3/5/21: Margin Debt: Things are FOMOing up...

 Debt, debt and more FOMO...

Source: topdowncharts.com and my annotations

Ratio of leveraged longs to shorts is at around 3.5, which is 2014-2019 average of around 2.2. Bad news (common signal of upcoming correction or sell-off). Basically, we are witnessing a FOMO-fueled chase of every-rising hype and risk appetite. Meanwhile, margin debt is up 70% y/y in March 2021, although from low base back in March 2020, now back to levels of growth comparable only to pre-dot.com crash in 1999-2000. Adjusting for market cap - some say this is advisable, though I can't see why moderating one boom-craze indicator with another boom-craze indicator is any better - things are more moderate. 

My read-out: we are seeing margin debt acceleration that is now outpacing the S&P500 acceleration, even with all the rosy earnings projections being factored in. This isn't 'fundamentals'. It is behavioral. And as such, it is a dry powder keg sitting right next to a campfire. 

30/6/20: Long-Term Behavioral Implications of COVID19 Pandemic

My article on the behavioural economics and finance implications of COVID19 pandemic is now available on @TheCurrency website: https://www.thecurrency.news/articles/19675/debt-distress-and-behavioural-finance-the-post-pandemic-world-be-marked-by-deep-and-long-lasting-scars.

Hint: dealing with COVID19 impact will be an uphill battle for many and for the society and economy at large.

This is a long read piece, covering general behavioural fallout from the pandemic, and Ireland-specific data.

21/1/20: Investor Fear and Uncertainty in Cryptocurrencies

Our paper on behavioral biases in cryptocurrencies trading is now published by the Journal of Behavioral and Experimental Finance volume 25, 2020:

We cover investor sentiment effects on pricing processes of 10 largest (by market capitalization) crypto-currencies, showing direct but non-linear impact of herding and anchoring biases in investor behavior. We also show that these biases are themselves anchored to the specific trends/direction of price movements. Our results provide direct links between investors' sentiment toward:

1. Overall risky assets investment markets,
2. Cryptocurrencies investment markets, and
3. Macroeconomic conditions,

and market price dynamics for crypto-assets. We also show direct evidence that both markets uncertainty and investor fear sentiment drive price processes for crypto-assets.

9/1/20: Herding and Anchoring in Cryptocurrency Markets

Our new paper, with Daniel O'Loughlin, titled "Herding and Anchoring in Cryptocurrency Markets: Investor Reaction to Fear and Uncertainty" has been accepted to the Journal of Behavioral and Experimental Finance, forthcoming February 2020.

The working paper version is available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3517006.

Abstract:
Cryptocurrencies have emerged as an innovative alternative investment asset class, traded in data-rich markets by globally distributed investors. Although significant attention has been devoted to their pricing properties, to-date, academic literature on behavioral drivers remains less developed. We explore the question of how price dynamics of cryptocurrencies are influenced by the interaction between behavioral factors behind investor decisions and publicly accessible data flows. We use sentiment analysis to model the effects of public sentiment toward investment markets in general, and cryptocurrencies in particular on crypto-assets’ valuations. Our results show that investor sentiment can predict the price direction of cryptocurrencies, indicating direct impact of herding and anchoring biases. We also discuss a new direction for analyzing behavioral drivers of the crypto assets based on the use of natural language AI to extract better quality data on investor sentiment.

11/1/19: Herding: the steady state of the uncertain markets

Markets are herds. Care to believe in behavioral economics or not, safety is in liquidity and in benchmarking. Both mean that once large investors start rotating out of one asset class and into another, the herd follows, because what everyone is buying is liquid, and when everyone is buying, they are setting benchmark expected returns. If you, as a manager, perform in line with the market, you are safe at the times of uncertainty and ambiguity. In other words, it is better to bet on losing or underperforming alongside the crowd of others, than to bet on a more volatile expected returns, even though these might offer a higher upside.

How does this work? Here:

Everyone loves Corporate debt, until everyone runs out of it and into Government debt. Everyone hates Government debt, until everyone hates corporate debt. It's ugly. But it is real. Herding is what drives markets, even though everyone is keen on paying analysts top dollar not to herd.

