Asset Selection Research Articles

Crypto Price Prediction as an Investment Opportunity: An Empirical Study of Three Global Cryptocurrencies

Aims: To determine the investment feasibility of evaluating cryptocurrency opportunities as an investment product under the possibility of crypto price valuation selection. The study analyzes three indicators: asset price returns in unrelated time, selection of cryptocurrency investment price weights, and crypto price forward contract opportunities on ARCH-GARCH probability forecasts in the selection of price valuations by individual cryptocurrency prices.
 Study Design: Quantitative research.
 Place and Duration of Study: The period from 10 September 2021 to 4 September 2022 using sample data downloaded from the Yahoo Finance website database with metric data retrieval bound in amount, data quantity, or distance relative to writing opportunities to examine the distribution of the amount of research data.
 Methodology: This study employed Bitcoin (BTC), Ethereum (ETH), and Tether (USDT) cryptocurrencies as the research objects with used panel and multiple regression analysis methodologies and using forecasting the appropriate ARCH and GARCH methods
 Results: The results show that the prediction of future crypto price selection in BTC and ETH tokens has a probability of 78.6% and 59.6%, respectively. The study highlights the prediction of future BTC and ETH price selection with 79.21% and 78.64% forecast results as found in the ARCH-GARCH(1, 0, 1) technique. Meanwhile, USDT token has no possibility to be forecasted in the future, leaving a 7.3% possibility of crypto price selection under probability by investors in the form of high (or different) price fluctuation inequalities.
 Conclusion: Conclusions could state that the partial (combined) selection of crypto coin price assessments and individual crypto assets can reduce the expected return from the selection of the asset price so that this form of investment in crypto assets can reduce the level of observation of return on wealth from crypto assets for investors especially in expecting the chance on that investment.

Asset selection based on estimating stress-strength probabilities: The case of returns following three-parameter generalized extreme value distributions

<abstract><p>Analyzing the statistical behavior of the assets' returns has shown to be an interesting approach to perform asset selection. In this work, we explore a stress-strength reliability approach to perform asset selection based on probabilities of the type $ P(X < Y) $ when both $ X $ and $ Y $ follow a generalized extreme value (GEV) distribution with three parameters. At first, we derive new analytical and closed form relations in terms of the extreme value $ \mathbb{H} $-function, which have been obtained under fewer parameter restrictions compared to similar results in the literature. To show the performance of our results, we include a Monte-Carlo simulation study and we investigate the application of the reliability measure $ P(X < Y) $ in selecting financial assets with returns characterized by the distributions $ X $ and $ Y $. Therefore, rather than the conventional approach of comparing the expected values of $ X $ and $ Y $ based on modern portfolio theory, we delve into the metric $ P(X < Y) $ as an alternative parameter for assessing better returns.</p></abstract>

Brief Review on Asset Selection and Portfolio Construction: Diversification, Risk and Return

Brief Review on Asset Selection and Portfolio Construction: Diversification, Risk and Return

Brief Review on Asset Selection and Portfolio Construction: Diversification, Risk and Return

Brief Review on Asset Selection and Portfolio Construction: Diversification, Risk and Return

Two-Stage Portfolio Optimization Integrating Optimal Sharp Ratio Measure and Ensemble Learning

The traditional portfolio theory has relied heavily on historical asset returns while ignoring future information. Based on ensemble learning and maximum Sharpe ratio portfolio theory, this paper proposes a two-stage portfolio optimization method by considering asset forecast information, aiming to improve the performance and robustness of a portfolio. In the first stage, concerning the underlying asset selection, we integrate six individual prediction models using the ensemble learning method to forecast the future return of assets where the assets with higher potential returns are selected for portfolio optimization. In the second stage, we propose a novel investment strategy by combining the forecasted returns of selected assets with the maximum Sharpe ratio portfolio model. In the empirical analysis, we employ the constituent stocks of the China Securities Index 300 (CSI 300) to test the out-of-sample performance of the proposed strategy with several traditional portfolio strategies, including minimum variance portfolio strategy, traditional maximum Sharpe ratio portfolio strategy, 1/N portfolio strategy, and CSI 300 index. Our analytical results show that (1) compared to individual forecasting models, the ensemble learning method is more accurate in forecasting stock returns, and (2) the proposed portfolio strategy largely outperforms most of its competitors in terms of the Sharpe ratio, Sortino ratio, Omega ratio, and Calmar ratio. This indicates that the proposed two-stage portfolio optimization method is of potential to construct a promising investment strategy due to its trade-off between historical and future information of assets.

