Gann allows us to make outrageous forecasts that no other discipline can match. The trick? With Gann we base our views on “science.”

Gann is not going to win the day with traders who don’t believe in charts. Traders tend to be first and foremost skeptical of anything and everything. I don’t blame them. There is a lot of stuff out there that is just sheer marketing hype. But Gann is not marketing hype. It is real. The more you study Gann, the more you realize the “Truth in the Ticker Tape.” The truth in the charts. The charts don’t lie.

A short video tutorial demonstrating the power of Gann analysis.

The position of the planets in relation to each other has some sort of energy that can influence the markets. I don’t understand it; I just know from observation that it works. Gann’s ideas came from a scientific study of the planets and how they relate to the markets. It is fascinating stuff. Not everyone will see it that way, but so it goes.

I’m still mystified that more traders don’t even know who Gann was. Gann was a genius. No hype. Just hard work and incredible diligence to his craft. And brilliant insight.

“A Tutorial to WD Gann Analysis” is a collaboration between Catapult Research and Jeanensis Capital Markets. Complimentary Gann Analysis charts could be requested by contacting either or

Policy, Laws And Compliance

Artificial Ignorance is a term that relates to machine learning and anomaly analysis, but as a concept, it can remind us to put in a process that ignores the noise of data overload and to focus on solving a problem, winning an opportunity, or opptimizing a business. In this post, we tie this to an important risk management concept: Identify, Measure, Monitor, and Report.


Initially, Artificial Ignorance sounded like a parody that expressed a blissful retreat from the hype and fears of Artificial Intelligence. Synonyms for ignorance includes bewilderment and unlearned, portraying Artificial Ignorance is an automated way to do nothing.

It is not. In this case, ignorance is the act of ignoring unimportant data, allowing you to focus on what is important and necessary in order to create value.


Artificial Ignorance is a type of machine learning. It is mostly associated with monitoring log files and network traffic to spot security intrusions, identify root cause of system crashes, and other issues.

See the animated diagram below for this quick explanation:

  • In the normal course of business, your organization generates data activity.
  • A machine drops in and monitors the activity over time in order to learn what is normal.
  • If something new enters and affects your environment, the machine identifies what is not normal, or anomalies, at very fine detail.
  • It brings to your attention potential problems or perhaps, opportunities.

Since it is machine learning, issues are picked up that have not already been documented, mitigating risk against the unknown.


The expression, “Can’t see the forest for the trees” is used to describe someone who is overcome by detail and fails to see the big picture. In this post, we consider the opposite, where value is derived by seeing specific trees for the forest. Depending on the business challenge, you may be looking for opportunites (trees with nuts) or problems to be fixed (diseased trees).

Mobile computing, real time analytics, and financial transactions from the edges of your world generates huge amounts of data in an instant. Depending on the situation, it may be optimal to ignore most of it (the forest) and focus on key events (some of the trees), optimizing the decision process.

Outside of the network infrastructure scenarios described above, the concept of Artificial Ignorance can be applied closer to the decision making process if you can get creative with the right tools and data assets in order to identify what is not normal (or what is special).


A deviation in consumer behavior could flag a new business opportunity. Deviation of compliance based events could contribute key facts to a fraud examination and provide a compelling example to auditors of compliance monitoring and controls. Here are two examples:

  • A trader action that deviates from what is considered normal trade activity could trigger a red flag. It may be worth monitoring that trader by additional and more descriptive means (chat, voice, review trade history, etc.).
  • For trading strategy, the “something new” described in the animated diagram above, could be non traditional data like weather or location intelligence that puts a particular risk measure out of normal limits.


A standard process in Risk Management is to identify, measure, monitor and report. Measuring, monitoring, and reporting comes to naught if relevant issues can’t be identified. There is often the desire to bring in more and more data but the tools need to be put that identifies what is important.

