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Portfolio Theory in the Context of Litigation Finance (pt. 1 of 2)

Portfolio Theory in the Context of Litigation Finance (pt. 1 of 2)

The following article is part of an ongoing column titled ‘Investor Insights.’  Brought to you by Ed Truant, founder and content manager of Slingshot Capital, ‘Investor Insights’ will provide thoughtful and engaging perspectives on all aspects of investing in litigation finance.  Executive Summary
  • Modern Portfolio Theory (MPT) – a mathematical framework based on the “mean-variance” analysis – argues that it’s possible to construct an “efficient frontier” of optimal portfolios offering the maximum possible expected return for a given level of risk
  • MPT states that assets (such as stocks) face both “systematic risks” – market risks such as interest rates – as well as “unsystematic risks” – mostly uncorrelated exposures that are characteristic to each asset, including management changes or poor sales resulting from unforeseen events
  • Post-modern Portfolio Theory (PMPT) adds a layer of refinement to the definition of risk
  • Diversification of a portfolio can mitigate the impact of unsystematic risks on portfolio performance – although, it depends on its composition of assets
  • Behavioural Finance (BF) introduces a suggestion that psychological influences and biases affect the financial behaviors of investors and financial practitioners, also applicable to litigation finance
Slingshot Insights:
  • Portfolio theory is important to the commercial litigation finance asset class due to its inherently high level of unsystematic risks
  • Slingshot’s Rule of Thumb: a portfolio should contain no less than 20 investments in order to provide the benefits associated with portfolio theory
  • Diversification is critical for every fund manager
  • Specialty fund managers may play a positive role in a comprehensive litigation finance investing strategy by assisting with meeting a particular performance objective when defined in the context of acceptable “mean-variance” targets
  • Diversification provides optionality for an under-performing manager to ‘live to fight another day’ if their first fund achieved sub-par performance
  • Portfolio theory is applicable to consumer litigation finance
For those new to the commercial litigation finance sector, one aspect worth discovering from an investment perspective is the existence of unique risks attributable to this asset class.  For investment managers looking to get started in the industry, it is critical to understand the implications of the risks inherent in the asset class, especially for those with a limited track record in litigation finance.  Accordingly, significant attention should be paid to portfolio construction and diversification, in particular during the early stages of the life cycle of an industry where investments possess both idiosyncratic and binary risk, and where there is much less empirical data to guide investment decisions.  Portfolio risk is generally influenced by three main factors: volatility of results, correlation (of outcomes within a given portfolio) and the size of the portfolio.  For the purposes of this article, I have assumed that correlation within a portfolio is non-existent, as each case stands on its own and is not influenced by others in the portfolio. However, to the extent correlation does exist, it can have a significant impact on the value of portfolio theory.  As the industry evolves so too will its data requirements When the litigation finance industry first originated, the concept of portfolio theory was less important, given the recognition within the industry of a requisite level of experimentation (i.e. risk) to be assumed in order for a conclusion to be drawn about the attractiveness of the asset class. Therefore, the industry attracted the appropriate level of risk capital correlating to the risk/reward promise of litigation finance.  As the asset class matures and managers prove out the return profile, the early risk money is being supplemented with institutional capital, which is less inclined to assume the same level of risk as that of high net worth and family office investors.  Accordingly, in order to attract such capital, an element of data and analysis will need to be captured and compiled to assist the investor in understanding the dynamics inherent in the industry (returns, duration, volatility, correlation, etc.), which is partly why I believe the concepts in this article will grow increasingly significant in the near future. Portfolio Theory Concepts Before we discuss the applicability of portfolio theory to litigation finance, let’s dig into some portfolio theory concepts. While an in-depth study into portfolio theory is beyond the scope of this article, the following will provide readers with some theoretical concepts that have been developed and refined over the last 70 years.  Multitudes of research studies and articles have been published over the years and are publicly available.
  1. Modern Portfolio Theory (“MPT”)
