One of the most important components of successful private market investing is to get your investment pacing right so that you can balance your liquidity with your asset allocation goals. The strategy and timeline by which you commit capital to various private equity funds requires careful deliberation so as to achieve the necessary diversification while also managing risk and aligning the portfolio with the specific investment objectives. This is as much an art as it is a science because the specific pacing strategy will vary greatly depending on your unique goals and circumstances.

In this article, we’re going to explore how analytical LP portfolio management tools can help portfolio managers with this strategic decision-making through data-driven analytics that mitigate against human bias.

Why is investment pacing important?

A portfolio manager is always trying to find the balance between three sets of expectations: profitability, risk diversification, and cash flows. To achieve this, the manager must make investment pacing decisions that ensure that the portfolio has adequate liquidity to meet capital calls and re-allocations to other asset classes while still maintaining a sustainable and reasonable pace of capital commitment.

Private equity funds typically have a lifetime of between 7 and 12 years. The committed capital is drawn down progressively during the investment period while the net asset value (NAV) grows continuously until it reaches a peak at around the halfway point of the fund’s lifetime. Once this peak has passed and the fund moves into its de-investment period, the NAV along with the capital calls declines as the fund makes distributions. For a single private equity fund, the maximum investment level is only reached for a short period of time. Consequently, it is critical to take advantage of the uncalled capital and early distributions by recommitting those resources into new funds that counterbalance the opportunity cost.

This is especially important in today’s market environment where high interest rates, low IPO activity, and slowed M&A activities have had serious macroeconomic effects – meaning that the cash flows in private market portfolios need to be managed carefully. As the pace of distribution activity slows, the length of the investment cycle increases, GPs hold their assets for longer given challenged exit environments, and therefore there is a longer time required for value creation.

An LP portfolio manager needs to have a clear understanding of what cash flows are expected so as to avoid liquidity issues. Additionally, they need to stay on top of all fund commitments and honour all capital calls across the investment lifecycle. As such, the investment pacing mandates the portfolio manager to increase or reduce the stake in private market investments.

Anticipating and keeping track of the cash flows generated from a private market portfolio is crucial for successful investment pacing. It takes a solid understanding of the expected demand for contribution, taking into consideration the distributions available for reinvestment for the next quarters and years.

Especially challenging is the pacing for pension schemes with hard regulatory constraints and hard allocation limits on private markets. The denominator effect, caused by reduced value in other asset classes, can force the liquidation of holdings in secondary markets at a discount level of between 10 and 30%. Family offices are further challenged to provide a dividend stream for family members, and unsteady contributions from funds make it harder to plan.

In all of these cases, analytical LP portfolio management tools can assist in navigating these challenges and helping portfolio managers make the right decisions in the context of their portfolio and the wider investment objectives.

Investment pacing use cases for Analytical LP Portfolio Management

Analytical LP Portfolio Management tools help the portfolio manager master the challenges of investment pacing – enabling more accurate assumptions and models of the portfolio. By taking advantage of advanced data analytics methods, managers can limit the impact of human bias in investment decisions. This can then be augmented by advanced forecasting and modelling methods that create portfolio cash flow scenarios that reflect the investor’s view of the markets, based on rich data sets and powerful analytical engines.

Here are some key use cases for these software solutions.

Defining the portfolio implementation strategy

Executing investment decisions based on the guidance of the strategic asset allocation (SAA) is the starting point for any LP portfolio manager. Analytical LP Portfolio Management tools can be used to identify gaps between the current portfolio allocation and the SAA, thus allowing for investment priorities to be determined, and aligned with the various objectives.

These implementations need to take the following into account:

• Risk management. Analytical tools provide a comprehensive view of the portfolio’s risk profile. By assessing the historical and potential future risk of different asset classes and investment options, investors can make informed decisions to align their portfolios with their risk tolerance and financial goals.

• Asset diversification. Proper diversification is essential for managing risk and optimising returns. Analytical tools can help to plan, monitor, and report on this diversification at various time scales to assist with rebalancing and other investment decisions.

• Performance analysis. Analytical tools offer in-depth performance analysis, allowing investors to track how their portfolio is performing against their strategic goals. This also enables timely adjustments to maintain alignment with the chosen asset allocation strategy.

• Rebalancing decisions. As market conditions change, portfolios may drift from their intended allocations. Analytical tools help identify these deviations, facilitating efficient rebalancing by highlighting areas that require adjustment to bring the portfolio back in line with the strategic asset allocation.

