Diversified Portfolio Of Stocks Research Articles

A Research Perspective on Mutual Funds

This research explores mutual funds, which are investment pools where many people combine their money to buy a diversified portfolio of stocks, bonds, or other securities. The study investigates how mutual funds work, their benefits, and potential risks. By understanding these aspects, investors can make informed decisions about participating in mutual funds to achieve their financial goals. This research aims to provide a straightforward overview to help individuals navigate the world of mutual fund investments. However, a long-term investor has to consider other factors associated with such mutual funds (like asset under management, expenses ratio, number of stocks, and experience of the management) to finalise the selection of mutual funds. In this research paper, an attempt has been made to identify the relationship among the performance of selected equity mutual funds and the parameters considered by the investors for selecting the fund. 't-statistic’ has been used to identify such relationship. This research shows that there is no relationship between trailing return of any kinds of Equity mutual funds and the selected parameters by the investors. However, number of stocks and experience of management have a little impact on the rolling return of equity large cap mutual funds and Assets under management has a little impact on rolling return in case of mid-cap mutual fund only. Key Words: Investment Pools, Asset management companies, Mutual Funds, Trailing Return,

Machine Learning Approach for Predicting the Net Asset Value (NAV) of Mutual Funds based on Portfolio Holdings

Mutual funds, a cornerstone of modern investment portfolios, offer investors a diversified and professionally managed approach to financial growth. These investment vehicles collect small amount from multiple investors and invests the collective investment further in diversified portfolio of stocks, bonds, or other securities. Mutual funds are characterized by their net asset value (NAV), which represents the per-share value of the fund's assets minus its liabilities. Investors benefit from the expertise of seasoned fund managers who make investment decisions based on their analysis of market trends and company performance. By providing an accessible and diversified investment option, mutual funds have become a popular choice for both novice and seasoned investors seeking to achieve their financial goals. Apart from that, the role of AI is increasing exponentially in various areas. Such AI based tools and algorithms are utilized by various MFs expert to identify the suitable investment portfolios. This paper proposes a technique to analyse the performance of the different stock's portfolios based on the past data. Combining the isolated analysis of different stocks portfolios may be utilized to predict the net asset value prediction (NAV) of MFs. The experiments carried out in this research work uses linear regression analysis to analyse the stock's performance at sub level of the MFs. A case study for the Axis Bluechip fund's is also carried out by using the proposed ML approach. The Yahoo Finance dataset is used for the case study. Previously, machine learning algorithms like linear regression (LR), decision tree regression (DTR), and multivariate regression (MR) are taken to predict and analyse the dataset. These algorithms calculate the NAV of the mutual fund with good accuracy and analyse the best result for the test dataset. The various models are being created from different portfolios to analyse the NAV of the mutual funds. This work proposed hierarchical method to perform the linear regression analysis for the NAV predicting. This work uses R-Square (R2) statistical measures to determine the goodness of models. The obtained R2 is 0.86, implying that the created models have good accuracy and performance on the dataset.

A Study on Performance Evaluation of Balanced Investment Schemes Offered by Selected Asset Management Companies

Investing in mutual funds and diversifying the investment portfolio are two critical strategies for achieving long-term financial success. Mutual funds pool money from multiple investors to invest in a diversified portfolio of stocks, bonds, or other assets. This diversification helps reduce the impact of individual stock or bond price fluctuations, thus mitigating market risk. Mutual funds are managed by experienced fund managers who conduct extensive research and analysis. These professionals make informed investment decisions, saving investors from the need to constantly monitor the market and individual securities. Diversification allows investors to balance risk and return according to their risk tolerance. By spreading investments across different asset classes and risk levels, individuals can achieve their desired risk-return profile. By harnessing the expertise of professional fund managers and diversifying across various assets, investors can navigate market fluctuations with confidence while working toward a more secure financial future. It also contributes to the field of finance by offering a comprehensive analysis of the performance of balanced mutual fund schemes, employing a range of evaluation tools, and emphasizing the critical interplay between risk and return in the investment landscape. Keywords: Balanced mutual fund scheme, Performance evaluation, Asset Management Companies (AMCs), evaluation metrics.

