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the t-test indicated a greater than 80% probability that the effect was real and would hold up. Both long and short trades were profitable. In-sample there were 37 trades (40% winners). Out-of-sample, longs and shorts both continued to be profitable; it was surprising that the shorts were profitable because the market was in a steep up-trend during most of that period. Out-of-sample, the return was 39.5% annualized, with an 80% probability of being real; there were 11 trades (45% winners), and the average trade returned $5,640. These are excellent results. The observation that sharp down-turns can occur after solar flares has been supported. With the stop order, Minnesota Wheat, Soybeans, and Cotton were also profitable in both samples. Minnesota Wheat pulled 13.5% annually in-sample, again with a better than 80% chance of the results being real, and 30.5% out-ofsample, with almost an 85% probability of the effect not being due to chance. Interestingly, Light Crude and Heating Oil were highly profitable in-sample, but lost moderately out-of-sample. There were a few profits in other markets, but no consistency between in- and out-of-sample performance with the stop.
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Like seasonality and lunar phase, solar activity appears to have a real influence on some markets, especially the S&P 500 and Minnesota Wheat. As with lunar cycles, this influence is not sufficiently strong or reliable to be a primary element in a portfolio trading system; however, as a component in a system incorporating other factors, or as a system used to trade specific markets, solar activity is worth attention. We personally do not believe solar influences directly determine the market. We do suspect that they act as triggers for events that are predisposed to occur, or as synchronizers of already-present market rhythms with similar periodicities. For example, if a market is highly over-valued and unstable, as was the S&P 500 in October 1987, a large solar flare might be sufficient to trigger an already-imminent crash.
WHAT HAVE WE LEARNED
. Lunar and solar phenomena may have real impact on commodities markets. In the case of solar phenomena, such impact on the S&P 500 has been conhrmed. With lunar phenomena, there is more inconsistency in the results, but influence is clearly detectable. n The phenomena are probably worth including in a more elaborate trading model, e.g., as inputs to a neural network. n Models that capture such influences almost certainly need to be tuned to specific markets in a more effective way than has been done here. Such market specificity might be the reason for the better results observed when tests were conducted on individual markets.
. As with other countertrend models that pinpoint turning points (including seasonality), it is probably necessary to use an exit that includes a fairly tight stop. If a turning point is correctly identified, the stop is never hit and the trade quickly turns profitable. On the other hand, when a prediction error occurs, as frequently happens, the stop quickly kills the trade, cutting losses short. The use of tight stops may be another factor that accounts for better performance in our earlier studies (Katz and McCormick, June and September 1997) than in the current tests. . Given the highly probable influence of solar and lunar rhythms on certain markets, it might be worth exploring some other planetary rhythms, including the kinds of planetary configurations and cycles that are the focus of astrologers. . Entry orders interact with models. Limit orders, for example, do not always perform best. It seems a stop order sometimes improves performance. The reason may be that a stop order introduces verification of the beginning of a trend before entry can occur, something that may be important when using a prediction-based counter-trend strategy.
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