the Czech Republic

The Level of Awareness of Non-fungible Tokens as an Investment Tool in the Czech Republic

Жасыл экономиканы ынталандыру үшін мемлекеттік қаржылық реттеудің инновациялық құралдарын пайдаланудың әлемдік тәжірибесі

In the current reality, the issues of economic growth and the environment are cross-cutting and are becoming more and more relevant. As we know, the «resource-intensive economic model» is very limited in all directions, there are questions about the efficiency of using natural resources, these are definitely environmental risks, and this is a direct threat to the country's competitiveness. The strategic vector of sustainable development is aimed at the «green» economy model. In Kazakhstan, the transformation from one model to another requires significant investments. However, the difficulties of implementing such a transformation are related to the need for appropriate institutions and the implementation of both state and market support mechanisms. In this regard, consideration of foreign practices and its adaptation to our conditions will give a real impetus to the development of environmental investment tools. The purpose of the study is to assess the effectiveness of mechanisms to support financing of «green» projects in developed countries, in particular the USA, the EU, and China. As a result of the work carried out, the international practice of facilitating the financing of "green" projects was structured and an assessment of the effectiveness of the mechanisms used to maximize the concentration of private capital on such breakthrough projects of particular importance for the development of the country was carried out.

The time-varying effects of geopolitical risk on mutual fund risk taking.

Based on a time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV), this paper investigates the dynamic effects of geopolitical risk on mutual fund risk taking in China across three-time horizons and at three selected time points. Overall, the impulse responses are time-varying and we find a negative effect of geopolitical risk on mutual fund risk taking until 2015, with the short-term effect being the most pronounced, suggesting that when professional investors such as mutual fund managers are faced with the stock valuation uncertainty due to a geopolitical shock, they choose to reduce market risk exposures. After 2015, the short-term effect begins to diminish and gradually turns positive, which could be explained by the fact that with the increasing abundance and diversification of investment instruments, fund managers have more effective investment tools and more sophisticated trading strategies to hedge against geopolitical risk, rather than reducing market risk exposure. Further, we explore the heterogeneous effects of eight types of geopolitical risk and three types of mutual fund. The results indicate that the effect of geopolitical actions is stronger than that of geopolitical threats, while the effect of narrow geopolitical risk is stronger than that of broad geopolitical risk. Moreover, we find that the response of the risk taking of growth funds to the geopolitical risk is weaker than that of balanced and income funds.

Digital learning platform for cultural heritage: new normal tourism for community

PurposeThis study aimed to (1) elucidate the results of a social outcomes assessment and socio-spatial return analysis of a social innovation village (SIV) program in Thailand, (2) develop a social return on investment (SROI) tool for a digital learning platform of cultural heritage and (3) propose the application of an SROI calculator for policy decision-making.Design/methodology/approachSROI methodology was applied to analyze data from a sample of 110 individuals from the SIV in the Tha Ruea community, Nakhon Phanom province, Thailand, which is renowned for musical instrument making, to assess the social innovation objectives.FindingsImprovement in the grassroots economy led to increased income, translating to assessment outcomes and social values within the tourism community. Social and digital media platforms link physical and digital spaces to facilitate interactive learning and online tourism in the community.Research limitations/implicationsSince the COVID-19 pandemic led to a pronounced economic decline, especially in the tourism sector, the results only partially capture the actual benefits of SROI. To address the challenge of adapting the ecosystem of local tourism into the alternative market, training programs are recommended, including upskilling and new-skilling courses offered by state agencies, to support this technological trend.Originality/valueThis research illustrates how an integrative platform between digital technology and cultural heritage can address the significant shift in tourism behavior and promote a new era of cultural heritage tourism in virtual and physical spaces.

Investment Decision Factors of Non-Fungible Tokens in the Czech Republic: Survey

Abstract Non-fungible Tokens (NFTs), represent a revolution in the digital ownership paradigm. NFTs are a kind of digital asset built on blockchain technology, most commonly the Ethereum blockchain, that validate the uniqueness and ownership of a unique digital item in question. Each NFT carries specific information or attributes that make it original and non-fungible. Unlike cryptocurrencies like Bitcoin or Ethereum, which are identical to each other, non-fungible tokens cannot be exchanged on a like-for-like basis making them non-fungible. NFTs are traded for cryptocurrencies via online trading platforms. Investment in NFTs can present a risky situation due to the large volatility of the assets in a quite short time. This article focuses on identification of key aspects that influence decision making process of potential investors who are considering buying non-fungible tokens as an investment tool in the Czech Republic. From the point of view of investment decision-making, the primary factors appear to be the expected income from the investment, its payback period, and the risk that the investor undertakes. It has been proven that there is a degree of dependence between gender and the mentioned decision-making factors. The research showed that men are more inclined to make decisions based on expected returns, while women are more likely to make decisions based on perceived risk.

Revolutionizing Student Accommodation: A MERN-Based Rental System For Seamless Property Management

This web application facilitates the registration of individual homes or apartments to streamline the process of finding ideal rental properties. Users can conveniently search for their next rental in targeted areas through a user-friendly interface. The platform is designed to cater to diverse real estate needs in India, including property buying, selling, and leasing. It serves as a valuable tool for investment by maintaining a comprehensive database of various properties and agent information. Not only does it assist users in accessing updated property information globally, but it also enables agents to manage and update their listings. The platform alleviates the complexities associated with traditional property search methods by offering a simplified online form, minimizing the need for individual agent communication, appointment scheduling, and time coordination

EXPLORING THE DYNAMICS BETWEEN MUTUAL FUND AND INVESTORS BEHAVIOUR

The basic purpose is to know about the Mutual Fund Industry and to know the behavior of the Indian Investors regarding different investment tools. This project will provide me t he better platform to understand the History, Growth and various other aspects of Mutual Fund. It will also help me to understand the behavior of Indian investor regarding different inve stment tools. SEBI is the apex regulatory of capital markets. SEBI has enacted the SEBI (mutual fund) Regulations, 1996, which provides the scope of the regulation of the mutual fund in India. All Mutual funds are required to be mandatory registered with SEBI. The structure and formation of mutual funds, appointment of key functionaries, operation of the mutual funds, accounting and disclosure norms, rights and obligations of functionaries and investors, investment restrictions ,compliance and penalties are all defined under the SEBI regulations. Mutual funds have to send half yearly compliance reports to SEBI, and provide all information about their operations

Exploiting green energy potential via FinTech: The role of DLT-based crowdfunding in PV and ESS investments

This article explores the interplay of green energy potential and innovative financial technology (FinTech). To achieve this, an in-depth study on the technological, economic, and environmental aspects of residential-scale solar energy and storage investments was performed. As the first part of the study, residential-scale solar energy and associated energy storage investments were modeled in the assessed countries. As the next step, the effects of Distributed Ledger Technology (DLT) enabled crowdfunding on the modeled investments were highlighted with a Proof-of-Concept (PoC) DLT-enabled investment tool. As the last step, the Life Cycle Assessments (LCAs) of the investments were carried out for each of the assessed countries in order to provide better investment decisions to various investors. The results of this research indicate that with the integration of novel Fintech tools, the Levelized Cost of Storage (LCOS) values of the assessed investments may be reduced by up to 58%. Meanwhile, the LCA results highlighted that solar silicon production and battery cell manufacturing have the highest LCA results, accounting for 19.1 and 63.8 kgCO2eq/kWh respectively. Meanwhile, the transportation sector had an emission of 9 kgCO2eq/kWh. Thus, with cleaner manufacturing and transportation, the emissions from PV and ESS investments may be reduced drastically.