To Study Inflation Impact in Malaysia Against the Stock Portfolio Investment in Bursa Malaysia

Background: The rising of the inflation had triggered to need to choose the right investment to beat the inflation. The study will focus on the potential impact in investing in diversified stock portfolio within the stock market exchange in Bursa Malaysia. Aim: The goal of the research is target to understand the potential existence of the positive correlation and relationship between the KLSE market index and the inflation rate through the CPI. Method: The quantitative analysis method will be conducted to study the historical data based on the past 20 years’ trend of data from both inflation rate through CPI as well as the KLSE market index. The average yearly growth for the past 20 years performance will also be assessed to understand the comparison on the growth of the inflation and market index. Findings: The findings had observed that the investment for the portfolio investment representing through the KLSE market index had not been sharing the significant positive correlation and relationship with the inflation rate on CPI. However, the growth rate on average had obsvered higher rate of return on KLSE market index which is 5.666% over the inflation rate through CPI that recorded only 2.018% indicating that the investment in diversified stock portfolio will obsvered greater return to cover for the inflation rate in Malaysia.

Measuring and Managing the Opportunity Cost of Downside Risk Protection

Portfolio insurance targets the elimination of losses in excess of a predefined threshold while allowing for access to the upside potential of an underlying risky asset. This article studies the implications of the choice of the underlying asset on the frequency and magnitude of floor breaches and on the loss of performance associated with the protection against downside risk; it considers in particular the use of a diversified portfolio of stocks as opposed to a single stock. The authors find that gap risk is more prominent when insurance is applied to a stock than to a portfolio, but it can be reduced by implementing time-invariant portfolio protection or by letting the multiplier decrease when volatility rises. However, option-based insurance with dynamic option replication has a large probability of breaching, no matter the underlying asset. Among diversified portfolios, minimum variance portfolios minimize the opportunity cost of insurance, but the ranking of diversified portfolios in terms of long-term returns does not appear to be disturbed by insurance. <b>Key Findings</b> ▪ Floor breaches in a portfolio insurance strategy occur more frequently and larger if the risky asset of the strategy is a single stock than if it is a diversified portfolio of stocks. ▪ Option-based portfolio insurance with dynamic replication of the option involves large gap risk, whether the risky asset is a single stock or a diversified portfolio. ▪ The opportunity cost of insurance against downside risk, measured as the long-term return spread between the insured portfolio and its underlying risky asset, is minimized by taking the risky asset to be the minimum variance portfolio.

Stock portfolio selection based on investors’ risk preference

Stock portfolio investment gives investors the opportunity to engage in diversification among stocks which helps to achieve higher financial risk-adjusted return. A properly diversified stock portfolio should include stocks that have different economic variable such as interest rate, exchange rate and share price. It helps investors to achieve higher risk-adjusted return, nevertheless there are investors who love risk known as Risk Premium investors and Risk Averse investors who prefer lower risk. The risk value of stocks acts as an indicator in selecting stocks to be included in a portfolio. Risk value of stock portfolios is used in selecting stock portfolio that follows investors’ risk preference. This study forecast share price by using Geometric Brownian Motion and hence use the Variance Covariance to calculate Value at Risk of each stock and stock portfolio in future investment. The stock portfolios in this study are based on five sectors which are industrial-product, consumer, construction, technology, and trading and services. Thus, there are five stock portfolios that are produced for each type; Risk Averse and Risk Premium investors. Using the Value at risk of each stock portfolio, it is found that Stock portfolio from the Industrial Product Trading and Service sectors is the most suitable for Risk Averse and Risk Premium investors respectively. This study may serve as a guide for investors in creating and choosing stock portfolios for future investment based on their risk preference and also forecast its Value at Risk.

