Upstart (UPST)
Market Price (12/28/2025): $47.1 | Market Cap: $4.6 BilSector: Financials | Industry: Consumer Finance
Upstart (UPST)
Market Price (12/28/2025): $47.1Market Cap: $4.6 BilSector: FinancialsIndustry: Consumer Finance
Investment Highlights Why It Matters Detailed financial logic regarding cash flow yields vs trend-riding momentum.
| Strong revenue growthRev Chg LTMRevenue Change % Last Twelve Months (LTM) is 74% | Weak multi-year price returns2Y Excs Rtn is -41% | Expensive valuation multiplesP/EPrice/Earnings or Price/(Net Income) is 142x |
| Valuation becoming less expensiveP/S 6M Chg %Price/Sales change over 6 months. Declining P/S indicates valuation has become less expensive. is -35% | Significant share based compensationSBC/Rev LTMShare Based Compensation / Revenue (Sales), Last Twelve Months (LTM) is 13% | |
| Megatrend and thematic driversMegatrends include Artificial Intelligence, and Fintech & Digital Payments. Themes include AI Software Platforms, and Online Banking & Lending. | Not cash flow generativeCFO/Rev LTMCash Flow from Operations / Revenue (Sales), Last Twelve Months (LTM) is -38%, FCF/Rev LTMFree Cash Flow / Revenue (Sales), Last Twelve Months (LTM) is -40% | |
| Yield minus risk free rate is negativeERPEquity Risk Premium (ERP) = Total Yield - Risk Free Rate, Reflects the premium above risk free assets offered by the investment. is -3.4% | ||
| Significant short interestShort Interest % of Basic SharesShort Interest % of Basic Shares = (Short Interest Quantity) / (Basic Shares Outstanding). A high fraction of short interest can indicate potential risk of a short squeeze. is 24% | ||
| Key risksUPST key risks include [1] a heavy reliance on a concentrated number of lending partners for funding and revenue and [2] regulatory scrutiny targeting the fairness and transparency of its core AI-driven lending model. |
| Strong revenue growthRev Chg LTMRevenue Change % Last Twelve Months (LTM) is 74% |
| Valuation becoming less expensiveP/S 6M Chg %Price/Sales change over 6 months. Declining P/S indicates valuation has become less expensive. is -35% |
| Megatrend and thematic driversMegatrends include Artificial Intelligence, and Fintech & Digital Payments. Themes include AI Software Platforms, and Online Banking & Lending. |
| Weak multi-year price returns2Y Excs Rtn is -41% |
| Expensive valuation multiplesP/EPrice/Earnings or Price/(Net Income) is 142x |
| Significant share based compensationSBC/Rev LTMShare Based Compensation / Revenue (Sales), Last Twelve Months (LTM) is 13% |
| Not cash flow generativeCFO/Rev LTMCash Flow from Operations / Revenue (Sales), Last Twelve Months (LTM) is -38%, FCF/Rev LTMFree Cash Flow / Revenue (Sales), Last Twelve Months (LTM) is -40% |
| Yield minus risk free rate is negativeERPEquity Risk Premium (ERP) = Total Yield - Risk Free Rate, Reflects the premium above risk free assets offered by the investment. is -3.4% |
| Significant short interestShort Interest % of Basic SharesShort Interest % of Basic Shares = (Short Interest Quantity) / (Basic Shares Outstanding). A high fraction of short interest can indicate potential risk of a short squeeze. is 24% |
| Key risksUPST key risks include [1] a heavy reliance on a concentrated number of lending partners for funding and revenue and [2] regulatory scrutiny targeting the fairness and transparency of its core AI-driven lending model. |
Why The Stock Moved
Qualitative Assessment
AI Analysis | Feedback
Upstart (UPST) experienced a -35.2% stock movement from approximately August 31, 2025, to December 28, 2025, influenced by several key factors:
<b>1. Mixed Q3 2025 Earnings Report and Cautious Outlook.</b><br>
