FinVis: Applied Visual Analytics for Personal Financial Planning

Stephen Rudolph, Anya C. Savikhin, David S. Ebert{ srudolph | anya | ebertd } @ purdue.edu

Purdue University

Financial Planning for the Casual User

• Financial planning

• Individuals often make sub-optimal decisions when choosing a portfolio (Kroll et al., 1988)

• Casual users

• Americans increasingly responsible for own retirement

• < 25% of individuals over 50 have a financial plan (Lusardi et al., 2006)

• Individuals less likely to follow plan made for them – need to understand it (Lusardi et al., 2006)

Motivation

• Decisions when choosing a portfolio:

• Finding the right investment?

• Optimal allocation decisions?

• Decision support systems needed (Bhandari et al., 2008)

• Visual analytics needed for complex decision-making - exploring information interactively

• Casual information visualization field calls for new applications in finance (Pousman et al. 2007)

Contribution

• One of the first visual analytics work to address portfolio planning for casual users.

• General public could benefit from this tool

• Decisions are better when using FinVis

• Quantified using experimental economics

• Improved decisions, learning, exploration, confidence

A Casual User’s introduction to Wise Financial Planning

• Learn the lingo• Finance has a language all it’s own

• Do some market research• Analyze investment performance

• Do a little math• Calculate return, risk, and correlations then optimize your portfolio

Overwhelmed or Don’t Have the Time?

• How can FinVis help?• No need to learn about statistics or signals

• Allows for quick experimenting and prototyping

• Visual feedback

Cognitive Limitations

• Individuals have difficulty understanding risk and correlations

• When available information is too complex and uncertain, individuals use heuristics or “rules of thumb”

• Simplifies decision process

• However, results in sub-optimal decisions

• Visual analytics can reduce complex cognitive work (Savikhin et al., 2008)

Overcoming Cognitive Limitations

• Improves decision-making

• “Framing and Diversification Heuristic” Avoid allocating equal amounts to each asset (Goetzmann & Kumar, 2008)

• Engages subjects to explore dataset (learning)

• “Ambiguity Aversion” – inappropriate default portfolio

• Identify with plan and stick to it (Bhandari et al., 2008)

• Increases confidence - graphical representations increase confidence (Lusardi & Mitchell, 2005).

Visualizing Risk + Return + Time

• Casual information visualization (Pousman et al., 2007)

• 2D vs. 3D (Jarett, 1983; Lux, 1997)

• Temporal visualization (Havre et al., 2000)

• Risk visualization (Schreck et. al, 2007; Ziegler et al., 2008)

Visualizing Risk + Return + Time

Visualizing Risk + Return + Time

Temporal visualization

Risk visualization

Highlighting

Investment Boundaries

Newest investment closer to the center

FinVis Demo

Economics Experiment Benefits

• Quantify the benefits of various learning tools

• Testing multiple environments

• Remove context/emotion (abstracted from “real world”)

• Repeated trials –analyzing learning

• Controlled subject pool

• Financial incentives

Experiment Design

Asset Return Standard Dev.

Correlation

A 3 3 0

B 5 6B and C - 0.8C 7 12

D 2 0 0

Treatment

FinVis 6 periods with 3 decision years per period: allocate money in each year among 4 investments

Control: Tabular Version

• Rational investors should use diversification to maximize return for any level of risk (Markowitz, 1952)

• Overcome cognitive limitations to make decisions closer to the theoretical optimal?

Experimental Procedure

• 26 (13/13) Purdue undergraduate students participated at the experimental economics lab

• Subjects proceeded at their own pace

• Confidence and perceived usefulness questionnaire at the end

• Participants paid based on their portfolio’s return

• An outcome randomly chosen from the distribution based on return/risk profile of the portfolio

• Greater returns for same level of risk, lower risk for same level of returns. Closer to efficiency frontier (p-value 0.02 for last period) (Markowitz, 1952)

Finding 1 - Improved Decision-Making

Markowitz Benchmark

• 1/n heuristic less likely (4 times, compared to 10 times in the control)

Finding 1 - Improved Decision-Making

• Decrease in “ambiguity aversion”

• Investments modified 107 times, versus 55 in the control

• Statistically significant difference (individual period t-test p-values < 0.05)

• Similar number of investments at the end

• 7 min per decision, versus 5 in the control

Finding 2 - Greater Exploration and Learning Through Visual Analytics

• “Are you more, less or equally confident in your abilities to understand financial planning?” Increased confidence results in greater likelihood to create investment plan.

• “How likely are you to use this tool in practical application?”

Finding 3 - Increased Confidence and Perception of Usefulness

Treatment Less Confident Equally Confident More Confident

FinVis 15% 62% 23%

Control 15% 85% 0%

Treatment Not Very Likely Somewhat Likely Very Likely

FinVis 0% 61% 39%

Control 38% 46% 15%

• FinVis helps the non-expert user make improved personal finance decisions• Subjects using FinVis outperform baseline

• Improved confidence

• Subject interest in using FinVis in practice

• Subjects may not need “rules of thumb”

Conclusions

Future Work

• Transform FinVis into a highly-interactive consumer application

• Add practical extensions to the financial model• Automatically perform market research• Integrate a 2nd visualization to highlight the

diversification at a single point • OSS: http://code.google.com/p/finvis/

Acknowledgements

• The authors wish to thank Roman Sheremeta and Niklas Elmqvist for their help and feedback. This work has been funded by the US Department of Homeland Security Regional Visualization and Analytics Center (RVAC) Center of Excellence and the US National Science Foundation (NSF) under Grants 0328984 and 0121288.

• Call for collaboration: more work is needed in economic visual analytics!

• 26 (13/13) Purdue undergraduate students participated

• 60% male, average age 21

• Business, engineering, information technology, science, health science

•

Demographics

• 26 (13/13) Purdue undergraduate students participated

• Endowment of $1,000 experimental dollars each period

• Conversion: 100 experimental dollars = $1

• 2 of 6 periods selected for payment

• Participants paid based on their portfolio’s return

• An outcome randomly chosen from the distribution based on return/risk profile of the portfolio

• Earnings on average $9-18 (60 minutes)

Experimental Setup

Modification p-values