The current emphasis on impact measurement raises several challenges, including ethical ones. Rather than taking for granted that more and better measurements are crucial to the development of impact investing, the authors of this article question what drives this measurement mania and expose some related pitfalls. We also develop some practical and original ways to help the impact investing movement avoid some measurement-related traps, such as making numbers less salient and combining them with more qualitative elements, reducing the likelihood of metric overload, inviting investors to adopt the perspective of a beneficiary, or introducing random procedures (sortition) to give a voice to neglected stakeholders.

The Research Foundation Review 2017 summarizes the offerings from the CFA Institute Research Foundation over the past year—books, literature reviews, workshop presentations, and other relevant material.

Risk profiling is fraught with misunderstandings that lead to ill-advised approaches to determining suitable investment solutions for individuals. The author discusses how we should think about the crucial elements of (a) risk tolerance, (b) behavioural risk attitudes, and (c) risk capacity. He uses a simple thought experiment to examine a stripped-down investor situation and define the essential features and exact role of each of the components of an investor's overall risk profile. He examines options for eliciting and measuring risk tolerance and considers some promising avenues for future methods.

Behavioral finance is useful only if it can be applied to help people make better decisions. This chapter offers reflections on the good, bad, and ugly of practical applications of behavioral finance in a commercial banking setting. It explores the difficulties of nonexperts experimenting with behavioral finance, and how effective applications require a unique mix of expert knowledge and the ability to effect change through a business. Principles of good applications of behavioral finance are also presented, with information on how to start using behavioral finance within an organization. The importance of senior management’s acknowledging that behavioral finance practitioners do not necessarily know the correct answer and that they will need to use randomized control trials to learn is also discussed.

Behavioral finance is useful only if it can be applied to help people make better decisions. This chapter offers reflections on the good, bad, and ugly of practical applications of behavioral finance in a commercial banking setting. It explores the difficulties of nonexperts experimenting with behavioral finance, and how effective applications require a unique mix of expert knowledge and the ability to effect change through a business. Principles of good applications of behavioral finance are also presented, with information on how to start using behavioral finance within an organization. The importance of senior management’s acknowledging that behavioral finance practitioners do not necessarily know the correct answer and that they will need to use randomized control trials to learn is also discussed.

We study investor happiness in a panel survey of brokerage clients at a UK bank. When investors anticipate future happiness, they set their aspirations according to personal portfolio risk, objectives, investment horizon, con dence, and other individual characteristics. They are accurate in their forecasts, only rarely are investors unhappy with outcomes they predicted they would be happy with, and vice versa. However, determinants of experienced happiness only partially correspond to the ones found for anticipated happiness. In particular, relative performance plays an important role investors do not anticipate. Having outperformed other people contributes to investor happiness, as does active trading success.

An understanding of an investor's risk tolerance is essential to determining investment suitability and yet there is no universally agreed definition of what risk tolerance is. The authors discuss the need for a holistic approach to measuring risk tolerance that provides a consistent framework for constructing efficient multi-asset allocation and ensuring a suitable aggregate level of risk and return, given the investor's personality and financial circumstances. Risk tolerance is only stable when considered as a personality trait, which can be measured effectively using holistic psychometric scales. Attempts to measure risk tolerance at lower levels of granularity are unstable, they accentuate, rather than mitigate short-term behavioural distortions and cannot be meaningfully aggregated to provide an overall level of risk that is appropriate given the investor's risk capacity. This implies that the recent move towards mental accounting, goal-based, assessments of risk tolerance is misguided and exposes institutions to risks associated with providing unsuitable advice.

Abstract In comparing risk attitudes across individuals we usually aim to determine whether one can reliably measure a difference in risk attitudes between two individuals, the magnitude of that difference, and what factors should be controlled for to ensure comparisons are meaningful. These comparisons critically depend on the method of risk attitude measurement and elicitation used, and a clear understanding of what the modeled risk attitude represents. Individual risk attitudes are fragile and very specific to domain and framing effects, and thus comparisons should always be made within the same elicitation method. Depending on the measurement method, comparisons can be made on relative or absolute scales. In general, the more precise the measurement, the more sensitive it will be to slight changes in assessment. Psychometric measures are the simplest and most robust, but allow only for ordinal comparisons. Certainty equivalent and utility models of choices can give more precise, but noisy, cardinal estimates of individual risk aversion. The purpose and need for precision of the comparison will often drive the method a researcher chooses for eliciting risk attitudes, and advice is given for researchers seeking to design or analyze such studies.

In customer segmentation, a common strategy is to use individual differences as a predictor of future behavior. Recent advances in data management in large financial institutions give an unprecedented and potentially powerful source of data for identifying such differences. We show that spending data can substantially help target the direct marketing of financial products, and constitutes new information, not captured by demographics. In particular, a systematic combination of this independent source and more traditional measures can enhance the predictive power of marketing research and improve the relationship with customers as illustrated in a direct mailing selection method which substantially raises response rates.

With Keynes (1936), it is part of accepted theory that we have different motives for saving, including the need to secure means for the future. To bridge the gap between motives and observed behaviour, we assume it is necessary to understand how people actually try to achieve their saving goals. A new method of visualising existing saving concepts is introduced, which shows that individuals apply a range of saving strategies to organise their finances. Based on a financial personality survey it is shown how external as well as internal control for saving can be improved systematically.

