A 23% compounded average annual rate of return.  Not too bad.

During his career Shelby advised Thomas Dewey on economic matters when he ran for president in 1940 and 1944 and served as Governor Dewey’s First Deputy Superintendent of Insurance from 1944 to 1947 in New York. Later, he served as US ambassador to Switzerland under Presidents Nixon and Ford (1969 – 1975).

A philanthropist, he was head of the Shelby Cullom Davis foundation which disperses funds to higher education and to research on public policy and economics. Princeton, his alma mater, was one of the beneficiaries of his grants. He also served as chairman of the Heritage Foundation.

Shelby got around.

But what the heck does this have to do with Fintech?

In a previous post we found that the Tucson, AZ metro area made our top 15 list of top addressable Fintech Securities & Investments markets on a per employee basis in 2015 (ranked 14^th).

Can a contributing reason for this can be tied directly back to Shelby Cullom Davis?

Securities & investments sector in Tucson, AZ

Tucson, AZ probably does not first come to mind when thinking about hot spots of financial activity. So, what is going on in Tucson?

As we have done in previous posts, let’s start with a high-level view of Tucson’s Securities & Investments sector. As shown below, 62% of the 2015 addressable Fintech market is due to portfolio management. Another 27% is due to securities brokerage. This is generally consistent with the West regional average of 28% for securities brokerage we saw in an earlier post. But it reflects a much higher concentration of potential Fintech spend in the portfolio management sub-sector (62 vs 48%).

Again, our analysis uses ZIP Pointe Market Sizer.  Market Sizer is our Tableau-based market sizing tool based on ZIP Code-level Census data on over 7 million private sector business locations.

In terms of business location size by sub-sector, nearly 80% of the locations are small (1 – 4 payroll employees). This is a recurring theme. The sub-sector is dominated by small locations though these are not necessarily all stand-alone companies (i.e. they could be formal branch locations).

There are only 29 locations with 10 or more payroll employees. A bit over 40% of the locations are involved with securities brokerage, another 26% are portfolio management and another 18% are investment advice (see below).

With respect to “hot spots” of potential Fintech spend per employee, it is clearly the portfolio management sub-sector that stands out, with potential spend ranging from $5,337 to $7,682 per payroll employee. The top hot spot consists of 3 locations with 50 to 99 employees.

Note: The US Census source used by Market Sizer tracks only payroll employees. Independent contractors are not reflected in these numbers. Hence, the actual employment level can be higher, possibly much higher, in sectors that make extensive use of contract labor.

ZIP Code level view

As we have done in prior posts, another way to drill into the addressable Fintech market in Tucson is by ZIP code. The following map shows the ZIP codes in the metro area color coded for addressable market size per employee. At the ZIP Code level, addressable Fintech market size ranges from $1k to $6k per payroll employee.

But there is a distinct hot spot in ZIP Code 85706 (circled in red) at $6k per employee. According to Wealthminder.com, the largest Tucson financial advisory firm (in terms of assets under management) is located in this ZIP Code:  Davis Selected Advisors.

Other top firms in the Tucson metro area (indicated by red arrows) are Tci Wealth Advisors in ZIP Code 85718; Sonora Investment Management in Zip Code 85719; Strategic Equity Management in Zip Code 85715; and Sterling Investment Management in Zip Code 85718.

Which ZIP codes account for the bulk of potential Fintech spend?

We have already discussed two of the top 3: 85706 at 16% and 85718 at 20%. But these are surpassed by ZIP Code 85712 (indicated by the green arrow) which accounts for 27%. These three ZIP Codes together account for 63% of Tucson’s potential Fintech spend in the Securities & Investments sub-sector.

Another way to drill into the addressable Fintech market is on a per business location basis.  Dividing potential spend by the number of locations is another way to normalize the data. This map (shown below) looks quite a bit different and one ZIP Code stands out above all the rest: 85706. Although the ZIP code overall accounts for 16% of total potential Fintech spend, on a per location basis, it ranks at the top with $230k in potential spend per site.

ZIP Code 85706

Turns out at there are only 3 locations in ZIP Code 85706.  At least one of these (the larger one) is associated with, yep, Davis Selected Advisors. And it may be that the other sites are also associated with Davis as we could not find any other Securities & Investments firms (with at least 10 employees) in this ZIP Code (If anyone knows, please let us know!)

