ECB Action Sheds Doubts on Yield Differentials
Can the euro gain despite the latest rate cut? Before we cover the aftermath of Thursday's drama-fillled ECB decision, let's backtrack for a bit. A month ago, Bank of Finland chief ECB member Olli Rehn said it was important to come up with a significant and impactful policy package in September. "When you're working with financial markets, it's often better to overshoot than undershoot, and better to have a very strong package of policy measures than to tinker," he said. The euro had slumped on those headlines at the time on the expectation of a highly dovish hint. The problem with such remarks is they remove the element of surprise later down the line.
So the ECB went on a coordinated disinformation campaign. Speaker after speaker over the past month went on the offensive. Dutch central bank chief Knot said there was no need to resume QE, Germany's Weidmann agreed, and others spoke along the same lines. A series of leaks continued right up until the decision that suggested QE may not return, leaving the market off balance.
That set the stage for Draghi to deliver a Thursday 'surprise' of open-ended QE and open-ended guidance. Draghi opted for a 10-bp rate cut --not the 20 basis points -- that many were hoping for-- but emphasized in the press conference that downside risks remained. The forecast for 2020 inflation was lowered to 1.0% from 1.4%, suggesting that any trouble will be met by action.
Eurozone government yields fell alongside Italian yields down as much as 20 basis points to a record +0.75%. Yet the euro hung in there, which begs the question: Do interest rate differentials matter?
The answer could suggest that they don't. In 2007 you could buy the New Zealand dollar and sell the yen while collecting 8% in carry. Today, even at the extremes, the differences in developed markets are 200 basis points.
Flows vs Differentials?
Increasingly, money is chasing growth and/or value. That's always been the case, but it brings us back to the role of central banks today. The ECB's manufactured surprise should be viewed less through the lens of what it does mechanically, and more so on how it affects confidence and growth. In that sense, tiering for Eurozone banks (expempting banks with particular reserves from negative rates) will be a crucially positive step, helping to restore (or at least preserve) profitability in the banking sector. This factor helps explains why European banks sector shot up immediately after the tiering announcement from the ECB at 12:45 pm London time, which in turn proves euro-positive despite the latest rate cut.
Asset managers looking for bargains will not resist the temptation to buy beaten down European bank stocks with the decent internal fundamentals and some accomodation on negative rates from the ECB. EURUSD is on its way to complete its 2-weekly gain. It has not 3 consecutive weekly gains since June 2018. Could next week prove positive in the aftermath of the Fed?
The fiscal path
Draghi is now nearly done at the ECB. He engineered policies that were unthinkable on his arrival. He fought against the panic of a Eurozone breakup, and did everything he could within his mandate. Throughout his tenure, he asked for governments to do more. He made that call again on Thursday.
The fiscal argument has no doubt merit for it to be included Christine Lagardes playbook. She must draw a line at some point, warning that the risks of the ECB pushing further into experimental policy outweighs the rewards. With that, she will hand the baton to the fiscal side. The market is already looking ahead to this but the early take is that governments won't respond until they're already in recession. By that time, it will be too late.
Risk management occurs everywhere in the realm of finance. It occurs when an investor buys U.S. Treasury bonds over corporate bonds, when a fund manager hedges his currency exposure with currency derivatives, and when a bank performs a credit check on an individual before issuing a personal line of credit. Stockbrokers use financial instruments like options and futures, and money managers use strategies like portfolio diversification, asset allocation and position sizing to mitigate or effectively manage risk.
Inadequate risk management can result in severe consequences for companies, individuals, and the economy. For example, the subprime mortgage meltdown in 2007 that helped trigger the Great Recession stemmed from bad risk-management decisions, such as lenders who extended mortgages to individuals with poor credit; investment firms who bought, packaged, and resold these mortgages; and funds that invested excessively in the repackaged, but still risky, mortgage-backed securities (MBSs).
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Good, Bad, and Necessary Risk
We tend to think of "risk" in predominantly negative terms. However, in the investment world, risk is necessary and inseparable from desirable performance.
A common definition of investment risk is a deviation from an expected outcome. We can express this deviation in absolute terms or relative to something else, like a market benchmark.
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While that deviation may be positive or negative, investment professionals generally accept the idea that such deviation implies some degree of the intended outcome for your investments. Thus to achieve higher returns one expects to accept the greater risk. It is also a generally accepted idea that increased risk comes in the form of increased volatility. While investment professionals constantly seek—and occasionally find—ways to reduce such volatility, there is no clear agreement among them on how it's best done.
How much volatility an investor should accept depends entirely on the individual investor's tolerance for risk, or in the case of an investment professional, how much tolerance their investment objectives allow. One of the most commonly used absolute risk metrics is standard deviation, a statistical measure of dispersion around a central tendency. You look at the average return of an investment and then find its average standard deviation over the same time period. Normal distributions (the familiar bell-shaped curve) dictate that the expected return of the investment is likely to be one standard deviation from the average 67% of the time and two standard deviations from the average deviation 95% of the time. This helps investors evaluate risk numerically. If they believe that they can tolerate the risk, financially and emotionally, they invest.
