When you
1. segment a stock's time-series into a "price" (up, down) and "volatility" (rising, falling) component, and then
2. plot each of those on an axis, and then
3. color the coordinates according to forward returns at each of the [x, y] coordinates...
...you learn a lot about the way a stock trades that you wouldn't be able to learn by simply looking at a chart.
The above stock, for example, responds negatively to increases in volatility (↑), and positively to decreases on volatility (↓).
Seems useful to know.
But you'd *never* learn something like this just by looking at the time-series itself.
This may seem obvious, but to the extent that it is possible, it is good to view data from many angles, cross-sectionally, and in multiple dimensions.
Heatmaps give us three dimensions.
3-D color-coded scatter plots give us four!
Heck, add some interactivity and animation (sliders, time-progression) to a 3-D color-coded scatterplot and you're viewing *five* dimensions of data, or more.
This is advantageous!
Why did we tweet this?
Because we're on the verge of losing a bet with someone.
We thought that we'd start seeing more heatmaps and 3-D visualization in financial research and analysis within a few months. But this has not happened.
Please pull it together, people.
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