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Suppose I take a two moving averages to analyse stock market prices - one $200$ day moving average, $m_{200}$, and another $50$ day moving average, $m_{50}$.

How do you decide when to sell and when to buy a stock based on these moving averages so as to maximize your profits? For example what can be deduced when $m_{50} > m_{200}$?

Peter Košinár
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Ashwin
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  • I've back tested a market timing indicator using the 200-dma and 50-dma back to 1939 on a daily basis and found that there are conditions of the market relative to these two indicators under which being out of the market (in cash) would have avoided significant portfolio draw downs. Avoiding bear markets (declines exceeding 20%) while being fully invested at other times produces extraordinary results. I condensed these back-testing results into an indicator I call a Market Momentum Meter which I explain fully in my book "Run with the Herd" –  Jan 02 '14 at 03:39

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When $m_{50}>m_{200}$, it means that recently the stock has done better than it has done over a longer term. This suggests that the stock is on the rise, and might continue rising; buy. However, take easy rules about stocks with a boulder of salt.

T.J. Gaffney
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  • so, should we sell or buy when m50>200? – Ashwin Dec 29 '13 at 17:10
  • The phenomenon that stocks which have risen relative to the market, over certain timescales, tend to continue to rise, is measurable and there are traders who profit from carefully parametrised models of this. It is called momentum: see http://en.wikipedia.org/wiki/Momentum_%28finance%29 . Over other timescales the opposite effect, called mean reversion, occurs. The old wisdom is that "industries trend, companies revert". For private investors the chances are that overall market moves and your transaction costs will drown out these effects, and you cannot hope to time the market. So don't try! – HTFB Dec 30 '13 at 12:20
  • @HTFB : thanks. But I asked the question because I am trying to solve a programming questions in which I have to analyse the stock prices and decide when to sell and when to buy – Ashwin Dec 31 '13 at 02:42
  • If you have daily data on these rolling averages, you can reconstruct the actual daily price time series (making some assumptions about the 49 days prior to your first data point). From there I would try regression to find autocorrelations in the data or a Fourier transform to look for periodicities, either of which might theoretically be tradeable. – HTFB Dec 31 '13 at 11:42