In this goPeer case study, I use 5+ years of my own goPeer P2P lending data to explore why risk management and diversification can matter more than picking individual winners. Across 1,046 resolved loans, 908 have been fully repaid and 138 charged off, giving me an 86.8% repayment rate, while my portfolio has generated $3,513.76 in gross income and $2,398.80 in net income despite $1,125.96 in charge-offs. I’ll compare those results with my original PTJ post-earnings trading data, where even one of my strongest historical stock-selection buckets hit a +9% target before a -5% stop just 70.6% of the time, to show why better selection can improve your odds—but consistent position sizing, diversification, and controlling the inevitable losses may ultimately matter more.
One of the main reasons I’ve spent so much time collecting trading data throughout 2026 is because I want to get better at identifying the strongest post-earnings momentum setups.
In other words, I’m trying to become a better stock picker.
But there’s something slightly ironic about what the data keeps teaching me.
Even when the data identifies what looks like the superior stock-selection bucket, the data itself is telling me not to trust any individual stock too much. ANY setup can fail, reverse, and vaporize your capital if you’re not prepared for it.
That’s because even a 60%, 70%, or even 80% historical success rate still contains losers, which means better stock selection only takes me so far.
Eventually, risk management, diversification, and consistent position sizing have to do the rest.
Interestingly, I’ve already spent more than five years running another investment experiment that demonstrates this incredibly well.
My Canadian P2P lending portfolio through goPeer has now produced more than 1,000 resolved loan outcomes, and I’ve had 138 investments completely charged off.
That’s 138 FAILED investments… Yet the portfolio remains profitable.
The reason is NOT that I’ve become perfect at selecting borrowers. It’s that I’ve increasingly structured the portfolio so that I don’t need to be.
And there’s a perfect analogy between consistently profitable peer-to-peer lending and consistently profitable stock trading, which is what we’ll be exploring in the following articles.
Disclosure: This article contains affiliate/referral links. PTJ may receive compensation if you open or fund an account through links on this page. My historical performance does not guarantee future investment results.
Risk Management Handles the Losers.
Quick Answer: Is goPeer Legit for Borrowing Money in Canada?
Yes. goPeer is a legitimate Canadian peer-to-peer lending platform that allows eligible Canadians to either fund other people’s loans or to apply for personal loans. For investors, returns depend on factors such as borrower risk grade, interest rate, defaults, and diversification; in my own 5+ years using the platform, my portfolio has generated $3,513.76 in gross income and $2,398.80 in net income, despite $1,125.96 in charge-offs. Of my 1,046 resolved loans, 908 have been fully repaid and 138 charged off, producing an 86.8% repayment rate and roughly $3.12 in gross income for every $1 charged off. Investing or borrowing from goPeer is a legitimate way to invest, but it is NOT risk-free, which makes diversification and position sizing an important part of managing P2P lending risk.
Important: The goPeer investing opportunity discussed in this article is available to eligible Canadian investors only; my historical returns are personal results and do not guarantee future performance.
Key Statistics – goPeer P2P Lending, Diversification & Trading Risk
- 5+ years invested with goPeer: My P2P lending portfolio now provides a long-term, real-money example of how diversification and consistent position sizing can absorb individual failures.
- goPeer currently reports an annualized net return to investors since inception of 3%, with data through June 30, 2026
- 1,046 resolved loans: 908 have been fully repaid and 138 charged off, producing an 86.8% repayment rate and 13.2% charge-off rate among resolved loans.
- 1 default for every 6.6 repayments: Historically, my portfolio has experienced approximately one charged-off loan for every 6.58 loans fully repaid.
- $3,513.76 in gross income: Despite more than 100 individual loan failures, my account has generated $2,398.80 in reported net income after $1,125.96 in charge-offs.
- $3.12 generated for every $1 charged off: My historical gross-income-to-charge-off ratio is approximately 3.12:1. This isn’t directly equivalent to a trading risk-to-reward ratio, but it demonstrates how the magnitude and frequency of gains and losses work together to determine profitability.
- $10 standard P2P position size: For approximately the past three years, I’ve generally invested just $10 per new loan, reducing the influence any individual borrower can have on my overall portfolio.
- 2,102+ total loan outcomes/exposures: Combining 1,046 resolved loans with 1,056 currently in repayment means my portfolio now contains more than 2,100 resolved or active loan observations.