• Cost efficiency. Analytical tools can also assess the cost implications of different investment decisions including fees, taxes, and transaction costs, ensuring that the chosen asset allocation remains cost-effective.

• Reporting. Finally, reports, tables, and visualisations from the analytical tool back up the investment narrative presented to the CIO and investors.

Cash flow projections

Due to the fact that the specific nature of the private markets cash flows is not in the control of the investor, the LP is dependent on the distributions provided by the fund or the contributions called. Therefore, in order to plan the liquidity of the portfolio over the investment cycle, the LPs must establish a robust cash flow projection model that is based on advanced forecasting methods to ensure accuracy. However, these models also have to accept the limitations of the available data points.

These cash flow projections influence many investment decisions and are shared across the organisation and investors. Here are some of the key considerations:

• Liquidity management. Cash flow projections allow investors to anticipate when capital commitments and distributions are expected in private equity, real estate, or other illiquid investments. Analytical tools provide an estimated timeline of these cash flows, enabling better liquidity management. This is particularly important because private market investments often come with lock-up periods, and investors need to ensure they have sufficient liquidity to meet their various obligations.

• Risk mitigation. Analytical tools help identify potential liquidity gaps or surpluses in advance. This insight enables investors to take proactive measures to mitigate liquidity risks by doing things such as securing credit lines or adjusting their investment pacing to align with cash flow needs. Managing liquidity effectively minimises the risk of having to sell investments prematurely at unfavourable terms.

• Portfolio optimisation. Cash flow projections also inform portfolio optimisation strategies. Investors can strategically allocate capital to different investment opportunities based on their projected cash flows, optimising their asset allocation strategy to maximise returns while meeting liquidity requirements.

Forecasting models

When making investment pacing decisions, portfolio managers will use different forecasting models to assess the various components of their investments over their lifecycles. Typically these can be separated into two distinct model types:

• Commitment models. These types of models focus on tracking the timing and amount of capital commitments. They estimate when these commitments will be called, typically considering the fund’s investment pacing.

• Distributions models. These types of models project cash inflows based on historical distributions, fund-specific characteristics, and expected exit activity. They help investors anticipate when they can expect returns from their investments.

Some cash flow projection models are well-established and are already widespread. These models provide guidance on assuming cash flows and they form the foundation for many investment decisions. The most famous of these is the Takahashi-Alexander model which was developed for use on the endowments of Yale University [1] . More recently, a number of AI-based approaches are gaining more traction. These technologies can analyse vast datasets, identify patterns, and make predictions that improve the accuracy of forecasts.

Scenario planning

Analytical tools allow investors to undertake robust scenario planning to assess the long-term balance of a portfolio, evaluating the impact of different market conditions or changes in cash flow patterns. This methodology plays a significant role in portfolio management by helping portfolio managers and investors anticipate and prepare for a range of potential future outcomes. It is a strategic tool that involves the creation of multiple scenarios detailing how the future might unfold and assessing the impact of each scenario on a portfolio.

Examples of how these scenario planning exercises impact investment performance include:

• Stress testing. Scenario analysis allows investors to stress-test their portfolios. By subjecting their private market holdings to extreme historic scenarios like a bubble, financial crisis, pandemic, etc., investors can assess the resilience of their portfolios and identify potential weaknesses that require attention or adjustment.

• Dynamic asset allocation. Scenario planning also helps investors determine the most suitable mix of asset classes, strategies, and geographies to achieve their financial goals under different conditions with changing parameters for interest rates or expected contributions and distribution patterns over time.

These scenarios can then be easily communicated with the CIO and other stakeholders, illuminating the underlying assumptions about the tactical asset allocation and expected outcomes, thus fostering transparency and building confidence.

Building on the scenario planning above, the next question to ask is which model should be used and how are the various impact factors weighted. And the truth is that these questions need to be answered by the specific investment teams in question because every situation is different. However, to evolve the expertise of the investment team, an Analytical Portfolio management tool can provide best practices in creating, comparing, and evolving scenarios. All of this serves to prepare multiple high-quality scenarios that can be powerful tools for communicating with stakeholders.

These scenarios can include: 

• Economic scenarios. Private equity investors often model different economic scenarios to understand how macroeconomic factors such as GDP growth, interest rates, and inflation could affect their portfolios.

• Currency and exchange rate scenarios. For international private equity investments, currency fluctuations can significantly affect returns. Scenario modelling helps investors assess the impact of currency movements on their investments and develop strategies to mitigate currency risk.