DOES THE CHOICE OF THE MULTIVARIATE GARCH MODEL ON VOLATILITY SPILLOVERS MATTER? EVIDENCE FROM OIL PRICES AND STOCK MARKETS IN G7 COUNTRIES

In this paper, we employ asymmetric multivariate GARCH approaches to examine their performance on the volatility interactions between global crude oil prices and seven major stock market indices. Insofar as volatility spillover across these markets is a crucial element for portfolio diversification and risk management, we also examine the optimal weights and hedge ratios for oil-stock portfolio holdings with respect to the results. Our findings highlight the superiority of the asymmetric BEKK model and the fact that the choice of the model is of crucial importance given the conflicting results we got. Finally, our results imply that oil assets should be a part of a diversified portfolio of stocks as they increase the risk-adjusted performance of the hedged portfolio.Keywords: Asymmetry, Multivariate GARCH, Stock market, Oil price, Volatility SpilloverJEL Classifications: C32, F3, G15, Q4DOI: https://doi.org/10.32479/ijeep.9469

Portfolio Risk and Return with a New Simple Moving Average of Price Change Ratio

Cluster analysis is a commonly used technique by investors to create a diversified portfolio. The approach aims at maximizing returns for a tolerable degree of risks. To diversify effectively, investors use similarity measures to enable clustering. Traditional price indexes, such as Return on Asset and Return on Equity, are known to perform inconsistently in identifying acceptable clusters. Our study proposes a novel indicator, Simple Moving Average of Price Change Ratios (SMA-PCR-N), for use as a similarity measure. It is an adjusted version of the traditional Simple Moving Average (SMA) calculated based on stocks’ closing price over a number of time periods, to observe price trend and potential changes. Instead, the SMA-PCR-N considers the daily opening prices, the closing prices and the average price of stocks. We demonstrate the use of k-means clustering with SMA-PCR-N to create a diversified stock portfolio. Data on approximately three hundred stocks were retrieved from the Stock Exchange of Thailand for the fiscal years 2015–2017, and were used experiments to evaluate the effectiveness of our SMA-PCR-N diversification approach. The results show that SMA-PCR-N based portfolios gave higher returns than portfolios created based on SMA clusters, in most cases.

ИНВЕСТИЦИОННЫЙ ПОТЕНЦИАЛ СЕКТОРНЫХ БИРЖЕВЫХ ФОНДОВ SPDR

The paper work is devoted to the study of the investment potential of sectoral exchange-traded funds SPDR, the advantages of investing in shares of such funds compared to investing in shares of companies or in units of mutual funds are examined, the dynamics of market prices of shares of sectoral stock funds in both favorable and crisis periods on the stock market has been studied, identified the most attractive sector exchange-traded funds for investments. The studies were conducted on the basis of publicly available statistical material on the market prices of the SPDR sector exchange-traded funds for the period from January 2007 to November 2019. Data processing was carried out using statistical methods, a comparison method, a graphical representation of the material. As a result of the studies, it was found that the shares of exchange-traded funds have a number of advantages over ordinary stocks of corporations and over the shares of ordinary mutual funds and are attractive enough for investment purposes. Among the main investment advantages of ETF securities are the following: the purchase of ETF securities means the acquisition of an already diversified portfolio of stocks, which reduces the risk of losses and provides some protection in case of an unforeseen drop in the price of one of the portfolio components; the stocks of exchange-traded funds are sold and bought during the whole trading session; securities of exchange-traded funds can be bought using a brokerage loan and short-selling; there are no fees typical for mutual funds when buying and selling securities, and the fact that an investor buying securities of an exchange-traded fund always knows which shares are in the portfolio of this fund. It was found, that stocks of exchange-traded funds the Select Sector SPDR Fund - Health Care (XLV) and Select Sector SPDR Fund - Technology (XLK) are the most attractive for investment among all others sectoral exchange-traded funds of the SPDR group. At the same time, Select Sector SPDR Fund - Technology (XLK) stocks have the highest growth potential in favorable periods in the stock market and moderate risk in crisis periods; stocks of Select Sector SPDR Fund - Health Care (XLV) have a fairly high growth potential in favorable periods in the stock market and low risk in times of crisis.

