Risk Adjusted Returns: A Sharpe Ratio Guide to P2P Lending

When two investments return 8% annually, most investors assume they’re equal. They’re not. One might achieve that return with minimal volatility; the other might swing wildly, exposing you to potential losses that could wipe out months of gains. Risk-adjusted returns exist precisely to expose this difference, and in peer-to-peer lending, where default rates and platform risk create a genuinely complex environment, understanding these metrics is the foundation of sound portfolio management. Not a nice-to-have. The foundation.

What Are Risk-Adjusted Returns?

A risk-adjusted return measures how much return an investment generates per unit of risk taken. Raw return figures tell you what happened. Risk-adjusted figures tell you whether it was worth the risk to get there.

Think about it this way: a P2P lending portfolio yielding 12% sounds attractive. But if that portfolio carries a standard deviation of 15% compared to a competitor’s 12% yield with a standard deviation of 6%, the second portfolio is objectively superior on a risk-adjusted basis. Same reward, 6% of volatility instead of 15% - roughly 40% as much.

Standard deviation, in this context, is simply how far a portfolio’s periodic returns tend to sit from their own average. A portfolio that returns 0.9%, 1.0% and 1.1% in three consecutive months has a low standard deviation. One that returns 3%, -2% and 2% has a high one, even though both can average out to a similar annual figure. Every metric in this guide is built on that one measurement, which is why the section below on where a P2P volatility number actually comes from matters more than the formulas do.

The core principle is simple: higher returns should compensate for higher risk. When they don’t, you’re being underpaid for the danger you’re accepting.

Key Takeaways

• Risk-adjusted returns measure reward relative to risk, not just absolute gains
• The Sharpe Ratio is the most widely used metric for this calculation
• P2P lending introduces unique risks that standard metrics must account for
• Multiple ratios give a more complete picture than any single measure

Why P2P Lending Demands Rigorous Risk Analysis

P2P lending sits outside traditional asset classes. You’re lending directly to individuals or businesses through a platform, bypassing banks entirely. The return potential is real: our own review of 19 European platforms puts a realistic net range at 5% to 12% a year after defaults, recovery delays, platform fees and cash drag, against advertised headlines that cluster around 12% to 15%. The platform-by-platform breakdown is in P2P Lending Realistic Returns. But the risk profile is equally distinctive.

P2P lending returns don’t follow a normal distribution the way equity returns roughly do. Default events are lumpy and correlated during economic downturns. A single recession can spike default rates across an entire loan book simultaneously, creating a left-tail risk that standard deviation alone doesn’t capture well.

Platform risk adds another layer. Envestio, Kuetzal, Monethera and Grupeer all collapsed within the first months of 2020, not gradually across 2019-2021: the Envestio website went dark on 22 January 2020 holding roughly €33 million from about 13,000 investors, and both Envestio and Kuetzal were declared bankrupt by June 2020 under the same Estonian bankruptcy trustee. The larger UK failures, Lendy and FundingSecure, came earlier, in 2019. Investors from both clusters are still in recovery processes years later. That is operational and counterparty risk bundled into a single event, not the kind of market risk a standard deviation figure will warn you about in advance, and it is why we keep a case-study log of P2P platforms that failed.

Liquidity risk matters too. Secondary markets for P2P loans exist on some platforms, but they are thin and the rules differ sharply. Mintos runs the largest one in EU P2P and has charged sellers 0.85% since May 2025. PeerBerry only launched a secondary market in January 2026, with a six-month holding period before a loan can be listed. Robocash has none at all, so the only exit is waiting for loans to mature. On EstateGuru a secondary market technically exists but is largely non-functional, because few buyers want exposure to the legacy portfolio. During stress periods, selling positions at fair value becomes difficult or impossible.

There is no daily price for a P2P loan

This is the part most Sharpe Ratio guides skip, and it changes how you should read everything below. A listed fund has a daily market price, so its volatility is observed. A P2P loan does not trade. It sits on the platform at face value plus accrued interest until something goes wrong with it. No market quote, no daily mark, and therefore no directly observable volatility.