24/11/18: Wisdom of Crowds, Meta-Knowledge and Behavioral Biases in Scalable Data

Social networks: scale, biases and the notion of the 'wisdom of crowds'... Here are my lecture notes from the Behavioral Finance and Economics course @MIIS:

28/4/18: Unintended Consequence of Tax Audits

The law of unintended consequences applies to all policies and all state systems design, including tax policies, tax laws and tax enforcement. This is a statement of truism. And it  works both ways. A well-designed policy to promote income supports and aligned incentives to work, for example, can have an unintended impact of increasing fraud. Conversely, a measure to enforce the policy to prevent fraud can result in undoing some of the positive impacts of the policy which it was designed to deliver. These statements are also a form of truism.

However, rarely do we see research into the unintended consequences of core tax policies delivering a negative view of the perceived wisdom of regulators and enforcers. Instead, we tend to think of tax laws enforcement as an unquestionable good. Fraud and tax evasion prevention are seen as intrinsically important to the society, and the severity of penalties and punishments imposed on non-compliance (whether by error or design) is seen as being not only just, but pivotal to the sustainability of the entire tax system. Put differently, there is an inherent asymmetry in the relationship between tax payers and tax enforcers: the former face potentially devastating penalties for even minor infringements, while the latter face zero cost for wrongfully accusing the former of such infringements. Tax audits are free of consequences to enforcers, and tax audits are of grave consequences to those being audited.

In this environment, tax audits can lead to severe distortions in the balance of intended and unintended consequences of the tax law. Yet, rarely such distortions are considered in the academic literature. The prevalent wisdom that the tax authorities are always right to audit and severely punish lax practices is, well, prevalent.

One recent exception to this rule is a very interesting paper, titled “Tax Enforcement and Tax Policy: Evidence on Taxpayer Responses to EITC Correspondence Audits” by John Guyton, Kara Leibel, Dayanand S. Manoli, Ankur Patel, Mark Payne, and Brenda Schafer (NBER Working Paper No. 24465, March 2018).  Five of the six authors work for Uncle Sam in either IRS or Treasury.

The paper starts by explaining how EITC audits work. "Each year, the United States Internal Revenue Service (IRS) sends notices to selected taxpayers who claim Earned Income Tax credit (EITC) benefits to request additional documentation to verify those claims." Worth noting here, that IRS' EITC audits are the lowest cost audits from the point of view of the taxpayers who face them: they are based on email exchanges between IRS and the audited taxpayer and request pretty limited information. In this, the EITC audits should create lower unintended consequences in the form of altering taxpayers' behavior than, say, traditional audits that require costly engagement of specialist accountants and lawyers by the taxpayers being audited.

So, keep in mind, fact 1: EITC audits are lower cost audits from taxpayer's perspective.

The study then proceeds to examine "the impacts of these correspondence audits on taxpayer behavior." The study specifically focuses on the labor market changes in response to audits. Now, in spirit, EITC was created in the first place to incentivise greater labor force participation and work effort for lower income individuals. The authors describe the EITC as "the United States’ largest wage subsidy antipoverty program."

Thus, keep in mind, fact 2: EITC was created to improve labor supply choices by lower income individuals.

As noted by the authors, "because these correspondence audits often lead to the disallowance of EITC benefits for many individuals, we are able to examine how the disallowance of EITC benefits affects individuals’ labor supply decisions." The authors use audits data for 2010-2012 and have accompanying administrative data for 2001-2016, so the "data allow for analysis of short-term changes in behaviors one year after the audit, as well as persistent or longer-term changes in behaviors up to six years after the audit".

The study "results indicate significant changes in taxpayer behavior following an EITC correspondence audit. In the year after being audited, we estimate a decline in the likelihood of claiming EITC of roughly 0.30, or 30 percentage points. The decrease in the likelihood of claiming EITC benefits persists for multiple years after the EITC correspondence audits, although the size of the effect is reduced over time." In year four, the likelihood of audited EITC filers still filing EITC claims is 1/4 of that for non-audited higher risk EITC filers.

Now, logical question is: was the decrease down to audits weeding out fraudulent claims? The answer is, not exactly. "Much of the decline in claiming EITC benefits following an EITC correspondence audit appears driven by decreases in the likelihood of filing a tax return." Authors suggest that 2/3rds of the decline in EITC filings post-audit is down to taxpayers stopping filing any tax returns post-audit. Which means that even some of the taxpayers who continue to file returns post-EITC audit are dropping out of EITC system.