International portfolio diversification

In this paper, several empirical tests are applied to evaluate: 1) the effectiveness of international diversified stock portfolios in bull and bear markets; 2) the potential gains of diversification strategies for both developed and developing country investors. The results suggest that the correlation structure of global equity market returns is not constant over time. Correlation coefficients rise during economic crisis and decline again when the crash is over, but do not drop back to the original level as before the crisis. The total risk of foreign investment can be decomposed into the local stock return risk and the currency risk. We also find that US investors accrue potential diversification gains in terms of return enhancement. However, the decrease on return offsets the decrease on standard deviation, making the performance of international portfolios no better than that of domestic investment.

International portfolio diversification

In this paper, several empirical tests are applied to evaluate: 1) the effectiveness of international diversified stock portfolios in bull and bear markets; 2) the potential gains of diversification strategies for both developed and developing country investors. The results suggest that the correlation structure of global equity market returns is not constant over time. Correlation coefficients rise during economic crisis and decline again when the crash is over, but do not drop back to the original level as before the crisis. The total risk of foreign investment can be decomposed into the local stock return risk and the currency risk. We also find that US investors accrue potential diversification gains in terms of return enhancement. However, the decrease on return offsets the decrease on standard deviation, making the performance of international portfolios no better than that of domestic investment.

Agent based Stock Clustering for Efficient Portfolio Management

ABSTRACT This research paper proposed agent based framework for portfolio management using non-hierarchical clustering method. The framework included various agents such as data agent, clustering agent, ranking agent, portfolio manager and user agent. The data agent collected financial ratio of Nifty 50 companies from financial database. Clustering agents generated clusters and DB index computed to find optimum cluster size of each method. Validation agent evaluated the performance of k -means, k -medoids and fast k -means using intra-class inertia. Clusters generated by k -means used for investment and portfolio analysis using Markowitz model. This research helped to assemble a diversified portfolio of stocks with the use of clustering Keywords Clustering, Data mining (DM), Davis-Bouldin (DB) Index, Dunn Index, k -means, k -medoids, Partitioning Around Medoids(PAM), Silhouette index 1. INTRODUCTION Data mining is a process of automatically discovering knowledge and predicting future trends from large financial markets. It creates opportunities for companies to make proactive and knowledge-driven decision in order to gain a competitive advantage. There are varieties of DM techniques available over past decades that include classification, similarity search, cluster analysis, association rule mining. Data mining techniques are also widely applied in number of financial areas, including predicting stock prices, predicting stock indices, portfolio management, portfolio risk management, trend detection, designing recommender [27, 28]. Portfolio management is one of major problem in financial domain. In today‟s competitive financial environment, an investor wants to earn maximum profit from his assets. An investor considers an investment in securities faces with the problem of choosing from among a large number of securities. He confuses in which security he has to invest. It depends upon the risk-return characteristics of individual securities. He selects most desirable securities and likes to allocate his funds over this group of securities. Again, he faces with the problem of deciding which securities to select and how much to invest in each. The investor chooses the optimal portfolio taking into consideration the risk and return characteristics of all possible portfolios. The research work describes about an agent based framework for portfolio management using non-hierarchical clustering methods. The proposed framework consist of various agents such as data agent, clustering agent, ranking agent, user agent and portfolio manager. This framework assists investors in strategic planning and investment decision-making. This research work can help to assemble a diversified portfolio of stocks with the help of clustering and also will help investor community in specific and in turn it helps the society and economy in general for better allocation of wealth. In this research paper,

