Last week we presented some evidence that the total daily trading volume for SPY could be predicted from the first minute’s trading volume. We accomplished this using an Archimedean copula, a mathematical construct for modeling multivariate data. Interpreting a copula from an intuitive standpoint can be difficult at first glance, so we thought that we should present an alternative way to estimate trading volume using k-means and logistic regression.
Initially we were motivated to investigate trading volume when we noticed that high volume days tend to be associated with higher volatility. Looking deeper, we saw the relationship between volume and value: high volume days are associated with falling prices and low volume with rising prices.
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