On November 4, 2025, Upstart reported its third-quarter 2025 results, which showed an earnings per share (EPS) beat, but revenue slightly missed analysts' expectations. Despite the EPS beat, the stock price declined by 2.59% in after-hours trading, reflecting a cautious market reaction. The stock also "slid after soft Q4 guidance" and mixed Q3 results.<br>
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<b>2. Analyst Downgrades and Lowered Price Targets.</b><br>
During this period, several financial firms revised their outlooks for Upstart. On October 13, 2025, Goldman Sachs maintained a "Sell" rating and significantly reduced its price target from $78.00 to $54.00, a decrease of over 30%. Additionally, on November 5, 2025, B of A Securities lowered its price target for Upstart from $81.00 to $71.00 while maintaining a "Neutral" rating.<br>
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<b>3. Persistent Macroeconomic Headwinds and Interest Rate Sensitivity.</b><br>
Upstart's business model is highly sensitive to changes in interest rates. Ongoing macroeconomic uncertainties, including inflation risks and the prospect of sustained higher borrowing costs, continued to influence loan demand and investor sentiment throughout the latter half of 2025.<br>
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<b>4. Increased Credit Risk from On-Balance Sheet Loans.</b><br>
Concerns arose as Upstart increased the amount of loans held on its balance sheet. As of Q2 2025, the company's held loans grew from $538 million to $700 million, indicating that Upstart was bearing more credit risk directly. This increased exposure to loan performance could have contributed to investor caution, particularly in a challenging lending environment.<br>
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<b>5. Valuation Concerns Amidst Profitability Challenges.</b><br>
Even with some revenue improvements, Upstart continued to grapple with profitability. As of April 2025, the company had reported an operating loss of $128 million over the preceding four quarters, resulting in a -20.4% operating margin. This, combined with a premium valuation compared to the broader S&P 500, suggested a limited margin of safety for the stock, further contributing to its decline.
Show moreStock Movement Drivers
Fundamental Drivers
The -17.2% change in UPST stock from 9/27/2025 to 12/27/2025 was primarily driven by a -26.3% change in the company's P/S Multiple.| 9272025 | 12272025 | Change | |
|---|---|---|---|
| Stock Price ($) | 57.35 | 47.47 | -17.23% |
| Change Contribution By | LTM | LTM | |
| Total Revenues ($ Mil) | 844.07 | 959.04 | 13.62% |
| P/S Multiple | 6.49 | 4.79 | -26.27% |
| Shares Outstanding (Mil) | 95.53 | 96.68 | -1.21% |
| Cumulative Contribution | -17.24% |
Market Drivers
9/27/2025 to 12/27/2025| Return | Correlation | |
|---|---|---|
| UPST | -17.2% | |
| Market (SPY) | 4.3% | 57.9% |
| Sector (XLF) | 3.3% | 52.0% |
Fundamental Drivers
The -25.2% change in UPST stock from 6/28/2025 to 12/27/2025 was primarily driven by a -42.8% change in the company's P/S Multiple.| 6282025 | 12272025 | Change | |
|---|---|---|---|
| Stock Price ($) | 63.43 | 47.47 | -25.16% |
| Change Contribution By | LTM | LTM | |
| Total Revenues ($ Mil) | 714.41 | 959.04 | 34.24% |
| P/S Multiple | 8.37 | 4.79 | -42.83% |
| Shares Outstanding (Mil) | 94.27 | 96.68 | -2.55% |
| Cumulative Contribution | -25.21% |
Market Drivers
6/28/2025 to 12/27/2025| Return | Correlation | |
|---|---|---|
| UPST | -25.2% | |
| Market (SPY) | 12.6% | 41.5% |
| Sector (XLF) | 7.4% | 36.0% |
Fundamental Drivers
The -30.2% change in UPST stock from 12/27/2024 to 12/27/2025 was primarily driven by a -56.8% change in the company's P/S Multiple.| 12272024 | 12272025 | Change | |
