The traditional 60/40 portfolio is an allocation of 60% of an account to equities and 40% of an account to bonds. We will attempt to understand-based on the current macroeconomic climate-why it has been an attractive allocation to date, but may not provide comparable performance going forward. In this article we will describe the 60/40 portfolio and implement it as a 'trading strategy' in QSTrader. One of the most common benchmarks utilised in the TAA space is the 60/40 equities/bonds static allocation. Such a method would fail to take into account the diversifying power of multiple asset classes or geographies. Hence utilising a geographically-dependent single asset class instrument such as a domestic market index makes less sense. When considering static and active asset allocation strategies specifically a broad range of potential asset class and geographic options are available to the investor. Since an investor, such as a pension fund allocator, is effectively choosing between multiple firms rather than likely investing in a market index directly this may seem like a more sensible comparison. However, an argument can be made that the benchmark could also be an index aggregating the performance of other institutional long-only equities funds. The usual 'go to' benchmark is a geographic market cap weighted index, such as the S&P500 for the US or the FTSE100 for the UK. Consider an active long-only equities strategy run by a large institutional fund. In particular, for a certain set of investor risk/reward preferences if the benchmark cannot be beaten with an active complex trading strategy then why spend operational time on its implementation?Ĭhoosing a benchmark is not as straightforward as it seems. One can think of the benchmark as the 'default option'. A benchmark is a particular choice of portfolio that provides a comparison mechanism against another trading strategy. This motivates the concept of a strategy benchmark. If we are to consider running a complex TAA strategy with non-trivial weighting rules it is necessary to ask whether one can achieve similar-or better-performance solely with simple static allocations. Some will utilise sophisticated portfolio construction methods to tactically weight allocations on a more frequent basis.Īs with all forms of investing it is prudent to be aware of the opportunity cost of allocating capital to one strategy or portfolio over another. Many TAA strategies consist of straightforward static asset allocations that are periodically rebalanced. All of the asset allocation approaches discussed in our TAA articles will have backtests provided via interactive charts helping you to determine if such a strategy should be added to your portfolio. QSTrader has recently been redeveloped 'from the ground up' to be easier to use, allowing straightforward implementation of TAA strategies. In addition to learning a new set of investment skills we are also going to make use of the current development version of our freely available open source backtesting engine QSTrader. The goal will be to provide a solid overview of TAA, via a series of example strategies, which you can then use a 'tool' in your systematic trading toolbox, potentially to diversify against higher frequency strategies. In this and subsequent articles we are going to start delving into some of these issues. The typical monthly rebalance frequency of these strategies lends itself to spending more time on research and less time on operational implementation. Many-if not all-TAA strategies rely on a good understanding of macroeconomic trends and an awareness of the behaviour of various asset classes over the long-term. In a recent article we introduced systematic tactical asset allocation (TAA) as a low-frequency example of quantitative trading strategy.įor those who are taking their first steps in systematic trading, are wanting to consider systematic trading in the context of their retirement planning or are simply wishing to minimise the day-to-day work of running a strategy TAA can be a great choice.ĭespite its seeming simplicity as a systematic trading mechanism there is a substantial body of theory behind some of the more complex implementations.
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