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How to Alleviate Correlation Neglect in Investment Decisions
Abstract
We experimentally study how presentation formats for return distributions affect investors’ diversification choices. We find that sampling returns alleviates correlation neglect and constitutes an effective way to improve financial decisions. When participants get a description of the probabilities for outcomes of the joint return distribution, we confirm the findings of others that investors neglect the correlation between assets in their diversification choices. However, when participants sample from the joint distribution, they change their allocation between two assets in response to a change in their correlation in the predicted direction. The results are robust across two experiments that have participants with varying experience (students versus private investors).
This paper was accepted by Manel Baucells, behavioral economics and decision analysis.
... When combining information from multiple sources (e.g., for financial forecasts: Budescu and Yu, 2007;Enke and Zimmermann, 2017;Hossain and Okui, 2021;Maines, 1996Maines, , 1990or constructing portfolios of correlated assets: Eyster and Weizsacker, 2016;Laudenbach et al., 2022), most participants exhibit "correlation neglect" (i.e., partially or fully failing to account for correlations), which often leads to reduced decision accuracy. Positive correlations have also been proposed to lead to overconfidence, which has been attributed to failing to account for redundancy (Eyster and Rabin, 2010;Glaeser and Sunstein, 2009;Ortoleva and Snowberg, 2015) or to the false assumption that consistency among information sources suggests higher reliability (Kahneman and Tversky, 1973). ...
... /2024 even in simplified cases, decisions in descriptive scenarios likely rely on very different cognitive mechanisms than decisions that, like ours, are based directly on relatively low-level sensory stimuli. For example, decisions under risk can vary substantially when based on description versus direct experience (Hertwig and Erev, 2009), and giving passive exposure to samples from distributions underlying two correlated assets has been shown to alleviate correlation neglect in subsequent allocation decisions (Laudenbach et al., 2022). ...
The brain forms certain deliberative decisions following normative principles related to how sensory observations are weighed and accumulated over time. Previously we showed that these principles can account for how people adapt their decisions to the temporal dynamics of the observations (Glaze et al., 2015). Here we show that this adaptability extends to accounting for correlations in the observations, which can have a dramatic impact on the weight of evidence provided by those observations. We tested online human participants on a novel visual-discrimination task with pairwise-correlated observations. With minimal training, the participants adapted to uncued, trial-by-trial changes in the correlations and produced decisions based on an approximately normative weighting and accumulation of evidence. The results highlight the robustness of our brain’s ability to process sensory observations with respect to not just their physical features but also the weight of evidence they provide for a given decision.
... Future studies could incorporate these interdependencies into experimental DfE research by including choices between multiple actions with complex causal relations or allowing for sequential decisions that can change the composition of a future decision environment (see Brehmer, 1992;Liang et al., 2019;Meyer, 2012). Also, little research has examined whether (and how) people learn and represent correlations between choice options from experience (Kareev, 2000;Laudenbach et al., 2022;Olschewski, Diao, & Rieskamp, 2021). In this context, it matters whether feedback from options that are not chosen is available or not (i.e., full vs. partial feedback; Ben Zion et al., 2010;Grosskopf et al., 2006). ...
In many important real-world decision domains, such as finance, the environment, and health, behavior is strongly influenced by experience. Renewed interest in studying this influence led to important advancements in the understanding of these decisions from experience (DfE) in the last 20 years. Building on this literature, we suggest ways the standard experimental design should be extended to better approach important real-world DfE. These extensions include, for example, introducing more complex choice situations, delaying feedback, and including social interactions. When acting upon experiences in these richer and more complicated environments, extensive cognitive processes go into making a decision. Therefore, we argue for integrating cognitive processes more explicitly into experimental research in DfE. These cognitive processes include attention to and perception of numeric and nonnumeric experiences, the influence of episodic and semantic memory, and the mental models involved in learning processes. Understanding these basic cognitive processes can advance the modeling, understanding and prediction of DfE in the laboratory and in the real world. We highlight the potential of experimental research in DfE for theory integration across the behavioral, decision, and cognitive sciences. Furthermore, this research could lead to new methodology that better informs decision-making and policy interventions.
This paper aims to explore the literature on Fintech for plausible research gaps. We systematically searched abstracts on the Fintech literature from the Scopus abstracted journals to accomplish this aim. We then used multiple criteria, including the Association of Business Schools (ABS) journal rankings, to narrow from 4,567 to 340 documents. These 340 documents were published in high-ranked journals and aligned with our study objective. After reviewing those documents, we came up with three critical research gaps relating to (1) the relationship between Fintech concepts and technologies, (2) the use of Fintech in financial institutions and services, and (3) the role of contextual or geographical factors in influencing the popularity of Fintech. Scholars in the future are encouraged to work in the direction of bridging one or more of these gaps.
