No one person can watch everything in the market all at once.

At MarketReader, we aim to solve this problem by automatically identifying and explaining significant asset price movement in real time.

Last week was an unusually volatile week for US stocks, especially given that it is August when many investors are typically on vacation. Our systems at MarketReader were certainly not on vacation and caught numerous compelling examples of how we can provide quick insights when the market is on the move. These insights can alert investors to new trade ideas and/or help them track volatility in their current portfolio.

Below is an example from Wednesday, August 17:

Semiconductor stocks were leading US stocks lower within tech (see tweet), and at 10:20 am EDT, MarketReader detected strong statistical (and causal) links to ADI (Analog Devices). Our system found that the price action in Texas Instruments (TXN) was strongly linked to ADI.

In other words, MarketReader helped highlight that the weakness in the entire semiconductor sector was linked to news around ADI and sympathy moves in other related stocks. MarketReader software did this before it was picked up by traditional news sources (even the professional service Bloomberg provides).

Here is a graphic that identifies the cause of the big move in ADI and the linkage to other semiconductor stocks, such as Texas Instruments (TXN):

It took Bloomberg another hour to catch on to the story:

Investors without access to professional tools, like Bloomberg, may not have gotten useful color on this for many hours—if even on the same day. Moreover, standard financial news sites (such as CNBC), are still not providing an explanation of the big move in TXN over the week.

This example, and others like it, demonstrate how the MarketReader engine is uniquely placed, using a multidimensional approach, to detect statistical and causal links in the market in near real-time. This is no easy task, but we are making substantial progress, and our models are now working in concert with one another to identify and explain market moves. We are excited to see our systems performing superbly through this market volatility and will continue to iterate on our models to ensure the success of this technology.