Market Timing

 Market Timing

Historical patterns of trading are the foundation of market timing methods. When applied to historical data, every system you're likely to read about performs admirably. You would never learn of its failure in the past if it had worked. The future, however, is constantly a mystery, and trends can and do shift. Investors would have avoided a steep fall had a mechanism been put in place for the market trends of the 1970s, which encompassed a two-year severe bear market. The 1980s were defined by a protracted bull market, therefore that wasn't necessary. Also, doing backtests on a system that was perfected in the 1980s would have yielded poor results compared to the 1970s. Investors have been more injured than helped by defensive strategies thus far in the 1990s.

Market timing isn't going to be easy if you feel emotionally secure knowing exactly what's occurring with your investments at all times. Despite your best attempts, market timing's performance and direction will frequently elude your comprehension. Furthermore, they will disobey logic. The market's moves could appear understandable in the absence of timing. There are many analyses of every blip that appear in print, online, and on the media every single day. Since economic and market tendencies tend to last, they give the impression of being somewhat reasonable. When you start timing your investments, though, everything changes. No one can tell you how your timing models function unless you built them and know them inside and out, or unless you do the math yourself on a daily basis. Asking yourself to buy and sell based on faith is a common task. Because timing performance is dependent on how your models engage with market trends, the reason behind your short-term results might not be immediately apparent. Your results may appear arbitrary when compared across years, quarters, and months.

As a general rule, most of us tend to assume that the status quo will persist. However, you can't do that with market timing. Immediate previous performance will have little bearing on future performance. So, there's no telling what's going to happen next. For the sake of this *timing simulator*, let's pretend that you have complete knowledge of the monthly returns of a successful approach over a 20-year timeframe. There will be a lot of positive returns and a lot of negative returns among those monthly returns. Picture this: you make a deck of cards with all the returns written on it, put them in a hat, and then start drawing them at random. Think of it this way: you have a stack of poker chips to work with. Chips are awarded if a positive return is drawn. But in this game, you have to give up some chips to *the bank* when your return is negative. You can be reasonably certain if the first six cards you get are all positive. And you'll be hoping that the fun times keep rolling. Your joy may be short-lived, though, if you get a card that represents a loss out of the blue. And if you lose a lot of chips on the first card you draw, you could start to question if you still want to play this game. Even if you know the drawing is completely random, it's still possible to feel like you're on a *negative roll* and assume that the upcoming quarter will be just like the last if you get two bad cards in a row and see your chip pile diminish. But you can't even begin to guess what the next card will be. All of this is readily apparent when one is merely engaged in a game of poker. Actually, though, it's more challenging. For instance, our Nasdaq portfolio strategy, which aimed to beat the Nasdaq 100 Index, generated a return of 5.9 percent in the fourth quarter of 2002, which was quite good for a portfolio that was solely invested in technology funds. However, Q1 2003 saw a decline of 7.8 percent after that. At least among those who were aware of this method, the majority of investors persisted. Nonetheless, the loss and the abrupt change in fortunes caused them great undue stress. This same thing occurred, but in much larger numbers, with our more aggressive tactics. Some people put their money into those portfolios in the winter of 2002, and they were surprised to see such large losses in the first quarter. Some people got out of the company because they thought the losses would keep piling up. Had they been patient for just a little longer, they could have made double-digit gains in the rest of 2003, more than making up for their losses. Naturally, though, it was impossible to predict in advance.

All market timing systems have been *fine-tuned* to fit historical data, although most timers will deny this. What this implies is that they are based on data that has been hand-picked to ensure that they enter and exit the market at optimal moments. Consider it in light of this comparison. Just pretend for a second that we were attempting to use the last 30 years to create a better Standard & Poor's 500 Index. Looking back, we could likely greatly enhance the index's performance with just a couple minor tweaks. It would be easy to *exclude* from the index, for example, the worst-performing stock industry and any companies that have declared bankruptcy during the last 30 years. That would get rid of most of the *junk* that was causing performance issues before. To boost the new index's return even further, we could increase the weightings of a handful of selected stocks—for example, Microsoft, Intel, and Dell—in the index. A new *index* would be created, which has historically outperformed the actual S&P 500 in terms of return on investment. We could think we've found something important. No one needs a crystal ball to see that this plan won't lead to better results in the following three decades. By playing around with historical data in this straightforward way, you can easily create a *system* that passes the eye test. The term "data-mining" describes the process of sifting through large amounts of historical data in search of relevant pieces of information that can be "fitted" into a preconceived ideology or reality model. Any inferences drawn from data-mining are ill-founded and should not be trusted as indicators of future events, according to academic researchers. One way or another, data-mining or optimization is the foundation of every market timing system. If you want to create a timing model, your only option is to look at previous periods and try to replicate their successes. The optimization principle underpins all market timing models. One issue is that some systems, like as the improved S&P 500 example, are overly optimized and discard historical data in a manner that may not be dependable going forward. To illustrate the point, we lately examined a system that included some *rules* for when to send a purchase signal, and then we included a filter that said such a buy may only be sent out during four particular months annually. Since it eliminates the ineffective purchases from the previous eight months, that system appears great on paper. No foolproof method exists for distinguishing between systems that are over-optimized and those that are sufficiently optimized. In general, though, you should seek out simpler systems rather than more complicated ones. It is more probable that an extremely complicated system will generate spectacular hypothetical returns than a simpler one. However, you may anticipate the simpler system to act in a predictable manner.

If you want to make it as an investor, you need to think about the big picture and be able to tune out the little things. For those who want to buy and hold, this might be a rather simple task. However, if you try to time the market, you'll get entangled in the process and end up thinking just about the here and now. It's not enough to just monitor short-term shifts; you'll also need to respond to them. Plus, you'll need to disregard them right away. Listen, I know it might be tough at times. Smart people in the real world usually do one last *gut check* to see how they really feel before making a big decision. Following a mechanical method, however, requires you to forego this commonsense measure in favor of immediate action. On occasion, this becomes challenging.

There will be extended stretches when your results are lower than or higher than the market average. You need to broaden your definition of typical, anticipated behavior to encompass participating in a falling market and exiting a rising one. On occasion, your earnings may fall short of money market fund rates. And if you're timing your short bets, you can end up broke while everyone else is flush with cash. Is that something you can roll with the punches as an investor? Avoid putting money into that plan if you can help it.

Poor outcomes are possible with even the best timing system. Even while it may seem apparent, market timing is only another chance to be right or wrong and another layer of complexity to investing. No matter how well your timing model predicts market movements, it will provide unexpectedly good or bad results when applied to a fund that invests in anything other than the market. Use money that correspond well with your system for this purpose.

For me, the most important thing is that timing is really difficult. In my opinion, having someone else handle the actual timing moves is the way to go for most investors. A professional can handle it for you. Another option is to delegate the trade-making to a trusted coworker, friend, or family member. In this approach, you can avoid letting your emotions interfere with your discipline. With your system in place, you can relax and enjoy your holiday. Above all else, you won't have to deal with the emotional ups and downs of entering and exiting the market.

Oh my goodness!

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