Category: website

The Bubble Index and Daneric’s Elliott Wave Count Comparison

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The American stock market appears to be at a critical juncture. On the one hand, various trading blogs such as the ELLIOTT WAVE lives on, suggest a continuation of a Primary 5 Wave Impulse up-trend in place since the 2009 low. Other Elliott Wave traders, like Daneric’s Elliott Waves, place the current up-trend as a B Wave rebound from the 2008-9 A Wave correction. In a previous post I mentioned the connection between The Bubble Index and Anthony Caldaro’s EWC. In this post I suggest a connection with Daneric’s EWC. The graphs below show this connection with The Bubble Index: DJIA. I neither agree nor disagree with Caldaro/Daneric. The comparison is presented here for comment.

Graph 1. The Bubble Index: DJIA (5,040 Days) with Daneric’s EWC
Graph 2. The Bubble Index: DJIA (10,080 Days) with Daneric’s EWC
Graph 3. The Bubble Index: DJIA (20,160 Days) with Daneric’s EWC

The Bubble Index Composite

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A few months ago I posted (here) the distribution of the values of The Bubble Index on a given day (June 27, 2014). Building upon that idea, by creating an index which shows the distribution evolving through time, The Bubble Index Composite is an index which plots the median of the distribution for all monitored stocks. I plan to produce these composite indices for every global market. For example, I have about 1,400 US Stocks which have 1260 and 1764 day indices. Taking the median of these 1,400 values each trading day and plotting gives the following graphs:

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LPPL Model Working

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Honestly, I am shocked at how well The Bubble Index timed this market correction.

The Bubble Index: S&P 500 (1764 days) peaked on September 22, 2014. That was 21 days ago, the critical time variable, Tc, in the code.

Today’s S&P 500 sell-off was intense, to say the least… down 1.65%. And this wasn’t the only day with huge losses. Since the market peaked on September 19th, it has lost about 10%.

The Bubble Index: DJIA (20160 days)

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The Bubble Index: DJIA (20160 days) suggests that there are characteristics of the market which occur over very long time intervals. The importance of long memory processes in financial markets should not be underestimated, as suggested by Mandelbrot via Hurst’s studies of the Nile. The massive window, approximately 80 business years, indicates the fundamental importance of LPPL oscillations in economic and social history.

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Potential New Site

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In previous posts I have mentioned, with few details, a potential way to display the state of the trading network. Over the past few weeks I have been playing with the idea and progress is being made. The site, Particle Markets, will be the center of these new ideas to publicly and freely display the state of trading networks.

Currently the site displays a simple, 2D Ising type model, progressing through time. There are 176,400 total traders, and they all start with Long positions. As time progresses many of the traders sell and become either neutral or short. All traders are long/short 1 share. The summation of their numbers after each time step determines the change in price of the traded security.

Short positions: RED
Long positions: GREEN
Neutral: BLUE

This is a simple model. Future models will incorporate more details. I hope to be able to use actual data one day and display daily changes in the actual human network; in the same way a meteorologist shows viewers his Doppler radar to warn of storms, Particle Markets will be able to show investors the “market weather.”

The concept applies mostly to the stock market, since a trader holds a contract which has no “delivery date.” I hope to apply the same ideas to commodity markets.

Future Update Ideas:

Major Changes

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UPDATE: Some of this is incorrect

There are a few changes which will be made to The Bubble Index website; changes which will require massive computation. So, over the next few weeks there will be fewer daily updates.

Changes include:

  • Adding search functionality in the navigation bar
  • The standardization of every Bubble Index
  • Creating a standard set of windows (52 days, 153 days, 256 days, 512 days, 1260 days, 1764 days, 2520 days, and 5040 days) for all stocks, indices, commodities, and currencies.
The last two changes will allow every Bubble Index for a given window to be comparable with each other. In other words, say I want to compare the relative herding in two securities — The Bubble Index: GOOG (512 days) with The Bubble Index: DJIA (512 days). This will be possible after the changes are complete. The standardization will set 100.00 as the level of The Bubble Index on October 23, 1929 for all windows.
So, if The Bubble Index: GOOG (512 days) is at 18.65 today, then I would know that herding behavior is only 18.65% of that seen in The Bubble Index: DJIA (512 days) on October 23, 1929, several days before the Wall Street Crash of 1929.

As of today, the issue with The Bubble Index for a single security is that the index levels are relative to their own values. This hinders the usefulness of time series with shorter time horizons, like Tesla (TSLA) with only three years of history. In addition, on time series with long time horizons, like the DJIA, the massive difference in absolute price affects the level of the index (not the shape and structure, just the relative size of the “spikes”). This is bothersome and I should have corrected this a long time ago. Example of the normalization change in the DJIA (RED is the non-normalized curve):