- 70.6% PTJ target-hit rate in one of my strongest historical buckets: Post-earnings setups with an initial 5–10% move reached a +9% target before a -5% stop 70.6% of the time, with 12.97% average MFE and just 2.72% average MAE in that sample.
- Nearly 3× difference from stock selection alone: The strongest initial-move bucket reached the +9% target approximately 2.8× as frequently as the 20%+ bucket—evidence that selecting better statistical categories can meaningfully improve the odds.
- But even the strongest bucket failed 29.4% of the time: This may be the most important number in the entire article. Even when my PTJ data identifies a substantially superior stock-selection bucket, the same data tells me that I still can’t trust any individual stock too much.
- Short interest showed a similar pattern: In another PTJ sample, setups with 5%+ short float averaged 15.39% MFE and reached +9% 70.6% of the time, compared with 7.67% MFE and a 33.3% target rate for setups below 5% short float.
The long-term lesson: Better data can help identify better opportunities, but it can’t eliminate losing outcomes. Selection improves the odds; diversification and risk management determine whether you survive when the odds don’t work in your favour.
My P2P Portfolio Has Picked 138 Losers
Before getting into the trading side of this discussion, I want to start with the historical data from my own P2P lending account.
P2P loans and stocks are obviously very different investments, but once we strip them down to wins, losses, position sizes, risk, reward, and a large sample of outcomes, the mathematics start to look surprisingly similar.
My five-plus years of goPeer data gives us a real-world example of what happens when we stop worrying about picking every individual winner and instead focus on diversification, consistent position sizing, and the performance of the portfolio as a whole.
After more than five years of investing through goPeer, my account currently shows:
- 908 loans fully repaid
- 138 loans charged off
- 1,056 loans currently in repayment
- 1,046 resolved loans
- 86.8% resolved-loan repayment rate
- 13.2% resolved-loan charge-off rate
- $3,513.76 gross income
- $1,125.96 charged off
- $2,398.80 reported net income
- ~3.12 in gross income for every $1 charged off
That means approximately 13.2% of my resolved loans have been charged off, or roughly one default for every 6.6 fully repaid loans. Put another way, if the historical distribution were to repeat perfectly over another 100 resolved loans, I’d expect approximately:
87 repayments and 13 defaults.
I obviously don’t expect the future to match those numbers perfectly. But that’s exactly how I want to think about the portfolio, as well as how traders should think about their brokerage accounts.
When trading or investing, one does NOT need to know which borrowers or which trades will fail.
You simply need to build a portfolio or a trading system so that those defaulted loans or failed trades don’t matter very much.
The Math Matters More Than Identifying Every Winner
My dashboard reports $3,513.76 in gross income compared with $1,125.96 charged off.
That works out to a risk-to-reward ratio of approximately: $3,513.76 ÷ $1,125.96 = 3.12
So historically, the portfolio has generated roughly $3.12 in gross income for every $1 charged off.
I don’t consider that a literal 3.12:1 trading risk-to-reward ratio because P2P lending and trading generate returns in fundamentally different ways. But the mathematical questions are extremely similar.
- How often does the investment succeed?
- How often does it fail?
- How much do the successful outcomes collectively generate?
- How much do the failures cost?
And after everything is combined: Does the strategy remain profitable?
Which is exactly what I’m trying to calculate with my PTJ trading data.
My Biggest P2P Mistake Was Position Sizing
When I started investing through goPeer, I wasn’t particularly systematic about how much I put into individual loans. Some positions were $20. Others were $30. Some were $50.
In other words, I was placing disproportionately sized bets. I had little knowledge about trading or investing at the time.
So if I liked one borrower more, I’d put more money into the loan, hoping to earn a higher return. This is the exact same concept as putting all your eggs in one basket, or oversizing a trading position.
Eventually, however, I realized that I was creating a completely separate risk.
A $50 loan had five times as much influence over my results as a $10 loan.
Yet it’s completely impossible to know which borrowers would eventually become one of those 138 defaults. After all, even a loan with an A+ rating can default.
Despite that uncertainty, I was effectively saying: “I’m more confident about this one, therefore I’m willing to make being wrong five times more expensive.”
Traders do exactly the same thing. And I’ve certainly done it myself.
Fortunately, risk size is something that both P2P lenders, traders and investors CAN control.
Why I Now Invest $10 Per Loan (And Keep Trading Position Size Consistent)
For approximately the past three years, I’ve generally standardized my new goPeer investments at $10 per loan. I no longer make $20 or $30 investments, no matter how good a borrower might look.