Davis Selected Advisors

So, the Davis name is a major player in the Tucson metro Securities & Investments sector. Any relation to Shelby Cullom Davis perhaps?

Davis Selected Advisers was started in 1969 in New York by Shelby Davis…not Shelby Cullom Davis but his son Shelby Moore Cullom Davis. Following his father’s footsteps, Shelby M.C. Davis graduated from Princeton and rose through the ranks of the New York financial industry – specifically at the Bank of New York.

In the late 1970s the non-portfolio operations of Davis Selected were moved to Sante Fe, N.M. In 1997, a service center was opened in Tucson and personnel were hired locally and moved from Santa Fe. By 2000, Tucson had became Davis Selected’s global headquarters, specifically in ZIP Code 85706 where the company operates out of its own commercial property.

Today, Davis Selected Advisers is led by Shelby’s son, Chris Shelby, grandson of Shelby Cullom Davis.  “Davis Selected controls the nine-fund Davis group, the Selected group of funds, which is a no-load, four-fund group, private accounts of no less than $5 million, and other managed money programs.” (Inside Tucson Business).  Currently, Davis Selected has about $30b in assets under management.

So, there you have it…from Switzerland to the Sonoran Desert! 

Ok, maybe New York to Arizona…

The post Top 15 Metros – From Switzerland to the Sonoran Desert – Fintech Securities & Investments appeared first on KDD Analytics.

And so will tax incentives.

The Raleigh-Durham-Chapel Hill combined statistical area (CSA), with over 2 million people, is the second largest metro area in North Carolina following Charlotte to the south. It is home to 3 major universities: Duke, University of North Carolina (UNC) and North Carolina State. Duke and UNC form two of the vertices of the “Research Triangle,” an area known for a high concentration of corporate operations and research companies and startups, especially in life sciences. The third vertex is formed by North Carolina State University in Raleigh.

The Raleigh metro area was number two on the 2015 Forbes list of the best place for businesses and careers. Durham ranked number 20 while Charlotte was 14th. Companies located in the Raleigh-Durham area include heavy hitters like IBM, GlaxoSmithKline, Cisco, SAS Institute and Lenovo.

As one of the largest research parks in the world, Research Triangle Park (RTP), sits mostly within the Durham-Chapel Hill metro area. RTP employs nearly 50,000 “high-tech” workers and is home to over 250 companies including Credit Suisse, Dell, EMC and Fidelity Investments. None of these are tobacco companies.

North Carolina has been very aggressive in wooing companies to locate in the state.  Lucrative incentives have been offered including outright grants, rebates of employee withholding taxes and sales tax refunds. Raleigh-Durham-Chapel Hill has directly benefited from these incentive programs as has the Charlotte-Concord-Gastonia metro area.

So, what does this have to do with Fintech?

Two million people and a healthy economy means there’s a lot of money that needs good advice. And a healthy program incentivizing companies to locate in the state doesn’t hurt. This combination has pushed the Durham-Chapel Hill Metro to #6 in our ranking of the Top 15 List of Fintech Securities & Investments Markets on a per employee basis in 2015.

Securities & Investments sector in Durham-Chapel Hill

What does the Securities & Investments sector look like in Durham-Chapel Hill, NC? To start, as shown below, over 50% of the 2015 addressable Fintech market is due to securities brokerage. Another 27% is due to portfolio management. This is generally consistent with the South Atlantic regional average of 45% and 28% that we saw in an earlier post.

Again, our analysis uses ZIP Pointe Market Sizer. Market Sizer is our Tableau-based market sizing tool based on ZIP Code-level Census data on over 7 million private sector business locations.

In terms of business location size by sub-sector, over 70% of the locations are small (1 to 4 payroll employees). There are only 7 locations with 100 or more employees. A bit over 40% of these locations are involved with securities brokerage, another 28% are portfolio management and 19% are investment advice (see below). With respect to “hot spots” of potential Fintech spend per employee, several business locations classified as Miscellaneous Intermediation (e.g. venture capital) have the highest Fintech spend per employee of around $12,000. In the chart below we have circled the largest locations.

