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Thanks for the interesting reference. I have never seen the S$P 500 returns presented in this exact format. The presentation is a bit puzzling; I’ll need time to interpret what it is telling us.
Market traders have been watching the S&P 500 pricing changes as a function of trading volume for a longtime now. The correlation between price and volume has had a directional tidal shift in the last decade or so.
Since World War II, the correlation coefficients between these two metrics were strongly positive. Correlations are transient, and positively peaked in the mid-1990s at a level just under a perfect One value. If you could forecast near-term volume you could have made a nice profit during that extended period.
Since the mid-1990s, the S&P 500 price to volume correlation has gone weakly negative. Today, on average, the US equity market has a slight price tendency to exhibit a counter reaction to volume. The data demonstrate a dynamic correlation reversal.
It is not clear what caused this shift. Of course during this extended period much has happened. Trading volume has increase by over an order of magnitude. The institutional investor has replaced the individual investor as a prime mover. Hedge funds have developed into a major trading force. Communications are instantaneously worldwide. Trading is now done using highly suspicious computer models that have limited validation. Seasonal patterns have mutated somewhat. National economies are tattered and uncertain. The list is almost endless.
All these changes have accelerated in the last decade or so and have made interpreting the correlation shift more complex and even more challenging. Market traders and authors write thick books about this subject.
Thanks for providing access to the plot, I think. I currently do not fully understand the curves themselves, and therefore can’t offer a definite explanation. Data collection timeframe is a likely culprit. Shifting investor sentiment and even vacation habits might be other explanatory reasons. Who knows? Perhaps the cloud will be lifted with further study.
Keep in mind the shortcomings of using only year-to-date data. It is probably not statistically significant, and is likely to be very ephemeral in character on a longer term basis, although the correlation has remained in the negative camp for about a decade. The issue for profitable investing, of course, is the capability to consistently forecast market volume given the current correlation.
Since I do not trade on a daily or even monthly basis, these data are marketwise interesting, but will not impact my portfolio management whatsoever. Given my market participation rate these data represent trading noise to me.
Comments
Thanks for the interesting reference. I have never seen the S$P 500 returns presented in this exact format. The presentation is a bit puzzling; I’ll need time to interpret what it is telling us.
Market traders have been watching the S&P 500 pricing changes as a function of trading volume for a longtime now. The correlation between price and volume has had a directional tidal shift in the last decade or so.
Since World War II, the correlation coefficients between these two metrics were strongly positive. Correlations are transient, and positively peaked in the mid-1990s at a level just under a perfect One value. If you could forecast near-term volume you could have made a nice profit during that extended period.
Since the mid-1990s, the S&P 500 price to volume correlation has gone weakly negative. Today, on average, the US equity market has a slight price tendency to exhibit a counter reaction to volume. The data demonstrate a dynamic correlation reversal.
It is not clear what caused this shift. Of course during this extended period much has happened. Trading volume has increase by over an order of magnitude. The institutional investor has replaced the individual investor as a prime mover. Hedge funds have developed into a major trading force. Communications are instantaneously worldwide. Trading is now done using highly suspicious computer models that have limited validation. Seasonal patterns have mutated somewhat. National economies are tattered and uncertain. The list is almost endless.
All these changes have accelerated in the last decade or so and have made interpreting the correlation shift more complex and even more challenging. Market traders and authors write thick books about this subject.
Thanks for providing access to the plot, I think. I currently do not fully understand the curves themselves, and therefore can’t offer a definite explanation. Data collection timeframe is a likely culprit. Shifting investor sentiment and even vacation habits might be other explanatory reasons. Who knows? Perhaps the cloud will be lifted with further study.
Keep in mind the shortcomings of using only year-to-date data. It is probably not statistically significant, and is likely to be very ephemeral in character on a longer term basis, although the correlation has remained in the negative camp for about a decade. The issue for profitable investing, of course, is the capability to consistently forecast market volume given the current correlation.
Since I do not trade on a daily or even monthly basis, these data are marketwise interesting, but will not impact my portfolio management whatsoever. Given my market participation rate these data represent trading noise to me.
Best Wishes.