Portfolio Choice with State-Dependent Adjustments: Analyzing Leveraged Positions Without Parametric Assumptions

In this paper, we discuss a new method for solving the portfolio choice problem which models state-dependent quantity adjustment using boundary crossing events. This new method has several advantages: first, we can solve for the optimal portfolio weights without parametric assumptions, by deriving them directly from the data. Next, we can describe the full distribution of the portfolio's value, not just its moments. Finally, we can easily deal with important practical issues, such as transaction costs, leveraged positions and no-ruin conditions, or the cost of margin financing. In particular, the method allows us to analyze leveraged positions in discrete time under zero ruin probability. Analyzing historical stock data suggests that historically, the log-optimal portfolio was a not too extensive leveraged purchase of a diversified stock portfolio, therefore leveraging does not necessarily imply risk-seeking behavior. We also show that depending on how much weight we allocate to this diversified stock portfolio, the downside risk measured as 5% VAR of the portfolio's value may be decreasing or increasing over time. Consequently, an objective functions which incorporates the VAR of the portfolio's value or operate with VAR constrains result in horizon-dependent portfolio weights. We also present some evidence suggesting that the log-optimal portfolio weights are time-dependent. Finally, irrespective of what weight we chose for the diversified stock portfolio, it is log-optimal to reduce exposure to the stock market if the predicted volatility is high, and increase it in low volatility periods.

Visualization of a stock market correlation matrix

This paper presents a novel application of Neighbor-Net, a clustering algorithm developed for constructing a phylogenetic network in the field of evolutionary biology, to visualizing a correlation matrix. We apply Neighbor-Net as implemented in the SplitsTree software package to 48 stocks listed on the New Zealand Stock Exchange. We show that by visualizing the correlation matrix using a Neighbor-Net splits graph and its associated circular ordering of the stocks that some of the problems associated with understanding the large number of correlations between the individual stocks can be overcome. We compare the visualization of Neighbor-Net with that provided by hierarchical clustering trees and minimum spanning trees. The use of Neighbor-Net networks, or splits graphs, yields greater insight into how closely individual stocks are related to each other in terms of their correlations and suggests new avenues of research into how to construct small diversified stock portfolios.

Oil price fluctuation, volatility spillover and the Ghanaian equity market: Implication for portfolio management and hedging effectiveness

This study attempts to contribute to the literature on stock markets and energy prices by examining the dynamic volatility and volatility transmission between oil and Ghanaian stock market returns in a multivariate setting using the recently developed VAR–GARCH, VAR–AGARCH and DCC–GARCH frameworks. In turn, the models' results are used to compute and analyze the optimal weights and hedge ratios for oil-stock portfolio holdings. For comparison purposes and to put the paper more in the perspective of West Africa, the Nigerian stock market is also included in the analysis. Our findings point to the existence of significant volatility spillover and interdependence between oil and the two stock market returns. While spillover effects are stronger for Nigeria, the transmission of volatility is much more apparent from oil to stock than from stock to oil in the case of Ghana. Also, the study demonstrates evidence of short-term predictability in oil and stock price changes through time and reveals that conditional volatility changes more rapidly as result of substantial effects of past volatility rather than past news/shocks for all market returns. Moreover, we show that there is a slightly more effective hedge in the two stock markets under the DCC–GARCH framework (our preferred model) compared to the other two models, although hedging effectiveness is much greater for Ghana. On the whole, our findings for optimal hedge ratios are consistent with other studies and particularly the view that oil assets should be an integral part of a diversified portfolio of stocks and suggest that a better understanding of volatility links is crucial for portfolio management in the presence of oil price risk. Finally, the existence of multivariate asymmetric effects and dynamic conditional correlations as revealed by the VAR–AGARCH and DCC–GARCH models make it clear that the assumptions of symmetric effects and constant conditional correlations are not supported empirically.