Another way to drill into this market is by ZIP code. The following map shows the ZIP codes in the metro area color coded for addressable market size per employee. At the ZIP Code level, addressable Fintech market size ranges from $1k to $6k per employee.

According to Wealthminder.com, the largest financial advisory firms (in terms of assets under management) are located in ZIP Codes 22701 (Amundi Smith Breeden and Verity Asset Management), 27713 (Piedmont Investment Advisors), 27703 (Gordon Asset Management) and 27707 (NCM Capital Management Group).

 

ZIP Code 27709

So, on a per business location basis as well as a total basis, there is a very heavy concentration of potential Fintech spend in ZIP Code 27709. Focusing on the distribution of sites in this ZIP Code we see that the largest locations in the metro area are also in this ZIP Code (see the chart above with the red circle). Since this ZIP Code partially covers Research Triangle Park (RTP), these large locations are likely Credit Suisse and Fidelity Investments (see the RTP company directory).

Note: The US Census source used by Market Sizer tracks only payroll employees. Independent contractors are not reflected in these numbers. Hence, the actual employment level can be higher, possibly much higher, in sectors that make extensive use of contract labor.

Fidelity Investments and JIDGs

Fidelity is one of the larger employers in the Durham-Chapel Hill metro area. Fidelity first began to operate in North Carolina in 2002 “when it acquired an employee benefits business. Four years later, the firm designated North Carolina as a regional technology center, added a data center and a customer contact center and based Fidelity Charitable in the Triangle.” wral.com 

Fidelity further expanded its presence starting in 2006 and in 2015 announced plans to add an additional 600 jobs. Projections are for 4,200 jobs by 2018, mostly at the Research Triangle Park campus.

Fidelity, as well as other business, was likely incentivized to locate and expand in North Carolina by Job Investment Development Grants or JIDGs. These state grants are a

“performance-based, discretionary incentive program that provides cash grants directly to new and expanding companies to help offset the cost of locating or expanding a facility in the state. The amount of the grant is based on a percentage of the personal income tax withholdings associated with the new jobs.” Economic Development Partnership of North Carolina.

Companies can use JIDG funds for any purpose.

As these grants are performance-based, companies receive the full incentive only after meeting certain benchmarks.  These requirements can be in the form of a certain number of jobs created and retained, generation of a targeted $ amount of gross state product and/or the generation of a certain positive net revenue amount over the life of the grant.

Recently, North Carolina has “sweetened the pot” of these incentives, with an eye towards “transformative projects”, those that invest at least $4 billion and create 5,000+ jobs (ahem…listening Amazon?).

All states have some type of incentive program to entice businesses to locate and expand in their states. North Carolina appears to be one of the more aggressive ones. And this definitely was a contributing factor to why the Durham-Chapel Hill, NC metro area is #6 on our list.  #2 Omaha powered its way there more organically it seems.

All regions are different.  And Fintech companies looking to successfully disrupt these segments will need to be able to take these differences into account.

The post Top 15 Metros – Fidelity Durham – Fintech Securities & Investments appeared first on KDD Analytics.

Is tone really that important? And if so, can it be measured and scaled?

Why might tone be important?

Analysts and investors are seeking an edge as to where a company is headed next. So, they focus not only on the numbers, but on the words the executives use in their introductory remarks and in the answers to analysts’ questions. Executives can unintentionally or even intentionally “tip their hand” and provide a clue as to the company’s direction.

But aside from making good CNBC commentary, is there a connection between tone and future company performance? Is it possible to detect tone without listening to the call?

Financial textual analysis has matured

Before the advent of natural language programming (NLP), text analytics and enhanced computing power, written transcripts and audio recordings were all analysts had to go on. There is only so much that the human mind can discern. Subtle shifts in tone and word usage can go unnoticed when listening and tone seems lost when reading transcripts of calls you didn’t have time to cover.

But now, new tools have emerged that can help. Going beyond simple sentiment, nuances in tone emerge when analyzed with advanced computational linguistics. A new academic field of inquiry developed, financial textual analysis, out of this ability to decompose earnings transcripts and financial documents into their most granular bits. Academic researchers were (and are) keen to determine if tone can presage future company performance.