Audits seem to trigger reductions in tax liabilities post-audit for self-employed taxpayers (ca $300 in a year following the audit) and no changes in tax liabilities post-audit for wage earners. This suggests that post-audit reported incomes either fall (for the self-employed) or remain static for those in employment. This, in turn, suggests that EIDC audits do not lead to improvements in income status for those audited by the IRS. In other words, audits do not reinforce or improve the stated objectives of EITC (see fact 2 above).

"For the Self-Employed, we estimate an increase in labor force participation (where labor force participation is defined in terms of having positive W-2 wage earnings), possibly indicating some reallocation of labor supply from self-employment to wage employment. In contrast, for Wage Earners, we estimate a decrease in labor force participation following the EITC correspondence audits."

Thus, we have fact 4: self-employed are likely to switch their income from self-employment to wages post-audit, while wage earners tend to drop their labor force participation post-audit.

The former part of fact 4 suggests can be reflective of fraudulent behavior by some self-employed who might over-state their self-employment income prior to audit in order to draw EIDC tax credits. The latter effect, however, clearly contravenes the stated objective of the EIDC system. On the first point, quick clarification via the authors of the study:"Intuitively, some lower-income individuals may increase reported self-employment (non-third-party verified) income, possibly by choosing to disclose more income, invent income, or not disclose expenses, to claim the EITC, but if they are detected by audit, they may become averse to inventing self-employment income for purposes of claiming EITC and without this income they may not file a tax return. These taxpayers may perceive the payoff from not filing as better than the payoff from filing and correctly reporting income."

Now, one can think of the effect on self-employment to be a relatively positive one. "Following the disallowance of EITC benefits due to an EITC correspondence audit, taxpayers with self-employment income on their audited returns appear more likely to have wage earnings in the next year, perhaps to offset the loss of EITC as a financial resource." But that is only true if we consider self-employment as a substitute for employment. In contrast, if self-employment is viewed as potentially entrepreneurial activity, such substitution harms the likelihood of entrepreneurship amongst lower earners. The study does not cover this aspect of the enforcement outcomes.

In measured terms, if EITC audits were successful in reinforcing EITC intended objectives, post-audits, we should see increases in wages and earnings for EITC audited individuals. Thus, we should see migration of lower earners EITC recipients to higher earners. Put differently, the share of higher earners within EITC eligible population should rise, while the share of lower earners should fall.

This is not what appears to be happening. Instead, we see increase in density (share) of lower earnings and slight decreases in densities of higher earnings:

Unambiguously, however, the study shows the damaging effects of audits: they tend to reduce labor force participation, offsetting the intended positive effects of the EITC program, and they tend to increase income tax non-filing, effectively pushing taxpayers into a much graver offence of income tax non-compliance.

Yet, still, we continue to insist that punitive, aggressive audit practices designed to impose maximal damage on tax codes violating taxpayers is a good thing. There has to be a more effective way to enforce the tax codes than throwing pain of audits around at random.

8/4/18: Talent vs Luck: Differentiating Success from Failure

In their paper, "Talent vs Luck: the role of randomness in success and failure", A. Pluchino. A. E. Biondo, A. Rapisarda (25 Feb 2018: https://arxiv.org/pdf/1802.07068.pdf) tackle the mythology of the "dominant meritocratic paradigm of highly competitive Western cultures... rooted on the belief that success is due mainly, if not exclusively, to personal qualities such as talent, intelligence, skills, efforts or risk taking".

The authors note that, although "sometimes, we are willing to admit that a certain degree of luck could also play a role in achieving significant material success, ...it is rather common to underestimate the importance of external forces in individual successful stories".

Some priors first: "intelligence or talent exhibit a Gaussian distribution among the population, whereas the distribution of wealth - considered a proxy of success - follows typically a power law (Pareto law). Such a discrepancy between a Normal distribution of inputs, suggests that some hidden ingredient is at work behind the scenes."

The authors show evidence that suggests that "such an [missing] ingredient is just randomness". Or, put differently, a chance.

The authors "show that, if it is true that some degree of talent is necessary to be successful in life, almost never the most talented people reach the highest peaks of success, being overtaken by mediocre but sensibly luckier individuals."

Two pictures are worth a 1000 words, each:

Figure 5 taken from the paper shows:

• In panel (a): Total number of lucky events and
• In panel (b): Total number of unlucky events 

Both are shown as "function of the capital/success of the agents"

Overall, "the plot shows the existence of a strong correlation between success and luck: the most successful individuals are also the luckiest ones, while the less successful are also the unluckiest ones."

