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The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time.

This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization.

From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods.

This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

The purpose of this paper is to evaluate two different asset selection methodologies and further examine these by forming optimal portfolios.

This paper deals with the problem of portfolio formation, broadly in two steps: asset selection and asset allocation by using the two different approaches for the first step and then well‐known mean variance portfolio optimization. In addition, the resulting portfolios are compared using Sharpe ratio.

The empirical observations prove the applicability of the methodology adopted in the research design, ordered weighted averaging (OWA)‐heuristic algorithm gives us a better portfolio from the sample observations. Also the asset selection procedures adopted in the research proves to be of help when an investor has to narrow down the number of assets to invest in.

The analysis provides two different methodologies for portfolio formation – though the asset allocation is based on the mean variance portfolio optimization, the asset selection methods adopted provide a systematic approach to select the efficient securities.

This paper shows that OWA can be used to decide the order of inputs for the heuristic algorithm. Also an attempt is made to use data envelopment analysis to find a solution to the problem of portfolio formation.

This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.

The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.

In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.

The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.

The purpose of this paper is to contrast three investment choices within the reach of individual investors: naive portfolios of Brazilian stocks; actively managed stock funds; and the Ibovespa index, which represents passive management as well as to offer insights on the performance of professional asset managers in this large emerging market.

Equally weighted portfolios contained between 5 and 30 stocks to keep transaction costs low. Stock selection used the Ibovespa constituents and considered value (dividend yield (DY) and price-to-book ratio), momentum (past returns), and liquidity, as well as the Sharpe ratio (SR) over the 2003-2012 period, rebalancing three times a year.

Cumulative returns of naive portfolios are large. They frequently outperform the index for all values of n. They also outperform stock funds, particularly when the invested amount exceeds US$25,000, due to transaction costs. Yet, expected out-of-sample SRs corrected for errors in estimates are very low, suggesting that one should not count on this historical performance in the future. Naive portfolios may simply be more exposed to additional value, size, and momentum risks. Results are sensitive to time period selection.

Naive portfolios may be attractive to individual investors in Brazil relative to stock funds, which seem to strive to keep volatility low and may be better when the investment amount is low. There may be merit for value or momentum stock selection strategies when forming small equally weighted portfolios.

The paper contrasts realistic stock investing alternatives for individuals, it provides a view of stock fund performance in Brazil, and offers practical implications that may be pertinent in other emerging stock markets.

Contrastar três opções de investimento ao alcance de investidores individuais: carteiras ingênuas de ações brasileiras; fundos de ações de gestão ativa; e o índice Ibovespa, que representa a gestão passiva. Oferecer informações sobre o desempenho de gestores de ativos profissionais neste grande mercado emergente.

As carteiras igualmente ponderadas continham entre 5 e 30 ações para manter os custos de transação baixos. A seleção de ações utilizou os componentes do Ibovespa e considerou o valor (rendimento de dividendos e relação preço/valor patrimonial), momentum (retornos passados) e liquidez, bem como o Índice de Sharpe no período 2003-2012, rebalanceando três vezes ao ano.

Os retornos acumulados de carteiras ingênuas são grandes. Eles frequentemente superam o índice para todos os valores de N. Eles também superam os fundos de ações, particularmente quando o montante investido excede US$ 25,000, devido aos custos de transação. Contudo, os Índices de Sharpe esperados fora de amostra corrigidos por erros nas estimativas são muito baixos, sugerindo que não se deve contar com este desempenho histórico no futuro. As carteiras ingênuas podem simplesmente estar mais expostas a fatores riscos adicionais, tal como os de valor, tamanho e momentum. Os resultados são sensíveis à seleção do período de tempo.

As carteiras ingênuas podem ser atrativas para os investidores individuais no Brasil em relação aos fundos de ações, que parecem se esforçar para manter a volatilidade baixa e podem ser melhores quando o valor do investimento é baixo. Pode haver mérito para estratégias de seleção de ações de valor ou momentum ao formar carteiras igualmente ponderadas pequenas.

O artigo contrasta alternativas realistas de investimento em ações para indivíduos, oferece uma visão do desempenho dos fundos de ações no Brasil e oferece implicações práticas que podem ser pertinentes em outros mercados emergentes.

Purpose: The present study aims to test the Quadratic Programming model for Optimal Portfolio selection empirically.

Need for the Study: All the investors who buy financial products are motivated to obtain higher profits or, in other words, to maximise their returns. However, the high returns are often accompanied by higher risks, and avoiding such risks has become the primary concern for all investors. There is a great need for such a model to maximise profits and minimise risk, which can help design an investment portfolio with minimum risk and maximum return. The Quadratic Programming model is one such model which can be applied for selected shares to build an optimised portfolio.

Methodology: This study optimises the stock samples using a two-level screening of correlation coefficient and coefficient of variation. The monthly closing prices of the NSE-listed Indian pharmaceutical stocks from December 2019 to January 2022 have been used as sample data. The Lagrange Multiplier method is used to apply the model to achieve the optimal portfolio solution. Based on the market reality, the transaction costs have also been considered. The Quadratic programming model is further optimised to achieve the optimal portfolio for the select stocks.

