I have written a new paper: ‘A Behavioural Finance Toolkit’, which aims to provide an introduction to the topic and look at how we can apply its lessons. The paper concludes by offering the following six steps to improve our investment decision making:
From a behavioural perspective the notion that there is some form of premium attached to investing in higher quality companies is something of a puzzle. If anything, one would expect loss-averse investors to overpay for securities with perceived stability and downside protection, and bear a cost in terms of lower risk-adjusted returns. Research, however, suggests that the reverse is true[i]. I have written previously about the potential behavioural explanations for value and momentum factors, but for the quality factor developing such an account is more challenging.
Compared to value and momentum, quality suffers from a somewhat amorphous definition, with a vast range of often distinct metrics employed to describe it. Some combination of earnings stability, low financial leverage and high profitability appear to be the most consistently applied characteristics. There is also debate around under what conditions the quality factor becomes apparent (delivers superior risk adjusted returns); often this is when applied in a long-short structure (long high quality / short low quality). It is also often effective when held as a complement to another factor (such as value or size), rather than on a standalone basis.
It is certainly possible that quality might not be a genuine factor. There are many who argue that excess returns to quality are a consequence of a market regime defined by a prolonged decline in interest rates – leading to a sustained re-rating for stocks with ‘bond-like’ qualities*. Furthermore, the aforementioned definitional uncertainty also leads to a potential data mining problem – if you test enough ‘quality’ metrics, some are likely to prove significant. The purpose of this post, however, is not to debate whether the quality factor is robust, but rather if it is – can there be a behavioural explanation?
It is important to make a distinction between the legitimacy of a quality equity factor and whether investing in quality companies can be an effective investment strategy. If you can select companies with quality characteristics that are able to ‘beat the fade’ embedded in their valuation you are likely to be successful. This, however, is reliant on having the skill to selectively identify these names in advance. By contrast, the existence of a quality factor suggests that there is a return advantage to systematically filtering companies by a certain set of defined criteria.
Of course, the quality factor does not require a behavioural explanation to exist. One of the main problems with factor premiums is that there is no certainty about what causes an anomaly and we are forced instead to speculate. This inability to truly understand the drivers of a factor means that we can never observe when something changes and the premium expected from a previously robust factor is extinguished.
In the case of the quality factor, a premium might be caused by a structural issue such as that highlighted by Frazzini and Pedersen[ii], who argue that constrained investors (who cannot utilise leverage) instead allocate to higher beta assets to boost a portfolio’s overall market sensitivity, leading to lower risk adjusted returns from higher beta assets. This argument can also be extended to tracking error and beta restricted active equity strategies that have limitations on holding significant amounts of higher quality stocks. Whilst there are behavioural elements embedded in this argument, these are more structural explanations**.
However, let’s assume that some form of quality premium exists in equity markets because of behavioural issues. What could they be?
Incentives: As always, incentives matter. If we assume that the richest rewards come to professional investors who either: a) Generate abnormally strong performance and raise a large amount of assets, or b) Generate strong returns by levying performance fees, then they may be inclined to embrace / overpay for volatile stocks with the potential for the highest payoffs[iii].
Present bias: We have a tendency to overweight near-term rewards. Quality stocks deliver a long-term payoff (through the compounding of high returns on capital), whereas lower quality stocks provide the possibility of a high near term payoff. If this is the case, we are likely to overvalue the potential short-term benefits of low quality companies (with an option like payoff), and undervalue the long-term benefits of quality companies.
Overconfidence: As investors we have an exaggerated belief in our own abilities and therefore may be reticent to invest in higher quality, stable companies and instead feel that our expertise is best rewarded by selecting companies with higher leverage, more variable earnings and greater earnings upside potential.
Focus: The myopic nature of financial markets may simply mean that investors focus on the wrong things – obsessing over short-term earnings announcements, corporate news flow and macro-economic issues – rather than long-term profitability.
As with all equity factors, attempts at identifying behavioural explanations for the existence of a premium for high quality stocks is a somewhat futile exercise – we will never know the answer. There is, however, some credibility to the notion that incentive structures, perceived pay-off profiles and temporal valuation issues could lead to a sustained mispricing in this area***, which could be systematically exploited. Yet given the oceans of behavioural research conducted in recent years, it is possible to create mildly plausible explanations to justify virtually any equity risk factor.
