Investigating financial opportunities for traditional clothing industry in South Asia based on an analysis of internationally diversified portfolio using ARCH and GARCH models

This paper investigates the benefits of forming an internationally diversified portfolio in the stock markets of Bangladesh, India and Pakistan using the stock market indices data from April 2013 to March 2020. The portfolio comprises of three stock market indices from Pakistan, India and Bangladesh. The goal is to identify financial opportunities for traditional clothing industry in South Asia. Bangladesh, India and Pakistan are neighbouring countries in South Asia. Tradition, culture and specific ethnic elements influence traditional clothing in the case of the selected country cluster consisting of Bangladesh, India and Pakistan. Our empirical results indicate that internationally diversified portfolio does not reduce the risk due to global market integration in the background. Furthermore, ARCH and GARCH models reveal that large change in conditional variance is followed by large changes in conditional variance whereas small change in conditional variance is followed by small changes in conditional variance.

How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature

Using extensive and comprehensive databases to select a subset of research papers, we aim to critically analyze previous empirical studies to identify certain patterns in determining the optimal number of stocks in well-diversified portfolios in different markets, and to compare how the optimal number of stocks has changed over different periods and how it has been affected by market turmoil such as the Global Financial Crisis (GFC) and the current COVID-19 pandemic. The main methods used are bibliometric analysis and systematic literature review. Evaluating the number of assets which lead to optimal diversification is not an easy task as it is impacted by a huge number of different factors: the way systematic risk is measured, the investment universe (size, asset classes and features of the asset classes), the investor’s characteristics, the change over time of the asset features, the model adopted to measure diversification (i.e., equally weighted versus optimal allocation), the frequency of the data that is being used, together with the time horizon, conditions in the market that the study refers to, etc. Our paper provides additional support for the fact that (1) a generalized optimal number of stocks that constitute a well-diversified portfolio does not exist for whichever market, period or investor. Recent studies further suggest that (2) the size of a well-diversified portfolio is larger today than in the past, (3) this number is lower in emerging markets compared to developed financial markets, (4) the higher the stock correlations with the market, the lower the number of stocks required for a well-diversified portfolio for individual investors, and (5) machine learning methods could potentially improve the investment decision process. Our results could be helpful to private and institutional investors in constructing and managing their portfolios and provide a framework for future research.

Method and information technology to research the component architecture of products to justify investments of high-tech enterprise

The development of enterprises in strategic industries depends on funding made innovative products that are in demand in the markets for high-tech products. The interest of investors depends on the innovation and competitiveness of the products that the enterprise can produce. The enterprise should make a new, diversified portfolio of orders to attract funding from potential investors. The innovativeness of the product is determined by the novelty of the components in its composition. Therefore, the pressing challenge is to study the innovation of high-tech products based on their component architecture. It makes it possible for investors to assess the possibility of enterprise financing while making a promising diversified portfolio of orders. The study develops a method to justify investments into the new orders that are based on the research of the component architecture of the complex product. The tasks analyzed the product component architecture innovation and investment attractiveness, justify and select the diversified portfolio of orders, simulate and assess orders portfolio feasibility are stated and solved. The paper proposes the component method that makes it possible to evaluate the architecture of the new product in terms of innovation and investment attractiveness. The research of innovation is conducted depending on the composition of the components in the architecture of the whole product. These components can be either new that require a new cycle of creation, or “old” ones, taken from previous experience with the possible adaptation to the technical requirements of the new product. By using the proposed multifactor planning of the experiment, the possible options are considered and the main indicators of the new product are assessed: investment attractiveness, costs, timelines, and risks of order fulfillment. Using lexicographic ordering of alternatives the compromised selection of the optimal option in terms of limited capabilities of the enterprise is conducted. To optimize the diversified portfolio of orders the method of integer (Boolean) programming is used. Investment attractiveness is used as a target function. The restrictions consider allowable costs, timelines, and risks of the orders portfolio fulfillment. In the last part of the paper, the method of simulation agent modeling in a form of applied information technology is used to assess the timeline for order fulfillment and the impact of risks on the feasibility of the diversified portfolio of orders. The novelty of the results is related to the justification of the choice of a diversified portfolio of orders, which in contrast to the already existing approaches, is based on the advanced component architecture of complex products and the simulation of orders portfolio selection considering innovation and investor interests. The proposed method and information technology are planned for the future development of an enterprise that makes it possible to assess the competitiveness of products, as well as the possibility to attract funding.

Optimized Portfolios: All Seasons Strategy

Our study explores the efficient frontier of optimal investment, taking behind the Markowitz’s theory, while advocating a diversified portfolio to reduce risk. To perform it, six portfolio models are proposed, and its formation are made by a solver, where the selected solving method is the GRG Nonlinear engine for linear solver problems. Our main goal is to design portfolios that resists to financial crisis but at the same time persists in a wealthy period. We analyze the decade where we assisted to two crashes (2000–2010) and a semi-decade where we assist to a wealthy period (2011–2018). The assets used are varied, such as Equities indexes form various countries, sector equities, bonds, commodities, EURUSD exchange and VIX. Results show that the GRG Nonlinear engine is powerful, providing excess returns in all six models.

Evaluating Various Portfolio Optimization Strategies using High-Dimensional Covariance Matrix Estimators

We compare the performance of multiple covariance matrix estimators for the purpose of portfolio optimization. This evaluation studies the ability of estimators like Sample Based Estimator (SCE), Ledoit-Wolf Estimator (LWE), and Rotationally Invariant Estimators (RIE) to estimate covariance matrix and their competency in fulfilling the objectives of various portfolio allocation strategies. In this paper, we have captured the effectiveness of strategies such as Global Minimum Variance (GMVP) and Most-Diversified Portfolio (MDP) to produce optimal portfolios. Additionally, we also propose a new strategy inspired from MDP: Most-Diversified Portfolio (MMDP), that enables diversification upon minimizing risk. Empirical evaluations show that by and large, MMDP furnishes the maximum returns. LWE are relatively more robust than SCE and RIE but RIE performs better under certain conditions.

MAPS: Multi-Agent reinforcement learning-based Portfolio management System.

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing return. However, these models often fail to consider and adapt to the continuously changing market conditions. In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS). MAPS is a cooperative system in which each agent is an independent "investor" creating its own portfolio. In the training procedure, each agent is guided to act as diversely as possible while maximizing its own return with a carefully designed loss function. As a result, MAPS as a system ends up with a diversified portfolio. Experiment results with 12 years of US market data show that MAPS outperforms most of the baselines in terms of Sharpe ratio. Furthermore, our results show that adding more agents to our system would allow us to get a higher Sharpe ratio by lowering risk with a more diversified portfolio.

Diversified Portfolio Model of Adaptability: A Natural History Perspective

Using a natural history perspective, the diversified portfolio model (DPM) of adaptability developed by Chandra and Leong (2016) is described in this article as a representation of the latter author’s life work in researching diversity and adaptability. The primary thesis of the DPM is that a diversified portfolio of activities, roles, and experiences will lead to greater adaptability in life. The DPM was intended to address a gap in the literature by illuminating the antecedents of adaptive processes that have been studied in current models, including those for self-complexity, risk and resilience, and self-efficacy. This description is followed by an example of the application of the DPM in academic careers. The mechanisms underlying the DPM are then discussed in relation to mental models and mindfulness. Next, the negative effects of nondiversification are illustrated with examples of workaholism and loneliness. Finally, the DPM is linked to the author’s own diversified portfolio of roles and activities to illustrate its positive impact.