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Few countries have sufficiently long and detailed returns data for real estate to permit sophisticated analysis. This paper aims to examine the potential diversification of private real estate investments using returns data for major regional centres in Ireland and the UK.

Optimal real estate‐only portfolios are constructed using total returns, income returns and appreciation returns for office and retail real estate in ten cities within Ireland and the UK. The analysis uses IPD data for the period 1984 to 2002. Total return, income return and appreciation returns are treated as separate asset streams in the modelling of portfolios.

The results show different risk levels; in particular the income stream carries low risk, whereas the capital appreciation element is much more volatile and risky. Optimal portfolios, office or retail, whether income, appreciation or total returns, indicate that provincial markets perform well and are capable of pushing London out of the optimised portfolios.

Limitations stem from the optimal portfolios being based on return series without a consideration of market depth. Future research will seek to construct weighted portfolios.

The paper constructs optimal portfolios for three scenarios: low return; medium return; and high return across sectors, return streams and major regional centres in Ireland and the UK. The results show that regional centres perform well and can exclude London real estate from optimal portfolios.

This paper presents a combined method of ensemble learning and genetics to rebalance the corporate portfolio. The primary purpose of this paper is to determine the amount of investment in each of the shares of the listed company and the time of purchase, holding or sale of shares to maximize total return and reduce investment risk.

To achieve the goals of the problem, a two-level combined intelligent method, such as a support vector machine, decision tree, network Bayesian, k-nearest neighbors and multilayer perceptron neural network as heterogeneous basic models of ensemble learning in the first level, was applied. Then, the majority vote method (weighted average) in the second stage as the final model of learning was collectively used. Therefore, the data collected from 208 listed companies active in the Tehran stock exchange (http://tsetmc.com) from 2011 to 2015 have been used to teach the data. For testing and analysis, the data of the same companies between 2016 and 2020 have been used.

The results showed that the method of combined ensemble learning and genetics has the highest total stock portfolio yield of 114.12%, with a risk of 0.905%. Also, by examining the rate of return on capital, it was observed that the proposed method has the highest average rate of return on investment of 110.64%. As a result, the proposed method leads to higher returns with lower risk than the purchase and maintenance method for fund managers and companies and predicts market trends.

In the forthcoming research, there were no limitations to obtain research data were easily extracted from the site of Tehran Stock Exchange Technology Management Company and Rahvard Novin software, and simulation was performed in MATLAB software.

In this paper, using combined machine learning methods, companies’ stock prices are predicted and stock portfolio optimization is optimized. As companies and private organizations are trying to increase their rate of return, so they need a way to predict stock prices based on specific indicators. It turned out that this algorithm has the highest stock portfolio return with reasonable investment risk, and therefore, investors, portfolio managers and market timers can be used this method to optimize the stock portfolio.

The homogeneous and heterogeneous two-level hybrid model presented in the research can be used to predict market trends by market timers and fund managers. Also, adjusting the portfolio with this method has a much higher return than the return on buying and holding, and with controlled risk, it increases the security of investors’ capital, and investors invest their capital in the funds more safely. And will achieve their expected returns. As a result, the psychological security gained from using this method for portfolio arrangement will eventually lead to the growth of the capital market.

This paper tries to present the best combination of stock portfolios of active companies of the Tehran Stock Exchange by using the two-level combined intelligent method and genetic algorithm.

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.

Purpose – Study the investment and risk management approach of sovereign wealth funds when national wealth including natural resources is accounted for rather than only financial asset.

Methodology/Approach – Using a range of widely used asset classes, we simulate sovereign wealth fund returns when considering only financial assets but also under varying levels of national wealth holdings in oil. We optimize two-asset financial portfolios and three-asset portfolios when including oil to maximize the risk-adjusted returns.

Findings – Sovereign wealth funds by failing to invest for the national wealth portfolio are overlooking a major source of volatility. To reduce the level of volatility associated with yearly national wealth returns, allocating a higher percentage of fixed assets to high-quality fixed income and low-risk equities will maximize the risk-adjusted returns of national wealth for sovereign wealth fund states.

Social implications – By focusing solely on the financial assets managed by sovereign wealth funds, states are exposing themselves to significant national wealth risk.

Originality/Value of the paper – This is the first work to estimate the impact on national wealth of oil-dependent states by failing to account for volatile commodity prices through the investment strategies of sovereign wealth funds.

We propose an alternative rationale for why some firms employ veterans, driven not solely by benevolence but also by the prospect of enhanced outcomes. Financially, hiring veterans could correlate with improved stock market performance for the hiring company while aligning with corporate social responsibility (CSR) initiatives. Our study centers on the stock market performance of companies hiring veterans. It aims to underscore a lesser-known facet of the veteran employment discourse and its connection to the hiring firm's financial performance.