* Valuation matters. Even if there is a structural premium for a certain equity factor, if it performs very well and becomes expensive, then you are unlikely to be enjoying that premium in the future (momentum will be an exception to this given the fluctuating composition).
** I acknowledge that a high quality equity strategy is not analogous to a low volatility or low beta equity approach; however, I would expect in most scenarios higher quality stocks to exhibit a lower beta and lower volatility than lower quality stocks.
*** It is certainly possible to argue that some of the behavioural explanations I give for the existence of the quality factor would seem to contradict the value factor (which is, I think, more robust than quality).
[ii] Frazzini, A., & Pedersen, L. H. (2014). Betting against beta. Journal of Financial Economics, 111(1), 1-25.
[iii] Bali, T. G., Cakici, N., & Whitelaw, R. F. (2011). Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics, 99(2), 427-446.
Rational choice theory dictates that our decision making process should involve assessing all available options and then selecting the best possible one, or ‘maximising utility’. This model is an example of a sound concept that fails when it encounters the real world. A major critique of this approach came in the 1950’s when Herbert Simon suggested that rather than attempt to make the optimal choice, cognitive and environmental limitations mean that we often ‘satisfice’ – that is make a decision when we find an option that is ‘good enough’ and meets some minimum threshold criteria.
The friction between satisficing and maximising behaviour is interesting when we consider investment decision making. Our natural reaction is perhaps to assume that we should be maximisers and exhaustively seek the best possible outcomes, and there is perhaps a stigma attached to selecting something that is simply ‘good enough’. But should this really the case? I would contend that for many investors attempts to maximise are not only close to impossible, but the constant search for the best option can actually lead to very poor outcomes. There are a number of features about investment choices that make maximising behaviour particularly problematic.
– Too Many Options: Maximisation may prove effective if there is a narrow and well-defined set of options, but in investment it is impossible to perform an exhaustive search across all available choices. The opportunity set is enormous and fluid, and any attempt to ensure that we have selected the best possible option will be ongoing and fruitless.
– Vague Criteria: Successful maximisation is also reliant on their being known and objective quality criteria – is it possible to easily differentiate between different options based on the most important factors? In investment it is incredibly difficult to confidently isolate these criteria and distinguish between distinct choices.
– Wrong Criteria: In the absence of a certain set of meaningful criteria through which we can judge quality, we tend to focus on one woefully inadequate comparative measure – historic performance. We rely on past returns from an asset or investment fund to gauge its quality. This approach is worse than simply ineffective; attempting to select the very best option based on what has been the strongest performer in the past can lead to deeply sub-optimal selections.
– Shifting Criteria: In addition to past performance being the most frequently used but damaging criteria for investment / asset selection, it also suffers from a lack of stability. Not only is it a weak metric for maximisation at any single point in time, but it is also changeable. The random and uncertain movement of markets mean that if we rely on past performance to compare options, our view on what is the best possible choice will be highly variable.
– Switching is Easy: One of the most problematic features of attempting to maximise in an investment context is the ability to switch between options in a relatively ‘frictionless’ and simple fashion. If we allow past performance to dictate our view on what is the ‘best possible option’ we are likely change our mind frequently and act on this by regularly shifting between investments*. Whilst the optical switching costs may be reasonably low, the total cost of lurching between different investments is exorbitant.
For these reasons, the attempt to maximise in investment decision making is highly problematic yet unfortunately common. Performance chasing in asset classes, stocks and mutual funds, alongside egregious overtrading and short termism are symptomatic of investors consistently and unproductively seeking to ‘maximise’ their choices. Maximisation is another of the many investment behaviours that ‘feels’ conceptually right – of course we should be seeking the best possible option – but has severely deleterious consequences.
In addition to the problems of maximisation specific to investment decision making, it has been argued that there are other negative ramifications. Research has suggested that individuals who maximise are likely to suffer lower optimism, life satisfaction and self-esteem[i]. Barry Schwartz also notes that as the range of options expands people’s threshold for an acceptable outcome becomes too high and they are more likely to blame themselves for disappointing results rather than circumstance or environment[ii] – there are so many options available why couldn’t you pick a good one?
If we are always seeking the very best outcome among a multitude of choices, discontent will follow as there is likely to be consistent regret from failing to select the best option[iii]. The randomness of outcomes allied to the sheer range of options in investment means that there will always be new shiny objects to attract us.