So when this guide refers to a P2P portfolio’s standard deviation, the input is one of three things, and you need to know which one you are holding:

• Your own account statement. Monthly net interest received, minus write-offs, divided by average capital deployed. This is the honest version for a retail investor, and it needs roughly 24 to 36 monthly observations before the standard deviation means anything at all.
• A platform-published monthly return series. Very few European platforms publish one. Most publish a single “average return” headline instead, which is a point estimate with no dispersion attached and therefore cannot be fed into any of these formulas.
• A modelled series built from default and recovery data. Our default rate barometer collects the underlying inputs across the platforms we cover.

None of the three is a market price, and all three understate real volatility, for the reason set out under volatility smoothing further down. Treat every ratio in this article as a tool for comparing portfolios measured the same way, not as a physical property of the asset.

This complexity is exactly why applying multiple risk-adjusted return metrics to P2P portfolios gives you far more clarity than simply comparing headline yields.

The Sharpe Ratio: The Foundation

The Sharpe Ratio, developed by Nobel laureate William Sharpe and published in his 1966 paper “Mutual Fund Performance” in the Journal of Business, remains the most widely applied risk-adjusted performance measure. Sharpe originally called it the reward-to-variability ratio; the name it carries today was attached by other people. Its logic is elegant: subtract the risk-free rate from your portfolio return, then divide by the portfolio’s standard deviation.

The formula:

Sharpe Ratio = (Portfolio Return - Risk-Free Rate) / Standard Deviation of Portfolio Returns

If your P2P portfolio returns 9% annually, the risk-free rate sits at 4%, and your portfolio’s standard deviation is 8%, your Sharpe Ratio is:

(9% - 4%) / 8% = 0.625

All three inputs have to be annualized and measured over the same period. If you are working from monthly data, the usual shortcut is to compute the ratio monthly and multiply it by the square root of 12, which comes with a caveat that matters more in P2P than almost anywhere else; it is explained under volatility smoothing below. And if you invest in euros, do not reach for the US Treasury bill that most textbooks assume. The section on the risk-free rate further down sets out what to use instead.

Interpreting Sharpe Ratio Values

The thresholds below are practitioner rules of thumb, widely repeated in institutional portfolio management. They are conventions rather than standards published by any regulator or professional body, and different houses draw the lines in different places. They give you a starting point for comparison, nothing more.

For P2P lending, a Sharpe Ratio between 0.5 and 1.0 is realistic for a well-diversified portfolio. Anything above 1.0 warrants scrutiny: either the platform is genuinely exceptional, or the standard deviation figure is being smoothed by infrequent or imprecise valuation of non-traded loans. One further quirk of the table: once the ratio goes negative it stops ranking things properly. Between two losing portfolios, the one with higher volatility produces the less-negative number, which is the opposite of what you want the metric to tell you. Below zero, read the sign and stop there.

The Sharpe Ratio’s Limitations in P2P Contexts

The Sharpe Ratio assumes returns are normally distributed. P2P lending returns aren’t. Default clustering during recessions creates negative skewness and excess kurtosis, meaning bad outcomes occur more frequently and more severely than a normal distribution would predict.

There’s another problem worth naming directly. The ratio treats upside and downside volatility identically, so a portfolio that occasionally delivers exceptional months alongside consistent moderate returns gets penalized in exactly the same way as one that swings between gains and losses. For most investors, that’s a meaningless distinction.

Standard deviation compounds the issue for illiquid assets. Loan values that aren’t marked to market daily produce artificially smooth reported returns, which lowers measured standard deviation and inflates the Sharpe Ratio. P2P portfolios can look better-managed than they are for precisely this reason.

The Sortino Ratio: Focusing on Downside Risk

The Sortino Ratio addresses the Sharpe Ratio’s most significant flaw. Instead of dividing by total standard deviation, it divides by downside deviation, measuring only the volatility of negative returns below a target threshold (usually zero or the risk-free rate).

The formula:

Sortino Ratio = (Portfolio Return - Target Return) / Downside Deviation

Two details decide whether the number you get out is meaningful.

