Dividend Discount Models and Portfolio Risk: Direct Evaluations

The Function of Dividend Discount Models in Valuation

Dividend Discount Models (DDMs) demonstrate a persistent relevance for valuing shares and, to a much lesser extent, bonds. The conceptual core of the DDM is comfortable: the value of a stock equals the present value of its expected dividends. Practitioners often fall back on the Gordon Growth Model, a neat version of DDM, for stocks with steady dividend growth. There is elegance in its simplicity. The inputs—projected dividends, required return, and growth rate—are well-defined, at least in theory. Reality, though, shakes things up. Dividends fluctuate, businesses reinvest or pivot, and “constant” growth proves slippery over time. Xu (2022) points out the danger: apply DDM to firms with irregular or negligible dividends, and the outputs veer toward fiction. What’s left is a tool most trustworthy for established, dividend-paying firms—think utilities, old banks, blue chips seeking little drama.[1]

Moreover, DDM imposes rough limitations. It aims for simplicity, but real valuation calls for texture. Discounted Cash Flow (DCF) models frequently outpace DDM in accuracy because they acknowledge non-dividend cash outflows and capital reinvestment patterns. Still, dividend-based models stick around for pedagogical reasons; they clarify the mechanics of value, linking firm policy and investor expectations directly. DDM works best as a first pass, a way to screen for plausibility rather than a final verdict. It loses sharpness with younger firms or those engineering their payout ratio for tax or signaling reasons. Lurking underneath, the assumption of rational investors and market efficiency imparts more stability to the model than is visible in the wild.

Turning to bond valuation, DDM is not directly constructed for fixed-income instruments. Bond cash flows—coupons and principal repayment—are contractually defined, not discretionary like dividends. Models such as yield-to-maturity, present value, and related constant interest rate computations are standard. Nevertheless, some academics have explored hybrid techniques when convertible bonds are involved, especially where firm dividend policy interplays with bondholder returns. Conceptually, bonds appear simpler—promised returns, limited risk if held to maturity barring default. Consequently, applying DDM to bonds is usually an exercise in analogizing, not innovating. What stands, however: bonds can be valued via present value calculations, echoing the technique behind DDM but not its theoretical stance.[1]

Risk and Return: Incorporated but Not Fully Captured

Risk, in DDM, is conceptualized via the required rate of return. The numerator of risk—the tangible price of uncertainty—is a direct input. In some ways, this seems circular: investors, wary of bankruptcy, dividend cuts, or regulatory shocks, mark up their required return. High perceived risk, high discount rate, low present value—that’s the chain. The model “incorporates” risk, but it compresses the complexity. Market beta, liquidity premium, business cycle sensitivity, all must be folded (at times handwaved) into the discount rate.

Academic scrutiny has exposed deeper cracks. Gálvez (2021) finds that constant-growth models often suppress the subtleties in risk pricing, especially for businesses in transition phases or those nesting unpredictable payout policies. Advanced variations—the H-model, multi-stage DDMs—inject greater realism, modeling dividend growth in phases: high, then low, then stable. Even then, only idiosyncratic, dividend-related risk gets properly counted. Systematic risk, captured loosely by the required rate and historically by proxies like the equity premium, is less tractable. Investors also weigh the risk of market volatility, tax changes, and monetary policy shifts—factors difficult to package within the model’s limited levers. Thus, the DDM serves more as a starting point: calculable, explainable, but not comprehensive.[2]

It’s important to underscore: empirical studies reveal that risk and return do mix into DDM, but the granularity seldom matches the real world. Practitioners tweak the required return to reflect firm-specific and market risks, but the risk remains only as precise as the inputs are honest. In the end, model outputs hinge on subjective discount rates, exposing valuation to error and bias. More sophisticated practitioners integrate scenario analysis, stress testing, or combine DDM with market-multiples and DCF approaches to buffer against its inherent narrowness. For an investor with patience and skepticism, the DDM may offer a pulse-check, but never enough for final surgery.