Dividend-yield variation as an asset selection criterion in momentum strategies of Brazilian real estate investment trusts

RESUMO Nosso objetivo foi mostrar o uso da variação do rendimento de dividendos (dividend-yield [DY]) como critério de seleção dos fundos de investimentos imobiliários brasileiros (FII-BR) em estratégias de momento. A identificação de momento em FII-BR com base nesse critério de seleção é, até onde sabemos, nova e origina diferentes estratégias que capturam retornos anormais. Este estudo proporciona uma nova compreensão da dinâmica entre preços e pagamentos de dividendos, que compõem os retornos dos FII-BR. Isso assume grande importância, considerando a relevância do DY para a classe de ativos. As estratégias estudadas apresentaram resultados acima dos índices do setor com expressiva magnitude, sua elaboração é simples e elas podem ser aplicadas por qualquer investidor individual ou institucional. Os desempenhos das carteiras foram mensurados por meio do índice de Sharpe (IS), do alfa do modelo de três fatores de Fama e French e dos retornos excessivos obtidos em relação ao índice FII-BR. Testes estatísticos foram aplicados para verificar a significância dos resultados. Este estudo mostrou que estratégias em torno do efeito de momento que compram (vendem) FII-BR com maiores (menores) variações de DY em meses anteriores tendem a apresentar desempenho superior ao índice do setor.

Performance of Social Pillar-Based Portfolio in Developing Capital Market

Research Question: This study examined the preconditions and efficiency of socially responsible investing (SRI) in the developing capital market, specifically the Belgrade Stock Exchange (BSE). Motivation: Considering the increasing trend of SRI (GSIA, 2020) and importance of information on corporate social responsibility (CSR) to investors (Miralles‐Quiros, Miralles‐Quiros, & Arraiano, 2017), especially on social issues (Giese, Nagy, & Lee, 2021), we explored the influence of applying the social criteria in asset selection on investment portfolio performance at the BSE. This study builds on the existing literature that is mostly focused on developed capital markets of Europe, the United States and Canada (Von Wallis & Klein, 2015) by analysing the issue of SRI in the developing market. The provided setup for socially driven portfolio structuring based on market trends and social performance disclosure proved to be efficient in the long-term. Idea: The core idea of this paper was to analyse the terms for SRI and empirically evaluate the performance of social pillar-based portfolio on the BSE in order to provide evidence to support the trend of SRI in developing markets. The structure of the portfolios was set using the best-in-class strategy, while the classes were determined on the basis of the stocks’ return trends. Data: The analysis was conducted using trading data and information from financial and non-financial reports of the companies listed on the BSE. Tools: Cluster analysis was used for classification of stocks, while performances of portfolios were evaluated implementing return, volatility and risk-adjusted measures. Findings: The social pillar-based portfolio outperformed the conventional one and benchmarking indices in the observed five-year period. However, mixed results were obtained in the short-term indicating that specific effects of CSR practice on financial performance of the companies in observed developing market could still be obscured. Contribution: This paper expands the existing research related to SRI in the developing markets and offers practical recommendations for potential socially conscious investors.

