Active Management

Active Management and Active Return

Active management is a strategy employed by investors with the goal of generating portfolio returns that surpass the benchmark returns, once all costs are accounted for, at an acceptable level of risk. The surplus return, also known as active return (R_A), of an actively managed portfolio is determined by the weight difference between the active portfolio and the benchmark.

The active return can be mathematically expressed as:

$$R_A = \sum_{i=1}^{N} \Delta W_i R_i$$

Where:

• \( R_i = \) the return on security \( i \)
• \( \Delta W_i = \) the difference between the portfolio weights \( W_{pi} \) and the benchmark weights \( W_{Bi} \). \( \Delta W_i \) is also referred to as the active weight.

A successful active manager will generate positive active returns if the active weights are managed effectively.

Portfolio Construction

Portfolio construction is a strategic process of adjusting the active weights of securities to generate active returns. This can be achieved by creating long-term exposures to rewarded risks, identifying mispricing in securities, sectors, rewarded risks, and assuming excessive idiosyncratic risk. Regardless of the manager’s approach or strategy, the sources of active return remain the same.

Historically, any excess return over the benchmark was termed “alpha”. However, recent research suggests that much of what was previously considered as alpha is actually “alternative beta” – exposure to rewarded risks that can be obtained at a lower cost. These rewarded risks are investment risks for which investors expect to be compensated through a long-run return premium, such as exposure to market risk and liquidity risk.

Decomposition of Ex Post Active Returns

The decomposition of ex post active returns can be expressed in the following equation:

$$R_A = \sum (\beta_{pk} – \beta_{bk}) \times F_k + (\alpha + \varepsilon)$$

Where:

• \(\beta_{pk}\) = the sensitivity of the portfolio \(p\) to each rewarded factor \(k\)
• \(\beta_{bk}\) = the sensitivity of the benchmark to each rewarded factor
• \(F_k\) = the return of each rewarded factor
• \((\alpha + \varepsilon)\) = the part of the return that cannot be explained by exposure to rewarded factors
• \(\alpha\) = the active return of the portfolio that can be attributed to the specific skills/ strategies of the manager
• \(\varepsilon\) = the idiosyncratic return, often resulting from a random shock, such as a company announcing unexpected earnings. It could also be called noise or luck (good or bad).

Building Blocks of Portfolio Construction

The building blocks used in portfolio construction include exposure to rewarded risks, alpha, and luck. The market portfolio encompasses all securities, and the weight of each security is proportional to its market capitalization. The benchmark would have an exposure (or beta, \(\beta\)) of 1 to the Market factor and no net exposure to other rewarded factors, such as Size, Value, and Momentum.

Active managers can add value over and above the market portfolio by choosing exposures to rewarded risks that differ from those of the market. The growing understanding of rewarded factors is profoundly changing the view of active and passive investing. There are many investment products that allow investors to directly access such factors as Value, Size, Momentum, and Quality.

The performance of the actively managed fund can be analyzed in terms of factors. Some portion of returns will not be explained by factors, which may be attributable to the unique skills and strategies of the manager (alpha), an incomplete factor model that ignores relevant factors, or exposure to idiosyncratic risks that either helped or hurt performance.

Generating Alpha in a Zero-Sum Game Environment

The ability to generate positive alpha in a zero-sum game environment is a crucial skill for active managers. This task is challenging as the alpha generated must be sufficient to cover the higher fees associated with active management.

Previously, exposures to rewarded factors, accessible via rule-based indexes, were considered a source of alpha. However, the current consensus is that the successful timing of this exposure, known as factor timing, can be a source of alpha. For instance, a manager who can predict when rewarded factor returns might be greater or less than their average returns can generate alpha.

Zero-Sum Game and Alpha Generation

In a zero-sum game, an investor can only outperform at the expense of someone else. For example, during a market downturn, a manager with a beta \((\beta)\) substantially less than 1 will outperform the market and likely other managers. Conversely, a beta greater than 1 in a rising market would drive strong portfolio performance relative to the market.

Timing Exposure to Unrewarded Factors

Alpha can also be generated from timing exposure to unrewarded factors, such as regional exposure, sector exposure, the price of commodities, or even security selection.

Example of this is a manager who correctly anticipated the decline in the price of oil from June 2014 to March 2016. By reducing exposure to the energy sector, especially to smaller, less integrated, and more indebted energy companies, the manager would have been amply rewarded despite oil prices not being a rewarded factor.

