Abnormal Return

An abnormal, or excess, return occurs when the performance of an investment exceeds what is expected based on its risk

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In finance, understanding key metrics and concepts is crucial for investors navigating the market. Investors, both experienced and new, always aim to improve returns and manage risk. Abnormal returns, also known as excess returns, are a critical aspect. This article explores abnormal returns, detailing what excess returns involve, how they differ from risk-free rates, and the methods to compute them.

Before diving deeper into the concept of abnormal returns, let’s establish a basic understanding of what these terms mean.

Key Highlights

  • Excess returns are returns on an investment that exceeds what is expected based on risk and market conditions.
  • Excess Returns = Actual Return less Expected Return.
  • Excess returns provide a tool for investors to quantify to what degree an investment has outperformed.
  • Excess returns can be calculated across a wide number of different investment scenarios, making it a simple yet powerful tool.

What are Excess Returns?

Excess returns are essentially the performance of an investment that exceeds what is expected based on its risk and market conditions. An excess return serves as a quantitative measure of an investment’s ability to outperform, providing investors with insights into the effectiveness of their strategies. The calculation of excess returns is often straightforward and involves subtracting the expected return of an investment from the actual return.

In finance, investors use models like the Capital Asset Pricing Model (CAPM) or the Arbitrage Pricing Theory (APT) to predict the expected return. The goal is to compare this expected return with the actual return achieved, helping us assess how well the investment performed compared to what the market expected.

Excess Returns vs. Risk-Free Rates

To understand why excess returns matter, it’s important to know how they differ from risk-free rates. Excess returns measure an investment’s outperformance, while risk-free rates act as a benchmark for evaluating this performance. Usually represented by government bond yields, risk-free rates offer a baseline return without any material risk.

Comparing excess returns to risk-free rates enables an investor to determine if the extra risks taken on an investment are justified by higher returns. This analysis is crucial for making informed decisions about the trade-off between risk and return in a portfolio.

Calculating Excess Return

The computation of excess returns involves a straightforward formula:

Excess Return = Actual Return − Expected Return

The actual return includes all gains or losses from holding an investment, like capital gains, dividends, or interest income. Meanwhile, the expected return comes from financial models, predicting the investment’s performance based on its risk.

This calculation is valuable for investors, fund managers, and quantitative analysts. It quantifies how well an investment surpasses market expectations.

What is the Difference Between Excess Return and Actual Return?

The fundamental difference between actual returns and excess returns lies in the context of expected returns. Actual return quantifies the realized performance of an investment, encompassing all gains and losses experienced during its holding period. On the flip side, excess returns are a measure of the deviation from the anticipated performance, factoring in the investment’s risk profile and prevailing market conditions.

This nuanced approach offers investors a deeper insight into the effectiveness of their investment strategies. By assessing how well an investment aligns with or diverges from expected returns, investors can gauge the relative success of their approaches. It allows them to discern whether their strategies are consistently delivering excess returns, meeting expected returns precisely, or perhaps falling short.

In essence, the comparison to expected returns provides a valuable metric for investors to fine-tune their strategies. It prompts a reflective analysis, helping them understand the effectiveness of their decision-making in various market conditions. This evaluative tool becomes especially crucial in an ever-changing financial landscape, allowing investors to adapt and optimize their strategies to achieve more consistent and satisfactory returns over time.

Is Excess Return the Same as Relative Return?

It can be tempting to simply equate excess returns and relative returns as having exactly the same meaning. However, excess return is not the same as relative return. While the terms are related, they have distinct meanings.

Excess return specifically refers to the outperformance of an investment compared to expectations. On the other hand, relative return is a broader term that encompasses various metrics comparing an investment’s performance to a benchmark, index, fund, or other comparable assets. Excess return is a subset of relative return, focusing specifically on the excess performance beyond what was expected.

Practical Applications and Examples

To show you how these ideas work, let’s look at real examples involving different types of investments and situations.

Example 1: Equities and the Market Portfolio

Consider two funds investing in a portfolio of equities. A fund’s return refers to the profit or loss generated by a financial investment fund over a specific period. This return is typically expressed as a percentage and encompasses any capital gains or losses, dividends, and interest earned during the given time frame.

Fund A delivers a total return of 12%, while Fund B achieves a return of 15%. However, the market, represented by a broad index, has a total return of 14%.

To calculate the excess return for the two funds, an investor can use the formula:

Excess Return = Actual Return − Expected Return

For Fund A: Excess Return for A = 12% − 14% = -2%

For Fund B: Excess Return for B = 15% − 14% = 1%

These calculations reveal that Fund A underperformed the market because the excess return of the fund is negative. Fund B, however, outperformed the market, with a positive excess return.

Outperforming the market by generating excess returns, sometimes referred to as alpha in the fund management industry, is typically considered the primary objective of any fund manager. However, other factors do come into play when assessing the holistic performance of a fund manager. An investor will also want to measure the risk taken by the fund manager to achieve any excess return, for example.

Example 2: Fixed Income and Yield Considerations

Now, let’s explore a fixed income example, particularly bonds. Imagine you own a corporate bond with an expected return of 7% based on the yields of similar bonds with the same credit rating. However, the bond actually gives you a return of 8%. The risk-free rate, which is a safe investment represented by the yield on a government bond, is 3%. In calculating excess return, you subtract the expected return from the actual return:

Excess Return on Bond = 8% − 7% = 1%

The actual return on the bond can also be compared to the government bond, which will give us the relative return:

Relative Return on Bond = 8% − 3% = 5%

Example 3: Using the Capital Asset Pricing Model

Let’s consider an example of calculating the excess return for an equity using the Capital Asset Pricing Model.