Relevant Examples: DDM in Practice

Take Iflytek Co., Ltd as a plain illustration. Xu (2022) dissected its share price with DDM and found logical divergences between the model’s predictions and market price. The primary culprit was dividend volatility, derived from underlying earnings instability. While DDM provided a quick screen of intrinsic value, rigorous analysis required fallback on DCF and market-based comparables to rectify errors. Furthermore, major global banks illustrate DDM’s power and limits. Analysts value firms like HSBC or JP Morgan with variants of DDM for their core banking divisions, but often revert to sum-of-the-parts valuations when faced with conglomerate complexity, fluctuating payout policy, or regulatory rotation.[1]

Bond examples are less convincing. Convertible bonds, when tied explicitly to dividend payout outcomes, may loosely borrow DDM techniques for a portion of the analysis, yet traditional present value models dominate fixed-income valuation. In other words, DDM’s best application remains within the sphere of predictable, distributive equity, not contractual debt instruments.

Portfolio Risk Reduction: Less Is Actually More

Portfolio theory hinges on one claim: the risk of a diversified portfolio of shares is routinely lower than the average risk of its constituent assets. Bikeri (2022) outlines the division: systematic risk—market-wide and inescapable—and unsystematic risk—firm or sector-specific, and therefore diversifiable. By spreading capital across multiple shares from disparate industries or regions, investors drive down exposure to random shocks—factory fires, accounting errors, sudden CEO departures. The mathematics underpinning this point is not elegant; it is robust. Provided the returns of each stock do not march perfectly in lockstep, diversification suppresses unsystematic risk.[3]

Most textbook illustrations miss the rawness of actual investing. A hypothetical portfolio spread across ten equally weighted shares, each with unique vulnerabilities but moderate correlations, will deliver a total volatility lower than the average volatility of its ingredients. The extremes wobble less. Bikeri (2022) notes that mutual funds and institutional investors purposefully allocate across stocks and asset classes to maximize this effect, even weighing in correlations to optimize. In actual application, Herfindahl-Hirschman Index (HHI) and similar concentration metrics help measure how well risk has been scattered across the portfolio. Real data: diversification raised the liquidity position and lowered distress costs in a sample of Kenyan investment firms spanning several years.[3]

However, systematic risk—interest rate spikes, regional wars, pandemics—remains lurking and cannot be dodged by asset mixing. Diversification slashes idiosyncratic risks, not macro threats. The average investor, with limited resources and market insight, can trim the noise but must always face the music when the entire market turns sour. That is the subtle distinction: portfolios can lower total risk, but never erase systemic shocks.

Illustrative Example: Risk Mitigation in Action

Imagine a portfolio with five shares: A, B, C, D, and E. Assume each has an annual standard deviation of 20%, but low correlation with the others. Pairwise correlations hover near zero. When calculating risk, the aggregate volatility of the portfolio lands at about 9%—well under the average of the components. The explanation: price swings of A and B partially offset those of C, D, or E. Even in an abrupt loss for one company, gains in another may soften the total loss. It is not insurance, more like sensible buffering.

What if the shares are tightly linked—say, all are UK banks? Risk drops less because their fates are intertwined. The lesson is stark: true diversification demands attention to co-movements and sector concentration. Random asset selection will not suffice. Quantitative portfolio models, such as Markowitz’s mean-variance optimization, make this plain but the intuition is accessible: open the gates to difference and risk starts to behave.

Refunding the Original Thesis

Dividend Discount Models are useful in their constrained domain: plain vanilla share valuation, predictable payouts, easy growth patterns. They incorporate risk and return through the required rate of return, but with a bluntness that can mislead when market conditions stray from the textbook. More complex models and pragmatic judgment fill in the gaps. For bonds, present value models retain the edge; DDM’s analogy is thin and rarely essential. Portfolio risk, by contrast, can truly astonish—mixing assets can choke down overall volatility to levels well below the weighted average risk. Diversification functions best with low correlations and an escape from sector or regional clustering. Evidence from empirical studies bears out the mathematics—risk reduction by combinations is no mere theory, but a consistent practical outcome.