• Market disruption scenarios. Private equity is not immune to market disruption and investors must prepare for such events. Scenario modelling allows investors to explore how market shocks, industry-specific challenges, or unforeseen events may impact their portfolios.

• Fund performance scenarios. Investors use historical data and assumptions to model different performance scenarios for private equity funds. By considering variables like exit multiples, holding periods, and fund-level cash flows, investors can assess how variations in fund performance impact overall returns.

Scenarios will have an impact on the portfolio at a fund level as well as on an asset level, and ideally, the model should aggregate the outcomes on all layers to come to accurate overall portfolio projections. To keep all the variables in line and avoid inconsistencies a rule-based approach is beneficial, allowing the modification of parameters of key drivers like interest yield curves and inflation rates to maintain a model consistent from a top-down and bottom-up approach.

A common method to predict the outcomes from scenarios is the Monte Carlo simulation. Monte Carlo simulations utilise statistical methods to model various scenarios and outcomes. By incorporating various input factors and their distributions, it provides a range of possible scenarios. However, the effectiveness of this scenario modelling is often determined by the actions taken after the scenario has been generated. It’s important to look back on it in retrospect and assess the quality of that scenario’s predictions to gain key insights. This is done by comparing the scenario results with actual results. Typically this can spark inspiring discussions and help with fine-tuning future scenarios, while also allowing multiple people with different outlooks to share their perspectives. By collaborating in this process, the investment team can constantly improve and update their view of the future. The ability to explain how and why the actual performance differed from the scenario offers a treasure trove of insights for the organisation.

Managing over-commitment strategies

Approximately a third of investor capital is never put to work. Moreover, some of the capital is used fairly late and thus bears significant opportunity costs. Assuming that investors are authorised to do so, it could make sense to over-commit to funds systematically.

However, implementing such a strategy can lead to liquidity gaps on capital calls. As such, scenarios need to be modelled very carefully to avoid surprises. Having to liquidate stakes in funds in secondary markets will cost 15 – 60 % of the NAV and ruin the performance of the asset class. However, on the other side of the coin, taking a too-conservative view will limit the upside potential.

In cases where there is not enough liquidity to contribute to a capital call, the investor has two options:

• Consider secondary market transactions to sell or transfer existing fund commitments.

• Use credit lines to temporarily cover capital calls, providing flexibility in managing over-commitment. The interest rates attached to these credits are good parameters to model the risk of such a strategy.

As the decision to apply an over-commitment strategy needs to be shared across the organisation, the additional risk on short-term liquidity needs and temporary out-of-band asset allocation has to be accepted and acknowledged. However, with solid cash-flow projection models and robust scenario planning in place, the risks of overcommitment strategy are better controlled.

Conclusion

Investment pacing within illiquid private market investing is crucial because there are far fewer rebalancing opportunities during the term of an investment cycle. As such, it makes a lot of sense to use analytical portfolio management tools that can support data-driven decision-making, manage risk effectively, optimise diversification, and maintain the portfolio’s alignment with long-term financial objectives.

It is essential to employ best-practice models and techniques for cash flow forecasting in private equity as these models help to provide investors with insights into their expected cash flows over various time scales. Additionally, scenario planning empowers investors to anticipate and prepare for a wide variety of market conditions. The collaboration and communication processes inherent in this also offer valuable learning opportunities for the investment team to improve their decision-making.

Robust technological solutions that offer this level of analysis make a world of difference and can be the catalyst for a new paradigm in terms of data-driven decisions in uncertain, illiquid asset classes.

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About Apliqo LP Portfolio Management

Apliqo LP Portfolio Management provides an easy-to-use and innovative solution for the investment pacing challenges that come with managing a private markets portfolio.

Optimise your pacing decisions with a single, unified view of your entire portfolio, and achieve the following:

• Learn from advanced scenario modelling that can be used to predict cash flows and manage liquidity risks across the lifecycle of the investment.
  
• Leverage more effective communication with key stakeholders with accurate and consistent performance reporting and analysis for private market investments
  
• Reduce your efforts through powerful data management to increase velocity with automation.

Learn more about the solution at ais.apliqo.com and embark on the product tour to explore how the software can help you with:

• Performance reporting;
  
• Cash flow projections;
  
• Investment pacing;
  
• Fund manager track record analytics;
  
• Scenario development and tracking; and
  
• Asset-level analytics.