Risk Management Example
For example, during a 15-year period from Aug. 1, 1992, to July 31, 2007, the average annualized total return of the S&P 500 was 10.7%. This number reveals what happened for the whole period, but it does not say what happened along the way. The average standard deviation of the S&P 500 for that same period was 13.5%. This is the difference between the average return and the real return at most given points throughout the 15-year period.
When applying the bell curve model, any given outcome should fall within one standard deviation of the mean about 67% of the time and within two standard deviations about 95% of the time. Thus, an S&P 500 investor could expect the return, at any given point during this period, to be 10.7% plus or minus the standard deviation of 13.5% about 67% of the time; he may also assume a 27% (two standard deviations) increase or decrease 95% of the time. If he can afford the loss, he invests.
Risk Management and Psychology
While that information may be helpful, it does not fully address an investor's risk concerns. The field of behavioral finance has contributed an important element to the risk equation, demonstrating asymmetry between how people view gains and losses. In the language of prospect theory, an area of behavioral finance introduced by Amos Tversky and Daniel Kahneman in 1979, investors exhibit loss aversion. Tversky and Kahneman documented that investors put roughly twice the weight on the pain associated with a loss than the good feeling associated with a profit.
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Often, what investors really want to know is not just how much an asset deviates from its expected outcome, but how bad things look way down on the left-hand tail of the distribution curve. Value at risk (VAR) attempts to provide an answer to this question https://www.gold-pattern.com/en
The word habit is pulled from the Latin words habere, which means "have, consist of," and habitus, which means "condition, or state of being." It also is derived from the French word habit (pronounced \ah-bee\), which means clothes. In the 13th century, the word habit first just referred to clothing. The meaning then progressed to the more common use of the word, which is "acquired mode of behavior."
Habit formation is the process by which a behavior, through regular repetition, becomes automatic or habitual. This is modeled as an increase in automaticity with the number of repetitions up to an asymptote. This process of habit formation can be slow. Lally et al. (2010) found the average time for participants to reach the asymptote of automaticity was 66 days with a range of 18–254
There are 3 main components to habit formation: the context cue, behavioral repetition, and the reward. The context cue can be a prior action, time of day, location, or anything that triggers the habitual behavior. This could be anything that one's mind associates with that habit, and one will automatically let a habit come to the surface. The behavior is the actual habit that one exhibits, and the reward, such as a positive feeling, therefore continues the "habit loop". A habit may initially be triggered by a goal, but over time that goal becomes less necessary and the habit becomes more automatic. Intermittent or uncertain rewards have been found to be particularly effective in promoting habit learning.
A variety of digital tools, online or mobile apps, have been introduced that are designed to support habit formation. For example, Habitica is a system that uses gamification, implementing strategies found in video games to real-life tasks by adding rewards such as experience and gold. However, a review of such tools suggests most are poorly designed with respect to theory and fail to support the development of automaticity.
Shopping habits are particularly vulnerable to change at "major life moments" like graduation, marriage, the birth of the first child, moving to a new home, and divorce. Some stores use purchase data to try to detect these events and take advantage of the marketing opportunity.
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Some habits are known as "keystone habits," and these influence the formation of other habits. For example, identifying as the type of person who takes care of their body and is in the habit of exercising regularly, can also influence eating better and using credit cards less. In business, safety can be a keystone habit that influences other habits that result in greater productivity.
A recent study by Adriaanse et al. (2014) found that habits mediate the relationship between self-control and unhealthy snack consumption. The results of the study empirically demonstrate that high self-control may influence the formation of habits and in turn affect behavior.
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The habit–goal interface or interaction is constrained by the particular manner in which habits are learned and represented in memory. Specifically, the associative learning underlying habits is characterized by the slow, incremental accrual of information over time in procedural memory. Habits can either benefit or hurt the goals a person sets for themselves.
Goals guide habits by providing the initial outcome-oriented motivation for response repetition. In this sense, habits are often a trace of past goal pursuit. Although, when a habit forces one action, but a conscious goal pushes for another action, an oppositional context occurs. When the habit prevails over the conscious goal, a capture error has taken place.
Behavior prediction is also derived from goals. Behavior prediction acknowledges the likelihood that a habit will form, but in order to form that habit, a goal must have been initially present. The influence of goals on habits is what makes a habit different from other automatic processes in the mind. https://www.gold-pattern.com/en
The following is a description of a classic goal devaluation experiment (from a Scientific American MIND guest blog post called Should Habits or Goals Direct Your Life? It Depends) which demonstrates the difference between goal-directed and habitual behavior: