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One of the biggest challenges in implementing Tactical Asset Allocation (TAA) portfolios is coming as close to the theoretical returns as possible. Theoretical returns are based on index returns, which are not available in the real world. In this post, I’ll explore the major items that keep investors from achieving published theoretical returns of TAA strategies, and discuss some ways to minimize the gap between theory and reality. This is definitely an advanced topic, but a critical one that I really never seen addressed in the financial blogosphere. First, let’s look at the three big reasons for the gap between theoretical and real returns for TAA portfolios. Poor index replication: TAA portfolio returns are based on indexes, e.g. small cap momentum, for some of which no reasonable investable ETF exists. This is becoming less and less for an issue – for e.g., the PowerShares DWA SmallCap Momentum Portfolio ETF (NYSEARCA: DWAS ) is a potential candidate for small cap momentum – but many of these new ETFs are still quite small. Even if an investable ETF exists, there will be some tracking error between its index and the ETF. Fees: There are two sources of fees – trading fees and management fees. Many of the ETFs in TAA portfolios are available as commission-free ETFs, but some are not. And of course, every ETF has a management fee, which detracts directly from the index returns. Slippage: This is the largest source of the gap between theoretical TAA returns and real TAA returns. TAA portfolios are based on monthly investment signals. Monthly investment signals are based on closing ETF prices. Actions based on those signals are done on the following trading day. Any difference between the closing ETF price and your trade price the following day constitutes slippage. For example, a sell signal was generated on August 31, 2015 when the Vanguard Small Cap Growth ETF (NYSEARCA: VBK ) closed at $124.77. On the following day, September 1, VBK traded in a range from $123.53 to $121.06. Selling VBK in that range would generate a difference from the theoretical sell price (the previous close) of 1-3%, depending on where you sold during the day. And this does not even account for the bid-ask spread. Needless to say, that would impact your returns. Usually, it is not as bad as this example, and the slippage can even go in your favor, but in general, it detracts significantly from theoretical returns. Now, let’s put these reasons into context. I ran some backtests with the AGG3 and AGG6 strategies with some different slippage numbers. Since these backtests use real ETFs, all management fees are taken into account. The results from March 2007 through mid-September 2015 are below. If you were able to trade at the theoretical closing price of the ETFs, then with AGG3, the return would have been 13.77% annualized over the period. With just 0.25% negative slippage on every trade, that return would have decreased by 2%, annualized to 11.77%. And with 0.5% negative slippage per trade, that annualized return would have been only 9.85% annualized. As I like to say, slippage kills! BTW, any portfolio strategy has the exact same issues – even “buy and hold”. The issues are exacerbated when a strategy is more active, and thus, trades more often. OK, so what can we do about this? Let’s address each reason individually. For poor index replication, we can always be on the lookout for better-constructed ETFs that more closely match the indexes, and do so at reasonable costs. As I said earlier, this is less and less of an issue today. As far as fees go, we can look for the lowest-cost commission-free ETFs that best implement the index. Sometimes, this can conflict with the first goal of good index replication. For example, is DWAS a better choice for small cap momentum at 0.6% per year in fees, versus VBK, which is really a small cap growth ETF (not momentum), but is only 0.09% per year in expenses? In other words, the better index replication may not be worth the extra fees. And then there is the big one – slippage. In theory, the solution is easy. Trade as close to the theoretical model price as possible. At the minimum, this ideally means the use of high volume, low bid/ask spread ETFs. I’ll give you my favorite example. The Vanguard Long-Term Government Bond Index ETF (NASDAQ: VGLT ) trades 50K shares per day, at an average bid/ask spread of 0.2%. The iShares 20+ Year Treasury Bond ETF (NYSEARCA: TLT ) trades 9M shares a day, at an average bid/ask of 0.02%. Which one would best minimize slippage? TLT by a long shot, despite the slightly higher management fee (0.15% versus 0.12%). So, to minimize slippage, we may sometimes actually end up using different ETFs. You may also want to change from end-of-month portfolio signals to some other day during the month to avoid volatile month-end periods due to options expirations, portfolio window dressing, etc. Then, at the advanced end of the spectrum, you can actually ‘trade the close”, i.e., execute your trades as near the end of trading as possible on the last day of the month (the day that generates your portfolio signals). The use of conditional orders and MOC (market on close) orders greatly simplifies this strategy. I’ve been working on this strategy most of the year, and have found it quite effective in minimizing slippage. Also, even the choice of brokerage can impact slippage. I have stopped using TAA strategies at certain brokers, due to poor execution prices. In summary, there will always be a difference in model returns versus real-world returns. The question is, how can we minimize these gaps? With attention to detail in choosing the best, liquid, low bid/ask, low-cost ETFs and some smart trading strategies, you can keep the gap down to a minimum.