Commodities and Their Roles for MENA Investors

Commodities investing and trading has thrived since essentially the dawn of civilization. But the investment role of commodities in a portfolio has evolved substantially in recent decades. Commodities strategies can play a major and multidimensional role in a diversified portfolio of stocks, bonds, and private market investments by repositioning risks, providing meaningful diversification, and adding value.

The Decision Support System for Hierarchical Portfolio Management

Securities investment in the capital market is one of the key factors in the socialism economic system.In order to optimize the return on investment, investors will build portfolio of their securities. The decision support system is intentionally developed for investment to select the securities in the Thai stock market, from a properly diversified stock portfolio, by using multi-criteria decision making technique. The hierarchical methods like AHP make complex decisions become easy make it easy in multi-criteria chosen. The system lets the user to create a rating for each criterion, calculates a relative weight to each criterion and then ranks securities listed on the stock market. Finally, the top rank security are collected to the portfolio. For further study, a decision support system for the capital investment planning and analysis to resolve, how much securities, bonds, gold, properties, and cash in hand needed to be invested, will be developed. involving an interface between the individual and computer, from problem formulation to solution (4). The proposed decision support system is developed for investment decision-making to select the securities in the Thai stock market, from a properly diversified stock portfolio. The multi-criteria selection which is applied to optimize the return on investment is financial ratio including: operating profit margin, debt to equity, price earnings ratio, and price to book value. The measurement of these ratios is usually used for indicating the performance of the enterprises. An interactive computer-based system includes three major parts: database; model base; and user interface.

Analysis of cross-correlations between financial markets after the 2008 crisis

We analyze the cross-correlation matrix C of the index returns of the main financial markets after the 2008 crisis using methods of random matrix theory. We test the eigenvalues of C for universal properties of random matrices and find that the majority of the cross-correlation coefficients arise from randomness. We show that the eigenvector of the largest deviating eigenvalue of C represents a global market itself. We reveal that high volatility of financial markets is observed at the same times with high correlations between them which lowers the risk diversification potential even if one constructs a widely internationally diversified portfolio of stocks. We identify and compare the connection and cluster structure of markets before and after the crisis using minimal spanning and ultrametric hierarchical trees. We find that after the crisis, the co-movement degree of the markets increases. We also highlight the key financial markets of pre and post crisis using main centrality measures and analyze the changes. We repeat the study using rank correlation and compare the differences. Further implications are discussed.

How Valuable are the Commodity Assets to Investors?

This study examines how commodity assets affect investors. Our main findings can be summarized as follows. First, the Sharpe ratio of commodity indexes is higher than that of stocks and bonds over the last ten years. Second, commodity (traditional) assets are positively (negatively) related with inflation, which implies that commodity assets provide better hedge against inflation. Third, a break-even analysis indicates that including commodity assets in diversified portfolio of stocks and bonds enhances the performance of the portfolio. Fourth, the numeraire portfolio approach of Hentschel et al.(2002) shows that, to some extent, there are gains by including commodity assets in a portfolio of stocks and bonds. For example, transaction cost of 0 to 92 basis points would keep a log-utility investor from including the Rogers International Commodities Index (RICI) in one’s portfolio. In sum, commodity assets enhance the performance of portfolio, and the performance gain is especially pronounced during the bear stock market.

Oil prices, stock markets and portfolio investment: Evidence from sector analysis in Europe over the last decade

This article extends the understanding of oil–stock market relationships over the last turbulent decade. Unlike previous empirical investigations, which have largely focused on broad-based market indices (national and/or regional indices), we examine short-term linkages in the aggregate as well as sector by sector levels in Europe using different econometric techniques. Our main findings suggest that the reactions of stock returns to oil price changes differ greatly depending on the activity sector. In the out-of-sample analysis we show that introducing oil asset into a diversified portfolio of stocks allows to significantly improve its risk-return characteristics.