Modern Portfolio Theory was developed by Harry Markowitz and published under the title “Portfolio Selection” in the journal of Finance in 1952, and remains one of the most important and influential economic theories dealing with finance and investment.  In essence, the theory suggests that investors can reduce risk through diversification.  Risk, in the context of modern portfolio theory, is the concept of the standard deviation of return as compared to the average return for the markets.  The theory states that the risk for individual stock returns has two components: Systematic Risk – These are market risks that cannot be diversified away. Interest rates, recessions and wars are examples of systematic risks in the context of public equities. Unsystematic Risk – Also known as “specific risk,” this risk is specific to individual stocks, such as a change in management or a decline in operations. This kind of risk can be diversified away as one increase the number of stocks in one’s portfolio. It represents the component of a stock’s return that is not correlated with general market moves. One of the limitations of MPT is the fact that it assumes a normal distribution of outcomes in the shape of a ‘normal bell curve’, which may be applicable for markets where there is perfect information, but not applicable to many private market investments where there is a meaningful information asymmetry among market participants (thereby resulting in skewed performance distributions and potentially heavy tails).  Essentially, MPT is limited by measures of risk and return that do not always represent the realities of the investment market. Nonetheless, it laid the foundation for additional theories which have served to refine the original, underlying one.
  1. Post-modern Portfolio Theory (“PMPT”)
The term ‘post-modern portfolio theory’ has its roots in research undertaken at the Pension Research Institute at San Francisco University in 1983, and was created in 1991 by software entrepreneurs Brian M. Rom and Kathleen Ferguson, in order to differentiate the portfolio-construction software developed by their company from those provided by traditional MPT.  The PMPT theory uses the standard deviation of negative returns as the measure of risk, while MPT uses the standard deviation of all returns as a measure of risk. The authors determined that the normal distribution curve which represents the basis for MPT does not accurately reflect all markets and is merely a subset of PMPT. Essentially, different than MPT which tends to focus on risk in the context of derivation from mean market returns, PMPT focuses on risk and reward relative to an expected Internal Rate of Return (“IRR”) required for a given set of risks, which is more of a risk-adjusted return philosophy.  However, a key limitation of both MPT and PMPT is that they are both premised on the assumption of efficient markets, being the theory that all participants in a market have the same access to information. Enter Behavioural Finance…
  1. Behaviour Finance (“BF”)
I think we can all agree that most financial markets are anything but rational, which means there must be something else influencing their behaviour and, hence, their performance.  Behavioural Finance is a conceptual framework to study the influence of psychology on the behavior of investors and financial analysts. It also recognizes the subsequent effects on markets. BF focuses on the fact that investors are not always rational, have limits to their self-control, and are influenced by their own biases.  BF believes that investors are subject to a variety of judgment errors or biases, which are broadly defined as Self-Deception (you think you know more than you do), Heuristic Simplification (information processing errors), Social Influence (how our decisions are influenced by others) and Emotion (your mood’s impact on rational thinking at the time of investment).  The applicability of BF cannot be overstated in the context of litigation as there is the potential for many biases to enter the decision-making process, especially by litigators who’s own experience may be impacting their decisions. While many theories exist to explain market behaviour and how investors should position their portfolios to address risk, I have focused on the three above as they are among the most prominent.  While they serve as a guide to address risk in the context of portfolio construction, they also serve to highlight an investor’s inherent limitations, and give rise to questions litigation finance managers should be asking themselves: are my biases working their way into my portfolio construction?  Of course, much of the research on which these theories are predicated relate to the public equities marketplace, which simplifies analysis via transparency and quantum of data.  In the context of litigation finance, we have a private market which is not large and not very transparent.  In addition, it is a market that is very inefficient due to the confidential nature of litigation – because it is a private market – and due to its relative nascency.  This is, in part, one of the reasons that I am presently pursuing the Slingshot Data Project (more to come in future articles) through a “Give to Get” model, where value (in the form of analytics) will be provided to a variety of participating constituents.