For certain types of investor some form of maximisation is inherent in what they do and the service they offer, but these are the exceptions. For most of us attempting to maximise our investment decision making simply leads to value destruction as we chase yesterday’s winners, trade too frequently and live in constant regret that the investments we don’t own are performing better than those we do . Instead of this, we should be content to satisfice. Find an investment plan that is good enough – based on sound principles (around issues like fees, rebalancing, diversification and compounding) and suited to our objectives. Then stick with it.
[i] Peng, S. (2013). Maximizing and satisficing in decision-making dyads.
[ii] Schwartz, B. (2000). Self-determination: The tyranny of freedom. American psychologist, 55(1), 79.
[iii] Roets, A., Schwartz, B., & Guan, Y. (2012). The tyranny of choice: A cross-cultural investigation of maximizing-satisficing effects on well-being. Judgment and Decision Making, 7(6), 689.
* There is also evidence of maximising behaviour leading to choice paralysis. For example, in the famous jam example where more choice led to less purchase decisions, or in pension plans where so many options are offered individuals are reluctant to participate at all because they are unsure of the best choice.
An individual decides to drive home after an evening out despite being knowingly over the legal alcohol limit; before completing their journey they are stopped by the police and charged with driving under the influence. In a parallel universe, the same scenario occurs but with one key difference – prior to being pulled over by the police the individual’s intoxication leads to an accident that causes serious injuries for passengers in another car. How should we view the perpetrator in these two incidents?
Our reaction from an ethical[i], and legal, standpoint is often to judge the second version more harshly – because the consequences were far more severe, but should this be the case? In both cases the main failing is identical – the initial flawed decision to drive after excessive alcohol intake. The relative results, however, are due to luck; the individual in the first instance experienced good luck (comparatively), and the other bad. Such judgements are often heavily influenced by the results, even if they are reliant on chance; an example of outcome bias[ii].
Our tendency to judge the quality of a decision by the ultimate consequence is a simple concept. In many instances it is also a prudent one; results often provide a useful gauge of the value of the actions that led to them. However, as with many things, once you add a healthy dose of randomness things start to become problematic.
“A good decision cannot guarantee a good outcome. All real decisions are made under uncertainty. A decision is therefore a bet, and evaluating it as good or not must depend on the stake and the odds, not on the outcome”[iii] (Ward Edwards)
Financial markets are the perfect breeding ground for outcome bias – results are obvious and easy to obtain, whilst judging process and decision quality is incredibly difficult, which means we rely heavily on the former. We also grossly understate the sheer level of unpredictability, largely due to the wonders of hindsight bias and our susceptibility to a compelling narrative.
In reality, our faith in the information provided by any outcome, should be scaled by the amount of luck there is involved. In certain endeavours results provide a good measure of decision quality; in others we hugely exaggerate the importance of outcomes.
Take chess as an example; it is a heavily structured game, dominated by skill, not chance, and with limited luck or randomness in its results. If I played 100 chess matches with Magnus Carlsen, I would lose each one and these outcomes would prove an excellent indication of our relative abilities. You wouldn’t need to watch each match to know this. Outcome bias is rarely a problem in such activities.
Now imagine that I had to enter a portfolio management competition against my seven year old son, where we each had to pick a portfolio of 30 stocks. As much as I might like to believe that I would hold a significant advantage, I know the probability of my selections outperforming his over a single year are not much greater than 50%. Whilst the odds may tilt in my favour as the time horizon extends there are no guarantees – maybe he has picked some stocks that go on to enjoy dramatic growth, or given his portfolio a factor tilt that is in vogue for a number of years. Not only am I faced with prospect of my diligent investment decision making being improved upon by the haphazard selections of a child, but outcome bias means that my son’s investment success may see him appearing on Bloomberg and asked to give his opinion on the Fed’s next move.
Despite the problems of using results as a barometer of decision quality, it remains endemic in investment. We use outcomes as a simple indicator and then weave narratives around these views. We take a difficult problem, simplify it (are results good or bad?) and then create a story to justify the outcome. This pattern of behaviour is evident in a range of poor investment decisions, such as: susceptibility to financial frauds, participation in investment bubbles, performance chasing and excessive short-term trading.