Suppose you have an equity investment with an actual total return of 15%. According to the Capital Asset Pricing Model, the expected return for this equity, based on its risk profile and the overall market conditions, is calculated as follows:

Expected Return = Risk Free Rate + (Equity Beta × Market Risk Premium)

Assume the current risk-free rate is 3%, and the market risk premium is 8%. If the equity’s Beta is 1.2, the expected return would be:

Expected Return = 3% + (1.2 × 8%) = 3% + 9.6% = 12.6%

To find the excess return, subtract the expected return from the actual total return:

Excess Return on Equity = 15% − 12.6% = 2.4%

In this example, the equity has generated a positive excess return of 2.4%, indicating that it has outperformed expectations based on the Capital Asset Pricing Model. Investors can use this information to evaluate whether the additional risk associated with the equity investment is compensated by the higher returns achieved.

Example 4: Real Estate: Capitalizing on Property Investments

Real estate, as an alternative investment class, presents unique opportunities for excess returns. Investors in real estate assess the performance of properties by comparing the actual total return, comprising rental income and property appreciation, to the expected returns based on factors such as location, market trends, and property type.

Consider Rental Property Y, which was projected to yield an annual return of 8%. However, the actual return realized is 10%. To calculate the excess return for Rental Property Y, we use the formula:

Excess Return for Property Y = 10% − 8% = 2%

In this scenario, Rental Property Y has delivered a positive excess return of 2%, indicating that the property has outperformed expectations and provided investors with a premium return.

Advanced Concepts and Strategies

As we continue, let’s dig into more advanced ideas connected to excess returns. This includes understanding how quantitative investing plays a role, considering risk premiums, and applying these concepts to effectively manage risk

Quantitative Investing and Excess Returns

Quantitative investing involves leveraging mathematical models and data analysis to make investment decisions. In the context of excess returns, quantitative, or quant, strategies aim to identify patterns, trends, and anomalies in market data that can be exploited to achieve superior performance. By utilizing quantitative models, quantitative fund managers and investors can enhance their ability to generate excess returns above any expected return in a consistent, systematic way.

Investors employ various quantitative strategies to generate excess returns. Here are some common quant strategies:

  • Statistical Arbitrage: This strategy involves using statistical models to identify pricing inefficiencies in the market. Quantitative analysts look for patterns and relationships in historical data to make short-term trades based on expected price movements.
  • Algorithmic Trading: Using computer algorithms to execute trades, an algorithmic trading strategy seeks to capitalize on market inefficiencies, price discrepancies, or specific patterns. These algorithms can process vast amounts of data and execute trades at high speeds.
  • Mean Reversion: This strategy assumes that asset prices will tend to move back to their historical average over time. Quants identify assets that have deviated significantly from their historical average and expect them to revert to the mean.
  • Momentum Investing: In contrast to mean reversion, a momentum strategy capitalizes on an existing trend. Quants invest in assets whose trend has shown strong historical performance and who expect that trend to continue.
  • Factor Investing: Investors focus on specific factors, such as value, size, or quality, that historically contribute to excess returns. These factors are identified through quantitative analysis and integrated into investment strategies.
  • Machine Learning-Based Strategies: Utilizing advanced machine learning algorithms, quants analyze large datasets to identify patterns and trends that may not be apparent through traditional analysis. Machine learning can adapt to changing market conditions.

These quant strategies leverage mathematical models, statistical analysis, and advanced technologies to gain an edge in the financial markets and generate excess returns. Each strategy has its own risk profile, and the success of each strategy requires expertise in financial markets and robust quantitative modeling.

The Role of Data in Quantitative Investing

Quantitative investing relies heavily on the analysis of vast amounts of data to identify patterns, trends, and anomalies. With the advent of technology and the availability of extensive datasets, quantitative investors leverage sophisticated algorithms and mathematical models to gain a competitive edge.

The utilization of data-driven insights allows fund managers and institutional investors to make informed decisions, enhance their predictive capabilities, and systematically generate excess returns.

Risk Premium and Excess Returns

Understanding the concept of risk premium is critical to grasping the concept of excess returns. Risk premium represents the extra return expected from an investment to compensate for its level of risk.

Excess returns essentially capture this risk premium by measuring how far an investment’s total return deviates from the expected return. If investors aim for higher returns, they need to be aware of the risk premium they’re taking on and evaluate whether the potential rewards are worth the associated risks.

Managing Risk with Excess Returns

Successfully managing risk is an integral part of any successful investment strategy. Excess returns are crucial here because they give investors a way to measure if the returns they are getting are worth the risks they are taking.

Fund managers and investors use excess returns to check the risk and return balance in their portfolios. This helps them spot areas in their portfolio that might need changes to match their comfort with risk and their investment goals.


This article delves into the important concept of abnormal returns, also known as excess returns, in finance. Excess returns are defined as an investment’s performance surpassing expectations based on risk and market conditions. Excess returns offer a measurable way to gauge how well an investment outperforms, giving insights into the effectiveness of investment strategies.

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