Traditional Investment Vehicles Research Articles

Analyzing the Relationship between Real Estate Investments and Portfolio Diversification

Real estate has long been considered an attractive investment option for individuals and institutions seeking to build wealth and diversify their portfolios. Unlike traditional investment vehicles such as stocks and bonds, real estate offers unique characteristics that can potentially enhance returns and mitigate risk. This analysis aims to explore the role of real estate investments in portfolio diversification and assess their potential impact on overall portfolio performance. Portfolio diversification is a fundamental principle in investment management, as it helps to spread risk across different asset classes and mitigate the impact of market fluctuations on a portfolio's overall value. By including assets with low or negative correlations, investors can reduce the volatility of their portfolios and potentially achieve higher risk-adjusted returns. Real estate investments, including direct property ownership, real estate investment trusts (REITs), and other real estate-related securities, have traditionally exhibited low correlations with other asset classes, such as equities and bonds. This low correlation can be attributed to the unique characteristics of real estate, including its tangible nature, the presence of rental income streams, and the potential for capital appreciation. Furthermore, real estate investments can provide a hedge against inflation, as property values and rental rates tend to increase during periods of rising prices. This feature makes real estate an attractive diversification option, particularly for investors seeking to protect their portfolios from the eroding effects of inflation. This analysis will delve into the historical performance of real estate investments, examine their risk and return characteristics, and evaluate their potential contribution to portfolio diversification. By examining empirical data and leveraging portfolio optimization techniques, we aim to provide insights into the optimal allocation of real estate investments within a diversified portfolio.

Gaining a Seat at the Table: Enhancing the Attractiveness of Online Lending for Institutional Investors

This paper investigates the suitability of online loans as an investment through the lens of a portfolio optimization framework. We propose general characteristics-based portfolio policy (GCPP), a framework which overcomes unique challenges associated with building a portfolio of online loans. GCPP directly models the portfolio weight of a loan as a flexible function of its characteristics and does not require direct estimation of the distributional properties of loans. Using an extensive data set spanning over one million loans from 2013 to 2020 from LendingClub, we show that GCPP portfolios can achieve an average annualized internal rate of return (IRR) of 8.86% to 13.08%, significantly outperforming an equal-weight portfolio of loans. To assess the attractiveness of online loans, we then compare the performance of the GCPP portfolio to traditional investment vehicles including stocks, bonds, and real estate. The results demonstrate that a portfolio of online loans earns competitive or higher rates of return compared to traditional asset classes with limited comovement. These results indicate that online loans are an attractive novel asset class for investors. Together, we demonstrate that GCPP is an approach that can help platforms better serve both borrowers and lenders en route to growing their business.

RELATIONAL STUDY BETWEEN MACROECONOMIC VARIABLES AND GOLD PRICE: LATEST MALAYSIAN EVIDENCE

Gold is one of the world-leading commodities, which is sought after as jewellery as well as an investment. Despite gold advantage as a reliable store of value, its price has been as fluctuating as other commodities or assets such as crude oil and stock. Therefore, investors should be aware that gold is not totally resistant to market turmoil and economic crisis. Gold could be as vulnerable as other traditional investment vehicles. Hence, this paper focuses on investigating the macroeconomic variables affecting gold prices in Malaysia using monthly data from 2014 to 2018. This study uses Pearson Correlation and Multiple Linear Regression tests to determine significant relationships and effects between the independent variables, namely interest rates, inflation rates, crude oil price, and exchange rates with the dependent variable, namely gold price. The findings show that the interest rate has a significant negative effect on gold price while inflation rate, crude oil price, and exchange rate have a significant positive effect on gold price in Malaysia, respectively. The results suggest valuable information and insight to policymakers, researchers, and investors in decision making.