This paper evaluates the stock market performance of three VETS portfolios (made of companies that hire veterans) compared to the benchmark SPDR S&P 500 ETF. Using a modular approach, we create three VETS passive indices: VETSEW (equal-weighted index), VETSPW (price-weighted index) and VETSVW (value-weighted index). The study analyzes the annual returns, portfolio allocations, risk-adjusted performance metrics and style analysis of the portfolios from January 1, 2020, to December 31, 2022.

The findings indicate that all three VETS portfolios outperformed the benchmark, with higher ending balances and superior risk-adjusted ratios such as the Sharpe and Sortino ratios. Notably, the portfolios demonstrated resilience during challenging periods, including the COVID-19 pandemic, subsequent recovery and an inflationary period.

Limitations include the paper's focus solely on stock returns, suggesting a need for broader financial and management ratios. Moreover, a deeper exploration into how veterans contribute during turbulent times is suggested for further investigation. Although the study touches upon the financial performance of veteran-focused companies during challenging economic times, it does not extensively delve into the specific ways in which veterans add value under such circumstances, presenting an opportunity for further exploration.

Firms that employ veterans amid the COVID-19 pandemic demonstrate favorable risk-adjusted returns, underscoring the potential of veterans as valuable crisis-time assets. Our research further underscores the correlation between veteran hiring and enhanced financial prowess. These insights carry significant policy implications, including CSR initiatives for hiring veterans, skill translation and training and collaboration with veteran organizations.

The paper's findings suggest significant implications: (1) Policymakers could incentivize firms to hire veterans through tax benefits or grants, leveraging their skills for organizational resilience. (2) Collaborative efforts between policymakers and firms can promote responsible hiring, boosting a company's reputation through diversity and inclusion, positively impacting society. (3) Support for skill translation from military to civilian jobs is crucial. Programs certifying skills and tailored education aid veterans' successful transition into the workforce. (4) Collaborations between policymakers, veteran organizations and private sector entities can create networks, job placements and support systems for veterans' employment.

Numerous prior studies within the domain of corporate social responsibility have predominantly neglected the contributions veterans offer to businesses and the underlying reasons behind firms' decisions to employ them. Our research uniquely concentrates on the stock market performance of companies that choose to hire veterans.

Lowering of investment barriers between European nations has led to increasing integration of their capital markets. Consequently, global investors may be well‐advised to evaluate European stocks, not on the basis of the country of listing, but on the basis of the transnational industrial sector to which the stocks belong. This study utilizes performance measures, grounded in modern portfolio theory, to assess the risk‐adjusted return that has accrued to major transnational industrial sectors in Europe, such as consumer products, technology, utilities and financial services. The empirical documentation generated here can be used by international investors as input in decision making for sectorial allocation of funds in the European component of their global stock portfolios.

The Treynor and Mazuy framework is a widely used return-based model of market timing. However, existing corrections to the regression intercept can be manipulated through derivatives trading. Because they are conceptually flawed, these corrections produce biased performance measures. This paper aims to get back to Henriksson and Merton’s initial idea of option replication to overcome this issue and adapt the market timing model to various kinds of trading strategies and return-generating processes.

This paper proposes a theoretical adjustment based on Merton’s option replication approach adapted to the Treynor and Mazuy specification. The linear and quadratic coefficients of the regression are exploited to assess the cost of the replicating option that yields similar convexity for a passive portfolio. A similar reasoning applies for various timing patterns and in multi-factor models.

The proposed framework induces a potential rebalancing risk and involves the delicate issue of choosing the cheapest option. This paper shows that these issues can be overcome for reasonable tolerance levels. The option replication approach is a workable approach for practical applications.

The adaptation of Merton’s reasoning to the Treynor and Mazuy model has surprisingly never been proposed so far. This paper has the potential to correct for a pervasive bias in the estimation of the performance of a market timer in the context of this very popular quadratic regression setup. Because of the power of the option replication approach, the reasoning is shown to be applicable to multi-factor models, negative timing and market neutral strategies. This paper could fuel empirical studies that would shed new light on the genuine market timing skills of active portfolio managers.

While there are numerous indices for the German stock and bond markets, no performance index exists for the German property market, yet foreign institutional investors in particular are interested in the creation of a performance index, in order to achieve an acceptable framework for international real‐estate investment. Presents a construction method for a real‐estate performance index based on published accounting reports of open‐end real‐estate investment funds. This total return index can be disaggregated into a net cash‐flow return as well as capital growth, and could serve investors as an information tool about the German property market. This index could not only be used within the scope of performance analysis, but also for the benchmarking of real estate portfolios or decision making in portfolio management asset allocation. At the same time, this would enhance the transparency, liquidity and professionalism of the German real‐estate market.