There exists no completely satisfactory theory of risk attitude in current normative decision theories. Existing notions confound attitudes to pure risk with unrelated psychological factors such as strength of preference for certain outcomes, and probability weighting. In addition traditional measures of risk attitude frequently cannot be applied to non-numerical consequences, and are not psychologically intuitive. I develop Pure Risk theory which resolves these problems – it is consistent with existing normative theories, and both internalises and generalises the intuitive notion of risk being related to the probability of not achieving one’s aspirations. Existing models which ignore pure risk attitudes may be misspecified, and effects hitherto modelled as loss aversion or utility curvature may be due instead to Pure Risk attitudes.

There have been few theoretical investigations of risk attitude within Cumulative Prospect Theory (CPT). Unlike expected utility theory, in CPT risk attitude is affected by loss aversion and decision weight distortions as well as utility curvature for both gains and losses. We introduce two variants of the risk premium—the total risk premium relative to expected value, and the behavioural risk premium relative to the imputed behavioural expected value. Approximate solutions for each using Pratt's [(1964). Risk aversion in the small and in the large. Econometrica, 32, 122–136] methodology show that the CPT risk premium is composed of two components: the first, analogous to the Pratt–Arrow coefficient of risk aversion, governs the contribution of the curvature of the value function to risk aversion; the second governs the first-order contribution of loss aversion. Both of these terms are made more complex by the introduction of decision weights. We analyse the contribution of each component and provide sufficient conditions to ensure risk aversion in CPT.

The Cognitive Science of Saving: Individual Financial Structures as Tools for Self-Control Philipp E. Otto (p.e.otto@warwick.ac.uk) Institute for Applied Cognitive Sciences, Department of Psychology University of Warwick, Coventry CV4 7AL, UK Greg B. Davies (gbd21@cam.ac.uk) Faculty of Economics and Politics, University of Cambridge Sidgwick Avenue, Cambridge CB3 9DD, UK Nick Chater (n.chater@warwick.ac.uk) Institute for Applied Cognitive Sciences, Department of Psychology University of Warwick, Coventry CV4 7AL, UK The derived results divide into two categories ‘tiered structures’ and ‘radial structures’ (Figure 1). The tiered structures (46% of the cases) serve as a sort of buffer with different levels. In the radial structures (54%) the income is distributed between different saving accounts. In all cases a number of accounts are linked in specific ways by tools which control or guide the transfers. Economic Rationality versus Self-Control and Mental Accounting Since Strotz (1955), the standard economic model of wealth distribution over the lifecycle as an overall utility maximization has been challenged repeatedly (i.e. Thaler, 1980). The two main observations contradicting the integration into one category of total SALARY discounted wealth are the additional utility of direct or anticipated consumption (self- control) and the segregation into financial CURRENT categories (mental accounting). Different ACCOUNT models to capture these behavioral characteristics have been proposed. Various patterns of SC have been described in the literature of financial behavior. In line with Schelling (1984) and Ainslie (1975) these can be categorized into three different types: precommittment, environment manipulation, changes in contingencies. Many behavioral pattern use different mechanisms in combination to guide saving. Often external control goes hand in hand with internal preparedness and are therefore difficult to distinguish from each other. The categorization above illustrates the variety of possible alternatives which can be applied. In this paper we evaluate whether people actually use SC strategies to guide saving behavior. SALARY SAVING ONE SAVING ONE RESERVE CURRENT ACCOUNT SAVING TWO TIER ONE HOLIDAY TIER TWO STOCKS SAVING TWO CAR MORTGAGE LOAN Figure 1: Tiered and radial structures Behavioral Differentiation A specifically developed questionnaire shows, with 57 saving behavior related items and based on 173 participants, that the demand for self-control tools is the most prominent factor in the saving domain. The individual differences in SC strongly demand tailored solutions and stress design components which support the understanding, the involvement, the evolution, and the flexibility of financial products. Saving Concepts References To evaluate the different approaches to the savings task it is necessary to know how people understand this problem and what their saving goal is. The construal and mental representation are important for the various SC initiatives. For understanding the mental representation of saving it is useful to know how people structure their finances. Ainslie, G. (1975). Specious Reward: A behavioral theory of impulsiveness and impulse control. Psychological Bulletin, 82(4), 463-496. Schelling, T. C. (1984). Self-command in practice, in policy, and in a theory of rational choice. American Economic Review, 74(2), 1-11. Strotz, R. H. (1955). Myopia and inconsistency in dynamic utility maximization. The Review of Economic Studies, Thaler, R. (1980). Towards a positive theory of consumer choice. Journal of Economic Behavior and Organization, Saving Structures All 13 participants, from a two hour open structured interview with a drawing board task, have some sort of financial structure in place to facilitate saving. But the general understanding of this structure is poor and is only revealed through the task.

We implement the Cumulative Prospect Theory (CPT) framework (Tversky and Kahneman 1992) into a model of individual asset allocation, building on earlier work by Hwang and Satchell (2003) where they derive explicit formulae for the asset allocation decision using a loss aversion utility function. We apply Prelec’s probability weighting function (1998) to continuous distributions and derive the formulae for the optimal asset allocation between risky and safe assets. US equity returns data are used to examine the feasible parameter space. The earlier results of Hwang and Satchell are confirmed and the more complex model is compatible with observed equity proportions. The parameters are highly interconnected, but feasible combinations indicate that more inverse-S shaped deviations from linear probability weightings are associated with lower risk taking behaviour.

There exists no satisfactory theory of risk in current normative decision theories. Notions based on utility curvature, loss aversion and probability weighting are derivative, cannot be applied to non-numerical consequences, and are not psychologically intuitive. I develop a Pure Risk theory which resolves these problems, is consistent with existing normative theories, and both internalises and generalises the intuitive notion of risk being related to the probability of not achieving one’s aspirations. The theory shows that existing models are misspecifed. Effects hitherto modelled as loss aversion or utility curvature may be due instead to Pure Risk.