Book authors, bloggers and even some pundits insist that market timing is easy because it has worked in the past. But the fact is that profits depend on future market behavior. This is what some market timers are afraid of.
I had an email exchange the other day with a prominent profession of finance who is currently working on one of the best books you will ever get to read on moving averages. He asked me what I thought were the advantages of moving average strategies. My reply was that in my opinion the complexity of the transition from strategic to capital is more important.
Passive funds when challenged about low or negative returns can blame the market and the investor that decided to go passive. But market timers can blame only their own abilities. This is a fundamental difference.
This is what some astute market timers are afraid of: a rapid decline of the market below its 12-month moving average in a period of less than a month before a bear market starts. The chart below shows why this could be problem.
Wherever that distance from the 12-month MA to last closing price in the above monthly S&P 500 chart moves above the longer-term average of roughly 3.20% calculated since the early 1990s, market timers get nervous.
At this point this distance is at 5.85%. The problem is not only the distance but how fast the market falls to cross the average.
For example, at the 2000 top, prices fell much below the 12-month MA but then recovered and the loss before getting a signal to get out was small. A similar situation occurred at the 2007 top. Market timers had time to get out with a small loss.
But what about if there is a sudden drop of more than 5.85%, for example 7% in a period of less than a month and prices continue to fall afterwards? A large loss will be realized in that case.
Success of market timing depends on how fast models can adapt to market conditions. The problem is of fundamental nature and has no easy solution: if the model adapts too fast, then it generates many losses during sideways markets and if it adopts too slow, then it generates larger losses at the top of uptrends and then misses a good part of the next uptrend. Obviously, most studies found in books, blogs and academic papers have optimized the lag of the models based on past market conditions and results look good. It is also obvious that the future may be different from the past and often in ways that cause damage to the majority of market participants.
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