References

• Xu, J. (2022). “Advantages and Disadvantages of Dividend Discount Model and Better Alternatives.” Atlantis Press. [Accessed 27 Oct 2025]
• Gálvez, J. (2021). “Measuring the equity risk premium with dividend discount models.” Banco de España. [Accessed 27 Oct 2025]
• Bikeri, G.M. (2022). “Effect of Portfolio Diversification on Financial Performance of Investment Firms in Kenya.” University of Nairobi. [Accessed 27 Oct 2025]
• Payne, T.H. (1999). “Effective teaching and use of the constant growth dividend discount model.” Journal of Financial Education.
• Musembi, M., & Jagongo, A. (2017). “Portfolio Diversification and Risk Reduction.” International Journal of Finance and Accounting.

DFI4002 Introduction to Finance Assessment Essay Coursework – Arden University

 

University	Arden University
Subject	DFI4002: Introduction to Finance

 

Assignment Brief

As part of the formal assessment for the programme you are required to submit a Introduction to Finance assignment. Please refer to your Student Handbook for full details of the programme assessment scheme and general information on preparing and submitting assignments.

Learning Outcomes:

After completing the module, you should be able to:

1. Understand the concept of the time value of money and the nature of interest rates.
2. Understand different sources of finance.
3. Calculate the fair value of bonds and shares.
4. Understand the relationship between risk and return and how appropriate diversification reduces risk.

Guidance

Your assignment should include: a title page containing your student number, the module name, the submission deadline and the exact word count of your submitted document; the appendices if relevant; and a reference list in AU Harvard system(s). You should address all the elements of the assignment task listed below. Please note that tutors will use the assessment criteria set out below in assessing your work.

You must not include your name in your submission because Arden University operates anonymous marking, which means that markers should not be aware of the identity of the student. However, please do not forget to include your STU number.
Maximum word count: 2500 words

Please refer to the full word count policy which can be found in the Student Policies section here: Arden University | Regulatory Framework

Please note the following:

Students are required to indicate the exact word count on the title page of the assessment. 

The word count includes everything in the main body of the assessment (including in text citations and references). The word count excludes numerical data in tables, figures, diagrams, footnotes, reference list and appendices. ALL other printed words ARE included in the word count.   

Students who exceed the wordcount up to a 10% margin will not be penalised. Students should note that no marks will be assigned to work exceeding the specified limit once the maximum assessment size limit has been reached.

Assignment Task 

Write an essay on both topics below using a maximum of 2,500 words in total:

1. Evaluate the usefulness of Dividend Discount Models in the valuation of bonds and shares and analyse how they incorporate the relationship between risk and return. Support your discussions with academic research and relevant examples.
   (50 marks)
2. Discuss why the risk of a portfolio of shares is normally lower than the average risk of the shares in the portfolio. Use academic research to underpin your discussions and a hypothetical example for illustrative purposes.
   ( 50 marks)

(2500 words)
(100 marks)
(LOs: 1-4)

Formative Feedback 

You have the opportunity to submit a partial draft of up to 1000 words to receive formative feedback.

The feedback is designed to help you develop areas of your work and it helps you develop your skills as an independent learner.

If you are a distance learning student, you should submit your work, by email, to your tutor, no later than 2 weeks before the actual submission deadline. If you are a blended learning student, your tutor will give you a deadline for formative feedback and further details.

Formative feedback will not be given to work submitted after the above date or the date specified by your tutor – if a blended learning student.

Referencing Guidance

You MUST underpin your analysis and evaluation of the key issues with appropriate and wide ranging academic research and ensure this is referenced using the AU Harvard system(s).