Findings: The traditional portfolio theory and the modified quadratic model gives similar and consistent results. In other words, the modified quadratic model asserts the accuracy of the conventional portfolio model. The portfolio constructed in the present study gives a return much higher than the return of the benchmark portfolio of Nifty Fifty, indicating the usefulness of applying the Quadratic Programming model.

Practical Implications: The construction of an optimal portfolio using the traditional or modified Quadratic model can help investors make rational investment decisions for better returns with lower risks.

In this study, we produce mean-variance efficient portfolios for various universes in the U.S. equity market, and show that the use of a composite of analyst earnings forecast, revisions, and breadth variable as a portfolio tilt variable and an R&D quadratic term enhances stockholder wealth. The use of the R&D screen creates portfolios in which total active return generally rise relative to the use of the analyst variable. Stock selection may not necessarily rise as risk index and sector index returns are affected by the use of the R&D quadratic term. R&D expenditures of corporations may be integrated into a mean-variance efficient portfolio creation system to enhance stockholder returns and wealth. The use of an R&D variable enhances stockholder wealth relative to the use of capital expenditures or dividends as the quadratic term. The stockholder return implications of the R&D quadratic variable are particularly interesting given that most corporations allocate more of their resources to capital expenditures than R&D.

The purpose of this paper is to present a new technique to portfolio selection using a genetic algorithm (GA) and fuzzy synthetic evaluation (FSE). Portfolio selection is a multi-objective/criteria decision-making problem in financial management.

The proposed approach solves the problem in two stages. In the first stage, by using a GA and FSE, the weight of criteria will be calculated. Euclidean distance between the computed overall performance evaluation and the surveyed overall performance evaluation is used to determine the weight of criteria. In the second stage, by using a GA and FSE, portfolios will be prioritized. A multi-objective GA is used to determine return and risk in the efficient frontier. A decision making approach is based on FSE to select the best portfolio from among the solutions obtained by a multi objective GA.

The main advantage of the proposed approach is to help an investor to find a portfolio which has best performance, and portfolio selection does not rely on expert knowledge.

The value of the paper is in it using a new approach to determine the weight of criteria and portfolio selection. It surveys firms’ performance in the stock market, based on which the weight of criteria will be determined and portfolios will be prioritized.

Stock selection models often use momentum and analysts’ expectation data. We find that earnings forecast revisions and direction of forecast revisions are more important than analysts’ forecasts in identifying mispriced securities. Investing with expectations data and momentum variables is consistent with maximizing the geometric mean and Sharpe ratio over the long run. Additional evidence is revealed that supports the use of multifactor models for portfolio construction and risk control. The anomalies literature can be applied in real-world portfolio construction in the U.S., international, and global equity markets during the 1998–2009 time period. Support exists for the use of tracking error at risk estimation procedures.

While perfection cannot be achieved in portfolio creation and modeling, the estimated model returns pass the Markowitz and Xu data mining corrections test and are statistically different from an average financial model that could have been used to select stocks and form portfolios. We found additional evidence to support the use of Arbitrage Pricing Theory (APT) and statistically-based and fundamentally-based multifactor models for portfolio construction and risk control. Markets are neither efficient nor grossly inefficient; statistically significant excess returns can be earned.

Examines the impact of using “unsmoothing” techniques on real estate data to take pension‐plan asset‐allocation decisions. It is generally believed that valuation‐based real estate indices give rise to returns figures which are “smoothed” versions of the underlying transaction prices. Unsmoothing techniques can be used to develop real estate return data series that are believed to be a more accurate representation of underlying transaction prices. If this is done, the resulting data reveal greater volatility of real estate returns. When such data are applied to portfolio selection models, they often reveal a reduced allocation to real estate in efficient portfolios. Looks at the impact of unsmoothing data when taking pension‐plan asset‐allocation decisions. Finds here that the unsmoothed data are more closely correlated with pension plan liabilities. As a result, efficient pension plan portfolios sometimes contain more real estate, rather than less. In general, there is little change in the efficient real estate allocation. These results are very important. They reveal that so‐called “valuation smoothing” may distort property investment decisions less than is commonly thought.

This study aims to examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of liquidity-adjusted value-at-risk (LVaR), to multivariate optimization of investment portfolios.

This paper examines the modeling parameters of LVaR technique under event market settings and discusses how to integrate asset liquidity risk into LVaR models. Finally, the authors discuss scenario optimization algorithms for the assessment of structured investment portfolios and present a detailed operational methodology for computer programming purposes and prospective research design with the backing of a graphical flowchart.

To that end, the portfolio/risk manager can specify different closeout horizons and dependence measures and calculate the necessary LVaR and resulting investable portfolios. In addition, portfolio managers can compare the return/risk ratio and asset allocation of obtained investable portfolios with different liquidation horizons in relation to the conventional Markowitz´s mean-variance approach.

The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within machine learning and artificial intelligence, expert systems and smart financial applications, financial technology (FinTech), and within big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk.

The proposed optimization algorithms can aid in advancing portfolios selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints. Furthermore, the robust risk-algorithms and portfolio optimization techniques can aid in solving some real-world dilemmas under stressed and adverse market conditions, such as the effect of liquidity when it dries up in financial and commodity markets, the impact of correlations factors when there is a switching in their signs and the integration of the influence of the nonlinear and non-normal distribution of assets’ returns in portfolio optimization and management.