Investment fund selection techniques from the perspective of Brazilian pension funds

ABSTRACT The aim of this article was to evaluate the effectiveness of investment fund selection techniques from the perspective of Brazilian pension funds. Asset liability management (ALM) and liability driven investment (LDI) strategies are usually adopted to guide pension fund managers in relation to strategic allocation in asset classes that should compose their investment portfolios and to the liquidity needed in each period, but not specifying in which assets to allocate resources from among the infinity of assets available in the financial market. This article contributes to tactical management in the fixed income and stock segments outsourced via funds and demonstrates that adopting simple indicators can increase investment performance. The article broadens the knowledge on pension fund investment decisions and creates confidence in the adoption of the Sharpe ratio as a technique for choosing investment funds. We analyzed the returns obtained by hypothetical portfolios built using the following techniques: (i) the Sharpe ratio; (ii) the alpha of a multifactor model; (iii) data envelopment analysis (DEA) efficiency; and (iv) the different combinations of these techniques. We considered information on 369 funds from 2013 to 2018, adopting 12 temporal windows for choosing and re-evaluating the portfolios. The returns obtained were compared with the mean actuarial goal of the benefits plans administered by the pension funds, by means of the unplanned divergence (UD). When outsourcing pension fund investments in fixed income and stock investment funds it was verified that the Sharpe ratio contributes significantly to pension fund performance, compared with other indicators and techniques or a combination of them.

Econometric Modeling to Measure the Efficiency of Sharpe’s Ratio with Strong Autocorrelation Portfolios

Sharpe’s ratio is the most widely used index for establishing an order of priority for the portfolios to which the investor has access, and the purpose of this investigation is to verify that Sharpe’s ratio allows decisions to be made in investment portfolios considering different financial market conditions. The research is carried out by autoregressive model (AR) of the financial series of returns using Sharpe’s ratio for evaluations looking over the priority of financial assets which the investor can access while observing the effects that can cause autocorrelated series in evaluation measures for financial assets. The results presented in this study confirm the hypothesis proposed in which Sharpe’s ratio allows decisions to be made in the selection of investment portfolios under normal conditions thanks to the definition of a robustness function, whose empirical estimation shows an average 73% explanation of the variance in the degradation of the Spearman coefficient for each of the performance measures; however, given the presence of autocorrelation in the financial series of returns, this similarity is broken.

Financial Asset Management Using Artificial Neural Networks

Investors typically build portfolios for retirement. Investment portfolios are typically based on four asset classes that are commonly managed by large investment firms. The research presented in this article involves the development of an artificial neural network-based methodology that investors can use to support decisions related to determining how assets are allocated within an investment portfolio. The machine learning-based methodology was applied during a time period that included the stock market crash of 2008. Even though this time period was highly volatile, the methodology produced desirable results. Methodologies such as the one presented in this article should be considered by investors because they have produced promising results, especially within unstable markets.

Differences in Investment Concepts between Chinese and Western Investors from the Cultural Perspective

The investment concept, reflecting the investor's investment purpose and willingness, is a value that embodies the investor's investment personality characteristics, prompts investors to carry out investment analysis, judgment, decision-making, and guides investor behaviors. Due to different maturity of the capital market in China and Western countries, there are many differences in the regulatory level, cultural and behavioral patterns of the supervision and management departments of the capital market between Chinese and Western investment philosophy. This article analyzes the differences in investment ideas between Chinese and Western investors from the culture perspective. This thesis studies on the basis of four cultural differences: "The Golden Mean" and "Interest Maximization"; the face-culture and individualism; rule of man and rule of law; and gambler psychology and adventure spirit. Based on these four aspects of cultural differences, four different investment concepts of Chinese and Western investors are analyzed: long-term investments and short-term speculation; "Herd Effect" and independent decision; grapevines and public information; and leveraged trading and allocation of funds. This thesis adopts several cases to analyze the differences between Chinese and Western investors in financial products such as stocks, gold, and futures, and in investment behavior such as the long-term investment, short-term speculation, leveraged trading, and investment portfolios. With cultural differences between China and the West probed into, the differences between Chinese and Western investors' investment concepts are justified. It is hoped that this effort will help investors deepen the understanding of the capital markets in China and the West, enable Chinese investors to learn the Western mature investment concepts, and facilitate the regulators to manage the capital market effectively.

Religion vs ethics: hedge and safe haven properties of Sukuk and green bonds for stock markets pre- and during COVID-19

Purpose This study aims to examine the hedge and safe-haven properties of the Sukuk and green bond for the stock markets pre- and during the COVID-19 pandemic period. Design/methodology/approach To test the hedge and safe-haven characteristics of Sukuk and green bonds for stock markets, the study first uses the methodology proposed by Ratner and Chiu (2013). Next, the authors estimate the hedge ratios and hedge effectiveness of using Sukuk and green bonds in a portfolio with stock markets. Findings Strong safe-haven features of ethical (green) bonds reveal that adding green bonds into the investment portfolios brings considerable diversification avenues for the investors who tend to take fewer risks in periods of economic stress and turbulence. The hedge ratio and hedge effectiveness estimates reveal that green bonds provide sufficient evidence of the hedge effectiveness for various international stocks. Practical implications The study has significant implications for faith-based investors, ethical investors, policymakers and regulatory bodies. Religious investors can invest in Sukuk to relish low-risk and interest-free investments, whereas green investors can satisfy their socially responsible motives by investing in these investment streams. Policymakers can direct the businesses to include these diversifiers for portfolio and risk management. Originality/value The study provides novel insights in the testing hedge and safe-haven attributes of green bonds and Sukuk while using unique methodologies to identify multiple low-risk investors for investors following the uncertain COVID-19 pandemic.

