Should investors include Bitcoin in their portfolios? A portfolio theory approach

Many papers in recent years have examined the benefits of adding alternative assets to traditional portfolios containing stocks and bonds. Bitcoin has emerged as a new alternative investment for investors which has attracted much attention from the media and investors alike. However relatively little is known about the investment benefits of Bitcoin and therefore this paper examines the benefit of including Bitcoin in a traditional benchmark portfolio of stocks and bonds. Specially, we employ data up to June 2018 and analyse the potential out-of-sample portfolio benefits resulting from including Bitcoin in a stock-bond portfolio for a range of eight popular asset allocation strategies. The out-of-sample analysis shows that, across all different asset allocation strategies and risk aversions, the benefits of Bitcoin are quite considerable with substantially higher risk-adjusted returns. Our results are robust to rolling estimation windows, the incorporation of transaction costs, the inclusion of a commodity portfolio, alternative indices, short-selling as well as two additional optimization techniques including higher moments with (and without) variance-based constraints (VBCs). Therefore, our results suggest that investors should include Bitcoin in their portfolio as it generates substantial higher risk-adjusted returns.     

Do commodities add value in multi-asset portfolios? An out-of-sample analysis for different investment strategies

An essential motive for investing in commodities is to enhance the performance of portfolios traditionally including only stocks and bonds. We analyze the in-sample and out-of-sample portfolio effects resulting from adding commodities to a stock-bond portfolio for commonly implemented asset allocation strategies such as equally- and strategically-weighted portfolios, risk-parity, minimum-variance as well as reward-to-risk timing, mean-variance and Black–Litterman. We analyze different commodity groups such as agricultural and livestock commodities that currently are critically discussed. The out-of-sample portfolio analysis indicates that the attainable benefits of commodities are much smaller than suggested by previous in-sample studies. Hence, in-sample analyses, such as spanning tests, might exaggerate the advantages of commodities. Moreover, the portfolio gains greatly vary between different types of commodities and sub-periods. While aggregate commodity indices, industrial and precious metals as well as energy improve the performance of a stock-bond portfolio for most asset allocation strategies, we hardly find positive portfolio effects for agriculture and livestock. Consequently, investments in food commodities are not essential for efficient asset allocation.     

Liquidating large security positions strategically: a pragmatic and empirical approach

This article investigates static liquidation strategies for large security positions in illiquid markets. Under the assumption that the liquidation horizon is given exogenously, a discretionary liquidity trader solves for the optimal sales trajectory so as to maximize an objective function that considers the expected liquidation revenues and their standard deviation. Although existing literature tends to focus on theoretical aspects with the intention of deriving closed-form solutions for special types of market impact functions, this article considers a framework that is able to capture important empirical phenomena in the stock market, such as the intraday U-shaped pattern of price impact and the resiliency of the order book. The new model is very flexible since it allows for liquidation intervals of varying length and foregoes the assumption of constant speed of trading. Examples with real-world order book data demonstrate how the setup can be implemented numerically and provide deeper insight into relevant properties of the model.     

Portfolio performance of linear SDF models: an out-of-sample assessment

We evaluate linear stochastic discount factor models using an ex-post portfolio metric: the realized out-of-sample Sharpe ratio of mean–variance portfolios backed by alternative linear factor models. Using a sample of monthly US portfolio returns spanning the period 1968–2016, we find evidence that multifactor linear models have better empirical properties than the CAPM, not only when the cross-section of expected returns is evaluated in-sample, but also when they are used to inform one-month ahead portfolio selection. When we compare portfolios associated to multifactor models with mean–variance decisions implied by the single-factor CAPM, we document statistically significant differences in Sharpe ratios of up to 10 percent. Linear multifactor models that provide the best in-sample fit also yield the highest realized Sharpe ratios.     

Further evidence on performance evaluation: Portfolio holdings,recommendations, and turnover costs

This paper analyzes the performance of mutual funds in Spain between January 1980 and June 1990. The robustness of results to alternative measurements and benchmarks are analyzed. The results indicate that, with monthly returns alone, it is not possible to distinguish between selectivity and timing. We are only able to measure the magnitude of total performance. To be more precise about the reasons behind performance, portfolio holdings are necessary. This work employs a new data set based on monthly portfolio holdings of a representative sample of funds. A comparison of results using monthly returns and monthly portfolio holdings is made. In particular, thanks to the availability of portfolio holdings, we are able to separate selectivity and timing. Finally, the impact of turnover costs is considered.