There is a lot of excitement and hype that comes with big data, real time analytics, artificial intelligence, the fintech revolution, and so forth. However, there is still a fundamental need to focus on business strategy (conceptualize and act on what is important) and data strategy (capture and identify what is important).

Depending on your business drivers, you probably don’t need all of the data that is being created. This is a good concept to keep in mind. As it relates to Artificial Intelligence, there could be very good reason to apply Artificial Ignorance first.

“Use Location Intelligence to Combat Financial Crime”

This video accompanies the article by Kenneth Goodwin, Jeanensis Capital Markets, and Paul Lashmet, North Castle Integration called, , featured in TabbFORUM.

Policy, Laws And Compliance

Intelligence – artificial or real – is only as good as the information it uses. Location intelligence helps contextualize a situation and can augment machine learning and other applications of artificial intelligence. How can location intelligence be applied to regulatory compliance initiatives to help combat financial crime?

Intelligence (artificial or real) is only as good as the information it uses. A higher volume of relevant information provides deeper context to a situation, resulting in heightened intelligence, increased value, and better outcomes.

Location intelligence is a data asset that helps contextualize a situation and can augment machine learning and other applications of artificial intelligence. This post considers how location intelligence can be applied to regulatory compliance initiatives, specifically fraud examination and forensic audit, to combat financial crime.

Fraud Examination and Forensic Audit

Fraud examination is the discipline of resolving allegations of fraud from tips, complaints, or accounting clues. It involves obtaining documentary evidence, interviewing witnesses and potential suspects, writing investigative reports, testifying to findings, and assisting in the general detection and prevention of fraud*.

Forensic audit is the process of investigation used to build a case that supports fraud examination. It is the intersection of financial principles and the law, and therefore, applies the following:

  1. Technical skills of accounting, auditing, finance, quantitative methods, and certain areas of the law and research;
  2. Investigative skills for the collection, analysis and evaluation of evidential matter; and
  3. Critical thinking to interpret and communicate the results of an investigation.

Points 2 and 3, above, are particularly relevant to this post. The collection and analysis of evidential matter needs a variety of data assets to support a comprehensive investigation. Critical thinking – sometimes referred to as lateral thinking, or thinking “outside the box” – is a disciplined approach to problem solving and guides our thought process and related actions. Critical thinking is required to contextualize patterns and connections across evidential matter.

Location Intelligence Is Context

Think about where you are right now. You may be at a specific address. If you have location services enabled on your phone, you are also located at specific coordinates relative to the rest of the world. However, the intelligence of your location is more than that.

The neighborhood that you are in can be characterized by demographics and consumer preferences. It also can be described in relation to the proximity of other neighborhoods that may have similar or very different characteristics. The people around you have mobile devices that are interacting with social media, streaming a live event, or making a purchase. Video is capturing movement, and machine sensors are analyzing usage and scheduling maintenance.

Location intelligence applies the multiple layers of information that surround you. The better you are able to logically connect these layers of information, the more location intelligence you have.

Back to Evidential Matter

Location intelligence continues to grow as a critical component of analytics, and it is being applied to more and more scenarios across industries through social media, connected devices, distributed systems, the internet of everything, and overlaying all of that with just about any dataset that can be collected.

In financial services, location intelligence provides an important and actionable level of information that ties together a customer profile, from on-boarding to know-your-customer and financial crime investigations. Referring back to Fraud Examination and Forensic Audit, above, location intelligence can be a critical component of evidential matter.

A Scenario

Party A is a bank customer that, by all accounts, is squeaky clean.

  • Party A executes a financial transaction with Party B from a place Party A has never been to before.
  • Party B registered their place of business as being in an upscale business park, but a satellite view shows that the actual location is in an abandoned industrial area.
  • Party B makes many transactions with Party C that very closely correlates to the sum that was originally sent from Party A.
  • Party C is located in very close proximity to a HIFCA designated zone. [To get a sense of what proximity risk is, look at High Intensity Drug Trafficking Areas (HIDTA) and High Intensity Financial Crime Area (HIFCA).