EVERY INVESTMENT IS $10!
It’s true that if I made a $20 investment, it would earn a higher return over time.
But by splitting that $20 into two separate $10 investments, I can systematically reduce concentration risk and improve my chances of earning a positive return.
I still select which loans I’m interested in, generally those with better credit risk ratings.
But once I’ve made that selection, I don’t need to turn my confidence into a dramatically larger position.
If a $20 loan defaults, that hurts. But if a $10 loan defaults and the other is fully repaid, that loss barely matters to a portfolio spread across hundreds or thousands of individual exposures.
That’s diversification doing its job.
The goal isn’t to prevent defaults. It’s to prevent one default from becoming important.
That distinction has become increasingly relevant to how I think about trading.
My PTJ Data Is Showing Me the Same Thing
This is where the numbers from my post-earnings momentum dataset get really interesting. One of the clearest relationships I’ve found involves the size of the stock’s initial earnings move.
In one PTJ sample, setups with a 5% to 10% first-hour earnings move produced:
- 17 setups
- 12.97% average MFE
- 2.72% average MAE
- 70.6% +9% target hit rate
Stocks with a 10% to 20% initial move produced:
- 28 setups
- 11.89% average MFE
- 6.37% average MAE
- 42.9% +9% target hit rate
And stocks that initially moved more than 20% produced:
- 12 setups
- 9.00% average MFE
- 10.66% average MAE
- 25.0% +9% target hit rate
That’s exactly the kind of information I want my dataset to uncover.
I’ve literally found data that helps me pick better stock setups. Just like P2P lenders can screen borrowers based on credit risk ratings, loan sizes, and loan terms.
But here’s the part I think matters even more:
Even the superior bucket failed to reach that target first 29.4% of the time. Just like with P2P lending, many of your investments WILL default over the lifetime of your portfolio.
My data can tell me which bucket has historically been better. But it still can’t tell me whether the next stock inside that bucket will be one of the winners or one of the losers.
And that is exactly why I shouldn’t trust any individual stock too much.
Better Stock Selection Improves the Odds—Not Certainty
I’ve found similar relationships elsewhere in the PTJ dataset.
For example, one short-interest study showed that setups with less than 5% short float produced approximately 7.67% average MFE, while setups with 5% or more short float produced approximately 15.39% average MFE.
The +9% target rate also increased substantially: 33.3% for lower-short-interest setups versus 70.6% for setups with 5%+ short float.
Again, that’s potentially useful information. But once again, 70.6% is NOT 100%.
Nearly three out of every ten observations still landed on the other side of the distribution.
So the lesson isn’t:
“High short interest means buy the stock.”
The lesson is:
“This characteristic may improve the historical probability, but I still need to size the trade as though it can fail.”
That’s a completely different mindset.
P2P Lending or Trading – The Goal Isn’t to Avoid Every Default
After more than five years of P2P lending, 138 of my loans have been charged off, resulting in $1,125.96 in charge-offs.
Yet the portfolio has generated $3,513.76 in gross income and $2,398.80 in reported net income, or roughly $3.12 in gross income for every $1 charged off.
That’s why I don’t think successful P2P investing is about finding borrowers who will never default.
It’s about using the available data to select reasonable opportunities, keeping individual investments small, spreading risk across many borrowers, and accepting that some loans will inevitably fail.
My trading data keeps reinforcing the same idea.
If my strongest historical stock-selection bucket succeeds around 70% of the time, that same statistic tells me it fails around 30% of the time.
Better selection can improve the odds, but diversification and position sizing are what make being wrong manageable.
Conclusion: Better Selection Doesn’t Eliminate Risk
After more than five years of P2P lending, 138 of my loans have failed.
Yet my portfolio has generated a 47.4% simple return, $2,398.80 in net income, and an 86.8% repayment rate among resolved loans.
And that’s really the lesson.
No trader or investor needs to identify every borrower who will repay, or to pick every winning stock. They simply need to improve the odds, keep positions small, and diversify enough that being wrong doesn’t matter very much.
My PTJ data reinforces the same idea: even one of my strongest historical setup buckets hit its +9% target first 70.6% of the time—which means 29.4% still failed to do so.
Better data helps us select better opportunities. Risk management makes sure the inevitable losers don’t derail the entire strategy.