Figure 7 shows:
In panel (a): Distribution of the final capital/success for a population with different random initial conditions, that follows a power law.
In panel (b): The final capital of the most successful individuals is "reported as function of their talent".

Overall, "people with a medium-high talent result to be, on average, more successful than people with low or medium-low talent, but very often the most successful individual is a moderately gifted agent and only rarely the most talented one.


Main conclusions on the paper are:

• "The model shows the importance, very frequently underestimated, of lucky events in determining the final level of individual success." 
• "Since rewards and resources are usually given to those that have already reached a high level of success, mistakenly considered as a measure of competence/talent, this result is even a more harmful disincentive, causing a lack of opportunities for the most talented ones."

The results are "a warning against the risks of what we call the ”naive meritocracy” which, underestimating the role of randomness among the determinants of success, often fail to give honors and rewards to the most competent people."

22/10/17: Framing Effects and S&P500 Performance

A great post highlighting the impact of framing on our perception of reality: https://fat-pitch.blogspot.com/2017/10/using-time-scaling-and-inflation-to.html.

Take two charts of the stock market performance over 85 odd years:

The chart on the left shows nominal index reading for S&P500. The one on the right shows the same, adjusted for inflation and using log scale to control for long term duration of the time series. In other words, both charts, effectively, contain the same information, but presented in a different format (frame).

Spot the vast difference in the way we react to these two charts...

3/10/17: Ambiguity Fun: Perceptions of Rationality?

Here is a very insightful and worth studying set of plots showing the perceived range of probabilities under subjective measure scenarios. Source: https://github.com/zonination/perceptions

The charts above speak volumes about both, our (human) behavioural biases in assessing probabilities of events and the nature of subjective distributions.

First on the former. As our students (in all of my courses, from Introductory Statistics, to Business Economics, to advanced courses of Behavioural Finance and Economics, Investment Analysis and Risk & Resilience) would have learned (to a varying degree of insight and complexity), the world of Rational expectations relies (amongst other assumptions) on the assumption that we, as decision-makers, are capable of perfectly assessing true probabilities of uncertain outcomes. And as we all have learned in these classes, we are not capable of doing this, in part due to informational asymmetries, in part due to behavioural biases and so on. 

The charts above clearly show this. There is a general trend in people assigning increasingly lower probabilities to less likely events, and increasingly larger probabilities to more likely ones. So far, good news for rationality. The range (spread) of assignments also becomes narrower as we move to the tails (lower and higher probabilities assigned), so the degree of confidence in assessment increases. Which is also good news for rationality. 

But at that, evidence of rationality falls. 

Firstly, note the S-shaped nature of distributions from higher assigned probabilities to lower. Clearly, our perceptions of probability are non-linear, with decline in the rate of likelihoods assignments being steeper in the middle of perceptions of probabilities than in the extremes. This is inconsistent with rationality, which implies linear trend. 

Secondly, there is a notable kick-back in the Assigned Probability distribution for Highly Unlikely and Chances Are Slight types of perceptions. This can be due to ambiguity in wording of these perceptions (order can be viewed differently, with Highly Unlikely being precedent to Almost No Chance ordering and Chances Are Slight being precedent to Highly Unlikely. Still, there is a lot of oscillations in other ordering pairs (e.g. Unlikely —> Probably Not —> Little Chance; and We Believe —> Probably. This also consistent with ambiguity - which is a violation of rationality.

Thirdly, not a single distribution of assigned probabilities by perception follows a bell-shaped ‘normal’ curve. Not for a single category of perceptions. All distributions are skewed, almost all have extreme value ‘bubbles’, majority have multiple local modes etc. This is yet another piece of evidence against rational expectations.

There are severe outliers in all perceptions categories. Some (e.g. in the case of ‘Probably Not’ category appear to be largely due to errors that can be induced by ambiguous ranking of the category or due to judgement errors. Others, e.g. in the case of “We Doubt” category appear to be systemic and influential. Dispersion of assignments seems to be following the ambiguity pattern, with higher ambiguity (tails) categories inducing greater dispersion. But, interestingly, there also appears to be stronger ambiguity in the lower range of perceptions (from “We Doubt” to “Highly Unlikely”) than in the upper range. This can be ‘natural’ or ‘rational’ if we think that less likely event signifier is more ambiguous. But the same holds for more likely events too (see range from “We Believe” to “Likely” and “Highly Likely”).

