Enhancing investment outcomes through liquidity analytics

In discussions on fundamental investment processes, topics such as stock selection and drivers of performance and risk tend to take center stage. Under the hood, however, the consideration of liquidity and management of fund flow is equally important. When investment opportunities are found in less liquid parts of the market, or when there is material fund inflow or outflow, planning contributes towards better portfolio outcomes.

In collaboration with our active equity teams, we developed a set of proprietary analytics to add rigour to such discussions. These analytics are designed to provide data to help on position sizing, align timelines with key stakeholders with respect to large flows, and provide a data-informed reference for decision making in the event of uncertain market conditions.

A thorough understanding of company fundamentals - such as business models, management quality, and competitive positioning - builds towards the decision on which stocks to buy or sell. A crucial next step is the decision on position sizing, where liquidity risk is one important consideration. Being aware of how to buy/scale up or exit/scale down a position is critical as execution challenges can arise at both the individual position level and across the broader portfolio.

At the position level, the ability to increase or reduce the desired number of shares is affected by a stock’s liquidity and the varying profiles of short-term vs. long-term investors. Our toolkit provides data points such as the number of trading days needed to exit, fund ownership ratio at the stock-level, and tracks these metrics over time at the aggregated portfolio-level, which helps to manage liquidity risk. (Fig. 1)

Fig. 1. Dashboards for tracking portfolio liquidity profile

Source: Eastspring Investments. Analysis date: 30-Sep-2025. Sample dashboards tracking the liquidity profile of a Japan Equity strategy.

These analytics are built on top of Transaction Cost Analysis (TCA) data sets, allowing us to use both current and historical trends for more precise stock-specific liquidity assumptions. This is important as stock liquidity profiles can vary significantly across countries and sectors. Differences in liquidity profiles are also likely to be magnified during periods of market stress.

We illustrate this in Fig. 2. by simulating the same trade value into a market capitalisation-weighted ASEAN and Asia ex Japan index. The result informs us of two different trajectories (i.e. the speed and acceleration achieved in completing the trade).

Fig. 2. Comparing the pace of execution flows in different equity markets

Source: Eastspring Investments. Analysis date: 30-Sep-2025. Simulation of an investment inflow into hypothetical ‘market portfolios’ as represented by the MSCI ASEAN and MSCI Asia ex Japan indexes.

At the portfolio level, execution challenges can result from portfolio strategy and portfolio construction intents. For example, if there is a large flow to execute, portfolio size can impact the type of stocks that a portfolio holds due to liquidity reasons. A larger portfolio would find it challenging to continue holding smaller and less liquid stocks as the portfolio grows. At the same time, trades can be used to reshape portfolio style exposures, reduce concentration, rebalance regional allocations, or trim a stock that has exceeded or is approaching its forecasted intrinsic value. Portfolio analytics can help us evaluate how portfolio characteristics evolve and ensure that they are aligned with the target portfolio strategy.

Some examples of decisions which would benefit from data and discussion prior to trading include:

Running portfolios through hypothetical scenarios helps to surface potential issues which would benefit from further debate. A key building block of scenario analysis is the choice of execution approach. This allows us to evaluate how the portfolio changes through the trading horizon.

Some examples of execution approaches are:

Fig. 3. shows the time taken for a trade flow to be completed using the three different execution approaches. As expected, the ‘Firesale’ approach prioritises speed over the need to have a targeted portfolio shape at the end of execution period. The ‘Pro-rata’ and ‘Hybrid’ approaches take the same amount of time to complete the trade flow although the latter allows for a quicker deployment at the start. The differences between the ‘Pro-rata’ and ‘Hybrid’ approaches are only in the sequencing and volume of stocks to be traded across the execution horizon.

Fig. 3. Flow projection of different execution approaches

Source: Eastspring Investments. Analysis date: 30-Sep-2025. Trading simulation is based on a hypothetical portfolio investing in a large and liquid Asian market.

As mentioned earlier, it will take longer to exit or build a position in less liquid stocks. If speed is a priority, and these less liquid stocks do not play a critical role in the portfolio, we could consider freezing these positions and buying or selling more of the other stocks. By simulating the trajectory differences, our portfolio managers can quantitatively weigh the benefits of not trading positions which are less liquid. See Fig. 4.

Fig. 4. Portfolio flow simulation

Source: Eastspring Investments. Analysis date: 30-Sep-2025. Note: In the ‘Illiquid Positions Not Traded’ simulation, we execute the same required level of flow without securities which have poor liquidity profiles. Trading simulation is based on a hypothetical portfolio investing in a large and liquid Asian market.

However, in the event where there is a large and urgent outflow, where even the sale of less liquid stocks may be required to raise cash, then the participation¹ can be nudged higher but this may result in greater market impact. Nevertheless, the portfolio manager may prefer this approach to avoid skewing the portfolio towards less liquid and/or small capitalisation stocks.

Fig 5. shows that we can reduce the execution time by increasing the level of trade participation. This approach, however, should be accompanied by a thorough cost-benefit analysis, as increased execution speed might be offset by higher trading costs.

Fig. 5. Analysing the result of larger trade participation if speed is required

Source: Eastspring Investments. Analysis date: 30-Sep-2025. Participation refers to the level of traded flow relative to the average traded value per security, typically measured over a period of time. For example, if a security typically trades USD1 million a day, a 20% participation suggests trading up to USD200k. Trading simulation is based on a hypothetical portfolio investing in a large and liquid Asian market.

The resulting analytics from the portfolio simulation can be used to engage the client around reasonable timeframes and flow sizes. In the above scenario, a win-win solution could be to break the larger outflow into several more manageable tranches.

We find that stress-testing is a helpful tool for discussions around strategy capacity, and liquidity management particularly for strategies with a tilt towards Small-and-Mid (SMID) capitalization stocks. The heatmap in Fig. 6. Illustrates the results of such a stress test. We simulate a significant fund flow of up to USD 1 billion into a diversified Asia ex Japan index to approximate what a typical trading horizon would look like.

The questions we ask ourselves next are typically:

Fig. 6. Using a large trade flow to evaluate portfolio liquidity thresholds

Source: Eastspring Investments. Analysis date: 30-Sep-2025. The values within the cells refer to the percentage of completion to the total trade flow required. Trading simulation is based on a hypothetical portfolio investing in a large and liquid Asian market.

Liquidity analytics is key in supporting decision-making throughout the investment process, from idea generation (position-sizing) to ongoing portfolio management (fund flow planning). These tools provide structure and equip our portfolio managers with data to debate and decide more confidently. By anticipating liquidity constraints across the region and structuring trades through scenario analysis, these frameworks instill discipline and enhance transparency throughout the investment process.

While such tools cannot fully eliminate liquidity risks, they help to highlight areas of concern and make them more manageable. Meanwhile, a scenario approach complements the investment process of active fundamental strategies as it allows the portfolio managers to test a range of assumptions and investigate the range of possible outcomes on a stock-by-stock basis.

In a market environment where liquidity can shift quickly and unpredictably, a structured decision approach is essential for maintaining consistency and driving optimal investment outcomes.