Although academic researchers have always been interested in studying financial disclosures, it wasn’t until the early 2000’s that interest in financial textual analysis really took off. The bibliography of a survey of financial textual analysis studies shows the exponential growth in the number of studies starting in the early 2000’s. The authors attribute this growth to the application of “highly evolved” computational linguistics technologies to financial documents and enhanced computing power.

So, does tone matter? Simple answer is YES.

What do these academic studies conclude about tone? Can executives’ tone on earnings calls “tell” analysts something about future company performance?

It is beyond the scope of this post to survey the entire field of financial textual analysis. But we can say the answer is “yes,” tone matters. Here is a taste of what the academic literature has found:

We note that these studies tend to be sophisticated statistical studies that attempt to estimate the relationship between tone and company or market performance after controlling for other factors that may affect such performance (i.e. they tend to be more than simple correlation studies).

A study by Price, Doran, Peterson and Bliss finds that “conference call linguistic tone is a significant predictor of abnormal returns and trading volume.” The researchers further find that the Q&A portion of calls has an incrementally greater effect than the introductory portion, which is typically a duplication of the earnings press release. The study concludes that “conference call tone dominates earnings surprises over the sixty trading days following the call.”

Another study by Price with Blau and DeLisle finds a positive relation between companies’ stock returns and earning call tone. But the researchers go on to argue that tone can be subtle and that more sophisticated investors (e.g. short sellers) process the information differently than the average investor. Empirically they find that “short sellers target firms with simultaneous high earnings surprise and abnormally high management tone.”

Druz, Petzev, Wagner and Zeckhauser looked at changes in earnings call tone from quarter to quarter finding that higher negativity of executives’ tone “strongly predicts lower future earnings and greater uncertainty.” Interestingly, the researchers find that decreased negativity only “weakly predicts the opposite.”

Finally, Chen, Demers, and Lev’s research showed that the overall negative tone of earnings call Q&A increased as the morning progressed, dipped slightly after lunch and then rose again until the market close. They also found that the more negative the tone, the more negative the stock returns over the five trading hours after the call. Those negative stock returns associated with negative calls continued downward for up to 15 trading days.

So, it’s clear that “tone matters” and computational linguistics can detect it. Analysts, investors and the market react to the choice of words used by executives in their earnings calls. Investors need to factor this information into their trading strategies, but with care to consider external factors as well.

Boulder Earnings Call Tracker

KDD Analytics is partnering with Boulder Equity Analytics in the development of the Boulder Earnings Call Tracker. This Tableau-based set of dashboards examines earnings call tone among other attributes of earnings calls. It provides our clients with highly relevant signals for sophisticated trading strategies, especially short sellers. On the flip side it provides CEOs and investor relations with unbiased metrics to improve their conference call performance.

The post Negative Earnings Call Tone? Go Short (but not always) appeared first on KDD Analytics.

Analysts under time and deadline pressure need to know that the information distilled by an AI solution is relevant to their context and is not simply the result of key word searches.  Understanding context is foundational to the collaborative AI solution offered by our partner BEA.

In another guest article, Tom Marsh, CTO at Boulder Equity Analytics (BEA), talks about context and why, without it, computer algorithms will never be able to truly know “you”.

Since Robert Scoble and Shel Israel just released their new book “The Fourth Transformation”, I decided to revisit “Age of Context“, a global survey of the contributions to the forces influencing technology.  The five forces were mobile, social media, data, sensors and location.  Scoble called these the “five forces of context”, the future of computing.  The five forces are still there but hardly tamed and in the rear view mirror.

Revisiting this topic three years later (see my ai-one post), there is still a lot of work to do in mainstream applications of cognitive computing.  For our BEA clients, it remains a critical challenge to building analytics for analysts under time and deadline pressure, faced with exploding amounts of information.

Why is context so important

First, context is fundamental to our ability to understand the text we’re reading and the world we live in.  When reading a sentence, you draw on the semantics of the words, the sentence, the paragraph, the context of the page, chapter, book and prior works or conversations.  Added to your ability to understand the sentence is your education and experience, and your reasons and objectives for reading it.  This diagram from Chris Campion’s blog is instructive in this regard.

Second, if you broaden the challenge it overlaps with personal intelligent agents (Siri, Alexa, Cortana, Google Now), the bigger problem of complexity.  The inability to provide context has always made it difficult for computers and people to understand each other.  Three years ago Scoble felt Google Glass could be that enabling breakthrough and now maybe VR will be the answer.