There are many more points worth discussing in the context of this exercise. But on the net, the data suggests that the rational expectations view of our ability to assess true probabilities of uncertain outcomes is faulty not only at the level of the tail events that are patently identifiable as ‘unlikely’, but also in the range of tail events that should be ‘nearly certain’. In other words, ambiguity is tangible in our decision making. 

Note: it is also worth noting that the above evidence suggests that we tend to treat inversely certainty (tails) and uncertainty (centre of perceptions and assignment choices) to what can be expected under rational expectations:

In rational setting, perceptions that carry indeterminate outruns should have greater dispersion of values for assigned probabilities: if something is is "almost evenly" distributed, it should be harder for us to form a consistent judgement as to how probable such an outrun can be. Especially compared to something that is either "highly unlikely" (aka, quite certain not to occur) and something that is "highly likely" (aka, quite certain to occur). The data above suggests the opposite.

21/5/16: Manipulating Markets in Everything: Social Media, China, Europe

So, Chinese Government swamps critical analysis with ‘positive’ social media posts, per Bloomberg report: http://www.bloomberg.com/news/articles/2016-05-19/china-seen-faking-488-million-internet-posts-to-divert-criticism.

As the story notes: “stopping an argument is best done by distraction and changing the subject rather than more argument”.

So now, consider what the EU and European Governments (including Irish Government) have been doing since the start of the Global Financial Crisis.

They have hired scores of (mostly) mid-educated economists to write, what effectively amounts to repetitive reports on the state of economy . All endlessly cheering the state of ‘recovery’.

In several cases, we now have statistics agencies publishing data that was previously available in a singular release across two separate releases, providing opportunity to up-talk the figures for the media. Example: Irish CSO release of the Live Register stats. In another example, the same data previously available in 3 files - Irish Exchequer results - is being reported and released through numerous channels and replicated across a number of official agencies.

The result: any critical opinion is now drowned in scores of officially sanctioned presentations, statements, releases, claims and, accompanied by complicit media and professional analysts (e.g. sell-side analysts and bonds placing desks) puff pieces.

Chinese manipulating social media, my eye… take a mirror and add lights: everyone’s holding the proverbial bag… 

20/5/16: Business Owners: Not Great With Counterfactuals

A recent paper, based on a “survey of participants in a large-scale business plan competition experiment, [in Nigeria] in which winners received an average of US$50,000 each, is used to elicit beliefs about what the outcomes would have been in the alternative treatment status.”

So what exactly was done? Business owners were basically asked what would have happened to their business had an alternative business investment process taken place, as opposed to the one that took place under the competition outcome. “Winners in the treatment group are asked subjective expectations questions about what would have happened to their business should they have lost, and non‐winners in the control group asked similar questions about what would have happened should they have won.”

“Ex ante one can think of several possibilities as to the likely accuracy of the counterfactuals”:

1. “…business owners are not systematically wrong about the impact of the program, so that the average treatment impact estimated using the counterfactuals should be similar to the experimental treatment effect. One potential reason to think this is that in applying for the competition the business owners had spent four days learning how to develop a business plan… outlining how they would use the grant to develop their business. The control group [competition losers] have therefore all had to previously make projections and plans for business growth based on what would happen if they won, so that we are asking about a counterfactual they have spent time thinking about.”
2. ”…behavioral factors lead to systematic biases in how individuals think of these counterfactuals. For example, the treatment group may wish to attribute their success to their own hard work and talent rather than to winning the program, in which case they would underestimate the program effect. Conversely they may fail to take account of the progress they would have made anyway, attributing all their growth to the program and overstating the effect. The control group might want to make themselves feel better about missing out on the program by understating its impact (...not winning does not matter that much). Conversely they may want to make themselves feel better about their current level of business success by overstating the impact of the program (saying to themselves I may be small today, but it is only because I did not win and if I had that grant I would be very successful).”

The actual results show that business owners “do not provide accurate counterfactuals” even in this case where competition awards (and thus intervention or shock) was very large.