We document the asset-pricing implications of the model-free option-implied dependence (MFID), a measure that exhibits information on linear and non-linear dependence between random variables. We show that stocks with high exposure to MFID generate significantly higher risk-adjusted returns in bad times. This finding is robust when we condition on the exposure to implied correlation and other common risk factors. This suggests that the non-linear component drives the outperformance. Overall, the results are consistent with time-varying preferences, implying increased demand for assets that offer a hedge in bad times.
We introduce the Berlin Numeracy Test, a new psychometrically sound instrument that quickly assesses statistical numeracy and risk literacy. We present 21 studies ( n =5336) showing robust psychometric discriminability across 15 countries (e.g., Germany, Pakistan, Japan, USA) and diverse samples (e.g., medical professionals, general populations, Mechanical Turk web panels). Analyses demonstrate desirable patterns of convergent validity (e.g., numeracy, general cognitive abilities), discriminant validity (e.g., personality, motivation), and criterion validity (e.g., numerical and non-numerical questions about risk). The Berlin Numeracy Test was found to be the strongest predictor of comprehension of everyday risks (e.g., evaluating claims about products and treatments; interpreting forecasts), doubling the predictive power of other numeracy instruments and accounting for unique variance beyond other cognitive tests (e.g., cognitive reflection, working memory, intelligence). The Berlin Numeracy Test typically takes about three minutes to complete and is available in multiple languages and formats, including a computer adaptive test that automatically scores and reports data to researchers ( http://www.riskliteracy.org ). The online forum also provides interactive content for public outreach and education, and offers a recommendation system for test format selection. Discussion centers on construct validity of numeracy for risk literacy, underlying cognitive mechanisms, and applications in adaptive decision support.
DEBATES IESA • Volumen XXV • Número 4 • octubre-diciembre 2020
LA GENERACIÓN DEL MILENIO (también conocida como generación Y o milenial) abarca unos 2.000 millones de personas nacidas entre, aproximadamente, 1980 y 2000. La industria financiera ha estudiado las características de esta generación, que tiene entre 20 y 40 años de edad, y sus diferencias con las generaciones precedentes. A medida que alcancen su edad de mayor ingreso y gasto, los mileniales tendrán un impacto enorme en la economía global. «Los mileniales han alcanzado la mayoría de edad en una época de cambio tecnológico, globalización y trastornos económicos», señalaba en 2014 un informe elaborado por el banco de inversión Goldman Sachs (2014). «Eso les ha dado un conjunto de comportamientos y experiencias diferentes de las de sus padres. Han tardado más en casarse y mudarse por su cuenta, y han mostrado diferentes actitudes hacia la propiedad que han ayudado a crear una "economía compartida"».
Constituyen también la primera generación de nativos digitales y tienen un estilo de vida activo que influye en las tendencias de todos los ámbitos. En 2015 la firma de auditoría Deloitte calculó que para 2020 el patrimonio neto agregado de los mileniales en todo el mundo oscilaría entre 19 billones y 24 billones de dólares.
Si bien muchos mileniales desconocen su potencial de ingresos, en los próximos años ocurrirá un cambio masivo (Deloitte, 2020): Primero, los mileniales están a punto de entrar en sus años de ingresos óptimos, lo que resultará en un aumento significativo de activos. En segundo lugar, ser autónomo como empresario es un modelo clave que acelerará el aumento de activos. Además, los mileniales se beneficiarán de la riqueza de sus padres baby boomers. Un informe de Ernst and Young (2017) prevé que los mileniales recibirán unos treinta billones de dólares en herencias durante los próximos veinte años.
Los mileniales alcanzaron la mayoría de edad en medio de una gran incertidumbre económica y mercados financieros volátiles. Enfrentan un conflicto entre el bajo riesgo que están dispuestos a asumir por sus inversiones y la necesidad de obtener alto retorno para financiar su jubilación.
In recent years, public officials have shown a growing interest in using evidence from the behavioural sciences to promote policy goals. Much of the discussion of behaviourally informed approaches has focused on ‘nudges’; that is, non-fiscal and non-regulatory interventions that steer (nudge) people in a specific direction while preserving choice. Less attention has been paid to boosts, an alternative evidence-based class of non-fiscal and non-regulatory intervention. The goal of boosts is to make it easier for people to exercise their own agency in making choices. For instance, when people are at risk of making poor health, medical or financial choices, the policy-maker – rather than steering behaviour through nudging – can take action to foster or boost individuals’ own decision-making competences. Boosts range from interventions that require little time and cognitive effort on the individual's part to ones that require substantial amounts of training, effort and motivation. This article outlines six rules that policy-makers can apply in order to determine under which conditions boosts, relative to nudges, are the preferable form of non-fiscal and non-regulatory intervention. The objective is not to argue that boosts are better than nudges or vice versa, but to begin to spell out the two approaches’ respective conditions for success.
Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy.
oTree is an open-source and online software for implementing interactive experiments in the laboratory, online, the field or combinations thereof. oTree does not require installation of software on subjects’ devices; it can run on any device that has a web browser, be that a desktop computer, a tablet or a smartphone. Deployment can be internet-based without a shared local network, or local-network-based even without internet access. For coding, Python is used, a popular, open-source programming language. http://www.oTree.org[www.oTree.org] provides the source code, a library of standard game templates and demo games which can be played by anyone.
Policy makers have embraced financial education as a necessary antidote to the increasing complexity of consumers' financial decisions over the last generation. We conduct a meta-analysis of the relationship of financial literacy and of financial education to financial behaviors in 168 papers covering 201 prior studies. We find that interventions to improve financial literacy explain only 0.1% of the variance in financial behaviors studied, with weaker effects in low-income samples. Like other education, financial education decays over time; even large interventions with many hours of instruction have negligible effects on behavior 20 months or more from the time of intervention. Correlational studies that measure financial literacy find stronger associations with financial behaviors. We conduct three empirical studies, and we find that the partial effects of financial literacy diminish dramatically when one controls for psychological traits that have been omitted in prior research or when one uses an instrument for financial literacy to control for omitted variables. Financial education as studied to date has serious limitations that have been masked by the apparently larger effects in correlational studies. We envisage a reduced role for financial education that is not elaborated or acted upon soon afterward. We suggest a real but narrower role for “just-in-time” financial education tied to specific behaviors it intends to help. We conclude with a discussion of the characteristics of behaviors that might affect the policy maker's mix of financial education, choice architecture, and regulation as tools to help consumer financial behavior.
This paper was accepted by Uri Gneezy, behavioral economics.
Human subjects are proficient at tracking the mean and variance of rewards and updating these via prediction errors. Here, we addressed whether humans can also learn about higher-order relationships between distinct environmental outcomes, a defining ecological feature of contexts where multiple sources of rewards are available. By manipulating the degree to which distinct outcomes are correlated, we show that subjects implemented an explicit model-based strategy to learn the associated outcome correlations and were adept in using that information to dynamically adjust their choices in a task that required a minimization of outcome variance. Importantly, the experimentally generated outcome correlations were explicitly represented neuronally in right midinsula with a learning prediction error signal expressed in rostral anterior cingulate cortex. Thus, our data show that the human brain represents higher-order correlation structures between rewards, a core adaptive ability whose immediate benefit is optimized sampling.
We study the effect of variation in correlation on investment decision in an experimental two asset application. Comparison of allocations across problems suggests that subjects neglect probabilistic information on the joint distribution of returns and base their allocations on the observed return levels for the two assets. When asked to predict future returns, subjects try to replicate the historical distribution, thereby falling into the probability-matching bias. Predictions drastically vary when correlations become negative, while allocations are not significantly affected by changes in sign of correlation. The observed allocation patterns contradict the predictions of standard models of choice; the inconsistency is attributed to common behavioral bias in financial decision. Field implications of the results are discussed.
Economic theory assigns a central role to risk preferences. This article develops a measure of relative risk tolerance using responses to hypothetical income gambles in the Health and Retirement Study. In contrast to most survey measures that produce an ordinal metric, this article shows how to construct a cardinal proxy for the risk tolerance of each survey respondent. The article also shows how to account for measurement error in estimating this proxy and how to obtain consistent regression estimates despite the measurement error. The risk tolerance proxy is shown to explain differences in asset allocation across households.
z-Tree (Zurich Toolbox for Ready-made Economic Experiments) is a software for developing and conducting economic experiments. The software is stable and allows programming almost any kind of experiments in a short time. In this article, I present the guiding principles behind the software design, its features, and its limitations. Copyright Economic Science Association 2007
According to prospect theory, people overweight low probability events and underweight high probability events. Several recent papers (notably, Hertwig, Barron, Weber \& Erev, 2004) have argued that although this pattern holds for ``description-based'' decisions, in which people are explicitly provided with probability distributions over potential outcomes, it is actually reversed in ``experience-based'' decisions, in which people must learn these distributions through sampling. We reanalyze the data of Hertwig et al. (2004) and present a replication to determine the extent to which their phenomenon can be attributed to sampling error (a statistical rather than psychological phenomenon) versus underestimation of rare events (i.e., judgmental bias) versus actual underweighting of judged probabilities. We find that the apparent reversal of prospect theory in decisions from experience can be attributed almost entirely to sampling error, and are consistent with prospect theory and the two-stage model of decision under uncertainty (Fox \& Tversky, 1998).
