Skip to main content
SpringerLink
Log in

The Application of Visual Analytics to Financial Decision-Making and Risk Management: Notes from Behavioural Economics

  • Chapter
  • First Online:
Financial Analysis and Risk Management

Abstract

Understanding how individuals and organizations make financial decisions under uncertainty and with different information settings is fundamental to informing the theory and practice of information management. Due to limitations on cognitive ability and problems of information overload, complex information sets may not be fully understood, resulting in suboptimal economic decision-making. We have applied visual analytics (VA), which enables users to interactively discover information from large information sets, to improve the financial decision-making process. Using an experimental methodology, we find evidence that VA reduces the cost of obtaining information, improves decisions, and increases confidence of users in a range of different financial decision tasks involving risk. This is a nascent area of research, and additional work is needed to develop and evaluate VA tools for financial decision-making and risk management. Best practices guidelines for presenting complex information sets may only develop through rigorous evaluation of the effect of information presentation on actual choice. In addition, the impact of VA in collaborative decision-making environments is not fully understood. The future of applied VA for financial decision-making and risk management must involve an interdisciplinary team of behavioural economists, VA researchers, computer scientists, and cognitive scientists.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Incentives are also present in practice, as individuals often make choices that are motivated by monetary and non-monetary incentives. For example, individuals preparing a financial portfolio will realize monetary outcomes and therefore have an incentive to select a portfolio that is closest to their risk preference. As another example, in the workplace, individuals receive bonuses as an incentive to improve performance. For a discussion of the use of financial incentives in experiments, see Camerer and Hogarth 1999.

  2. 2.

    Note that design research uses a qualitative approach as well (see Chap. 4, Users: Qualitative Research, Cooper et al. 2007).

  3. 3.

    According to the ‘utility of winning’ hypothesis, individuals gain utility not just from acquiring an item but also from the act of winning itself. See Sheremeta (2010) and Parco et al. (2005) for examples of utility of winning in experiments with a lottery contest.

References

  • Andersen S, Harrison GW, Lau MI, Rutström EE (2008) Eliciting risk and time preferences. Econometrica 76:583–618

    Article  Google Scholar 

  • Andreoni J, Sprenger C (2010) Estimating time preferences from convex budgets. National Bureau of Economic Research Working Paper No. 16347

    Google Scholar 

  • Arrow KJ (1965) The theory of risk aversion. In: Aspects of the Theory of Risk Bearing. Yrjo Jahnssonin Saatio, Helsinki. Reprinted in (1971): Essays in the theory of risk bearing. Markham Publ Co, Chicago

    Google Scholar 

  • Barsky RB, Juster FT, Kimball MS, Shapiro MD (1997) Preference parameters and behavioral heterogeneity: an experimental approach in the health and retirement study. Q J Econ 112:537–579

    Article  Google Scholar 

  • Benbunan-Fich R, Hiltz SR, Turoff M (2003) A comparative content analysis of face-to-face vs. asynchronous group decision making. Decis Support Syst 34:457–469. doi:10.1016/S0167-9236(02)00072-6

    Article  Google Scholar 

  • Bornstein G, Yaniv I (1998) Individual and group behavior in the ultimatum game: are groups more “rational” players? Exp Econ 1:101–108. doi:10.1007/BF01426217

    Google Scholar 

  • Brodbeck D, Chalmers M, Lunzer A, Cotture P (1997) Domesticating bead: adapting an information visualization system to a financial institution. In: Proceedings of the IEEE symposium on information visualization, pp 73–80. doi:10.1109/INFVIS.1997.636789

  • Camerer CF (1989) An experimental test of several generalized utility theories. J Risk Uncertain 2:61–104. doi:10.1007/BF00055711

    Article  Google Scholar 

  • Camerer CF, Hogarth RM (1999) The effects of financial incentives in experiments: a review and capital-labor-production framework. J Risk Uncertain 19:7–42

    Article  Google Scholar 

  • Capen EC, Clapp RV, Campbell WM (1971) Competitive bidding in high-risk situations. J Petrol Technol 23:641–653

    Google Scholar 

  • Caplin A, Dean M, Martin D (2009) Search and satisficing. Am Econ Rev (forthcoming)

    Google Scholar 

  • Casari M, Jackson C, Zhang J (2008) An experiment on risk preferences and strategic abilities (working paper)

    Google Scholar 

  • Chang R, Ziemkiewicz C, Green TM, Ribarsky W (2009) Defining insight for visual analytics. IEEE Comput Graph 29:14–17. doi:10.1109/MCG.2009.22

    Article  Google Scholar 

  • Cleveland W, Harris C, McGill R (1982) Judgments of circle sizes on statistical maps. J Am Stat Assoc 77:541–547