Follow this link to find the referencing guides for your subject: Arden Library

Submission Guidance

Assignments submitted late will not be accepted and will be marked as a 0% fail.

Your assessment can be submitted as a single Word (MS Word) or PDF file, or, as multiple files.

If you chose to submit multiple files, you must name each document as the question/part you are answering along with your student number ie Q1 Section A STUXXXX. If you wish to overwrite your submission or one of your submissions, you must ensure that your new submission is named exactly the same as the previous in order for the system to overwrite it.

You must ensure that the submitted assignment is all your own work and that all sources used are correctly attributed. Penalties apply to assignments which show evidence of academic unfair practice. (See the Student Handbook which is available on the A-Z key information on iLearn.)

Arden University © reserves all rights of copyright and all other intellectual property rights in the learning materials and this publication. No part of any of the learning materials or this publication may be reproduced, shared (including in private social media groups), stored in a retrieval system or transmitted in any form or means, including without limitation electronic, mechanical, photocopying, recording or otherwise, without the prior written consent of Arden University. To find out more about the use and distribution of programme materials please see the Arden Student Terms and Conditions.

Assessment Criteria (Learning objectives covered – all)

Level 4 is the first stage on the student journey into undergraduate study. At Level 4 students will be developing their knowledge and understanding of the discipline and will be expected to demonstrate some of those skills and competences. Student are expected to express their ideas clearly and to structure and develop academic arguments in their work. Students will begin to apply the theory which underpins the subject and will start to explore how this relates to other areas of their learning and any ethical considerations as appropriate. Students will begin to develop self-awareness of their own academic and professional development.

 

Grade	Mark Bands	Generic Assessment Criteria
First (1)	80%+	Outstanding performance which demonstrates the ability to analyse the subject area and to confidently apply theory whilst showing awareness of any relevant ethical considerations. The work shows an outstanding level of competence and confidence in managing appropriate sources and materials, initiative and excellent academic writing skills and professional skills (where appropriate). The work shows originality of thought.
	70-79%	Excellent performance which demonstrates the ability to analyse the subject and apply theory whilst showing some awareness of any relevant ethical considerations. The work shows a high level of competence in managing sources and materials, initiative and excellent academic writing skills and professional skills (where appropriate). The work shows originality of thought.
Upper second (2:1)	60-69%	Very good performance which demonstrates the ability to analyse the subject and apply some theory. The work shows a very good level of competence in managing sources and materials and some initiative. Academic writing skills are very good, and expression remains accurate overall. Very good professional skills (where appropriate). The work shows some original thought.
Lower second (2:2)	50-59%	A good performance which begins to analyse the subject and apply some underpinning theory. The work shows a sound level of competence in managing basic sources and materials. Academic writing skills are good, and expression remains accurate overall although the piece may lack structure. Good professional skills (where appropriate). The work shows some original thought.
Third (3)	40-49%	Satisfactory level of performance in some areas which demonstrates an understanding of the subject, its underpinning theory, and ethical considerations. The work shows a satisfactory use of sources and materials. Academic writing skills are limited and there are some errors in expression and the work may lack structure overall. There are some difficulties in developing appropriate academic skills (where appropriate). The work lacks original thought and is largely imitative.
Marginal Fail	30-39%	Limited performance in which there are omissions in understanding the subject, its underpinning theory, and ethical considerations. The work shows a limited use of sources and materials. There are numerous errors in expression and the work may lack structure overall. There are difficulties in developing appropriate academic skills (where appropriate). The work lacks original thought and is largely imitative.
Clear fail	29% and Below	A poor performance in which there is little evidence of appropriate reading and understanding, underpinning theory and ethical considerations. The work shows little evidence in the use of appropriate sources and materials. Academic writing skills are very weak and there are numerous errors in expression. The work lacks structure overall. Professional skills (where appropriate) are not developed. The work is imitative

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