People and the language used to describe the world is a complex system.  No matter how much data is crunched or how sophisticated the technology (e.g., new generation convolutional neural nets), you can’t be reduced to an algorithm.  Claiming personalization, these tools create approximations of you based on people like you, not a true you.

There’s a difference, especially when applied to automating the decisions of experts in investing, reinsurance contracts or road-maps for new technologies. The intelligent agents that make those decisions need to have a complete model of a complex world.

The five forces of context as the foundation

Pete Mortensen also addressed the problem of context at the same time in his article “The Future of Technology Isn’t Mobile, It’s Contextual.”  Mortensen argues that the five forces are finally giving computers the foundational information needed to understand “your context” and that context is expressed in four graphs.  These data graphs are
 
•    Social (friends, family and colleagues)
•    Interest (likes & purchases)
•    Behavior (what you do & where)
•    Personal (beliefs & values)
 
At BEA, we build the foundations for context by extracting Mortensen’s graphs from the complex data generated by your digital activity, your domain and a complete view of the material you’re researching.  We’ve been using our technology to process and store unstructured and structured text in diverse domains to deliver a representation of that knowledge through powerful visualizations of the models that before now existed only on the page and in your head. 

The five forces of context – and beyond

So first, we get the context right.  Then we add metadata and attributes using NLP, entity extraction, sentiment and other proprietary linguistic algorithms.  This is processed, indexed and stored via python middle-ware that allows us to extract all possible attributes for each paragraph. 

Running in the cloud and integrated with big data sources and ecosystems of existing APIs and applications, we can quickly create and test your investment models or add intelligence to old ones.  Our interactive Tableau visualizations respect the analyst’s expertise, neutralize human bias while acknowledging the limitations of the technology, never delivering “black box” results that aren’t transparent and verifiable.

You work with concepts and use context constantly in your offline life.  We work to bring the same intuitive and human experience to your work as an analyst, without compromising your privacy or creating an approximation of you from the data of others.
 

Tom

@tom_semantic

KDD Analytics and Boulder Equity Analytics are partnering to deliver collaborative artificial intelligence to the financial and competitive analysis industries.

The post Context, Then Concepts, Words Last appeared first on KDD Analytics.

Language and the human brain

How many geese in this picture?  You didn’t need to count. Your answer probably contained the words “approximately”, “average”, “almost”, “sort of”, “guesstimate” or “about”.  One of the most powerful features of your brain is that it does not treat language as math, a series of binary yes or no formal constructs.  Humans are masters of writing the same idea in many ways, understanding what you meant even if you didn’t say it perfectly.  You also know when someone is being so careful with their words, they’re lying (we’re all tested on this one daily). This critical skill is used by analysts all the time.

Many technical approaches to textual analysis try to convert sentences into math or use the presence of specific words to return a yes or no result.  Our approach at BEA overcomes this issue with software agents that compare arrays (models) of patterns and return “similarity scores”.  These normalized scores allow us to determine whether a paragraph is about the same as another and by how much. 

In my last post, I discussed the nature and definition of concepts and how our solution is built to find them. Whether it’s a concept you’ve created or an example you’ve found in a document, if you can’t describe it you can’t find it.

Describing concepts

You can spot a concept when you read one, but learning to describe one isn’t as easy as it sounds and its rarely exact. Our AI platform enables us to create and teach “intelligence agents” to “read” documents to score similarity to concepts.

The first step in the process is to provide examples in a simple text box. This is surprisingly difficult the first time because our brain is never reading text without injecting our own education, bias and assumptions into the process.  Unfortunately the software only sees exactly what you provide it, no more, no less. When one user asked us how to teach the concept of “fraud” to our agents, we had to take a step back.

Risk and opportunity in legal issues

In the financial filings we focus on, concepts are often expressed indirectly, through a description of the act or its consequences.  The objective of the analyst is to find indications of wrongdoing or their cover up in company filings, earnings calls, news and social media. The critical evidence is never a clear statement like “I just gave my friend inside information on our earnings announcement so they could trade ahead of our disclosure.”

