Benchmark outperforming

That said, there are two reasons why the performance of German swap spreads are related to Euro peripheral spreads. The first one is that, flows apart, the bond-swap spread reflects the yield difference between a government rate and the composition of a string of EURI-BOR rates (i.e., a swap fixed rate). As the average credit quality of the banks in the EURIBOR panel is A-AA, any increase in the investors preference for credit quality will make both swap and peripheral spreads widen versus the core Euro government rate, thus increasing the correlation between both differentials. Yet this increase in the correlation will be mainly due to the outperformance of the benchmark asset... 

As we discussed above, one draw is the scope to outperform. With yields among government markets all tightly compressed, the only way to add alpha (fund manager-generated outperformance versus a benchmark) in the government sector is via a yield curve or duration call. 

The second question that we address in this chapter follows naturally from the first, which is how does one beat a benchmark There are countless strategies that can be employed to outperform a benchmark. In this chapter we focus on nine important strategies that can be employed to achieve this goal. These strategies involve taking views on ... 

In contrast to MPT, researchers of Goldman Sachs— Fisher Black and Robert Litterman—proposed a technique that tackles most of the problems, commonly associated to the classical portfolio optimization methods. They start the process of optimization with the assumption that investor chooses his optimum portfolio within a finite group of assets. In essence, the BL model turns the MPT on its head—it does not compute the optimal portfolio from the historical data, but rather assumes that a given portfolio in fact is the optimal one. This idea is backed by several researches which show that it is very difficult for investor to systematically outperform well-diversified benchmark. BL then derive the expected remrns for different positions in the portfolio. If investor agrees with the market assessment, benchmark becomes the optimal portfolio and the funds should be invested accordingly. On the other hand, if someone has different opinions about the expected returns of some of the stocks in the portfolio, the BL approach allows him to adjust the weights according to his projections. The result is the optimal portfolio, based on investor s individual assessment of market potential. 

In the absence of additional information about the market s expected returns, it is safe to assume that weights from the benchmark represent the efficient weights. Otherwise, we have to introduce our own opinions. Investor views can be expressed in absolute or relative terms (i.e., Allianz will outperform Telefonica by 0.2% in the next month is a relative view, and Allianz will earn 1.2% in the next month is an absolute view). 

Table 21.4 shows that the TRP strategy significantly outperformed the benchmark in relation to the Sharpe ratio and ROI. Although minimum variance was identified as the safest method, it achieved the lowest return per unit of risk between all five strategies. Benchmark passive investment and the EQW portfolio produced similar results. 

In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmaik functions. 

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