Location Intelligence - How it works [Graphic by Paul Lashmet]

Location Intelligence – How it works [Graphic by Paul Lashmet]

Should Party A be investigated? It is a questionable circumstance, at least.

As it relates to forensic audit, location intelligence provides additional layers of information that tighten the interrelationship among auditing, fraud examination and financial forensics in a dynamic way that adapts to political, social, and cultural pressures over time.


Fraud examination and forensic audit encompasses much more than just the review of financial data; it also involves techniques such as interviews, statement analyses, public records searches, and forensic document examination to identify questionable circumstances.

Location intelligence can help identify transaction anomalies more quickly, verify customer place of business visually and more efficiently, and flag sensitive cross-border conditions and proximity risk. All of these are vital in securing the proper documentation needed in a robust forensic examination.


The organization’s obligation is to report suspicious activity, not to stop it, by submitting a SAR (Suspicious Activity Report). There are two levels to this:

  • Level 1 – “The Sniff Test”: A questionable activity or a correlation of circumstances is identified (“Something seems fishy here”).
  • Level 2 – Investigation: A disproportionate amount of time can be spent manually collecting supporting documentation. A good set of data access tools that links relevant data points, including location intelligence, can optimize this process, making it faster and with better quality.

This is article was first published in TabbFORUM “Rebuilding Risk and Compliance for the Age of Oversight”, sponsored by Cognizant.
Authors: Kenneth A. Goodwin Jr. of Jeanensis Capital Markets and Paul Lashmet of North Castle Integration.

China’s Exchange Rate Regime: A Crawling Managed Float

For most professionals in the financial industry, July 21, 2005, was a historic day. On this day, the “People’s Bank of China” (PBOC), with the authorization of the State Council (1), announced China’s new exchange rate regime from a peg to the U.S. dollar to a basket of currencies based on market supply and demand. In essence, the Renminbi (RMB) would be re-valued by about 2 percent to 8.11 yuan per U.S. dollar(2).  Moreover, the closing rate at the end of each trading day would become the central rate for a trading band on the following day. Nevertheless, the daily trading price of the U.S. dollar against the RMB in the inter-bank foreign exchange market would continue to float within a 0.3 percent band around the official parity published by the PBOC. The trading prices of the non-U.S. dollar currencies against the RMB would be allowed to move within a certain band announced by the PBOC. The PBOC would also make adjustments to the RMB exchange rate band, when necessary, according to market developments as well as the economic and financial situation.

Is the new currency exchange regime more flexible? Was there really ever a deviation in the structure of the exchange rate regime to a managed float system?

At first glance, it could be reasoned that the new currency rate regime is not as flexible as the previous one, but is instead a combination of two currency exchange rate methods with similar structures.  In fact, Chinese authorities have never abandoned the previous exchange rate regime of the peg but instead, maintained the current managed float system while making minor changes to certain variables. Thus, this combined system could be termed a “crawling managed float” which consists of the managed float system and a “basket band crawling system” (BBC). In both methods, all variables(3) remain the same except for the change in the target currency from the U.S. dollar to a basket of currencies. In order to better understand the crawling managed float, it would be beneficial to explore examples of both currency methods: the managed float regime and BBC system.

A managed float currency exchange rate regime involves the following steps:
• First, the PBOC agrees to an objective in stabilizing the effective exchange rate.
• Second, the PBOC assigns a weighting scheme to selected foreign currencies involved in the basket. Typically, the countries’ major trading partners are selected. If we were to factor just on the balance of trade, heavier weights would more than likely be assigned to the U.S dollar, Japanese yen and Hong Kong dollar in that order. In the short-term, the impact on Japan and the U.S. balance of trade would be minimal at least. Nonetheless, in the long-term, Japan and the U.S. trade gap imbalances with China may widen further as variables in the currency regime are changed.
• Third, the PBOC selects the main currency and assigns the highest percentage weight.
• Fourth, the PBOC stabilizes the effective exchange rate by intervening the rate of the domestic currency against the main currency in the foreign exchange market.
• Fifth, if the main currency appreciates against other currencies in the basket, the central bank would intervene to stabilize the effective rate and ensure that the domestic currency depreciates against the main currency. Conversely, if the main currency depreciates against other currencies in the basket, the domestic currency is revalued against the main currency to maintain a stable effective exchange rate.