The target has to appear on both sides. The figure you subtract in the numerator and the threshold you measure shortfalls against in the denominator must be the same number. Subtracting the risk-free rate on top while measuring shortfalls below zero underneath produces a ratio that cannot be compared with anyone else’s.

Downside deviation is not “the standard deviation of the negative months”. The correct calculation, set out by Rollinger and Hoffman in Sortino: A Sharper Ratio, squares the shortfall below the target for every period, counts every period above the target as a zero rather than deleting it, sums across all periods, divides by the total number of periods, and takes the square root. Dropping the positive months from the denominator - the most common mistake - inflates downside deviation and understates the ratio, by a different amount for every portfolio, which quietly destroys any comparison you were trying to make.

For P2P lending, this is a more honest measure. You don’t care that your portfolio occasionally outperforms expectations. You care about how often and how severely it falls below your target. The Sortino Ratio captures that asymmetry directly.

When Sortino Outperforms Sharpe as a Signal

A P2P portfolio with a Sortino Ratio of 1.2 and a Sharpe Ratio of 0.7 is telling you something: a large share of the volatility dragging down the Sharpe Ratio sits above the target rather than below it.

One correction to the way this comparison is usually presented, because it is stated as a signal far more often than it deserves. Downside deviation counts only shortfalls, while standard deviation counts moves in both directions, so for almost any portfolio the Sortino Ratio comes out higher than the Sharpe Ratio, including for portfolios you would not touch. “Sortino above Sharpe” on its own is close to a tautology and is evidence of nothing. What carries information is the size of the gap. A portfolio where the Sortino Ratio is well over one and a half times the Sharpe Ratio has genuinely asymmetric returns. One where the two sit almost on top of each other is taking nearly all of its volatility on the loss side, and that is the warning. A Sortino Ratio that actually falls below the Sharpe Ratio is rare, and means returns are clustered below your target: worth investigating rather than dismissing as a rounding artefact.

Practical Note: In practice, no European platform in our 19-platform coverage publishes either ratio, so the useful question sits one step earlier: will the platform give you a monthly net-return series, by year or by loan cohort, that you can run these calculations on yourself? Some will, on request, for a portfolio you already hold. A platform that will not release the underlying series while continuing to advertise a single average-return figure has told you something about its reporting culture. Our methodology sets out which disclosure signals we score and how they are weighted.

The Treynor Ratio: Systematic Risk Only

The Treynor Ratio swaps standard deviation for beta, measuring return per unit of systematic (market) risk rather than total risk.

The formula:

Treynor Ratio = (Portfolio Return - Risk-Free Rate) / Portfolio Beta

P2P loans theoretically carry low correlation with equity markets, which implies a low beta. If that holds, the Treynor Ratio looks spectacular for P2P portfolios. But does it hold?

During normal market conditions, yes. During systemic crises, the picture changes, and for a structural reason: the same recession that pushes equity markets down is what pushes borrowers into default, so the low correlation tends to disappear at exactly the moment you were relying on it. What we can point to from our own coverage is the March 2020 cluster:Mintos saw 17 loan originators fail with roughly €118 million of investor money at risk within months of the market dislocation. The Treynor Ratio, while useful for comparing funds within the same asset class, can mislead when applied across asset classes that include P2P lending.

Use the Treynor Ratio to compare different P2P platforms or loan categories against each other. Don’t rely on it to compare a P2P portfolio against an equity fund.

Jensen’s Alpha: Measuring Manager Skill

Alpha measures how much a portfolio returns above what its risk level predicts, based on the Capital Asset Pricing Model (CAPM). The CAPM, developed in the 1960s and foundational to modern portfolio theory, estimates expected return as a function of systematic risk exposure. The measure itself comes from Michael Jensen’s 1968 study of US mutual funds, which is where the “alpha” label originates.

The formula:

Alpha = Portfolio Return - [Risk-Free Rate + Beta × (Market Return - Risk-Free Rate)]

In P2P lending, alpha represents the excess return a platform or portfolio manager generates beyond what the systematic risk exposure would predict. Consistently positive alpha points to genuine skill in credit assessment or loan selection. Negative alpha means the platform is underdelivering relative to its risk profile.