Application to Commercial Litigation Finance

Before we can discuss the application of portfolio theory to commercial litigation finance, it is important to determine the risks that are inherent in the asset class. The litigation finance asset class exhibits a significant number of unique risks, some of which are Systematic and others Unsystematic, and some which fall into both categories.  As an example of a dual risk, collectability risk is inherent in any piece of litigation where one party is suing another (i.e. a Systematic Risk). In addition, there is the specific collection risk associated with a given defendant (are they more likely to settle and pay quickly, or delay, appeal and negotiate a settlement over a protracted period of time), which may be higher or lower than the overall risk inherent in litigation (i.e. an Unsystematic Risk)). Generally, I find the level of Unsystematic risks to be high in litigation finance given that the outcome of each case is idiosyncratic to the aspects of the case (case merits, credibility of the witnesses, the credibility of professional witnesses, the litigious nature of the defendant, legal counsel effectiveness, defense counsel effectiveness, judiciary effectiveness, jurisdiction and collectability – to name some of the more significant risks).  However, litigation finance also has a number of Systematic exposures (binary outcomes, duration, liquidity, counter-party, collectability, case precedent, regulatory, legislative, etc.) which may not be fully addressable through the application of portfolio theory. With respect to the influence of binary risk, I would add that while each case possesses binary risk at the outset, very few cases in fact are determined by a judicial decision (as with most litigation, the vast majority of cases are settled out of court). So, while binary risk (a Systematic risk) is endemic to the asset class, its application – in particular in the context of a portfolio – should not be overstated, because it rarely influences the performance directly – unless there is a series of highly correlated cases embedded in a portfolio (although the threat of a judicial outcome is a significant factor in any settlement).  In addition, certain case types have a higher propensity to be settled via a judicial decision (e.g. International Arbitrations) as opposed to others (e.g. Breach of Contract). Having said that, if one is only looking at the tail end of a portfolio, binary risk can be disproportionately higher, as those cases within the tail likely have a higher probability of being decided by a judiciary simply because they have had longer case durations which may indicate that neither side is willing to negotiate a settlement, or that the case is heading toward a trial decision. This proves that correlations – and thereby a degree of diversification – are not constant across a spectrum of case distributions. In the second part of this article, which can be found here, I apply the portfolio theories outlined above to the commercial litigation finance marketplace and offer some perspectives on responsible portfolio construction. Slingshot Insights Investing in a nascent asset class like litigation finance is mainly about investing in people.  Most managers simply don’t have the track record of a fully realized portfolio on which investors can base their investment decision.  Accordingly, much time and attention is spent on understanding how managers think about building their business and in particular their first portfolio.  In addition to the underwriting process, one of the most important considerations for investors to understand is how managers think about portfolio construction and diversification. Portfolio theory plays an integral role in terms of how managers should be thinking about constructing their portfolios from the perspective of the number of cases in the portfolio, but managers should also ensure their own personal bias is not entering into the portfolio and that they have thought about all of the systematic risks that can affect like cases. My general rule of thumb is that most first time managers should be targeting a portfolio of at least 20 equal sized commitments, appreciating that it is almost impossible to achieve equal sized deployments due to deployment risk. It is also not in the manager’s best long-term interest to take a short-cut on diversification for expediency sake (i.e. to raise the next larger fund) and to do so may be interpreted as poor judgment from an investor’s perspective! As always, I welcome your comments and counter-points to those raised in this article. Edward Truant is the founder of Slingshot Capital Inc. and an investor in the consumer and commercial litigation finance industry.
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A Framework for Measuring Tech ROI in Litigation Finance

This article was contributed by Ankita Mehta, Founder, Lexity.ai - a platform that helps litigation funds automate deal execution and prove ROI.

How do litigation funders truly quantify the return on investment from adopting new technologies? It’s the defining question for any CEO, CTO or internal champion. The potential is compelling: for context, according to litigation funders using Lexity’s AI-powered workflows, ROI figures of up to 285% have been reported.

The challenge is that the cost of doing nothing is invisible. Manual processes, analyst burnout, and missed deals rarely appear on a balance sheet — but they quietly erode yield every quarter.

You can’t manage what you can’t measure. This article introduces a pragmatic framework for quantifying the true value of adopting technology solutions, replacing ‘low-value’ manual tasks and processes with AI and freeing up human capital to focus on ‘high-value’ activities that drive bottom line results  .

A Pragmatic Framework for Measuring AI ROI

A proper ROI calculation goes beyond simple time savings. It captures two distinct categories:

  1. Direct Cost Savings – what you save
  2. Increased Value Generation – what you gain

The ‘Cost’ Side (What You Save)

This is the most straightforward calculation, focused on eliminating “grunt work” and mitigating errors.

Metric 1: Direct Time Savings — Eliminating Manual Bottlenecks 

Start by auditing a single, high-cost bottleneck. For many funds, this is the Preliminary Case Assessment, a process that often takes two to three days of an expert analyst's time.

The calculation here is straightforward. By multiplying the hours saved per case by the analyst's blended cost and the number of cases reviewed, a fund can reveal a significant hard-dollar saving each month.

Consider a fund reviewing 20 cases per month. If a 2-day manual assessment can be cut to 4 hours using an AI-powered workflow, the fund reallocates hundreds of analyst-hours every month. That time is now moved from low-value data entry to high-value judgment and risk analysis.

Metric 2: Cost of Inconsistent Risk — Reducing Subjectivity 

This metric is more complex but just as critical. How much time is spent fixing inconsistent or error-prone reviews? More importantly, what is the financial impact of a bad deal slipping through screening, or a good deal being rejected because of a rushed, subjective review?

Lexity’s workflows standardise evaluation criteria and accelerate document/data extraction, converting subjective evaluations into consistent, auditable outputs. This reduces rework costs and helps mitigate hidden costs of human error in portfolio selection.

The ‘Benefit’ Side (What You Gain)

This is where the true strategic upside lies. It’s not just about saving time—it’s about reinvesting that time into higher-value activities that grow the fund.

Metric 3: Increased Deal Capacity — Scaling Without Headcount Growth

What if your team could analyze more deals with the same staff? Time saved from automation becomes time reallocated to new higher value opportunities, dramatically increasing the value of human contributions.

