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V.14:4 (176-181): Predicting Major Market Moves by Gregory N. Hight
V.14:4 (176-181): Predicting Major Market Moves by Gregory N. Hight
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Predicting Major Market Moves by Gregory N. Hight
Here's a statistical method that compares the movement of the Standard & Poor's 500 index with the changes of interest rates. In addition, the author explains statistical tests that help validate the reliability of the relationships as well as a technique to identify anomalies, which may be used to anticipate changes in the S&P 500.
Major trend changes may be signaled when market activity takes a distinct turn from a previous pattern. The relationship between changes in interest rates and changes in the Standard & Poor's 500 index can be monitored by using change in the log of price as the preferred data. Methods exist to detect nonstationarity (that is, the rules driving the price movement seem to change randomly) and serial dependencies (that is, today's price is dependent upon yesterday's price), which may render data useless and so inferences from them meaningless. After these pretests confirm the relative health of the data, regression analysis can be applied to establish a range within which change in the log of price data is expected to fall. Finally, probabilities using the binomial distribution can confirm the presence of an anomaly, ultimately forecasting the late 1994 upturn in the S&P 500 index.
Here's a statistical method that compares the movement of the Standard & Poor's 500 index with the changes of interest rates. In addition, the author explains statistical tests that help validate the reliability of the relationships as well as a technique to identify anomalies, which may be used to anticipate changes in the S&P 500.
Major trend changes may be signaled when market activity takes a distinct turn from a previous pattern. The relationship between changes in interest rates and changes in the Standard & Poor's 500 index can be monitored by using change in the log of price as the preferred data. Methods exist to detect nonstationarity (that is, the rules driving the price movement seem to change randomly) and serial dependencies (that is, today's price is dependent upon yesterday's price), which may render data useless and so inferences from them meaningless. After these pretests confirm the relative health of the data, regression analysis can be applied to establish a range within which change in the log of price data is expected to fall. Finally, probabilities using the binomial distribution can confirm the presence of an anomaly, ultimately forecasting the late 1994 upturn in the S&P 500 index.
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