The challenge is that calculating meaningful alpha for P2P portfolios requires a reliable benchmark, and P2P lending lacks a universally accepted market index. Some analysts use high-yield bond indices as proxies. Others construct peer group comparisons across platforms.

Despite this limitation, tracking alpha over time within a single platform is worth doing. A platform whose alpha erodes from +2% to -1% over three years signals deteriorating credit quality or increasing competition for borrowers. Both are warning signs worth acting on before the headline yield figures catch up.

The Information Ratio: Evaluating Active Management

The Information Ratio measures how consistently a portfolio outperforms its benchmark relative to the variability of that outperformance.

The formula:

Information Ratio = (Portfolio Return - Benchmark Return) / Tracking Error

Tracking error is the standard deviation of the difference between portfolio returns and benchmark returns. A high Information Ratio means the manager consistently beats the benchmark without erratic swings in relative performance.

According to Grinold and Kahn’s Active Portfolio Management, a standard reference in institutional investment, an Information Ratio above 0.5 is considered good, and above 1.0 indicates strong active management. For P2P investors using managed accounts or fund structures, this ratio directly measures whether the manager’s active loan selection adds value.

Most P2P platforms don’t provide Information Ratio data. You’ll need to calculate it manually using monthly return data against a chosen benchmark. For significant allocations, that extra work pays off.

Calmar Ratio: Accounting for Drawdown

The Calmar Ratio divides annualized return by maximum drawdown, making it particularly relevant for P2P portfolios where drawdowns from default events can be severe and prolonged.

The formula:

Calmar Ratio = Annualized Return / Maximum Drawdown

A P2P portfolio returning 10% annually with a maximum drawdown of 20% (during a period of elevated defaults) has a Calmar Ratio of 0.5. Compare that to a portfolio returning 8% with a maximum drawdown of 8%, giving a Calmar Ratio of 1.0. The second portfolio is clearly superior on this measure despite lower absolute returns.

The Calmar Ratio is especially useful for evaluating P2P platforms through economic cycles, with one practical obstacle: no platform in our coverage publishes a Calmar Ratio, and most do not publish the drawdown data you would need to compute one.

The usable substitute is the share of the loan book stuck in recovery, which platforms do disclose. EstateGuru has 60.2% of its live portfolio in recovery as of 2026 against a stated 10.4% average return; Mintos sits at roughly 18.7%; PeerBerry and Robocash both report 0%. Our default rate barometer tracks these across the coverage universe.

One structural warning about drawdown metrics in this asset class. A platform with a short history and no defaults has no maximum drawdown to divide by, which makes its Calmar Ratio infinite rather than excellent. Maclear, our top-ranked platform, is the live example: its single default to date - an Italian SME, €150,000, July 2025 - was covered from the CEO’s personal funds rather than through collateral enforcement. That produces a flawless drawdown record and tells you almost nothing about how the recovery process would behave under stress. A clean number and an untested process look identical in a Calmar Ratio.

Comparing Risk-Adjusted Metrics Side by Side

Building a Risk-Adjusted Framework for P2P Portfolios

No single metric gives you the full picture. The most effective approach combines several measures, each answering a different question about your portfolio’s performance.

Start with the Sharpe Ratio for a baseline comparison across different investment options, including non-P2P alternatives. Then apply the Sortino Ratio to understand whether the volatility you’re accepting is concentrated on the downside or the upside. Use the Calmar Ratio to assess how the platform performed during stress periods.

For managed P2P funds or platforms with active loan selection, the Information Ratio adds a layer of accountability, forcing the question of whether active management is earning its fees.