One of the funds working with Lexity have reported a 2x to 3x increase in deal review capacity without a corresponding increase in overhead. 

Metric 4: Cost of Capital Drag — Reducing Duration Risk 

Every month a case extends beyond its expected closing, that capital is locked up. It is "dead" capital that could have been redeployed into new, IRR-generating opportunities.

By reducing evaluation bottlenecks and creating more accurate baseline timelines from inception, a disciplined workflow accelerates the entire pipeline. 

This figure can be quantified by considering the amount of capital locked up, the fund's cost of capital, and the length of the delay. This conceptual model turns a vague risk ("duration risk") into a hard number that a fund can actively manage and reduce.

An ROI Model Is Useless Without Adoption

Even the most elegant ROI model is meaningless if the team won't use the solution. This is how expensive technology becomes "shelf-ware."

Successful adoption is not about the technology; it's about the process. It starts by:

  1. Establish Clear Goals and Identify Key Stakeholders: Set measurable goals and a baseline. Identify stakeholders, especially the teams performing the manual tasks- they will be the first to validate efficiency gains.
  2. Targeting "Grunt Work," Not "Judgment": Ask “What repetitive task steals time from real analysis?” The goal is to augment your experts, not replace them.
  3. Starting with One Problem: Don't try to "implement AI." Solve one high-value bottleneck, like Preliminary Case Assessment. Prove the value, then expand. 
  4. Focusing on Process Fit: The right technology enhances your workflow; it doesn’t complicate it.

Conclusion: From Calculation to Confidence

A high ROI isn't a vague projection; it’s what happens when a disciplined process meets intelligent automation.

By starting to measure what truly matters—reallocated hours, deal capacity, and capital drag—fund managers can turn ROI from a spreadsheet abstraction into a tangible, strategic advantage.

By Ankita Mehta Founder, Lexity.ai — a platform that helps litigation funds automate deal execution and prove ROI.

Burford Capital’s $35 M Antitrust Funding Claim Deemed Unsecured

By John Freund |

In a recent ruling, Burford Capital suffered a significant setback when a U.S. bankruptcy court determined that its funding agreement was not secured status.

According to an article from JD Journal, Burford had backed antitrust claims brought by Harvest Sherwood, a food distributor that filed for bankruptcy in May 2025, via a 2022 financing agreement. The capital advance was tied to potential claims worth about US$1.1 billion in damages against meat‑industry defendants.

What mattered most for Burford’s recovery strategy was its effort to treat the agreement as a loan with first‑priority rights. The court, however, ruled the deal lacked essential elements required to create a lien, trust or other secured interest. Instead, the funding was classified as an unsecured claim, meaning Burford now joins the queue of general creditors rather than enjoying priority over secured lenders.

The decision carries major consequences. Unsecured claims typically face a much lower likelihood of full recovery, especially in estates loaded with secured debt. Here, key assets of the bankrupt estate consist of the antitrust actions themselves, and secured creditors such as JPM Chase continue to dominate the repayment waterfall. The ruling also casts a spotlight on how litigation‑funding agreements should be structured and negotiated when bankruptcy risk is present. Funders who assumed they could elevate their status via contractual design may now face greater caution and risk.

Manolete Partners PLC Posts Flat H1 as UK Insolvency Funding Opportunity Grows

By John Freund |

The UK‑listed litigation funder Manolete Partners PLC has released its interim financial results for the half‑year ended 30 September 2025, revealing a stable but subdued performance amid an expanding insolvency funding opportunity.

According to the company announcement, total revenue fell to £12.7 million (down 12 % from £14.4 million a year earlier), while realised revenue slipped to £14.0 million (down 7 % from £15.0 million). Operating profit dropped sharply to £0.1 million, compared to £0.7 million in the prior period—though excluding fair value write‑downs tied to the company’s truck‑cartel portfolio, underlying profit stood at £2.0 million.

The business completed 146 cases during the period (up 7 % year‑on‑year) and signed 146 new case investments (up nearly 16 %). Live cases rose to 446 from 413 a year earlier, and the total estimated settlement value of new cases signed in the period was claimed to be 31 % ahead of the prior year. Cash receipts were flat at about £14.5 million, while net debt improved to £10.8 million (down from £11.9 million). The company’s cash balance nearly doubled to £1.1 million.

In its commentary, Manolete emphasises the buoyant UK insolvency backdrop — particularly the rise of Creditors’ Voluntary Liquidations and HMRC‑driven petitions — as a tailwind for growth. However, the board notes the first half was impacted by a lower‑than‑average settlement value and a “quiet summer”, though trading picked up in September and October. The firm remains confident of stronger average settlement values and a weighting of realised revenues toward the second half of the year.