If we believe that investing is still a good idea, then now is the time to move the money there. Of course, one may also be happy about not having investment and keeping the dollars while people who invested are losing money day after day. But this reason to be proud of yourself will disappear as soon as some part of the market shoots up. And it certainly will, problem is, we never know which one. But what do we know?
Under quite a detailed scrutiny, market still looks like a random Markov process. In fact, the more one looks the more indications there are that there are no correlations whatsoever. Correction: you can find correlations, but only such that do not tell you anything about the sign of the price change.
Now, any strategy that can be coded by working with the price data and is a simple looking-back decision making program seems to return essentially random results. There are some that are skewed to perform better, and the dispersion is so big that on some stocks it would seem to perform really well, but it's all just noise. From what we can see, consequence of individual action on the stock market is pretty much 50/50 for most of them, except for particularly stupid ones. In fact, it is hard to find a consistently losing strategy (with 0 fees) because one would invert it (sell instead of buy) and turn it into winning strategy.
The things that are easy algorithmically do not neccessarily coincide with what people actually do while trading by hand. Algorithmically, almost every code that you write makes 100s of operations, while we usually are much more lazy and just want to buy once. So we have confirmed the 50/50 picture for algorithms like trendfollowing and mean-reversion, but these results do not help much in thinking about actually making a trade yourself this one time. So what would be the algorithms that shed some light on the effectiveness of most common trading practices? I hope to list a few things that I have tested here. I have two main interests:
Under quite a detailed scrutiny, market still looks like a random Markov process. In fact, the more one looks the more indications there are that there are no correlations whatsoever. Correction: you can find correlations, but only such that do not tell you anything about the sign of the price change.
Now, any strategy that can be coded by working with the price data and is a simple looking-back decision making program seems to return essentially random results. There are some that are skewed to perform better, and the dispersion is so big that on some stocks it would seem to perform really well, but it's all just noise. From what we can see, consequence of individual action on the stock market is pretty much 50/50 for most of them, except for particularly stupid ones. In fact, it is hard to find a consistently losing strategy (with 0 fees) because one would invert it (sell instead of buy) and turn it into winning strategy.
The things that are easy algorithmically do not neccessarily coincide with what people actually do while trading by hand. Algorithmically, almost every code that you write makes 100s of operations, while we usually are much more lazy and just want to buy once. So we have confirmed the 50/50 picture for algorithms like trendfollowing and mean-reversion, but these results do not help much in thinking about actually making a trade yourself this one time. So what would be the algorithms that shed some light on the effectiveness of most common trading practices? I hope to list a few things that I have tested here. I have two main interests:
- Stop-loss. In B&H strategy, one may take an active position and check how much is the portfolio worth every few days. If he sees that it is down by the last year's earnings, he might impulsively withdraw everything and wait for some time. I'd like to know how it affects the long-term returns (and e.v.) and what wait time and threshold to pick. Also maybe there are stocks that consistently reward traders who use this strategy.
- In picking which stocks to invest, one may focus on best performers and worst performers.
- The SR is the main instrument in thinking about stock as a random process, but for some of them it does not obey the naive sqrt(Ndays) dependence. In such way one can choose top SR and worst SR stocks, and maybe expect different strategies to work on them.
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