A practical framework for P2P portfolio evaluation:

• Sharpe Ratio above 0.5: Minimum acceptable threshold for a diversified P2P portfolio
• A wide gap between Sortino and Sharpe: the wider the gap, the more of the volatility sits above your target rather than below it. The two ratios sitting close together is the warning sign, not the other way round
• Calmar Ratio above 0.5: Indicates returns justify the drawdown risk experienced
• Positive and stable Alpha: Suggests the platform maintains credit quality over time
• Information Ratio above 0.5 (for managed accounts): Active selection is adding value

The Risk-Free Rate Question

Every ratio involving the risk-free rate requires a choice: which rate to use? The answer matters more than most investors realize, particularly in environments where central bank rates shift significantly.

Two rules settle most of it. Match the currency. If you invest in euros, a US 3-month Treasury bill is not your risk-free rate, because holding one would expose you to the EUR/USD exchange rate, and currency risk is the opposite of risk-free. Match the horizon. Use a short-dated rate, not a ten-year government bond yield.

For a euro-based investor that leaves three defensible choices:

• €STR, the euro short-term rate, published every day by the ECB. This is the euro area’s designated risk-free benchmark: the private-sector working group convened for the purpose recommended it as the euro risk-free rate in September 2018, and it replaced EONIA outright in January 2022.
• The ECB deposit facility rate, which is what a bank actually earns on overnight balances held at the central bank. Coarser than €STR, but easy to look up and stable between policy meetings.
• Short-dated German government bills (Bubills), if you would rather anchor on an instrument you could actually buy than on a reference rate.

EURIBOR is the wrong choice, even though it is the one most often reached for. EURIBOR is the rate at which European banks lend to each other on an unsecured basis, so it carries bank credit risk and a term premium inside it. Using it as your risk-free rate quietly subtracts somebody else’s credit spread from your return before you have measured anything. It is a benchmark rate, not a risk-free rate, and the ECB’s own working group on euro risk-free rates treats the two as different objects.

The remaining rule is consistency: use the same risk-free rate when comparing portfolios across time or against each other.

When risk-free rates rise sharply - the ECB deposit facility rate went from -0.50% to 4.00% across ten increases in about fourteen months, between July 2022 and September 2023 - the excess return in your calculation shrinks, naturally compressing Sharpe Ratios across all asset classes including P2P lending. Portfolios didn’t become worse. The opportunity cost of risk-taking increased. That’s a meaningful signal in itself. Whatever the rate happens to be on the day you run the numbers, take it from the ECB’s own key-interest-rates page rather than from memory or a secondary source.

Volatility Smoothing: A Hidden Distortion

P2P lending portfolios face a structural problem that inflates risk-adjusted metrics: because loans aren’t traded on secondary markets, their values don’t fluctuate daily. Reported returns look smoother than they actually are, which artificially lowers measured standard deviation and boosts Sharpe and Sortino Ratios.

This is a genuine feature of illiquid assets, not a deliberate misrepresentation, and it is well documented outside P2P. Getmansky, Lo and Makarov showed in 2004 that hedge funds holding illiquid securities report returns that are serially correlated and smoother than the underlying economics, which understates volatility and inflates the Sharpe Ratio; they went as far as constructing a smoothing-adjusted Sharpe Ratio to correct for it. A P2P loan book is a more extreme version of the same problem, because there is no market price at all rather than an infrequent one.

The same effect breaks the square-root-of-12 shortcut mentioned earlier. Scaling monthly volatility by the square root of twelve assumes each month is independent of the last. Smoothed returns are not independent - they are positively correlated from one month to the next - so true annual volatility is higher than the shortcut implies, and the annualized Sharpe Ratio you end up quoting is flattering.

One practical adjustment: compare the platform’s reported standard deviation against the volatility of comparable high-yield bond funds. If the P2P figures look significantly smoother than the bond fund, the smoothing effect is likely distorting your metrics. As a rough heuristic rather than a precise formula, adjusting your expected standard deviation upward before drawing conclusions gives a more conservative baseline. The right size of adjustment depends on loan duration and platform transparency, but erring toward caution here costs little and prevents overconfidence in the numbers.

Diversification and Its Effect on Risk-Adjusted Returns

Within a P2P portfolio, spreading across loans reduces idiosyncratic default risk and improves risk-adjusted returns without sacrificing yield. Spreading across 200 loans rather than 20 means a single default has a smaller impact on portfolio standard deviation.