In a “basket band crawl” (BBC), the effective exchange rate is also based on a basket of currencies of major trading partners. The difference lies with allowing for greater fluctuations in the short-term. This system maintains a country’s export competitiveness while tracking the currency trends of its major trading partners. Moreover, the system prevents speculators from knowing how authorities would react to movements in market exchange rates, thus reducing the likelihood of currency attacks. Singapore and Thailand are often cited as examples of countries in Asia that have exchange rates systems similar to a BBC.

It appears that Chinese officials favor maintaining a high growth rate in GDP terms while risking inflationary price pressures over the long-term. If so, targeting and maintaining a stable effective rate over the short-term will provide ample time to build on country-specific fundamentals such as increasing domestic demand and improving financial infrastructures while protecting gains on the export side. Moreover, if timing is what the Chinese authorities are seeking, then implementing a “crawling managed float” is the most appropriate currency rate regime. Nonetheless, the risk associated with this currency method requires a sustainability and further expansion of the current GDP level. This is economically impractical and non-beneficial. In the case of China, GDP growth has mainly been export driven and maintained by elements of capital controls and a favorable currency regime. Since export demand is not a constant variable, maintaining the current level of high GDP growth would require increases in either domestic demand and/or foreign direct investment. The PBOC has made some efforts to increase foreign direct investment and is encouraging more domestic consumption. Nonetheless, domestic inflationary price pressures may impede the PBOC’s efforts. In short, “all good things come to an end” and unforeseen market forces, once they subside, can hinder the objectives of the Chinese currency regime.

It goes without saying that the PBOC will continue to control the two most important elements in the foreign exchange marketplace: flexibility and transparency. Flexibility, at least in the foreign exchange marketplace, can be measured by the change in daily volatility and cumulative change over a period of time. Given these factors, market participants can trade from the volatility and speculate, with data received, the direction of the marketplace. Because there is little public information regarding the respective country weights of currencies in the basket, any possibilities for speculators to participate in the marketplace are eliminated(4).

That being said, the narrow band of 0.3 percent sets boundaries on the movement of the currency.

In summary, unfavorable long-term indicators, such as inflationary pressures, a change in the balance of trade between the U.S. and Japan and foreign direct investment inflows may cause Chinese officials to make some adjustments to the currency regime. Changes will occur but at a moderate pace and may include adjustments in the value of the currency, expansion of the currency band from 0.3 percent to 3.0 percent and just maybe, the announcement of the weight percentage of respective currencies in the basket.  Nonetheless, Chinese officials will continue to have a tight grip on the yuan as long as strict capital controls remain in place.  Eventually, Chinese officials will come to understand that financial markets, characterized by such fundamental elements as transparency and flexibility, tend to operate more efficiently.

(1) This illustrates that Chinese bureaucrats were cognizant of the effects of the currency re-valuation decision.
(2) The RMB was re-valued to 8.09 yuan per U.S. dollar.
(3) Variables include the target currency (s), trading band perimeter, and currency regime.
(4) In August 2005, Chinese officials revealed that the U.S. dollar, the Japanese yen, the Euro and the South Korean won were heavily weighted in the basket of currencies.

This article was published during Kenneth A. Goodwin Jr.’s professional secondment as a Mike Mansfield Fellow working at the Japan Financial Service Agency’s International Affairs division and the Bank of Japan. He is also a Nakasone Scholar, The Aspen Institute Socrates Program.