If you want to go deeper:
- Explore the Trading Statistics Hub to understand how different sectors behave across market cycles
- Study real setups inside the Trade Reviews section
- Learn the framework behind high-probability setups in the Post-Earnings Momentum Strategy
This is how you turn raw market data into repeatable trading edge.
More Trading Statistics…
Frequently Asked Questions
Is goPeer legit in Canada?
Yes. goPeer is a Canadian peer-to-peer lending platform that connects eligible Canadian borrowers with investors who help fund their personal loans. Like any form of lending or investing, however, legitimate does not mean risk-free, and investors can lose money when borrowers default.
Who can invest with goPeer?
The goPeer investing opportunity discussed throughout this case study is available to eligible Canadian investors. My results are based on more than five years of personally investing through the platform and should not be considered a guarantee of future returns.
How much have I made investing with goPeer?
My account has generated $3,513.76 in gross income and $2,398.80 in reported net income, despite experiencing $1,125.96 in charge-offs. Based on my calculations, that represents a 47.4% simple return over more than five years.
What is my goPeer repayment and default rate?
Of my 1,046 resolved loans, 908 have been fully repaid and 138 have been charged off. That works out to an 86.8% repayment rate and 13.2% charge-off rate, or approximately one default for every 6.6 fully repaid loans.
How much do I invest in each goPeer loan?
For approximately the past three years, I’ve generally invested $10 into each new loan. Earlier in my portfolio, I sometimes invested $20, $30, or $50 into individual loans, but I eventually realized those larger positions made individual defaults disproportionately important.
Why do I invest only $10 per P2P loan?
The primary reason is diversification. Instead of putting $50 into one borrower, I can potentially spread that same capital across five $10 investments, reducing the amount any single default can affect my overall portfolio.
The goal isn’t to prevent defaults. It’s to prevent one default from becoming important.
Can you lose money investing with goPeer?
Yes. My own portfolio demonstrates that very clearly: 138 of my loans have been charged off, representing $1,125.96 in charge-offs. P2P investors should expect that some borrowers may fail to repay and size their individual investments accordingly.
What information can goPeer investors use to evaluate loans?
P2P investors can evaluate available borrower and loan information before deciding which opportunities they’re comfortable funding. In my own approach, I still select individual loans and generally favour borrowers with better credit-risk ratings rather than investing blindly in every available opportunity.
Is a high repayment rate enough to make P2P lending profitable?
Not necessarily. Win rate—or repayment rate—only tells part of the story. Investors also need to consider how much income successful loans generate compared with how much money is lost through defaults.
In my portfolio, $3,513.76 in gross income compared with $1,125.96 charged off works out to approximately $3.12 in gross income for every $1 charged off.
What does P2P lending have to do with stock trading?
P2P loans and stocks are completely different investments, but they share some surprisingly similar risk-management mathematics. In both cases, investors can measure wins, losses, position sizes, risk, reward, and performance across a large sample of outcomes rather than depending on one individual investment.
Why can risk management matter more than picking the right stock?
Because even historically strong setups still fail. In one PTJ sample, my strongest initial-move bucket reached a +9% target before a -5% stop 70.6% of the time, meaning 29.4% still failed to reach that target first.
Better stock selection may improve the odds, but it can’t identify every future loser. Position sizing and diversification determine how much those inevitable failures are allowed to hurt.
Does better trading data eliminate the need for diversification?
No. In fact, I think better data can make the case for diversification even stronger. If historical data tells me my strongest setup succeeds 70% of the time, it’s simultaneously telling me that roughly 30% of comparable observations still fall on the other side of the distribution.
That’s the central lesson of this case study: better data helps identify better opportunities, while risk management helps make the inevitable losers manageable.
References
goPeer. (2026). Invest in Canadian private credit: High-yield passive income. goPeer investing
goPeer. (2024, September 3). Who can invest? goPeer Help Centre. goPeer investor eligibility
goPeer. (2024, September 3). What am I investing in? goPeer Help Centre. goPeer investment structure
goPeer. (2026). What is peer-to-peer lending? Earn or borrow through goPeer Canada. goPeer P2P lending overview
goPeer. (2024, September 3). Who can borrow through goPeer? goPeer Help Centre. goPeer borrower eligibility
goPeer. (2024, September 3). How do I apply for a loan? goPeer Help Centre. goPeer loan application process
goPeer. (2026). Borrower fees. goPeer borrower fees
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