Cross-platform diversification adds another dimension. Different platforms serve different borrower segments, geographies, and risk grades. Combining platforms with low return correlations (when one platform’s defaults rise, the other’s don’t necessarily follow) further reduces portfolio volatility.

But diversification has real limits in P2P lending. Recessions, regulatory changes, or platform failures tend to affect multiple platforms at once. Spreading across 500 loans on a single platform reduces individual loan risk while leaving you fully exposed to that platform’s operational and regulatory risk. True diversification requires multiple platforms, ideally across different geographies and borrower types, and it requires looking through the platform to the loan originator sitting behind it. PeerBerry sources over 83% of its loan book from a single group, Aventus, and every loan on Robocash comes from originators owned by one parent company. A portfolio of loans like that is one credit exposure wearing a hundred name tags, however many individual loan lines your dashboard shows. Our diversified P2P portfolio guide works through concrete allocations.

Diversification Reality Check: During the 2020 economic disruption, platforms operating across different European jurisdictions showed meaningfully different default trajectories. Investors concentrated on a single platform had no buffer against that platform’s specific borrower base deteriorating. The cleanest illustration in our coverage is not a return number at all: Reinvest24 has had withdrawals frozen since February 2024 and carries public warnings from three separate regulators - EFSA in Estonia, CNMV in Spain, and Finanstilsynet in Norway. For an investor concentrated there, no ratio computed before 2024 carried any information about what actually happened. Platform-level failure is not a volatility event, and none of the metrics in this guide will price it. The signals that do warn you are covered in how to spot a risky P2P platform.

Applying These Metrics: A Worked Example

Suppose you’re evaluating two P2P platforms over a 3-year period. The figures below are illustrative. They are not drawn from any platform in our coverage, and as noted above, no European P2P platform currently publishes the downside-deviation and drawdown inputs these calculations need - you would be computing them from your own statements. Note also that the Sortino Ratio here uses the risk-free rate as its target, which is what makes it directly comparable with the Sharpe Ratio in the row above.

Platform A: Annual return 10.5%, standard deviation 7%, downside deviation 3.5%, maximum drawdown 12%, risk-free rate 3.5%

Platform B: Annual return 11.8%, standard deviation 12%, downside deviation 8%, maximum drawdown 22%, risk-free rate 3.5%

The takeaway: Platform B has a higher absolute return. But across every risk-adjusted measure, Platform A wins. The extra 1.3 percentage points of annual return on Platform B do not compensate for 2.3 times the downside deviation (8% against 3.5%) and 1.8 times the maximum drawdown (22% against 12%). An investor choosing Platform B purely on headline yield is accepting substantially more risk for a marginal gain. This is exactly the insight risk-adjusted returns are designed to provide.

Frequently Asked Questions

What is a risk-adjusted return and why does it matter?

A risk-adjusted return measures investment performance relative to the risk taken to achieve it. Two portfolios with identical returns can carry very different risk levels. In P2P lending, where default rates and platform stability vary significantly across providers, comparing raw yields without adjusting for risk leads to poor allocation decisions.

What is the difference between the Sharpe Ratio and the Treynor Ratio?

The Sharpe Ratio divides excess return by total standard deviation, measuring performance relative to all volatility. The Treynor Ratio divides excess return by beta, isolating systematic (market) risk only. Use the Sharpe Ratio for standalone portfolio evaluation and the Treynor Ratio when comparing portfolios that form part of a broader diversified strategy.

What is considered a good risk-adjusted return?

A Sharpe Ratio above 1.0 is generally considered good, with values above 2.0 being excellent but rare. For P2P lending, a Sharpe Ratio between 0.5 and 1.0 is realistic for a well-managed portfolio. Sortino Ratios above 1.0 are strong. Always compare against similar asset classes rather than internal benchmarks alone, and sanity-check the numerator first: if the return you are feeding in is the platform’s advertised headline rather than what you actually netted, the ratio is measuring marketing copy. Our guide to realistic P2P returns covers the size of that gap platform by platform, and P2P vs ETF vs bank puts the asset class next to the alternatives.