• The authors found that “both the control and treatment groups systematically overestimate how important winning the program would be for firm growth… 
• “…the control group thinks they would grow more had they won than the treatment group actually grew”
• “…the treatment group thinks they would grow less had they lost than the control group actually grew” 

Or in other words: losers overestimate benefits of winning, winners overestimate the adverse impact from losing... and no one is capable of correctly analysing own counterfactuals.


Full paper is available here: McKenzie, David J., Can Business Owners Form Accurate Counterfactuals? Eliciting Treatment and Control Beliefs About Their Outcomes in the Alternative Treatment Status (May 10, 2016, World Bank Policy Research Working Paper No. 7668: http://ssrn.com/abstract=2779364)

27/11/2012: Neural Data and Investor Behavior

Fascinating stuff... really: a new study, titled "Testing Theories of Investor Behavior Using Neural Data" by Cary Frydman, Nicholas Barberis, Colin Camerer, Peter Bossaerts and Antonio Rangel (link) finds that "...measures of neural activity provided by functional magnetic resonance imaging (fMRI) can be used to test between theories of investor behavior that are difficult to distinguish using behavioral data alone."

How so? "Subjects traded stocks in an experimental market while we measured their brain activity. Behaviorally, we find that, our average subject exhibits a strong disposition effect [the robust empirical fact that individual investors have a greater propensity to sell stocks trading at a gain relative to purchase price, rather than stocks trading at a loss] in his trading, even though it is suboptimal."

More so: "We then use the neural data to test a specific theory of the disposition effect, the “realization utility” hypothesis, which argues that the effect arises because people derive utility directly from the act of realizing gains and losses. [Note to my Investment Theory (TCD) and Financial & Business Environments (UCD) students - we talked about direct utility derived from actual transactions, plus indirect utility effects of learning from same... remember?..] Consistent with this hypothesis, we find that

• activity in an area of the brain known to encode the value of decisions correlates with the capital gains of potential trades, 
• that the size of these neural signals correlates across subjects with the strength of the behavioral disposition effects, and that 
• activity in an area of the brain known to encode experienced utility exhibits a sharp upward spike in activity at precisely the moment at which a subject issues a command to sell a stock at a gain."

Awesome! We might not be wired for living in the world of uncertainty, but we might be somewhat wired for deriving utility out of uncertain gambles?

Now, that's what I call taking investment to MRI and getting results... well, might be not investable results, but...

5/11/2012: Academic research and market efficiency

Fascinating article on both the issue of markets efficiency (pricing-in of newsflows) and the impact of herding via learning (triggered by academic research) in finance: here.

A nice addition to our discussions both in TCD and UCD courses.

11/5/2012: Ignoring that which almost happened?

In recent years, I am finding myself migrating more firmly toward behavioralist views on finance and economics. Not that this view, in my mind, is contradictory to the classes of models and logic I am accustomed to. It is rather an additional enrichment of them, adding toward completeness.

With this in mind - here's a fascinating new study.

How Near-Miss events Amplify or Attenuate Risky Decision Making, written by Catherine Tinsley, Robin Dillon and Matthew Cronin and published in April 2012 issue of Management Science studied the way people change their risk attitudes "in the aftermath of many natural and man-made disasters".

More specifically, "people often wonder why those affected were underprepared, especially when the disaster was the result of known or regularly occurring hazards (e.g., hurricanes). We study one contributing factor: prior near-miss experiences. Near misses are events that have some nontrivial expectation of ending in disaster but, by chance, do not."

The study shows that "when near misses are interpreted as disasters that did not occur, people illegitimately underestimate the danger of subsequent hazardous situations and make riskier decisions (e.g., choosing not to engage in mitigation activities for the potential hazard). On the other hand, if near misses can be recognized and interpreted as disasters that almost happened, this will counter the basic “near-miss” effect and encourage more mitigation. We illustrate the robustness of this pattern across populations with varying levels of real expertise with hazards and different hazard contexts (household evacuation for a hurricane, Caribbean cruises during hurricane season, and deep-water oil drilling). We conclude with ideas to help people manage and communicate about risk."

An interesting potential corollary to the study is that analytical conclusions formed ex post near misses (or in the wake of significant increases in the risk) matter to the future responses. Not only that, the above suggests that the conjecture that 'glass half-full' type of analysis should be preferred to 'glass half-empty' position might lead to a conclusion that an event 'did not occur' rather than that it 'almost happened'.

Fooling yourself into safety by promoting 'optimism' in interpreting reality might be a costly venture...