Taken together these results appear to support the following conclusions. In the past there has been a significant relationship between some market indicators and subsequent stock market performance. The most successful indicators appear to be the cash position of mutual funds and the treasury bill rate. Other indicators that may have some forecasting ability include TOLSR, confidence index, specialists short sales, secondary distributions, and the inflation rate. The stability of a relationship involving these last mentioned variables, however, is subject to some doubt. In particular it may well be that indicators with forecasting ability in an earlier time period may be losing their value. This result would be expected to follow from increasing attention given to the indicators. An indicator that works well in one period may thereby attract enough attention to make it useless in a later period.
Further research might take several directions. One might add additional indicators to the test sample. Such additional variables would be interesting but it is not clear that they would add much explanatory power since they are likely to overlap phenomena already covered by existing variables. A second approach would involve playing around with the form of the independent variables. Mixed lag structures, distributed lags, nonlinear forms, and other adjustments to the independent variables could probably improve the fit. I was very careful not to do this since it comes much too close to data mining. Im-proving the R for one time period may or may not improve the forecasting ability of the model. If enough variations are tried, some will work just by chance. In all probability, however, they will not work in a future time period. It would also be useful to construct trading rules based on market indicators and then compare their use with a buy-and-hold strategy. Finally it would be interesting to test the power of market indicators on groups of stocks. For example, certain interest-sensitive stocks (savings and loans and other housing-related companies for example) may be particularly susceptible to forecasts based on monetary variables. Clearly this is a fertile ground for further work.
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