Which risk-adjusted return measure should I use for my portfolio?

No single measure is sufficient. Use the Sharpe Ratio for broad comparisons, the Sortino Ratio when downside risk is your primary concern, and the Calmar Ratio to assess drawdown tolerance. For P2P lending specifically, the Sortino and Calmar Ratios are most informative given the asymmetric nature of credit losses during economic stress periods.

How is risk-adjusted return calculated?

The most common calculation is the Sharpe Ratio: subtract the risk-free rate from the portfolio’s annualized return, then divide by the portfolio’s standard deviation. A portfolio returning 9% with a risk-free rate of 4% and a standard deviation of 8% produces a Sharpe Ratio of 0.625. Other metrics substitute downside deviation or maximum drawdown for total standard deviation.

What is Alpha in portfolio management?

Alpha is the excess return a portfolio generates above what its risk level predicts, based on the Capital Asset Pricing Model (CAPM). Positive alpha indicates returns beyond what systematic risk exposure explains, pointing to genuine skill in selection or risk management. In P2P lending, consistent positive alpha signals strong credit assessment and disciplined loan origination.

What is the Sortino Ratio and how is it different from the Sharpe Ratio?

The Sortino Ratio measures return per unit of downside deviation, considering only negative return volatility below a target threshold. The Sharpe Ratio uses total standard deviation, penalizing upside and downside volatility equally. For P2P lending’s asymmetric return profile, the Sortino Ratio gives a more honest read. Note that the Sortino Ratio comes out above the Sharpe Ratio for nearly every portfolio, simply because the denominator counts fewer observations, so the fact that it is higher tells you nothing on its own. The width of the gap is the informative part.

How do I calculate risk-adjusted return in Excel?

Use AVERAGE() and STDEV.S() on your monthly net-return series, then apply the formula directly. For the Sharpe Ratio: subtract the monthly risk-free rate from AVERAGE(), divide by STDEV.S(), and multiply by the square root of 12 to annualize. Two cautions. The square-root-of-12 step assumes each month is independent of the last, which is not true of an illiquid loan portfolio, so it will flatter the result. And for the Sortino Ratio, do not simply run STDEV.S() over the negative months - that drops the positive periods from the denominator and understates the ratio. Square the shortfall below your target for every month, count months above the target as zero, average across all months, then take the square root: =SQRT(SUMPRODUCT((range<target)*(range-target)^2)/COUNT(range)).

What is the Information Ratio and how do you evaluate active managers with it?

The Information Ratio divides a portfolio’s excess return over its benchmark by the tracking error of that excess return. It measures consistency of outperformance, not just average outperformance. According to Grinold and Kahn’s Active Portfolio Management, a ratio above 0.5 is good and above 1.0 is strong. For P2P fund managers, it directly quantifies whether active loan selection adds consistent value.

What are the limitations of risk-adjusted return metrics?

Standard metrics assume normally distributed returns, which P2P lending doesn’t produce. Default clustering creates fat tails that standard deviation understates. Illiquid loan portfolios report artificially smooth returns, inflating Sharpe and Sortino figures. Beta calculations become unreliable during crises when correlations spike. And the largest limitation is the one none of the formulas contain: platform failure, frozen withdrawals and regulator action are not volatility events, so a portfolio can show excellent risk-adjusted numbers right up to the point where the money stops coming back. No single metric captures all dimensions of risk, so a multi-metric approach - combined with the qualitative platform checks in our methodology - is necessary.

What to Read Next

• Want the return numbers these ratios operate on? P2P Lending Realistic Returns sets advertised yields against realized ones across 19 platforms.
• Want the default and recovery inputs? The default rate barometer and our sector statistics page carry the underlying data.
• Want to build the portfolio rather than measure it? Diversified P2P Portfolio covers allocation across platforms and originators.
• Want the risks the formulas cannot price? How to Spot a Risky P2P Platform and Are P2P Investments Safe?.

Capital at risk. P2P lending is not a deposit and is not covered by any deposit-guarantee scheme; you can lose some or all of the money you invest.