There is an increasing drive by financial regulators to assess the value for money provided by investment professionals by using simple comparative performance metrics, whilst this is an understandable approach to dealing with a fiendishly difficult problem; it creates a situation where a fluky dart thrower is often perceived to have offered a superior service to someone diligent yet unfortunate. These issues are also why performance fees for actively managed funds are so problematic – they egregiously reward the lucky and pay little heed to process or conscientiousness. There are no easy solutions here but being a beholden to outcomes alone is by no means a panacea.
In an investment context it actually seems wrong to refer to outcome bias; rather we should talk about the outcome heuristic. That is we use outcomes as a mental shortcut to simplify a highly complex and inherently unpredictable task. The use of rules of thumb is smart and effective in some domains, using outcomes as a proxy for sound decision making in investment is anything but.
[i] Gino, F., Moore, D. A., & Bazerman, M. H. (2009). No harm, no foul: The outcome bias in ethical judgments.
[ii] Baron, J., & Hershey, J. C. (1988). Outcome bias in decision evaluation. Journal of personality and social psychology, 54(4), 569.
[iii] Vlek, C., Edwards, W., Kiss, I., Majone, G., & Toda, M. (1984). What constitutes” a good decision”?. Acta Psychologica.
There is an excellent conversation detailed on the Farnam Street website between Shane Parrish and Adam Robinson about stupidity[i]; in particular, why we make decisions that seemingly lack intelligence, common sense or both. I was particularly taken with the definition used for stupidity:
“Stupidity is overlooking or dismissing conspicuously crucial information.”
This clearly has resonance when we consider investment decision making – although financial markets are awash with randomness and uncertainty, there are obvious, vital and, often simple, cues that as investors we seemingly choose to ignore or disregard. This results in poor choices and often disappointing outcomes.
We know that buying assets or funds after unusually strong performance is typically a bad idea, yet we still do it. We understand the challenges of short-term trading and the benefits of long-term compounding, but can rarely resist the urge to react to what is happening right now. These issues are not hidden from our view and they are paramount to our overall investment outcomes yet we often neglect them – but why?
Robinson notes seven factors, which can create situations where stupidity can flourish. I consider each of these from an investment perspective below, whilst adding two additional issues, which I believe can also lead us toward ‘sub-optimal’ investment decisions:
– Outside circle of competence: Economists predicting equity market moves, stock picking fund managers pontificating about macro-economics, amateur investors day trading. There are seemingly no boundaries in investment – if you are involved then you can (and must) have a view on every aspect. Investing is difficult enough without making decisions in areas in which you have no discernible skill, or where there is no evidence of anyone exhibiting consistently high levels of skill.
– Stress: If we engage with the constantly shifting narratives and random price fluctuations of financial markets it is almost inevitable that pressure and anxiety will lead us into decisions that are detrimental to our long-term goals.
– Rushing or urgency: The hyperbolic and frenetic reporting around financial news means that we often feel the urge to act immediately. We make decisions that will make us feel good in the very short-term, but come with a significant long-term cost.
– Outcome fixation: The problem of outcome bias is particularly pernicious in financial markets – this is because of the inherent level of randomness in results (particularly over short-time periods) which means that sensible decisions can often appear quite the reverse. Sometimes stupidity is rewarded.
– Information overload: There is simply too much noise in investment markets. It is a struggle to work out which information is relevant (the vast majority of it is not) or how we should use it. Given the sheer volume of data, our tendency is to react to it in an unpredictable fashion – considering information to be pertinent based on its salience, prominence, or availability.
– Group / social cohesion: We often make investment decisions in a group context, and what other people are doing matters greatly to us. Even if their judgements are seemingly irrational we will often seek to conform.
– Presence of authority (expertise): Perhaps in no other field do we behold such an array of experts. Each offers confident forecasts and compelling trade ideas – they are intelligent and confident, surely we should follow them?
– Overconfidence / Ego: We are often aware of the crucial information, but do not believe it applies to us. Even where the odds are stacked against us, we feel we have an uncanny ability to overcome them.
– External justification: For professionals to justify their role and fees we must be seen to act frequently, being a busy fool is often more highly valued than ‘doing nothing’.
There is possibly no more fruitful setting for stupid decisions than financial markets. Not only does the decision making environment lure us into mistakes, but the feedback we get is erratic. Stupid decisions sometimes work and work enough to keep us coming back (like a slot machine giving you enough small wins to keep you interested). Furthermore, for every sensible investment rule there are inevitable exceptions – survivorship bias and tiny samples (n=1) make us believe that either the evidence is erroneous or that we are the exception. We are not.
