Monthly Blog Archives: February 2014

Sentiment is Dead, Long Live Sentiment

Published: February 28, 2014

By: Kevin Coogan

Sentiment trading has been around a long, long time – and will continue to be. In this post, we argue that, although extremely important and integral to trading, the market’s focus on polarity sentiment for short-term trading is a bit exaggerated and that other uses and new forms of sentiment will begin to augment it.

Sentiment directly impacts asset prices through somewhat predictable shifts and overshoots – anyone who has spent a considerable amount of time on a trading desk can attest. Though sentiment has been around, its quantification is rather ‘new’, and its quantification in real-time even more so.

Measurement began with polls and surveys. These were and are still great. The problem of course is that they lack a real-time element. Additionally, issues with respondent selection are apparent.

Social media and the new world of full transparency have opened up financial market analysis to more modern types of sentiment. Looking at tweets, cashtags, other micro-blog posts, news, and/or user generated content can greatly improve insights into sentiment.

Why wait for an end-of-day or even end-of-week survey to be released? Sentiment can be yours right now. It is quicker and can be measured with an impressive amount of scientific certitude. All of this makes perfect sense and is a large benefit of the advent of Big Data.

The issue that has appeared, however, is that positive / negative polarity natural language processing (“polarity NLP”) sentiment has become too central when discussing Big Data and finance. In fact, when you mention Big Data and investing to most people, they automatically think of two things:

  1. sentiment based on polarity NLP scoring of social media, and
  2. short-term directional trading based on same.

Yes, these are important, but if your horizon ends there, you are frankly missing so much that the next generation of financial data and analysis has to offer. Furthermore, the rather clear impression is that going forward you will miss even more as Big Data continues to produce revolutionary insights.

Let’s state that sentiment derived from NLP polarity analysis is extremely important. It offers rapid interpretation of events and of market moods. It can also provide very interesting insights into market trends that would not be apparent otherwise. This post is not arguing to not use this type of sentiment, just that the market should better understand its limitations and be open to other new forms of data and even other measurements of sentiment.

To help portray these points, we will publish over the coming days two posts.

The first highlights a somewhat counter-intuitive use case for negative NLP polarity sentiment. To this point, the generally accepted interpretation is that an unusually large negative sentiment spike offers a great selling or short selling opportunity with potential follow through. In this case, the reverse is shown to be true in that after a few days of declines the stock bolted higher – thereby reinforcing the 360 degree approach that is required when interpreting any single variable.  In other words, do not rely or focus on just one variable.

The second highlights a new form of sentiment and how it can be used. Specifically, it highlights (what we believe) is a first in directional sentiment analysis, which has proven both intuitive to understand and highly useful in its application.

Big Data and Finance, Speed versus Insight

Published: February 26, 2014

By: Kevin Coogan

Big Data will revolutionize finance and investing. Nearly all investors will, at some point, begin to take advantage of the benefits it provides. Some, however, will leverage Big Data to focus on the speed at which information is gathered and understood, while others will prefer focus on deep comprehension and new insight based on this tidal wave of data.

The importance of getting information faster than others in the market has been a staple in the financial community, since… well… forever. Obtaining a piece of knowledge a little ahead of others allows an investor to get in to or out of market positions before the relevant knowledge impacts general pricing. Entire communication systems have been and deployed, and untold amounts of money have been invested for this purpose. Each wave of innovation in communication systems leads to the inevitable rush to slice some time off of financial relays.

Big Data and its related technologies allow for increases in the speed of communication and the dissemination of information. It, therefore, is the current embodiment of the long-term financial market trend of leveraging advancements in communications to gain information just ever so marginally faster. The best example at present is how social media is being used to displace, or at least buttress, traditional financial news. This is because, in many instances, the millions of everyday people using Twitter will be able to provide information about an event faster than a few thousand professional financial journalists. Traders have discovered that they can receive information nuggets many times faster through something like Twitter than through news channels. The underlying theme in speed-based approaches to Big Data is, “Get me the same information I already like using, just do it faster. I know how to use it and interpret it, as does the rest of the market, so any time gained will be a huge advantage.”

Gaining improved insight though Big Data is just as valuable, albeit less straightforward, than gaining a speed advantage. One of the issues associated with this ‘insight’ approach is that, historically, new forms and types of information must create whole new forms of analysis through which insight can then be gained. For instance, advanced technical analysis of stock trends only became possible when stock price historical records had been kept for a considerable amount of time. Similarly, discounted cashflow analysis only became possible with the advent of reliable accounting information. Finally, macro analysis was only devised through the standardization and availability of macroeconomic data. Arguably, each of these thoroughly different analysis techniques is a cornerstone for much of the investment analysis that currently takes place. It is difficult to envision investing without them. With all of this in mind, it is not unrealistic to imagine that the insights that Big Data analysis provides will become a new type of financial cornerstone in the near future.

A more recent phenomenon, high frequency trading, is also a product of changes in the availability and speed of information. New shorter term tick data, new higher powered computers, and faster, more reliable delivery of automated trades made high frequency trading a reality.

What is next for finance now that data is ubiquitous? If the government’s aggregation of national unemployment, inflation, and other forms of data can revolutionize economic analysis, and if standardizing and regulating quarterly accounting standards and information transparency can revolutionize fundamental analysis, how much more powerful will the transformation of financial analysis be due to the advent of total data?

Ubiquitous Data and Investing

Published: February 21, 2014

By: Kevin Coogan

Data is ubiquitous. Historians will likely peg the moment we as a society figured this out right around now. It will be seen as slightly after the infatuation with social media began to wear off, as Big Data became a buzzword and slightly before we realized that true privacy is antiquated.

The implications for almost all sectors are enormous, but for finance it seems like its impact will be immediate and universal.

The financial markets are a pricing mechanism and that mechanism relies on data. Looking at the most basic building blocks of investment theory, we see that data (or information) sits at its core. Investors simply would not be able to efficiently and effectively price an asset without relevant data.

Access to superior information has long been known as an advantage in the market. Taken to an extreme investors began to seek privileged information – which has been aggressively regulated in an attempt to create a level playing field. A company announcing its revenues or a round of layoffs, or the government announcing macro-economic data, like inflation or employment figures, is tightly controlled information which is often released to the market at specific times and in specific forums.

If data is ubiquitous, however, can such data be truly concealed until that final announcement? This does not infer data leaks or insider information. Data being everywhere, with increasingly interesting and novel collection and analysis, implies that clues are left for those willing to hunt for them. Finance in the 21st century will heavily rely upon discovering and leveraging these new sources of data and new analytics to get to insight prior to the ‘big’ announcement.

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