People don’t recite the definition of the crime when they talk about it. The language used is always more subtle and disguised. It also varies dramatically from one context to another (email vs. interview transcript for example). Tweets and text messages are full of acronyms, slang, phrases and partial sentences.

The guilty party is usually aware of the act and tries to avoid being discovered. He is more likely to say, “Hi Dave, here are some stats you might find interesting.” Is he talking about the company earnings report or his fantasy football team? If he says, “we’ll have a big surprise for you tomorrow”, is it a surprise birthday party or a merger announcement?

Kant’s tree concept (again)

To underscore this point, let’s revisit the example of the tree concept from my last post. The concept of a tree abstracted from descriptions of many trees is clear enough. The challenge in language analytics and many (most) language processing problems is that we are looking for the indirect effect or consequences resulting from the existence or actions of the tree.

This is easier to understand with examples. “The fall colors in New England are beautiful this time of year”.  Or “We need to get some shade for the yard at the nursery.” The concept of the tree is there but if I was searching for trunks, roots, branches and leaves, the “criminal” tree would escape detection!

Context is key

The context of the concept is critical.  Defining the context for the software can be complex.  But in most cases the patterns are there once you clear away the noise.

Capturing context can be as simple as setting filters.  For example, documents between specific individuals during specific time periods during which they had access to the information, resources and counterparts needed to commit wrongdoing.  Metadata from documents and entity attributes like organization, location and titles are commonly used for this purpose.

Where possible, we extract context from the source documents (and the source) to ensure that the more complex contextual factors are incorporated automatically into the intelligence agent. For example, the same person in the same period does not use the same language on twitter as they do in email.  Our powerful “almost engine” is what makes our system resilient and with our user in charge of how tight to set the “almost” meter, it adapts to a range of problems.

Is “big data” the answer?

We are all guilty of injecting our own bias and filters into understanding language. Good technical solutions capture the richness and subtlety in the context of the communications and ensure consistency of review.  This is not a problem solved by more and bigger data. 

We believe analysts have more than enough information.  They just don’t have enough time to find what matters.  BEA can help with that.

Want to know how we would use “almost” to solve your problems?  Drop us a line.

Tom @tom_semantic

KDD Analytics and Boulder Equity Analytics are partnering to deliver collaborative artificial intelligence to the financial and competitive analysis industries.

The post Good Concept Detection Requires an “Almost Engine” appeared first on KDD Analytics.

At Boulder Equity Analytics (BEA), “Our mission is to build an intelligent, enriched and fully interactive database from all the publicly available reports to improve the productivity and insight of the analyst covering an industry sector.”  We call it O.A.K.L.E.Y, collaborative artificial intelligence, a 2nd order of intelligence.  And we’re now creating the services around it.

Convenient, comparative, contextual and interactive services like ours will provide a foundation and no doubt be successful.  But that’s not our end goal.  

Competitive

We build tools for competitive analysis.  But more importantly we want to build tools that help you compete, in your job and in your markets. We can’t stop with a better version of what you can get elsewhere.  While there might be a market for a less expensive Bloomberg, what’s the competitive differentiator for you?  You have the same service as the rest of the market.  Which brings us to an interesting dilemma.  If we build a great service, after the early adopters exploit the advantage, everyone is equal again.

This is where our AI needs to augment your intelligence, not supply its own.

1st order intelligence

With our first products we’re using AI to “read” the market and company information so you can be more efficient in your research. This is the 1st order of intelligence.  It is like going to school to learn finance and then working the back room reading and reporting on company results.  It is the foundation for the next step in your career, using that knowledge to make investment decisions.

As the impact of MIFID II hits, our products will help you build a bigger and faster knowledge base with no bias and 100% recall at web speed and scale. Our products don’t get tired. That alone will help you beat the market.

But here’s where most take the easy way out: focus on speed and trades, not investing.  High-frequency trading (HFT) uses AI  It is in fact one of the most pervasive and advanced AIs in the world, second only to computer viruses and cyber bots.

This is a common execution of an AI strategy.  Go straight from the research department to the 3rd order of intelligence, an AI making autonomous decisions.  Think IBM Watson.  No humans required.

We think that’s the wrong approach. Black boxes powered by AI are as risky as they are powerful.