Project Portfolio Selection of Solar Energy by Photovoltaic Generation Using Gini-CAPM Multi-Criteria and Considering ROI Covariations

For some time, renewable solar energy generations using cellular photovoltaic panels have stood out among the options, especially in the segment of micro and small companies, where the return on investment is usually higher. In this context, when micro and small companies do not have the capital for the enterprises, several others, mainly small ones, have emerged to finance. However, significant difficulties occur for financiers in selecting investment portfolios, especially when considering the trade-off between return and risk and the covariations of return on investment, which are very common. In this type of selection, the Capital Asset Pricing Model criteria using the Gini risk can help significantly because this one is a more robust risk coefficient for assessments of non-normal probability distributions. However, searches for methods that meet the selection needs using the adjacent criteria are unsuccessful. Thus, this work seeks to help minimize the gap by presenting a new method for selection using the criteria. Historical and simulations data stochastic evaluations indicate that the portfolios selected by the method are attractive options for implementations. These portfolios have reasonable probabilistic expectations and satisfactory protection to avoid mistakes caused for not considering covariations in return on investment, which indicates a significant advance on the current knowledge frontier and will likely allow the increased use of the concept. The method also presents theoretical contributions in adaptations of the benchmark models, which help to minimize the adjacent literary gap of similar methods.

Self-Perfecting Model for Managing Individualized Investment Portfolios

Individualization is a process that adapts a ser vice or product to the needs of specific individuals. This process is a key element in recommender systems and is applied to establish innovative ser vices for technological solutions in the financial industry. For this purpose, a self-perfecting model for managing individualized investment portfolios is built, through which segmentation and evaluation of users are performed and for each of them a set of investment portfolios is recommended. This paper presents the process of recommending a portfolio of investment instruments, individualized according to the individual preferences of clients, methodology for its construction, and results of the approbation. Examples are shown of individualized portfolios, as well as a comparison of the main measures of the recommended portfolios in the respective clusters.

Cryptocurrency as a safe haven for investment portfolios amid COVID-19 panic cases of Bitcoin, Ethereum and Litecoin

PurposeThis article examines the susceptibility of cryptocurrencies to coronavirus disease 2019 (COVID-19) induced panic in comparison with major stock indices.Design/methodology/approachThe author employs the Bayesian structural vector autoregression to examine the phenomenon in Bitcoin, Ethereum and Litecoin from 2nd January 2020 to 30th June 2021. A similar analysis is conducted for major stock indices, namely S&P 500, FTSE 100 and SSE Composite for comparison purposes.FindingsThe results suggest that cryptocurrencies returns suffered immensely in the early days of the COVID-19 outbreak following declarations of the disease as a global health emergency and eventually a pandemic in March 2020. However, the returns for all three cryptocurrencies recovered by April 2020 and remained resistant to further COVID-19 panic shocks. The results are dissimilar to those of S&P 500, FTSE 100 and SSE Composite values which were vulnerable to COVID-19 panic throughout the timeframe to June 2021. The results further reveal strong predictive power of Bitcoin on prices of other cryptocurrencies.Research limitations/implicationsThe article provides evidence to support the cryptocurrency as a safe haven during COVID-19 school of thought given their resistance to subsequent shocks during COVID-19. Thus, the author stresses the need for diversification of investment portfolios by including cryptocurrencies given their uniqueness and resistance to shocks during crises.Originality/valueThe author makes use of the novel corona virus panic index to examine the magnitude of shocks in prices of cryptocurrencies during COVID-19.

Chinese-Funded Electricity Generation in Sub-Saharan Africa and Implications for Public Debt and Transition to Renewable Energy

While China has been increasingly contributing to the recent growth in electricity generation in sub-Saharan Africa (SSA), the effects of China-funded investment on host countries’ debt burden and transition to renewable energy sources have not been sufficiently explored. Drawing on secondary data, combined with deep dive studies of Ethiopia and Uganda, this paper shows that despite significant liberalisation of the power sector in SSA, Chinese investments in the electricity industry continue to follow state-led project contract-based models. We show that this approach has failed to encourage Chinese firms to build compelling investment portfolios for competitive procurements within the region and, instead and inadvertently, it has exacerbated the debt burden of host country governments. Second, in spite of the global drive towards climate resilient energy generation, Chinese funding of electricity generation in SSA is not sufficiently channelled towards modern renewable energy sources such as wind and solar power that could reduce vulnerability to climate change. While recognising that the private sector-led competitive model of power generation is not without limitations, we argue that SSA’s electricity generation strategy that leads to less public debt and more climate resilience involves increased involvement of Chinese investment in the competitive model, with more diversification of such investment portfolios towards modern renewables such as wind and solar energy resources.