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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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An LFJ Conversation with Eric Schurke, CEO, North America, Moneypenny

Below is our LFJ Conversation with Eric Schurke, CEO, North America at Moneypenny.

Eric Schurke is CEO, North America at Moneypenny, a global leader in customer conversations. He is passionate about the intersection of people, communication and technology, and how businesses can use AI to improve customer experience without losing the human judgment and empathy that build trust. He regularly writes and speaks on customer experience, AI and people-first leadership.

Moneypenny's consumer research found that 38% of people aren't comfortable using AI for any legal communication, rising to 51% among Baby Boomers. For funders and the firms they back, where is the line between what AI should handle at intake and what still needs a person on the phone?

I don't think the answer is to draw a fixed line between AI and people. The key is designing an intake process that understands when one should give way to the other.

AI can be incredibly useful at the beginning of an enquiry. It can answer straightforward questions, gather basic information, establish the reason for contact, capture key details and make sure an enquiry reaches the right place quickly. Those are all areas where speed and consistency can improve the experience.

But legal and funding conversations aren't always straightforward. Someone may be worried, confused or dealing with a difficult situation, and there will be moments when they want reassurance or need to explain something that doesn't fit neatly into a predefined process. That's where a person becomes essential.

Our research is a reminder that businesses can't assume everyone is comfortable with AI. The best systems give people choice and make the transition to a human seamless, with the context already captured so the claimant doesn't have to start again.

For me, the principle is simple: use AI for speed and structure, and people for judgment, reassurance and trust.

You've argued that funders often win or lose an opportunity before case review even begins. What actually happens in those first few minutes of contact that determines whether a claimant stays in the process?

Those first few minutes answer some very basic but important questions for the person making contact: Have I reached the right place? Does this organization understand what I need? Is someone taking me seriously? And what happens next?

Good intake needs to gather enough useful information to move an enquiry forward without making that first interaction feel like an interrogation. That's why I think first contact deserves more attention. It's not simply an administrative stage before the "real" work begins; it's where trust and momentum start to form.

If the experience is slow, confusing or impersonal, a claimant may disengage before the funding team has even had an opportunity to assess the case. Get it right, and the claimant understands the next step while the funder has the context needed to progress the enquiry.

Most intake technology is sold on volume - enquiries handled, calls deflected, hours saved. Why are conversion and client outcomes the better measures, and what does a funder lose by optimizing for the wrong one?

Volume tells you how busy the system is. It doesn't necessarily tell you whether it's working.

You could automate thousands of interactions and reduce handling time considerably, but if good enquiries are dropping out, information is incomplete or people are having to contact you again because nothing was resolved, you've created efficiency on paper rather than value for the business.

I'd ask: Did the enquiry progress? Was the right information captured? Did it reach the right person? Was unnecessary follow-up avoided?

That's the real ROI of a conversation. Passing a message is activity; gathering what's needed for the next stage creates progress. For funders, optimizing purely for volume risks making the top of the funnel look efficient while valuable opportunities are being lost underneath it.

There is a wider lesson here for AI, too. We're seeing businesses invest heavily in technology without always being clear about the outcome they're trying to improve. That's where an AI value gap can emerge; when adoption increases, but the commercial return doesn't necessarily follow.

Consumer legal funding is drawing more state-level regulation in the U.S., much of it centered on disclosure and how claimants are communicated with. How should that shape the way a funder designs an AI-assisted intake process?

It makes clear boundaries and good governance even more important.

I'm not a lawyer, so I wouldn't tell funders how to interpret individual state requirements, but from a customer communication perspective, AI should make a compliant process easier to follow, not harder to understand.

Funders need to define what an AI system can say and do, what information it can collect and when human involvement is required. If a conversation involves important disclosures, nuanced questions or judgment, there should be a clear escalation path.

Technology can support consistency, but it shouldn’t remove human oversight. In a regulated environment, knowing what your AI shouldn’t do can be every bit as important as knowing what it can. I’d build the guardrails at the beginning rather than adding them after deployment.

Looking out two to three years, what does a well-run intake operation at a litigation funder look like, and which parts of it do you expect will still be human?

I think the best intake operations will feel simpler to the claimant, even though the technology behind them will be more sophisticated.

AI will increasingly handle predictable work: answering routine questions, gathering and structuring information, identifying missing details, scheduling next steps and routing enquiries with the right context. It will also work behind the scenes, reducing administration and helping people find information quickly.

What won't disappear is the human role at the moments that matter most. When somebody has a complicated story, is uncertain about the process, needs reassurance or simply doesn't fit the expected pattern, judgment and empathy will remain essential.

So, I don't see the future as AI-led or human-led. It will be intelligently blended. The best technology will almost disappear into the experience; the claimant will simply feel understood and know they're moving forward.

Burford Prices Secured Notes at 8% as It Swaps $400M of 2028 Debt for $300M Due 2029

Burford Capital has set the terms on the refinancing it launched at the start of the week, pricing $300 million of senior secured notes at a coupon of 8.000% and locking in the cost of retiring its nearest maturity.

As reported by PR Newswire, the notes are due 2029 and will be issued by Burford Capital Global Finance LLC, an indirect wholly owned subsidiary. Burford Capital Limited is guaranteeing the paper, which is secured on a senior lien basis by substantially all of the issuer's assets and by the capital stock of certain subsidiaries, subject to exceptions.

The pricing carries a clear message about the funder's cost of capital. The 8.000% coupon on secured paper sits well above the 6.250% Burford is paying on the unsecured 2028 notes it is redeeming, and the company is putting up collateral to get there. Against that, the transaction takes $100 million of gross debt off the balance sheet, since net proceeds plus cash on hand will retire all $400 million of the 2028 notes.

The offering is expected to close on September 17, subject to customary conditions, with redemption of the 2028 notes to follow as soon as practicable afterwards.

The notes are being placed privately and have not been registered under the US Securities Act, with distribution limited to qualified institutional buyers under Rule 144A and to non-US persons under Regulation S, in each case also qualified purchasers under the Investment Company Act.

Tata Power Loss in Singapore Puts Arbitrator Disclosure of Funder Ties Under Scrutiny

A Singapore ruling upholding a US$490.32 million arbitration award against Tata Power is drawing attention across the arbitration bar for what it says about how far arbitrators must go in disclosing their connections to third-party funders.

As reported by the Deccan Chronicle, the Singapore International Commercial Court on August 26 dismissed all three of Tata Power Company Limited's applications challenging the award, which was issued in favour of Kleros Capital Partners along with legal costs and interest. Kleros pursued the claim with litigation funding from Omni Bridgeway.

Tata Power argued that two members of the tribunal, Prof Lawrence Boo and Stuart Isaacs KC, should have disclosed their appointments in other arbitrations involving Omni Bridgeway-funded parties. It also pointed to Prof Boo's professional and personal association with Mark Hughes, a member of Omni Bridgeway's investment committee.

The court rejected the apparent bias allegations, holding that undisclosed appointments in unrelated matters did not establish bias and that where the circumstances did not give rise to apparent bias, there was no need to decide separately whether a disclosure obligation had been breached. It also declined to treat third-party funders as parties for disclosure purposes.

"How far should arbitrators be required to disclose professional relationships with parties, lawyers and third-party funders, particularly when litigation financiers have economic interests in the outcome?" asked finance expert Biswanth Pradhan, framing the wider question the case raises.

Tata Power has indicated it will appeal to the Singapore Court of Appeal.