2018 was a horrendous year for many quantitative funds and their investors (I speak from personal experience). Although I do not wish to add to the commentary on the drivers of this particularly difficult period, it has brought into sharp contrast how different owning a systematic strategy is to holding a fund with a more traditional, human-led investment approach. Whilst both sets are often rightly grouped under the active banner, this definition belies the specific behavioural challenges investors face when holding a quant strategy – particularly when performance is poor:
Nobody Else to Blame – I have written previously about active fund investors suffering from a form of reverse disposition effect, that is a propensity to run winners and cut losers (unlike individual stock pickers). This is because fund selectors benefit from an attractive form of optionality – if the fund we have chosen delivers outperformance then it is due to our superior selection skills, whereas if it struggles we can claim that the underlying fund manager is behaving in a manner that is inconsistent with our expectations (a healthy dose of outcome bias is also at play here). This argument, however, does not hold for quant funds – in most cases we are investing in a defined system or process, if the strategy fails then it is far more difficult to apportion responsibility elsewhere – the process hasn’t changed, you picked it and it didn’t work. Unlike qualitatively driven funds, there is no get out of jail free card.
Curse of Consistency – Somewhat ironically, the majority of quant funds possess characteristics that are consistent with what most fund selectors say they seek in traditional active managers – a clear philosophy and a disciplined investment process / decision making structure that will be applied diligently through varying market conditions. Unfortunately, whilst prudent on paper, the stated preferences of most fund selectors do not really hold under stress. When active funds suffer marked underperformance the reaction of investors is typically not: ‘I’m a glad you are remaining faithful to your process through this difficult time’, but rather: ‘things are going wrong, show me what you are doing about it’. This attitude is a major problem for quant funds as in most circumstances their reaction to poor performance should be to consistently apply the process on the basis that it will deliver over the long run. A strategy doing the same thing when it is not working for a sustained period is often unpalatable for investors, even if it is the right approach to adopt.
Does the Factor Still Work? Perhaps the most significant problem for investors in quant funds pertains to factor based strategies, which are seeking to exploit market anomalies to deliver a risk premium. Owning such strategies requires a belief that the underlying factors exist (are robust) and will persist. It is this latter point that is the most challenging. Given that we can never have certainty why a particular factor has delivered a premium (we can only opine), we can equally never be sure as to whether it will continue to work. Perfectly valid factors can struggle for long spells and it is difficult / impossible to decipher whether these are the result of a structural shift extinguishing the factor premium, or a ‘temporary’ phenomenon. This uncertainty makes the task of myopic investors persisting with such strategies particularly difficult. Even if we pick the right factors we will have to sit through long periods when everybody is telling us they are broken.
Good Decision / Bad Outcome – Most quant funds are structured based on decision rules / algorithms that deliver on average, when applied over the long-term. By definition, this means that there will be phases when they do not and, with a liberal dose of leverage applied, these can be painful. Even a strategy with a high Sharpe Ratio, investing in proven factors, is prone to experience drawdowns that can be multiples of the long-term expected volatility. Averages hide a multitude of sins, and sensible decisions can come to look anything but.
Black Box Stigma: Quant funds unquestionably carry a stigma. They are blamed for a variety of ills, including (simultaneously) subdued market volatility and extreme bouts of volatility (apparently severe short-term market declines only began occurring with the onset of algorithmic trading). Of course, we should never invest in something we don’t understand – but this applies to all types of strategies. How much do we really know about the genuine drivers of decision making in a human-led investment process? Is the behaviour of a systematic trend following strategy more opaque than a discretionary global macro manager?
Discussing quantitative funds into one homogenous group is not particularly helpful and obscures the sheer array of approaches that can be broadly classified in this cohort. Each strategy should be assessed on its own merits – there are bad quant strategies as there are poor qualitative strategies. Investors, however, need to be acutely aware of the distinct behavioural challenges that arise from owning systematic strategies and be prepared to manage them if they are to successfully invest in such approaches.
It is without question that investors now have easy access to more information than ever to guide decision making; optically, this surfeit of data appears to be a positive – who doesn’t want more ‘evidence’ to inform their judgements? Yet there are a number of potential drawbacks, most notably the challenge of disentangling signals from a blizzard of noise in order to make consistent decisions. For this post, I want to specifically address the potential consequences of information growth and its impact on our precision and confidence levels. Whilst we often believe that more information can improve our accuracy (the number of correct decisions we make), in certain situations all it may be doing is increasing our (unfounded) confidence.