    Article  Google Scholar 

  • Cooper A, Reimann R, Cronin D (2007) About face 3: the essentials of interaction design. Wiley, India

    Google Scholar 

  • Cox JC, Hayne SC (2006) Barking up the right tree: are small groups rational agents? Exp Econ 9:209–222. doi:10.1007/s10683-006-9123-3

    Article  Google Scholar 

  • Csallner C, Handte M, Lehmann O, Stasko J (2003) FundExplorer: supporting the diversification of mutual fund portfolios using context treemaps. In: IEEE symposium on information visualization, pp 203–208. doi: 10.1109/INFVIS.2003.1249027

  • Davis DD, Holt CA (1993) Experimental economics. Princeton University Press, Princeton

    Google Scholar 

  • Dufwenberg M, Lindqvist T, Moore E (2005) Bubbles and experience: an experiment. Am Econ Rev 95:1731–1737. doi:10.1257/000282805775014362

    Article  Google Scholar 

  • Einhorn HJ (1970) The use of nonlinear, noncompensatory models in decision making. Psychol Bull 73:221–230

    Article  Google Scholar 

  • Einhorn HJ (1971) Use of nonlinear, noncompensatory models as a function of task and amount of information. Organ Behav Hum Perform 6:1–27. doi:10.1016/0030-5073(71)90002-X

    Article  Google Scholar 

  • Gigerenzer G, Goldstein D (1996) Reasoning the fast and frugal way: models of bounded rationality. Psychol Rev 103:650–669

    Article  Google Scholar 

  • Harrison GW, Rutström EE (2008) Risk aversion in the laboratory. Res Exp Econ 12:41–196. doi:10.1016/S0193-2306(08)00003-3

    Article  Google Scholar 

  • Heer J, Agrawala M (2008) Design considerations for collaborative visual analytics. Inf Vis 7:49–62. doi:10.1145/1391107.1391112

    Article  Google Scholar 

  • Holt CA, Laury SK (2002) Risk aversion and incentive effects. Am Econ Rev 92:1644–1655. doi:10.1257/0002828054201459

    Article  Google Scholar 

  • Hommes C, Sonnemans J, Tuinstra J, van de Velden H (2008) Expectations and bubbles in asset pricing experiments. J Econ Behav Organ 67:116–133. doi:10.1016/j.jebo.2007.06.006

    Article  Google Scholar 

  • Hur I, Yi J (2009) Simulsort: multivariate data exploration through an enhanced sorting technique. In: Jacko J (ed) Human–computer Interaction. Novel interaction methods and techniques. Springer, Berlin

    Google Scholar 

  • Iyengar SS, Lepper MR (2000) When choice is demotivating: can one desire too much of a good thing? J Pers Soc Psychol 79:995–1006. doi:10.1037/0022-3514.79.6.995

    Article  Google Scholar 

  • Jarvenpaa SL (1989) The effect of task demands and graphical format on information processing strategies. Manage Sci 35:285–303. doi:10.1287/mnsc.35.3.285

    Article  Google Scholar 

  • Johansen A, Sornette D (1999) Modeling the stock market prior to large crashes. Eur Phys J B: Condens Matter Complex Syst 9:167–174. doi:10.1007/s100510050752

    Article  Google Scholar 

  • Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:263–291

    Article  Google Scholar 

  • Keim DA, Mansmann F, Schneidewind J, Thomas J, Ziegler H (2008) Visual analytics: scope and challenges. In: Visual data mining: theory, techniques and tools for visual analytics. Lecture notes in computer science. Springer, Berlin

    Google Scholar 

  • Kocher MG, Sutter M (2005) The decision maker matters: individual versus group behaviour in experimental beauty-contest games. Econ J 115:200–223. doi:10.1111/j.1468-0297.2004.00966.x

    Article  Google Scholar 

  • Lurie NH, Mason CH (2007) Visual representation: implications for decision making. J Mark 71:160–177. doi:10.1509/jmkg.71.1.160

    Article  Google Scholar 

  • Lusardi A, Mitchell OS (2005) Financial literacy and planning: implications for retirement wellbeing. Michigan Retirement Research Center, Working Paper 2005-108

    Google Scholar 

  • Lybeck JA (2011) A global history of the financial crash of 2007–2010. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Markowitz H (1952) Portfolio selection. J Financ 7:77–91

    Google Scholar 

  • Parco JE, Rapoport A, Amaldoss W (2005) Two-stage contests with budget constraints: an experimental study. J Math Psychol 49:320–338. doi:10.1016/j.jmp.2005.03.002

    Google Scholar 

  • Payne JW, Bettman JR, Johnson EJ (1990) The adaptive decision-maker: effort and accuracy in choice. In: Hogarth RM (ed) Insights in decision-making: A tribute to Hillel J. University of Chicago Press, Einhorn