Exchange-Traded Funds

Over nearly a quarter of a century, exchange-traded funds (ETFs) have become one of the most popular passive investment vehicles among retail and professional investors because of their low transaction costs and high liquidity. By the end of 2016, the market share of ETFs exceeded 10% of the total market capitalization traded on US exchanges, representing more than 30% of overall trading volume. ETFs revolutionized the asset management industry by taking market share from traditional investment vehicles such as mutual funds and index futures. Because ETFs rely on arbitrage activity to synchronize their prices with the prices of the underlying portfolio, trading activity at the ETF level translates to trading of the underlying securities. Researchers have found that although ETFs enhance price discovery, they also inject nonfundamental volatility into market prices and affect the correlation structure of returns. Furthermore, ETFs impact the liquidity of the underlying portfolios, especially during events of market stress.

Crowdfunding: The collaborative economy for channelling institutional and household savings

The financial disintermediation mechanism known as “loan-based-crowdfunding” has recently come under regulation in several countries. This competitive investment and finance vehicle is already well established in the US and British markets.By compiling empirical data from a reference crowdfunding platform, this article compares loan-based crowdfunding with traditional investment vehicles such as investment funds, equities or pension funds.The conclusion of the study is that saving through crowdfunding allows the optimisation of a portfolio comprising both institutional and retail investors.

Exchange Traded Funds (ETFs)

Over two decades, ETFs have become one of the most popular investment vehicle among retail and professional investors due to their low transaction costs and high liquidity, taking market share from traditional investment vehicles such as mutual funds and index futures. Research has shown that in addition to the benefits of enhanced price discovery, ETFs add noise to the market: prices of underlying securities have higher volatility, greater price reversals, and higher correlation with the index. Arbitrage activity is a necessary component in minimizing the price discrepancy between ETFs and the underlying securities. During turbulent market episodes, however, arbitrage is limited and ETF prices diverge from those of the underlying securities.

Crowdfunding: The collaborative economy for channelling institutional and household savings

The financial disintermediation mechanism known as “loan-based-crowdfunding” has recently come under regulation in several countries. This competitive investment and finance vehicle is already well established in the US and British markets.By compiling empirical data from a reference crowdfunding platform, this article compares loan-based crowdfunding with traditional investment vehicles such as investment funds, equities or pension funds.The conclusion of the study is that saving through crowdfunding allows the optimization of a portfolio comprising both institutional and retail investors.

A Heuristic Approach to Asian Hedge Fund Allocation

The authors first note that, unlike traditional investment vehicles, hedge funds seem to produce return distributions with significantly non-normal skewness and kurtosis. Hedge fund managers that apply the mean-variance optimization approach to form optimal portfolios may find that approach no longer appropriate. Moreover, utilizing a portfolio optimizer to perform portfolio allocations will cause what is known as the “butterfly effect”—that is, small changes in inputs, especially mean returns, can cause large changes in the optimal asset weightings. This phenomenon, coupled with the illiquidity of hedge funds, may prompt hedge fund managers to consider alternative approaches to portfolio allocation. In this study, the authors introduce a practical heuristic approach using the semi-variance (that better accounts for non-normality in hedge fund returns) as a measure for downside risk. This heuristic approach is able to provide better forecasts, stable portfolio allocations, and more diversification than the optimization approach. The authors find the “butterfly effect” in their sample of Asian hedge funds when using portfolio optimizers resulting in dramatic changes in optimal weights over time. They also find that their risk-return approach recommends portfolio with higher returns when compared with optimizers. In risk-reward comparisons against the optimizers, the heuristic approach yields the highest return to standard deviation and return to semi-deviation ratio (trade-offs). <b>TOPICS:</b>Real assets/alternative investments/private equity, emerging, statistical methods, performance measurement

Optimal Hedge Fund Allocations

Hedge funds have return peculiarities not commonly associated with traditional investment vehicles. They are more inclined to produce return distributions with significantly non–normal skewness and kurtosis. Investor preferences may be better represented by bilinear utility functions or S–shaped value functions than by neoclassical utility functions, and mean–variance optimization is thus not appropriate for forming portfolios that include hedge funds. Portfolios of hedge funds formed using both mean–variance and full–scale optimization, given a wide range of assumptions about investor preferences, reveal that higher moments of hedge funds do not meaningfully compromise the efficacy of mean–variance optimization if investors have power utility; mean–variance optimization is not particularly effective for identifying optimal hedge fund allocations if preferences are bilinear or S–shaped; and, contrary to conventional wisdom, investors with S–shaped preferences are attracted to kurtosis as well as negative skewness.

