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Single-index model

Single-index model is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Single-index model rather than just read about it. In short: The single-index model (SIM) is a simple asset pricing model to measure both the risk and the return of a stock. The model was developed by William Sharpe in 1963 and is commonly used in the finance industry, including portfolio optimization.

Key takeaways

  • Single-index model belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Single-index model to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Single-index model from memory before moving on to harder problems.

Reference excerpt

The single-index model (SIM) is a simple asset pricing model to measure both the risk and the return of a stock.

The model was developed by William Sharpe in 1963 and is commonly used in the finance industry, including portfolio optimization.

Formulation Mathematically the SIM is expressed as:

r i t − r f = α i + β i ( r m t − r f ) + ϵ i t {\displaystyle r_{it}-r_{f}=\alpha _{i}+\beta _{i}(r_{mt}-r_{f})+\epsilon _{it}\,}

ϵ i t ∼ N ( 0 , σ i 2 ) {\displaystyle \epsilon _{it}\sim N(0,\sigma _{i}^{2})\,}

where:

rit is return to stock i in period t rf is the risk free rate (i.e. the interest rate on treasury bills) rmt is the return to the market portfolio in period t

α i {\displaystyle \alpha _{i}} is the stock's alpha, or abnormal return

β i {\displaystyle \beta _{i}} is the stock's beta, or responsiveness to the market return Note that r i t − r f {\displaystyle r_{it}-r_{f}} is called the excess return on the stock, r m t − r f {\displaystyle r_{mt}-r_{f}} the excess return on the market

ϵ i t {\displaystyle \epsilon _{it}} are the residual (random) returns, which are assumed independent normally distributed with mean zero and standard deviation σ i {\displaystyle \sigma _{i}}

These equations show that the stock return is influenced by the market (beta), has a firm specific expected value (alpha) and firm-specific unexpected component (residual). Each stock's performance is in relation to the performance of a market index (such as the All Ordinaries). Security analysts often use the SIM for such functions as computing stock betas, evaluating stock selection skills, and conducting event studies.

Assumptions To simplify analysis, the single-index model assumes that there is only 1 macroeconomic factor that causes the systematic risk affecting all stock returns and this factor can be represented by the rate of return on a market index, such as the S&P 500. According to this model, the return of any stock can be decomposed into the expected excess return of the individual stock due to firm-specific factors, commonly denoted by its alpha coefficient (α), the return due to macroeconomic events that affect the market, and the unexpected microeconomic events that affect only the firm. The term β i ( r m − r f ) {\displaystyle \beta _{i}(r_{m}-r_{f})} represents the movement of the market modified by the stock's beta, while ϵ i {\displaystyle \epsilon _{i}} represents the unsystematic risk of the security due to firm-specific factors. Macroeconomic events, such as changes in interest rates or the cost of labor, causes the systematic risk that affects the returns of all stocks, and the firm-specific events are the unexpected microeconomic events that affect the returns of specific firms, such as the death of key people or the lowering of the firm's credit rating, that would affect the firm, but would have a negligible effect on the economy. In a portfolio, the unsystematic risk due to firm-specific factors can be reduced to zero by diversification. The index model is thus based on the following:

Most stocks have a positive covariance because they all respond similarly to macroeconomic factors. However, some firms are more sensitive to these factors than others, and this firm-specific variance is typically denoted by its beta (β), which measures its variance compared to the market for one or more economic factors. Covariance among securities result from differing responses to macroeconomic factors. Hence, the covariance of each stock can be found by multiplying their betas and the market variance: The single-index model assumes that once the market return is subtracted out the remaining returns are uncorrelated:

E ( ( R i , t − β i m t ) ( R k , t − β k m t ) ) = 0 , {\displaystyle E((R_{i,t}-\beta _{i}m_{t})(R_{k,t}-\beta _{k}m_{t}))=0,}

which gives

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Single-index model

Start with the simplest possible case. Write down what Single-index model claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Single-index model before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Single-index model ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Single-index model

In research
Single-index model appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Single-index model in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Single-index model is common in secondary-school and first-year university syllabi. It links to neighbouring topics Financial economics, Financial models, Portfolio theories, so understanding it makes those chapters shorter.
In everyday life
Look for Single-index model outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Single-index model in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Single-index model means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Single-index model out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Single-index model in simple terms?

The single-index model (SIM) is a simple asset pricing model to measure both the risk and the return of a stock. The model was developed by William Sharpe in 1963 and is commonly used in the finance industry, including portfolio optimization.

Why does Single-index model matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Single-index model?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Single-index model.

Tags

  • Financial economics
  • Financial models
  • Portfolio theories

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