    Google Scholar 

  • Plaisant C, Milash B, Rose A, Widoff S, Shneiderman B (1996) Lifelines: visualizing personal histories. In: Proceedings of the SIGCHI conference on human factors in computing systems 221-ff. doi:10.1145/238386.238493

  • Porter DP, Smith VL (2003) Stock market bubbles in the laboratory. J Behav Financ 4:7–20. doi:10.1207/S15427579JPFM0401_03

    Article  Google Scholar 

  • Pousman Z, Stasko J, Mateas M (2007) Casual information visualization: depictions of data in everyday life. IEEE T Vis Comput Gr 13:1145–1152

    Article  Google Scholar 

  • Rudolph S, Savikhin A, Ebert DS (2009) FinVis: applied visual analytics for financial planning. In: IEEE symposium on visual analytics science and technology, pp 195–202. doi:10.1109/VAST.2009.5333920

  • Samuelson WF, Bazerman MH (1985) The Winner’s curse in bilateral negotiations. Res Exp Econ 3:105–137

    Google Scholar 

  • Savikhin A (2010) An experimental study of information search and decision-making with interactive technology: presentation based on work in progress with Hur I, Kim S, Yi JS. Purdue University economics department seminar series, March 2010

    Google Scholar 

  • Savikhin A, Ebert DS (2012) Exploring the effects of visual displays on financial decisions: an experimental study. Working paper, University of Chicago

    Google Scholar 

  • Savikhin A, Maciejewski R, Ebert DS (2008) Applied visual analytics for economic decision-making. In: IEEE symposium on visual analytics science and technology 2008, pp 107–114. doi:10.1109/VAST.2008.4677363

  • Savikhin A, Lam HC, Fisher B, Ebert DS (2011) An experimental study of financial portfolio selection with visual analytics for decision support. 44th Hawaii International Conference on Systems Sciences (HICSS) 2011, pp 1–10. doi:10.1109/HICSS.2011.54

  • Schreck T, Tekusova T, Kohlhammer J, Fellner D (2007) Trajectory-based visual analysis of large financial time series data. ACM SIGKDD Explor Newsl 9:30–37. doi:10.1145/1345448.1345454

    Article  Google Scholar 

  • Sheremeta RM (2010) Experimental comparison of multi-stage and one-stage contests. Game Econ Behav 68:731–747

    Article  Google Scholar 

  • Simon HA (1972) Theories of bounded rationality. In: McGuire CG, Radner R (eds) Decision and organization. North-Holland Publishing Company, Amsterdam

    Google Scholar 

  • Simon HA (1987) Models of bounded rationality: empirically grounded economic reason, vol 3. MIT Press, Cambridge

    Google Scholar 

  • Smith VL (1994) Economics in the laboratory. J Econ Perspect 8:113–131

    Article  Google Scholar 

  • Stigler GJ (1961) The economics of information. J Polit Econ 69:213–225

    Article  Google Scholar 

  • Thomas JJ, Cook KA (2005) Illuminating the path: the research and development agenda for visual analytics. IEEE Computer Society, Los Alamitos

    Google Scholar 

  • Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185:1124–1131. doi:10.1126/science.185.4157.1124

    Article  Google Scholar 

  • Varshney A, Kaufman A (1996) FINESSE: a financial information spreadsheet. In: Proceedings of the IEEE symposium on information visualization, vol 125, pp 70–71. doi:10.1109/INFVIS.1996.559222

  • Zhang J (2009) Communication in asymmetric group contest over public goods (Working paper)

    Google Scholar 

  • Ziegler H, Nietzschmann T, Keim D (2008) Visual analytics on the financial market: pixel-based analysis and comparison of long-term investments. In: International conference on information visualisation, pp 287–295. doi:10.1109/IV.2008.80

  • Zigurs I, Buckland BK (1998) A theory of task/technology fit and group support systems effectiveness. MIS Quart 22:313–334. doi:10.2307/249668

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Human Ecology, University of Wisconsin—Madison, Madison,  USA

    Anya C. Savikhin

Authors
  1. Anya C. Savikhin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anya C. Savikhin .

Editor information

Editors and Affiliations

  1. , Library, Archival & Information Stud., University of British Columbia, East Mall 1961, Vancouver, V6T 1Z1, British Columbia, Canada

    Victoria Lemieux

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Savikhin, A.C. (2013). The Application of Visual Analytics to Financial Decision-Making and Risk Management: Notes from Behavioural Economics. In: Lemieux, V. (eds) Financial Analysis and Risk Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32232-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32232-7_5

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32231-0

  • Online ISBN: 978-3-642-32232-7

  • eBook Packages: Business and EconomicsEconomics and Finance (R0)

Publish with us

Policies and ethics

Search

Navigation