An Application of Analytical Hierarchy Process to Financial Asset Selection

Asset selection involves picking up a particular asset within each asset class in such a manner that it would outperform the rest of the assets to maximize the investor’s goal of increasing value while mitigating risk. This paper aims to implement a widely used “multi-criteria decision making (MCDM)” technique known as Analytical Hierarchy Process (AHP) for best asset selection. In AHP, the qualitative problems are described and transformed quantitatively, and then the quantitative analysis is used to find the relationship among various decision criteria. In this study, ethical and suitability criteria are used along with the financial criteria to rank the assets based on individual investors’ preferences. To demonstrate the efficacy of the problem, a hypothetical data set is used for ethical and suitability criteria, and data set of 10 assets of Nifty 50 companies under the National Stock Exchange (NSE) is collected for financial criteria.

Testing of a Volatility-Based Trading Strategy Using Behavioral Modified Asset Allocation

The performance of volatility-based trading strategies depends, among other factors, on the asset selection and the associated risk preference. For this study, we conducted a representative survey for Germany to determine the asset preferences of individuals with lower-risk and higher-risk preference. These two types of behavioral modified asset allocations (lower-risk and higher-risk) form the basis for testing our volatility-based trading strategy with different risk and loss levels. The tests are based on historical asset price data over a period of nearly the last eleven years. The goal was to historically outperform the broad market by changing various factors, such as the initial asset allocation, the asset reallocation, and the risk and loss level underlying the trading strategy. We achieve this by using the riskier initial asset allocation and applying our trading strategy with a risk and loss level of 10% each. In this case, a historical return of 326% could have been achieved with our trading strategy over the period under review.

Dynamic portfolio optimization with inverse covariance clustering

Market conditions change continuously. However, in portfolio investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into a dynamic portfolio optimization process. Extensive experiments across three different markets, NASDAQ, FTSE and HS300, over a period of ten years, demonstrate the advantages of our proposed algorithm, termed Inverse Covariance Clustering-Portfolio Optimization (ICC-PO). The core of the ICC-PO methodology concerns the identification and clustering of market states from the analytics of past data and the forecasting of the future market state. It is therefore agnostic to the specific portfolio optimization method of choice. By applying the same portfolio optimization technique on a ICC temporal cluster, instead of the whole train period, we show that one can generate portfolios with substantially higher Sharpe Ratios, which are statistically more robust and resilient with great reductions in the maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets.

A brief analysis of investment alternatives

Bearing in mind the growing dynamism of the market, this article arose from the need to identify investments in the financial market that provide capital gain and profitability, as idle money does not change people’s economic situation. In this context, the following guiding question arose: what are the investment alternatives available in the Brazilian market that provide capital gain and profitability? In this way, the objective is to briefly report some investment alternatives, namely: public bonds, private bonds, shares, investment funds and savings. For this, the bibliographic review was adopted as a methodology. As a result, it was observed that saving allows the accumulation of unspent income. However, the investor who hopes to gain more value by making his resources available in the market should opt for other alternatives, such as government bonds, private bonds, shares and investment funds. However, although the earning alternatives are varied, in some of them, the exposure to risk is greatly increased, which constitutes a challenge for the selection of securities and other assets in which to invest. Finally, the analysis presented indicated that investment alternatives to obtain capital and profitability gains must observe certain premises, such as: investment liquidity; the calculation formula to decide in fraction of a second; the feasibility of acquiring or selling assets.

Sparse and risk diversification portfolio selection.

Portfolio risk management has become more important since some unpredictable factors, such as the 2008 financial crisis and the recent COVID-19 crisis. Although the risk can be actively managed by risk diversification, the high transaction cost and managerial concerns ensue by over diversifying portfolio risk. In this paper, we jointly integrate risk diversification and sparse asset selection into mean-variance portfolio framework, and propose an optimal portfolio selection model labeled as JMV. The weighted piecewise quadratic approximation is considered as a penalty promoting sparsity for the asset selection. The variance associated with the marginal risk regard as another penalty term to diversify the risk. By exposing the feature of JMV, we prove that the KKT point of JMV is the local minimizer if the regularization parameter satisfies a mild condition. To solve this model, we introduce the accelerated proximal gradient (APG) algorithm [Wen in SIAM J. Optim 27:124–145, 2017], which is one of the most efficient first-order large-scale algorithm. Meanwhile, the APG algorithm is linearly convergent to a local minimizer of the JMV model. Furthermore, empirical analysis consistently demonstrate the theoretical results and the superiority of the JMV model.