Sizing Positions

The third building block of portfolio management is sizing positions. This strategy is crucial in balancing a manager’s confidence in their alpha and factor insights while mitigating idiosyncratic risks. It significantly impacts the idiosyncratic risk and the potential impact of luck on performance.

Position sizing is similar to a manager deciding to invest in either 20 or 200 securities to achieve the same average exposure (beta) to the Value and Size factors. For example, a manager at a hedge fund might choose to invest in a diversified portfolio of 200 securities to minimize idiosyncratic risk, while a manager at a private equity firm might choose a concentrated portfolio of 20 securities, assuming a higher degree of idiosyncratic risk.

Portfolio Concentration

The choice of portfolio concentration reflects a manager’s belief in his investment skill. A factor-oriented manager, who believes in her ability to balance exposure to rewarded factors, will maintain a diversified portfolio. Conversely, a stock picker, who believes in his ability to forecast security-specific performance, will opt for a concentrated portfolio.

Position Sizing and Investment Approach

A manager’s choice of position sizing is influenced by her investment approach and the level of confidence she places on her analytic work. For instance, a manager with high confidence in her analysis of individual securities may be willing to assume high levels of idiosyncratic risk, focusing on the “\(\alpha + \epsilon\)” part of the equation. On the other hand, a manager focused on creating balanced exposures to rewarded factors is unlikely to assume a high level of idiosyncratic risk and is, therefore, quite likely to construct a highly diversified portfolio of individual securities.

Integrating the Building Blocks: Breadth of Expertise

Portfolio management is a complex process that involves the integration of three key building blocks:

• Exposure to rewarded risks: This refers to the potential gains that can be achieved by taking on certain risks. For example, investing in a startup company can be risky, but the potential rewards can be substantial if the company becomes successful.
• Timing of exposures to rewarded and unrewarded risks: This involves deciding when to take on certain risks. For example, investing in the stock market during a downturn can be risky, but it can also provide opportunities for high returns if the market recovers.
• Position sizing and its implications for idiosyncratic risk: This refers to the amount of money invested in a particular asset or investment. For example, investing a large portion of your portfolio in a single stock can increase the risk of significant losses if the stock’s price falls.

The success of a portfolio manager in integrating these building blocks into a portfolio is largely dependent on their breadth of expertise. A manager with a broad range of expertise is more likely to generate consistent, positive active returns. This concept is encapsulated in the fundamental law of active management.

The Fundamental Law of Active Management

The fundamental law of active management states that the expected active portfolio return, denoted as E(R), is determined by the following equation:

$$E(R_A) = IC \sqrt{BR} \sigma_{RA} TC$$

Where:

• IC = Expected information coefficient of the manager
• BR = Breadth (the number of truly independent decisions made each year)
• TC = Transfer coefficient (the ability to translate portfolio insights into investment decisions without constraint)
• \(\sigma_{BR}\) = the manager’s active risk

This law implies that a manager’s ability to outperform their benchmark increases when their performance can be attributed to a larger number of independent decisions. However, it’s important to note that even if a manager has positive information and transfer coefficients, it doesn’t guarantee that excess return will be positive every year. A long-term horizon is required to have a reasonable probability of generating the expected excess return.

Implication of Making Multiple Independent Decisions

Consider two managers, Manager A and Manager B, who both have similarly diversified portfolios and have outperformed the market over a specific period. Manager A follows a pure value style, favoring securities with a low price-to-book ratio. On the other hand, Manager B uses a multidimensional, factor-based approach that considers a wide range of metrics. The historical performance of Manager B may be a more reliable indicator of future outperformance due to the integration of several dimensions and metrics in her portfolio construction process.

Practice Questions

Question 1: An investment manager is employing an active management strategy for a portfolio. The manager aims to generate returns that exceed the benchmark returns, after adjusting for all costs, for a suitable level of risk. The excess return, also known as active return, of this actively managed portfolio is driven by the difference in weights between the active portfolio and the benchmark. If the manager effectively manages the active weights, what is the likely outcome for the active returns of the portfolio?

1. The active returns will be negative.
2. The active returns will be positive.
3. The active returns will remain neutral.

Answer: Choice B is correct.