2nd order intelligence

Our goal is to deliver collaborative AI, a 2nd order of machine intelligence.  Our soon to be announced Boulder Earnings Call Tracker (BECT) for Investor Relations, Analysts, Portfolio Managers and Independent Research is our first offering at the next level.  

Simply, the 2nd order is collaborative and pulls out multiple dimensions of sentiment and tone built with new analytics methods that provide the nuance missing from today’s offerings.  It enables you to create completely new strategies once you’ve been able to look at your markets and competitors at a deeper level and at machine speed and scale.

In the past, long term was long term and even when you were right, it may have been years before the market rewarded you for your positions. Information was created, reported and analyzed from the paradigm of quarterly reports and press releases.  Your technology, economic and geopolitical models were relatively fixed.  Sectors followed predictable patterns.

We think that has changed.  

Markets have moved to the “second half of the chessboard” where exponential changes will create and destroy large enterprises (wealth) in quarters not decades.  What are considered fundamental long term investment decisions today, will look like day trades in the future.

Our goal is to bring the 2nd order of intelligence to you, providing a foundation for contextual decisions.  This will empower you with individualized tools to compete and win, whether against a machine or another trader. 

You won’t be able to read fast enough.  You need to retool.  And the first ones to do it will win.  In the immortal words of Walter Gretzky, as passed on through his son Wayne: “Skate to where the puck is going, not where it has been.” 

Tom 

@tom_semantic

KDD Analytics and Boulder Equity Analytics are partnering to deliver collaborative artificial intelligence to the financial and competitive analysis industries.

The post Read Faster? Researchers Need to Retool to Compete appeared first on KDD Analytics.

AI, artificial intelligence, has been through several boom and bust cycles.  Today the pronouncements are everywhere with AI coming soon to everything from medicine to your underwear.  For those of us laboring in the dark for years, it feels good to be the most popular kid on the block.  Warning!  What the marketing gods grant, they can take away overnight.

 I have been lucky to work with AI technology in language for the last six years.  As we worked to find traction, I was exposed to a huge variety of application problems.  Now focused on financial reporting and compliance, my favorite inbound call was from a fund in the UK with $11B AUM.  

“We have been reading about all the wonderful things AI could do and we want to purchase some ‘AI’”. 

They did not have a data repository, everything was on analyst PCs and not the cloud and they had no analytics capability.  While we completed a small proof of concept, it failed to get approval because “our analysts say they already read everything anyway”.  Could have seen that coming (good news is that it was part of the inspiration to start Boulder Equity Analytics).

AI is not just software

If you’re in the field, you’ve probably run into the same request, either from a prospective customer or from your own management.  Maybe your marketing department has already asked you for some AI to put into a product or, worse yet, added to the website. Now you’re supposed to figure out what sort of machine learning algorithm you can put in the product.  It’s just software, right?

This feels like the ’90s when the internet came online and everyone had to have an internet “strategy”.  Executives that didn’t even know how to turn on a PC were jumping on the bandwagon; most thought a website was an internet strategy.  

Keep it grounded

My request to AI practitioners is to keep it grounded and fight back. Don’t be co-opted into the marketing BS and don’t let management that has no experience with AI try to build an “AI” strategy from a 15 minute YouTube session. Read the technical experts that are debunking the exaggerated claims (like this piece by Yoav Goldberg) so you know the real state of the field. Talk to experts and ask questions, challenge them on the fail modes, the testing and the risks.

Walk before you run

If you’re in management, know that this is going to change your life, your organization, your markets and your customers.  The claims are real but do your homework.  Implementing an “AI” strategy is not a software project or something you can buy.  You need to start small, build a small team and execute some very small projects.  You do not need a big budget but you do need AI technology, domain expertise, access to the data and good UX people. Before attempting this, you should already have a good analytics foundation and culture (at least in the business you’re starting with).  

That said, don’t hold back.  Everything you’ve read about the exponential nature of the progress and its impact is true and coming fast.  The impact will be personal. If you don’t start right now, you will get run over by the freight train that is AI powered innovation. 

Tom (@tom_semantic)

KDD Analytics and Boulder Equity Analytics are partnering to deliver collaborative artificial intelligence to the financial and competitive analysis industries.

The post Buzzword Overkill – AI is Not a Thing People, it’s a Discipline appeared first on KDD Analytics.