There have been a number of studies in this area, the majority of which reach similar conclusions. Tsai, Klayman and Hastie (2008)[i], tested the impact of additional information on an individual’s ability to predict the results of college football games and their confidence in doing so correctly. Participants in the study had to forecast a winner for a number of games based on anonymised statistical information. The information came in blocks of 6 (so for the first round of predictions the participant had 6 pieces of data) and after each round of predictions they were given another block of information, up to 5 blocks (or 30 data points), and had to update their views. Participants were asked to predict both the winner and their confidence in their judgement between 50% and 100%. The aim of the experiment was to understand how increased information impacted both accuracy and confidence. Here are the results (taken directly from the study):
The contrasting impact of the additional information is stark – the accuracy of decision making is flat, decisions were little better with 30 statistics than just 6, however, participant confidence that they could select the winner increased materially and consistently. When we come into possession of more, seemingly relevant, information our belief that we are making the right decision can be emboldened even if there is no justification for this shift in confidence levels.
For this research, the blocks of information were provided at random and the participants were amateurs – would the same relationship hold for professionals who were able to select the information they believed to be most pertinent? An unpublished 1973 study by Paul Slovic (cited by the CIA[ii]), takes a similar approach but in this case with experienced horse race handicappers. Unlike in the college football study, the handicappers were allowed to rank the available information by perceived importance (from a list of 88 variables) and then had to predict the winner of an anonymised race when in possession of 5 pieces of information, then 10, 20 and 40 (by order of their specified preference / validity). The results obtained were consistent with the aforementioned football study – accuracy was consistent despite more information becoming available, but confidence increased as the number of available statistics rose.
There are two important issues for investors to consider when looking at this type of outcome: i) There are probably less relevant pieces of information than we think, ii) There are a number of negatives around the accumulation of too much information – one of which is overconfidence.
More information does not necessarily lead to better decisions: In the investment industry it can often feel as if it is the amount of information or evidence that matters, rather than its validity. Provided a research report is long enough, the conclusion must be sound. I would contend, however, that for many investment decisions there are only a handful of information points that are relevant, distinct, and materially impact the probability of a positive outcome. If this is the case, why is there such a desire for more and more information?
– We don’t know what that relevant information is, therefore we include everything we can find.
– We struggle to realise that many pieces of information are telling us the same thing.
– In random markets, noise can be mistaken for relevant information.
– If a decision goes wrong, we at least want to show that we did a lot of research to support it.
– It is difficult to sell our investment wares if we simplify our decision making to a select few variables.
– It we make simple decisions based on a narrow range of information we can look lazy, inept and unsophisticated.
– We feel more comfortable / confident in a decision if it is ‘supported’ by more evidence.
– It is possible that information that was once relevant ceases to be so because of some ‘regime shift’.
This combination of factors (and others I have failed to mention) means that it is incredibly difficult not to focus more on the accumulation of information rather than seek to identify the information that matters.
More information can lead to overconfidence: It is not simply the case that more information might not result in greater decision making accuracy, but that it can lead to us becoming more overconfident and poorly calibrated in our judgements. Whilst we often believe that ‘new’ information bolsters the case supporting our choices, on many occasions this additional evidence may simply be a repetition of prior information (merely in a different guise) or be erroneous with no predictive power (a major problem in an environment marked by uncertainty and randomness where things that look like they matter, actually do not). As we receive more information, therefore, we are prone to believe that we are more accurate in our decisions, when there is often no justification for this. This can create an anomalous situation where behaviour consistent with being diligent and thorough, actually results in worse investment decisions being made.
Judging the balance between carrying out sufficient research and becoming overly confident by collecting reams of superfluous data is fraught with difficultly, however, all investors should think more about what is the most relevant information, rather than concentrate simply on the accumulation of more. For professional investors, a simple idea is to decide which pieces of information they would use if there was a restriction (of say only 5 or 10 items) and then monitor the outcomes of decisions made utilising only these select variables. Such an approach forces us to think about what evidence really matters to us, whether it is effective and what value we might add over and above such a basic method.
[i] Tsai, C. I., Klayman, J., & Hastie, R. (2008). Effects of amount of information on judgment accuracy and confidence. Organizational Behavior and Human Decision Processes, 107(2), 97-105.