If an investment manager effectively manages the active weights in an actively managed portfolio, the likely outcome for the active returns of the portfolio will be positive. Active return, also known as alpha, is the excess return of an investment relative to the return of a benchmark index. It is a measure of a portfolio’s performance that is due to active management. The active weights in a portfolio are the differences in the weights of the securities in the portfolio and their weights in the benchmark. If the manager effectively manages these weights, they can generate positive active returns by overweighting securities that outperform the benchmark and underweighting those that underperform. This requires skill and knowledge on the part of the manager to identify and exploit mispriced securities. Therefore, the success of an active management strategy largely depends on the manager’s ability to effectively manage the active weights in the portfolio.

Choice A is incorrect. If the manager effectively manages the active weights, the active returns will not be negative. Negative active returns would indicate that the manager’s decisions have resulted in underperformance relative to the benchmark. This would not be a likely outcome if the manager is effectively managing the active weights.

Choice C is incorrect. If the manager effectively manages the active weights, the active returns will not remain neutral. Neutral active returns would suggest that the manager’s decisions have neither added nor detracted value relative to the benchmark. This would not be a likely outcome if the manager is effectively managing the active weights, as the goal of active management is to generate positive excess returns.

Question 2: In the process of portfolio construction, strategic adjustments are made to the active weights of securities with the aim of generating active returns. This can be achieved through various methods such as creating long-term exposures to rewarded risks, identifying mispricing in securities, sectors, rewarded risks, and assuming excessive idiosyncratic risk. In the past, any excess return over the benchmark was termed “alpha”. However, recent research suggests that much of what was previously considered alpha is actually “alternative beta”. In this context, what does “alternative beta” refer to?

1. Exposure to unrewarded risks that can be obtained at a higher cost.
2. Exposure to rewarded risks that can be obtained at a lower cost.
3. Exposure to market risk and liquidity risk without any expected long-run return premium.

Answer: Choice B is correct.

Alternative beta refers to exposure to rewarded risks that can be obtained at a lower cost. In the context of portfolio construction, alternative beta is a term used to describe the systematic risk exposures that are not captured by traditional beta measures. These are risks that are rewarded over the long term and can be obtained at a lower cost than traditional alpha strategies. Alternative beta strategies aim to capture these rewarded risks through a systematic, rules-based approach, often using derivatives or other financial instruments. These strategies are designed to provide investors with exposure to a broad range of risk factors, beyond just market risk, that can generate returns over the long term. The concept of alternative beta has emerged as a result of advances in financial research and the increasing sophistication of investment strategies, which have revealed that many sources of returns previously attributed to manager skill (or alpha) can actually be explained by systematic risk exposures.

Choice A is incorrect. Alternative beta does not refer to exposure to unrewarded risks that can be obtained at a higher cost. Unrewarded risks are those that do not offer a positive expected return over the long term, and therefore, investors would not seek to obtain exposure to these risks, especially not at a higher cost.

Choice C is incorrect. Alternative beta does not refer to exposure to market risk and liquidity risk without any expected long-run return premium. While market risk and liquidity risk are important factors to consider in portfolio construction, they are not the primary focus of alternative beta strategies. Moreover, the goal of alternative beta strategies is to capture rewarded risks, which by definition offer an expected return premium over the long run.

Glossary

• Active Return \((R_A)\): The excess return of an actively managed portfolio, driven by the difference in weights between the active portfolio and the benchmark.
• Active Weight \((\Delta W_i)\): The difference between the portfolio weights \(W_{Pi}\) and the benchmark weights \(W_{Bi}\).
• Alpha: The active return of the portfolio that can be attributed to the specific skills/ strategies of the manager.
• Beta: The sensitivity of the portfolio or benchmark to each rewarded factor.
• Alternative Beta: Exposure to rewarded risks that can be obtained at a lower cost.
• Idiosyncratic Return: The return often resulting from a random shock, such as a company announcing unexpected earnings. It could also be called noise or luck (good or bad).
• Zero-Sum Game: A situation in which one person’s gain is equivalent to another’s loss, so the net change in wealth or benefit is zero.
• Factor Timing: The strategy of adjusting exposure to certain risk factors based on expected performance.

Portfolio Management Pathway Volume 1: Learning Module 3: Active Equity Investing: Portfolio Construction; LOS 3(a): Describe elements of a manager’s investment philosophy that influence the portfolio construction process