Time of COVID-19: stability analysis of stocks, exchange rates, minerals and metals markets

The coronavirus (COVID-19) pandemic has shaken the global economy and significantly affected the financial markets. This anxiety triggered panic reactions. The article aims to explain impact of abrupt changes in structural stability of four key asset markets due to pandemic. It includes a detail discussion regarding structural change in co-movement of stock indices, exchange rates, minerals and metal returns. The daily returns of 14 financial indices across four groups during a span of 25 January 2019 to 3 February 2021 are sampled. The selection of financial assets is based on trading volume and velocity in each of the four groups. The results of Markov Switching model explain that pandemic not only increase volatility of financial markets but also have a significant long-term impact on structural behaviour in daily returns. The analysis further reveals differences in co-movement of financial returns during two periods. However, on a positive note, pandemic does make a way for few financial assets to dominate the market, especially ones recognised as ‘safe-haven’ assets.

Long-term General Asset Allocation for individual investors in Chinese securities market

Facing the boom of online transaction applications in Chinese securities market, individual investors in China vacillate between different assets while allocating assets. On account of the individual investors’ inferior ability to take risk, portfolio will be their best choices. However due to their unacquaintance to modern portfolio theory, individual investors need some instructions. As proved by Brinson in 1986, over 90% of the success of a portfolio owe to general asset selection. Hence, this paper primarily focused on providing suggestions to individual investors on general asset selection to help them to construct a portfolio in China securities market at present. Initially, this paper utilized the Mean-Variance model and CML approach to depict the market and analyzed the long-term asset allocation of multi-asset for individual investors. Based on the results, this paper suggested that individual investors should focus more on bond asset: with a portfolio of 97.37% of bond asset and 2.63% of stock asset, investors will be able to acquire a yield of 4.54% (annualized). And through this study, this paper found that Chinese securities market is not mature enough and teemed with regulations, making it to be competent than other mature market.

MARKET TIMING IN PARAMETRIC PORTFOLIO POLICIES

We extend the parametric portfolio policies that exploit firm characteristics to optimize portfolios of stocks and are thus based on asset selection. In addition to this, our extension exploits market indicators for market timing purposes (i.e. optimal allocations between stocks and a risk-free asset). We demonstrate the mechanics of the proposed technique in simulation studies. Specifically, we show that the extended approach is able to produce portfolios based on selection and timing that outperform portfolios that only apply selection, when the applied market indicators have sufficient predictive power. In purely demonstrative empirical applications, we illustrate how investors can use our optimization approach using common market indicators.

Study of the Skills of Balanced Fund Managers in Poland

The market timing is one of the active methods used by portfolio managers to do their investments more effective. It allows to separate management skills on a micro and macro scale. The market timing applies to the appropriate selection of assets for the portfolio and the right moment to change its structure. The aim of the presented research was to check whether the managers of balanced open-end mutual funds operating on the Polish market apply market timing skills. Nine balanced funds have been accepted for the research, which have existed since at least 2003 year. The research period covered the years 2003-2019. WIBOR 1M was used as the risk-free rate, and the market factors were the main WSE indexes. The research was first conducted based on basic market timing research models, i.e. the Henriksson-Merton and Treynor-Mazuy. Then, these models were expanded to include factors related to the bond market. The Henriksson-Merton and Treynor-Mazuy models and their extensions with additional factors were compared among themselves. Studies show that models with additional factors have proven to be more appropriate for balanced open-end mutual funds. It has occurred that regardless of the model used, market timing skills were similar. In most cases the fund managers did not achieve higher results than the results of the relevant benchmark. Managers tried to follow the trend rather than anticipate it. In most cases, there was also no ability to select assets or market-timing. Most of the parameters standing by these variables were not statistically significant.

