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Mean-Variance Analysis in Behavioral Finance: Neoclassical vs. Behavioral Portfolios, Assignments of Behavioral Economics

An in-depth exploration of mean-variance analysis in behavioral finance. It discusses the concept of mean-variance analysis, the difference between neoclassical and behavioral mean-variance portfolios, and the strengths and weaknesses of behavioral finance. The document also includes learning activities such as pretests and a reaction paper.

Typology: Assignments

2020/2021

Uploaded on 04/05/2021

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Aldersgate College
Solano, Nueva Vizcaya, Philippines 3709
Telefax No.: (078) 326 –5085 Tel. No.: (078) 326-5136, 326-5645
E-Mail : Aldersgate@Hotmail.comWebsite:www.Aldersgate-College.com
SCHOOL of BUSINESS, MANAGEMENT and ACCOUNTANCY
Second Semester SY 2019-2020
L92 ELECT-1 BEHAVIORAL FINANCE
Christine B. Montano
BSBA FM 1A
MODULE– 3: QUANTITATIVE BEHAVIORAL FINANCE
LEARNING ACTIVITIES:
PRETEST:
Learning Activity-1:Answer Pretest and turn-in to Google March 30, 2021.
No. Discussion
1 Discuss Mean – variance analysis
Means Variance Return (Gross)
110%
105% Neoclassical Efficient MV Portfolio Return
100%
95% Behavioral MV Portfolio Return
90%
85%
80%
70%
2 Describe-Gross Return to Mean – variance Portfolio:
Behavioral Mean – Variance vs. Efficient Market Mean – Variance Return
1.03
1.02
1.01
1 Return to a Combination of the Market Portfolio and Risk – free security
0.99 Neo Classical MV Portfolio Return
0.98
[1]
pf3
pf4

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Aldersgate College Solano, Nueva Vizcaya, Philippines 3709 Telefax No.: (078) 326 –5085 Tel. No.: (078) 326-5136, 326- E-Mail : Aldersgate@Hotmail.comWebsite:www.Aldersgate-College.com SCHOOL of BUSINESS, MANAGEMENT and ACCOUNTANCY Second Semester SY 2019-

L92 ELECT-1 BEHAVIORAL FINANCE

Christine B. Montano BSBA FM 1A

MODULE– 3: QUANTITATIVE BEHAVIORAL FINANCE

LEARNING ACTIVITIES:

PRETEST:

Learning Activity-1:Answer Pretest and turn-in to Google March 30, 2021.

No. Discussion

1 Discuss Mean – variance analysis

Means Variance Return (Gross)

105% Neoclassical Efficient MV Portfolio Return

95% Behavioral MV Portfolio Return

2 Describe-Gross Return to Mean – variance Portfolio:

Behavioral Mean – Variance vs. Efficient Market Mean – Variance Return

1 Return to a Combination of the Market Portfolio and Risk – free security

0.99 Neo Classical MV Portfolio Return

0.97 Behavioral MV Portfolio Return

➔ Mean-variance analysis is the process of weighing risk, expressed as variance, against expected

return. Investors use mean-variance analysis to make investment decisions. Investors weigh how much

risk they are willing to take on in exchange for different levels of reward. Mean-variance analysis allows

investors to find the biggest reward at a given level of risk or the least risk at a given level of return.

This Mean-variance analysis is a tool used by investors to weigh investment decisions. The variance

shows how spread out the returns of a specific security are on a daily or weekly basis.

➔ Contrasting the return to a neoclassical mean-variance portfolio and the return to a behavioral mean-variance

portfolio, as functions of aggregate consumption growth in the economy. Special case of Figure 1, when behavioral mean-variance return function has the shape of an inverse-U. This figure also shows that the

neoclassical mean-variance return is approximately linear. In the CAPM (capital asset pricing model), the

mean-variance function is exactly linear.

➔ Behavioral mean-variance portfolios satisfy the two-fund separation theorem. However, the risky asset used to

construct behavioral mean-variance portfolios features the use of derivatives. It is a well-established fact that investors require compensation to assume risk. Risk can take any form in financial markets but, in broad terms, the neoclassical framework focuses on fundamental risk. The behavioral approach adds sentiment risk. Therefore, behavioral risk premiums serve as compensation for bearing both sentiment and fundamental risks. Behavioral risk premiums, like their neoclassical counterparts, will be associated with betas and factor pricing models.

3 Explain: Behavioral Finance has four major strengths:

1. It has proven itself to be productive. - For example, it has led to a series of new empirical findings.

Examples include over- and underreaction in share prices, the new issues and stock repurchase puzzles, a and the role of the stock split effect.

and motivate their actions? Investment bankers, client relationship and financial marketing managers, among others, would be interested in answers to these questions. Yet, so far, behavioral finance has little to say.

3. Behavioral finance must move beyond the narrow micro – level study of typical “mistakes”.

- If not, too much behavior remains unintelligible. Yes, US data suggest that CEOs, entrepreneurs and

investors tend towards unrealistic optimism, an error with perilous consequences. A more fundamental critique is to pose the related question: What is a mistake? Economists take a hard line. Error, they say, is strictly about the contrast between actions that are taken and actions that rationally should be taken in accordance with an individual agent’s costs and benefits. Economists’ chief concern is efficiency.

4. There is a disconnect between the emphasis in behavioral research on human frailties and

the reality that in many corners of the globe people lead a pretty good life.

- Rationality and well-being derive from organization, spontaneous or deliberate. Why are institutions so

crucially important? The reason is that everyone in society depends on everyone else. We sell 99% of what we produce, we buy 99% of what we consume, and we lead better lives for it. Incessant technological progress, product and service standardization, and economic organization are central. Technological artifacts often allow cheap replication. So, good products or ideas spread quickly. However, people and machines have to work together.. Technology can be easy or difficult to use. Similarly, administrative organization can be effective or ineffective. Smart technology and organization are human-centered.

Learning Activity-2: Prepare a reaction paper and turn-in to Google on March 31, 2021