Bayesian Networks: A Decision Tool to Improve Portfolio Risk Analysis

By Demirer, Riza; Mau, Ronald R. et al. | Journal of Applied Finance, Fall 2006 | Go to article overview

Bayesian Networks: A Decision Tool to Improve Portfolio Risk Analysis


Demirer, Riza, Mau, Ronald R., Shenoy, Catherine, Journal of Applied Finance


This paper demonstrates how Bayesian networks, a graphical modeling tool, can improve analysts' forecasts, portfolio decision-making, and risk analysis. Bayesian networks combine historical quantitative information with qualitative information in a systematic way. Findings in behavioral finance show buying and selling investment behavior that indicates biased decision-making, but most behavioral finance literature is descriptive, not normative. Our goal is to improve rational financial decision-making by helping analysts eliminate bias from their probability assessments and systematically improve value-at-risk forecasts. [G11, G19, C4]

Portfolio management is a very special problem in engineering, of determining the most reliable and efficient way of reaching a specified goal, given a set of policy constraints, and working within a remarkably uncertain, probabilistic, always changing world of partial information and misinformation, all filtered through the inexact prism of human interpretation (Ellis 1985, p. 53).

Security analysts evaluate a variety of information to decide whether to buy, sell, or hold a security. Research on security analysis concentrates primarily on two different areas. The first is pricing and valuation models. The second is the relation between firm or economic variables and earnings forecasts. We use a Bayesian network to model economic relations to produce earnings forecast for each stock in a portfolio and a return distribution for the portfolio. The output of the model is a probability distribution that combines historical information with current news.

Traditional pricing models such as the capital asset pricing model (CAPM) or arbitrage pricing theory (APT) describe how economic variables and firm characteristics are related to stock returns. The models are based on historical and quantitative data, and the results are averages for a typical firm. Most analysts use this historical quantitative analysis as part of an overall approach that also includes a wider variety of information. Analysts typically concentrate on special situations and individual cases, not on the average. Their information includes historical data and qualitative, imprecise evidence that may affect a firm.

An analyst may consider, for example, how effective or trustworthy a firm's management is. Or, what is the effect of China's entry into the World Trade Organization on a particular line of business? How reliable are a firm's financial statements? We show how to integrate this type of information with historical quantitative data using a graphical decision-modeling tool, Bayesian networks.

In portfolio management, analysts must assess a large amount of sometimes conflicting data to make a decision based on uncertain information. We suggest that Bayesian networks are especially well suited for this task. They help experts represent uncertain, ambiguous, or incomplete knowledge that portfolio managers and analysts often deal with.

The output of the model is a probability distribution of portfolio value. As the entire distribution is modeled, measures of risk and value-at-risk (VAR) are modeled naturally. When new evidence is added its effects on other variables in the model and on risk are also computed.

There are two types of inputs to the model - the graphical relation, and a set of equations and conditional probability distributions described by the graph. The conditional probability relations can be estimated from historical data or from expert judgment. Shenoy and Shenoy (2000) show how traditional expected return models such as the CAPM and APT can be used to model relations in a Bayesian network.

Here we show how to combine quantitative data with qualitative or soft information in a systematic way. Our example demonstrates how to combine macroeconomic factors and firm-specific factors, but the methodology is flexible enough to reflect an individual analyst's decision-making process. …

The rest of this article is only available to active members of Questia

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Default project is now your active project.
Project items
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Buy instant access to cite pages or passages in MLA 8, MLA 7, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

(Einhorn 25)

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Note: primary sources have slightly different requirements for citation. Please see these guidelines for more information.

Cited article

Bayesian Networks: A Decision Tool to Improve Portfolio Risk Analysis
Settings

Settings

Typeface
Text size Smaller Larger Reset View mode
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Help
Full screen
Items saved from this article
  • Highlights & Notes
  • Citations
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

matching results for page

    Questia reader help

    How to highlight and cite specific passages

    1. Click or tap the first word you want to select.
    2. Click or tap the last word you want to select, and you’ll see everything in between get selected.
    3. You’ll then get a menu of options like creating a highlight or a citation from that passage of text.

    OK, got it!

    Cited passage

    Style
    Citations are available only to our active members.
    Buy instant access to cite pages or passages in MLA 8, MLA 7, APA and Chicago citation styles.

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

    1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

    Cited passage

    Thanks for trying Questia!

    Please continue trying out our research tools, but please note, full functionality is available only to our active members.

    Your work will be lost once you leave this Web page.

    Buy instant access to save your work.

    Already a member? Log in now.

    Search by... Author
    Show... All Results Primary Sources Peer-reviewed

    Oops!

    An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.