There is a worldwide trend toward defined contribution saving plans and growing interest in privatized Social Security plans. In both environments, individuals are given some responsibility to make their own asset-allocation decisions, raising concerns about how well they do at this task. This paper investigates one aspect of the task, namely diversification. We show that some investors follow the "1/n strategy": they divide their contributions evenly across the funds offered in the plan. Consistent with this naive notion of diversification, we find that the proportion invested in stocks depends strongly on the proportion of stock funds in the plan.
How do investors perceive dependence between stock returns; and how does their perception of dependence affect investments and stock prices? We show experimentally that investors understand differences in dependence, but not in terms of correlation. Participants invest as if applying a simple counting heuristic for the frequency of comovement. They diversify more when the frequency of comovement is lower even if correlation is higher due to dependence in the tails. Building on our experimental findings, we empirically analyze U.S. stock returns. We identify a robust return premium for stocks with high frequencies of comovement with the market return.
We study the introduction of a wealth-management robo-adviser that constructs portfolios tailored to investors’ holdings and preferences. Adopters are similar to non-adopters in terms of demographics and prior interactions with human advisers but tend to be more active and have greater assets under management. Investors adopting robo-advising experience diversification benefits. Ex ante undiversified investors increase stock holdings and hold portfolios with less volatility and better returns. Already well-diversified investors hold fewer stocks, yet see some reduction in volatility, and trade more after adoption. All investors increase attention based on online account logins. We find that adopters exhibit declines in prominent behavioral biases, including the disposition, trend chasing, and rank effect. Our results emphasize the promises and pitfalls of robo-advising tools, which are becoming ubiquitous all over the world.
Received May 31, 2017; editorial decision August 21, 2018 by Editor Wei Jiang.
Using survey data and the instrument developed by Barsky et al. (1997), we estimate the distribution of attitudes towards income risk in a country where many employment and health‐related risks are generously covered by a tax‐financed social insurance system (Norway in 2006). Under a constant relative risk aversion assumption, the sample average for the coefficient of relative risk aversion is 3.8 with standard deviation 2.3. This number is then contrasted to that for five other OECD countries where risk attitudes have been measured using the same instrument and also prior to the financial crisis: Chile, France, Italy, the Netherlands and the USA. When we relate this distribution for stated relative risk aversion to that for generosity of social insurance and the risks related to employment and health expenditure, a picture emerges suggesting that more extensive welfare states induce higher risk tolerance for foreground risks—a relationship that is in line with the theory on risk vulnerability.
We estimate risk aversion from investors' financial decisions in a person-toperson lending platform. We develop a method that obtains a risk-aversion parameter from each portfolio choice. Since the same individuals invest repeatedly, we construct a panel data set that we use to disentangle heterogeneity in attitudes toward risk across investors, from the elasticity of risk aversion to changes in wealth. We find that wealthier investors are more risk averse in the cross section and that investors become more risk averse after a negative housingwealth shock. Thus, investors exhibit preferences consistent with decreasing relative risk aversion and habit formation.
We apply a new and innovative approach to communicating risks associated with financial products that should support investors
in making better investment decisions. In our experiments, participants are able to gain “simulated experience” by random
sampling of a previously described return distribution. We find that simulated experience considerably improves participants’
understanding of the underlying risk–return profile and prompts them to reconsider their investment decisions and to choose
riskier financial products without regretting their higher risk-taking behavior afterwards. This method of experienced-based
learning has high potential for being integrated into real-world applications and services.
This paper documents that investors are more likely to hold, buy, and sell the stocks of Finnish firms that are located close to the investor, that communicate in the investor's native tongue, and that have chief executives of the same cultural background. The influence of distance, language, and culture is less prominent among the most investment-savvy institutions than among both households and less savvy institutions. Regression analysis indicates that the marginal effect of distance is less for firms that are more nationally known, for distances that exceed 100 kilometers, and for investors with more diversified portfolios.
About a third of the assets in large retirement savings plans are invested in company stock, and about a quarter of the "discretionary" contributions are invested in company stock. From a diversification perspective, this is a dubious strategy. This paper explores the role of excessive extrapolation in employees' company stock holdings. I find that employees of firms that experienced the worst stock performance over the last 10 years allocate 10.37 percent of their "discretionary" contributions to company stock, whereas employees whose firms experienced the best stock performance allocate 39.70 percent. Allocations to company stock, however, do not predict future performance. Copyright The American Finance Association 2001.