Forward Guidance, De-Mystified and Automated
Today's research release from the European Central Bank (ECB) -- discovered via our PolicyScope Platform -- impacts profoundly how central banks and markets will communicate with each other in the near future. But to understand its significance, we need to provide a bit of background regarding both monetary policy formation and how advanced technology is changing the process of reading/cognition.
Monetary Policy/Forward Guidance 101
It is no mystery that market expectations and monetary policy have a symbiotic relationship. It is also no mystery that market dynamics can take on a life of their own, regardless of underlying "fundamentals."
Data-driven investors (and their algos) make investment decisions by understanding when a market trend reflects temporary sentiment/reaction functions or whether, instead, the trend reflects a structural shift. Understanding the difference between the two and being able to execute trades in the market successfully based on that understanding is what delivers significant alpha gains.
Central banks are on the other side of this equation, trying to set interest rate and liquidity policies in reaction to, and in anticipation of, medium-term expectations. Their language ("forward guidance") provides a formulaic approach to attempting to manage market expectations regarding future policy. Like investors, they seek concrete data to determine whether their words and actions are having the intended effect. The preferred mechanism to acquire this data is the old-fashioned market survey.
Both investors and central banks increasingly use advanced technology to enhance their work.
Advanced Technology/Enhanced Cognition
NOTE: BCMstrategy, Inc. customers who are already familiar with our platform can skip to the next section.
Here at BCMstrategy, Inc., we are in the business of automatically "translating" public policy language into structured data so that we can visualize and anticipate the next public policy move. Our patented process accelerates insight formation by making it possible to spot shifts in language (NOT sentiment) faster and objectively.
For example.....we can take 1000 words to describe a trend regarding, say, COVID19 policy.....or we can just show you a chart showing how policy shifted during the initial policy response phase in April 2020.
We could do the same for any number of issues, from digital tax policy, to LIBOR transition policy to Brexit policy (pictured below) as well as trade war, central bank digital currency, cryptocurrency, supply chains, etc.
The time series enables you intuitively understand the policy dynamic faster and better, even before reading the 1000 words. If you take the time to read the 1000 words, the dynamics from the reaction function become clear. If additionally the average junior analyst reads the underlying documents, the analyst becomes an instant expert by taking a 15-minute deep dive into the details while keeping an eye on policy process dynamics.
The efficiency and insight gains can be dramatic. However, many pundits and market experts find this profoundly disruptive.
If the average junior analyst a few years out of school can accurately anticipate outcomes when armed with this technology but without years of experience, experts will either have to work harder or they will have to deliver different value added. Adaptation is not easy for most people.
The better approach, of course, is that the experienced pundit also uses our platform. If the entire team uses it, their insight formation expands exponentially. They have team-level enhanced cognition because the technology automatically reads and automatically performs initial analytical functions.
We are not the only company automating the reading process, of course. Algorithmic traders for over a decade have been programming bots to read headlines and execute trades automatically based on the content.
At the upper echelons of finance, reading is increasingly an Internet of Things (IoT) activity in which machines provide an analytical layer that intermediates between human content generators (policymakers) and human portfolio managers (who program the bots).
We leave for another day why and how headlines generate misleading trading signals. The question answered below is whether survey data will remain a reliable mechanism for central banks to sample market views in the near future. Spoiler alert -- our answer is NO.
Today's ECB research paper places a high value on survey data.....and inadvertently also illustrates well the value proposition for capital market usage of our patented platform.
Today's ECB Research - Intuitive Yet Disruptive
Economists at the ECB published today the results of a quantitative study exploring the relationship between market sentiment and monetary policy. Their formal conclusion is quoted below. It is intuitive for economists and profoundly disruptive for monetary policy formulation. A plain-English translation follows below.
Why the conclusions are intuitive: Capital market participants and economists know well that yield curves have "informational value" regarding both the current...and near future... state of the economy. Investors take positions based on their assessment of whether (or not) a sovereign will be able to meet its repayment obligations given current and likely future economic conditions. Every time a bond trade is executed, investors send signals that central banks use to formulate monetary policy. Every economist reading the first part of the quote above yawned.
Why the conclusions are disruptive: The paper and its findings tackle -- and attempt to change -- one of the most important equations in economics. This is radical in its own right. When applied to the current market, in which investors increasingly can anticipate accurately shifts in policy by relying on advanced technology, the implications are massively disruptive.
For those of you that did not have to take economics in college....the Euler equation posits that consumers do not care whether they buy and use something today as opposed to in the future. Marginal utility derived from delayed future consumption is "discounted" compared with marginal utility associated with immediate consumption today. The equation is constructed to ensure that both values are equivalent through the use of a discount rate. Euler is such a giant that one of the hotels closest to the home of central banks in Basel (the Bank for International Settlements) is named after him.
Most people immediately react to Euler's assumption with skepticism. They point out that the it matters greatly whether the consumption in question is urgent or not. People buying toilet paper in mid-March as the global pandemic shutdown started were certainly not indifferent as to whether they could buy this item immediately....or not. We will leave to another day a discussion of liquidity, crises, and another famous economist (Herman Minsky).
The ECB study asserts that when central banks formulate monetary policy, they should NOT assume consumers are indifferent between today and tomorrow when it comes to making purchasing decisions that drive the economy forward. They should instead look at market expectations.
Many investors will also find this conclusion intuitively compelling. After all, market data reflects supply and demand. It daily re-sets, articulating in the process expectations about the near future. The problem is that models relying exclusively on this quantitative data are notoriously inaccurate.
What makes the ECB study so interesting is that they added an additional element to the model: "information on how market participants form their expectations and assess the macroeconomic outlook." Specifically, they relied on "survey data on real GDP growth and inflation expectations as well as the term structure of interest rates." This one change delivered the following outcome: "the explosive behaviour of the model after medium to long-term forward guidance announcements disappears."
Right now, investor expectations articulated through market elements (interest rate term structures) and survey data might be considered accurate only if humans are actually still driving investment decisions either directly (through transactions) or indirectly (by programming parameters and assumptions in their investing bots). But what happens when artificial intelligence acquires critical mass and starts making parameter adjustments automatically both in the private sector and in central banks? This day is just around the corner.
The information value from survey data will soon shift as well. With reading and analysis increasingly an IoT activity, how people think and form opinions at a minimum will require a slightly different approach to elicit meaningful information for central bank monetary policy formation. Our friends at Truthsayers Neurotech (who design surveys for a living based on neuroscience) might have views on what the next generation of monetary policy survey activity should look like.
At BCMstrategy Inc., we are thrilled with this research for a very parochial reason. Market expectations (via surveys or transaction data) have a symbiotic relationship with official sector activity.
Investors that can discern better and faster what direction policymakers are taking can out-perform other investors still using traditional mechanisms (and biased opinions) to drive investment decisions. By the time markets realize a policy shift occurred, our investors will be doing something different.
It is also really nice to see central banks thinking creatively about alternative data.
Thank you, ECB.
BCMstrategy, Inc. is a start-up company that is bringing the data revolution to the policy intelligence business through patented technology.The company measures and visualizes publicly policy risks using a web-based platform.Individuals can access the PolicyScope platform and the daily PolicyScope Risk Monitor through the company website.Customized, enterprise-level data delivering using APIs are available as well.To schedule a demo and to explore enterprise-level deployments, please contact us.