Optimal Hedge Fund Allocations: Do Higher Moments Matter?

Hedge funds have return peculiarities not commonly associated with traditional investment vehicles. Specifically, hedge funds seem more inclined to produce return distributions with significantly non-normal skewness and kurtosis. There is also growing acceptance of the notion that investor preferences are better represented by bilinear utility functions or S-shaped value functions than by neo-classical utility functions such as power utility. Many investors have therefore concluded that mean-variance optimization is not appropriate for forming portfolios that include hedge funds. We apply both mean-variance optimization and full-scale optimization to form portfolios of hedge funds, given a wide range of assumptions about investor preferences. We find that higher moments of hedge funds do not meaningfully compromise the efficacy of mean-variance optimization if investors have power utility. We also find, however, that mean-variance optimization is not particularly effective for identifying optimal hedge fund allocations if preferences are bilinear or S-shaped. Finally, we show that investors with bilinear utility dislike kurtosis and that, contrary to conventional wisdom, investors with S-shaped preferences are attracted to kurtosis as well as negative skewness. Mean-variance optimization is insensitive to these preferences.

Invest in Direct Energy

Direct energy investments should be evaluated as a distinct asset class. Producing oil and gas properties have unique characteristics that are not directly available through traditional investment vehicles such as stocks and bonds. Direct energy investment should be attractive to investors by virtue of its relatively low correlation with the traditional asset classes. It may also appeal to a broader audience as a hedge against inflation, allowing investors to maintain real purchasing power and protect against future nominal increases in the overall domestic price level. The potentially significant diversification benefits of direct energy investments establish them as a viable asset class to be considered in a long-term asset allocation policy.

An Econometric Model of Serial Correlation and Illiquidity in Hedge Fund Returns

The returns to hedge funds and other alternative investments are often highly serially correlated in sharp contrast to the returns of more traditional investment vehicles such as long-only equity portfolios and mutual funds. In this paper, we explore several sources of such serial correlation and show that the most likely explanation is illiquidity exposure, i.e., investments in securities that are not actively traded and for which market prices are not always readily available. For portfolios of illiquid securities, reported returns will tend to be smoother than true economic returns, which will understate volatility and increase risk-adjusted performance measures such as the Sharpe ratio. We propose an econometric model of illiquidity exposure and develop estimators for the smoothing profile as well as a smoothing-adjusted Sharpe ratio. For a sample of 908 hedge funds drawn from the TASS database, we show that our estimated smoothing coefficients vary considerably across hedge-fund style categories and may be a useful proxy for quantifying illiquidity exposure.

On the Performance of Hedge Funds

Empirical evidence indicates that hedge funds differ substantially from traditional investment vehicles, such as mutual funds. Unlike mutual funds, hedge funds follow dynamic trading strategies and have low systematic risk. Hedge funds' special fee structures apparently align managers' incentives with fund performance. Funds with “high watermarks” (under which managers are required to make up previous losses before receiving any incentive fees) significantly outperform those without. Hedge funds provide higher Sharpe ratios than mutual funds, and their performance in the period of January 1992 through December 1996 reflects better manager skills, although hedge fund returns are more volatile. Average hedge fund returns are related positively to incentive fees, fund assets, and the lockup period.

On the Performance of Hedge Funds

This paper investigates hedge fund performance and risk. The empirical evidence indicates that hedge funds differ substantially from traditional investment vehicles such as mutual funds. The funds with watermarks significantly outperform the funds without watermarks. The average hedge fund returns are related positively to incentive fees, the size of the fund, and the lockup period. Hedge funds follow dynamic trading strategies and have low systematic risk. There are low correlations among different strategies. Compared with mutual funds, hedge funds offer better risk-return trade-offs: they have higher Sharpe ratios, lower market risks, and higher abnormal returns. In the period of January 1994 to December 1996, hedge funds provide positive abnormal returns. Overall, hedge fund strategies dominate mutual fund strategies, hence hedge funds provide a more efficient investment opportunity set for investors.