Modern Machine Learning Solutions for Portfolio Selection

The primary goal of any financial investor is to maximize returns and minimize risks. The selection of the right assets and appropriate allocation of principal wealth is highly essential for the same. Investment specialists and portfolio managers have to manually analyze assets, determine potential returns and risks, and adhere to client preferences to create optimal portfolios. It is a complex and grueling process and demands significant skill and relevant experience for success. Like all other sectors in finance, machine learning models have seen tremendous success in the portfolio selection problem as well. In this article, a thorough review of several machine learning portfolio optimization techniques such as clustering based, Support Vector Machines based, genetic algorithm based, and more has been presented. This article, in its entirety, condenses and interprets the numerous approaches and the merits and limitations corresponding to their implementation. The conclusions presented by this review can be utilized to identify the advantages of these papers. This will help future researchers in their study of the domain and ensure the availability of essential data for further analysis in a systematic and comprehensive manner. With the help of this article, researchers will be well placed to identify areas that present scope for improvement and come up with novel or possibly hybrid techniques to achieve near perfection in portfolio selection.

Dynamic Portfolio Optimization with Inverse Covariance Clustering

Market conditions change continuously. However, in portfolio's investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into a dynamic portfolio optimization process. Extensive experiments across three different markets, NASDAQ, FTSE and HS300, over a period of ten years, demonstrate the advantages of our proposed algorithm, termed Inverse Covariance Clustering-Portfolio Optimization (ICC-PO). The core of the ICC-PO methodology concerns the identification and clustering of market states from the analytics of past data and the forecasting of the future market state. It is therefore agnostic to the specific portfolio optimization method of choice. By applying the same portfolio optimization technique on a ICC temporal cluster, instead of the whole train period, we show that one can generate portfolios with substantially higher Sharpe Ratios, which are statistically more robust and resilient with great reductions in maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets.

Impact of Covid-19 on sectoral stock prices: An event study based on US stock market

The impact of COVID-19 on stock prices of different United States economic sectors is examined using event study methodology. Results revealed that the first confirmed case in US on January 21, 2020 was underestimated by investors and did not disturb stock prices significantly. However, the first local transmission case on February 26, 2020 hit the stock market in the post event window. Due to this event, the stock prices of industries in energy, financials, industrials, real estate and utilities sectors declined but observed price increases in health care, information technology and telecom services industries. First “stay at home” order issued by California State in USA on March 19, 2020, a form of lockdown which restricted public gathering, reduced the market value of consumer staples, health care, industrials, real estate and utilities sectors. This order brought positive changes in stock prices of energy, consumer discretionary, financials, information technology and telecom services sectors. Results of the study can help portfolio managers and investors in the process of asset selection and risk minimization during these types of pandemics. On the other hand, these findings can assist policy makers in designing strategies in advance for financial stability as well as minimizing the negative consequences resulting from COVID-19 related potential health crises in future.

Assessment of portfolio investment effectiveness during the covid-19 pandemic – multivariate fundamental approach1

Making long-term investment decisions is possible using the portfolio approach. In this respect, there are classical (e.g., Markowitz model) and non-classical (e.g., fundamental model) approaches. The key element in the construction of models is not only the measurement of risk and rate of return, but also the method of selecting assets (companies) to the portfolio. The method of selecting assets often determines the direction of the investor's investment strategy. The portfolio construction and asset selection processes remain an open question. The aim of the study is to assess the effectiveness of portfolio investment using the concept of fundamental strength. The results of the study indicate that the fundamental approach, depending on the portfolio construction procedure used, gives different effects, but allows for risk diversification in times of crisis