|---|---|---|---|
| Stock Price ($) | 67.98 | 47.47 | -30.17% |
| Change Contribution By | LTM | LTM | |
| Total Revenues ($ Mil) | 552.43 | 959.04 | 73.60% |
| P/S Multiple | 11.09 | 4.79 | -56.85% |
| Shares Outstanding (Mil) | 90.12 | 96.68 | -7.28% |
| Cumulative Contribution | -30.54% |
Market Drivers
12/27/2024 to 12/27/2025| Return | Correlation | |
|---|---|---|
| UPST | -30.2% | |
| Market (SPY) | 17.0% | 57.7% |
| Sector (XLF) | 15.3% | 51.4% |
Fundamental Drivers
The 287.5% change in UPST stock from 12/28/2022 to 12/27/2025 was primarily driven by a 505.1% change in the company's Net Income Margin (%).| 12282022 | 12272025 | Change | |
|---|---|---|---|
| Stock Price ($) | 12.25 | 47.47 | 287.51% |
| Change Contribution By | LTM | LTM | |
| Total Revenues ($ Mil) | 998.40 | 959.04 | -3.94% |
| Net Income Margin (%) | 0.56% | 3.36% | 505.05% |
| P/E Multiple | 180.53 | 142.49 | -21.07% |
| Shares Outstanding (Mil) | 81.67 | 96.68 | -18.38% |
| Cumulative Contribution | 274.42% |
Market Drivers
12/28/2023 to 12/27/2025| Return | Correlation | |
|---|---|---|
| UPST | 7.5% | |
| Market (SPY) | 48.0% | 46.1% |
| Sector (XLF) | 51.3% | 41.2% |
Price Returns Compared
| 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | Total [1] | |
|---|---|---|---|---|---|---|---|
| Returns | |||||||
| UPST Return | 38% | 271% | -91% | 209% | 51% | -22% | 64% |
| Peers Return | 16% | 38% | -12% | 21% | 26% | 16% | 150% |
| S&P 500 Return | 16% | 27% | -19% | 24% | 23% | 18% | 114% |
Monthly Win Rates [3] | |||||||
| UPST Win Rate | 100% | 58% | 25% | 50% | 50% | 50% | |
| Peers Win Rate | 52% | 65% | 42% | 68% | 57% | 52% | |
| S&P 500 Win Rate | 58% | 75% | 42% | 67% | 75% | 73% | |
Max Drawdowns [4] | |||||||
| UPST Max Drawdown | 0% | 0% | -92% | -8% | -47% | -43% | |
| Peers Max Drawdown | -34% | -5% | -26% | -7% | -9% | -23% | |
| S&P 500 Max Drawdown | -31% | -1% | -25% | -1% | -2% | -15% | |
[1] Cumulative total returns since the beginning of 2020
[2] Peers: HPQ, HPE, IBM, CSCO, AAPL. See UPST Returns vs. Peers.
[3] Win Rate = % of calendar months in which monthly returns were positive
[4] Max drawdown represents maximum peak-to-trough decline within a year
[5] 2025 data is for the year up to 12/26/2025 (YTD)
How Low Can It Go
| Event | UPST | S&P 500 |
|---|---|---|
| 2022 Inflation Shock | ||
| % Loss | -96.9% | -25.4% |
| % Gain to Breakeven | 3123.1% | 34.1% |
| Time to Breakeven | Not Fully Recovered days | 464 days |
| 2020 Covid Pandemic | ||
| % Loss | -18.8% | -33.9% |
| % Gain to Breakeven | 23.1% | 51.3% |
| Time to Breakeven | 6 days | 148 days |
Compare to HPQ, HPE, IBM, CSCO, AAPL
In The Past
Upstart's stock fell -96.9% during the 2022 Inflation Shock from a high on 10/15/2021. A -96.9% loss requires a 3123.1% gain to breakeven.
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AI Analysis | Feedback
1. The AWS for lending.
2. The Tesla for credit scores.
3. The Zillow for personal loans.
AI Analysis | Feedback
- AI Lending Platform for Financial Institutions: This service provides banks and credit unions with an artificial intelligence platform to assess credit risk more accurately and automate the loan origination process.
- AI-Powered Personal Loan Origination: Upstart's platform facilitates unsecured personal loans for consumers by connecting them with lenders and using AI to evaluate creditworthiness beyond traditional scores.
- AI-Powered Auto Loan Origination: This service extends Upstart's AI capabilities to car dealerships and lenders, streamlining and improving the process of originating auto loans for consumers.
AI Analysis | Feedback
Upstart (UPST) primarily sells its AI lending platform and services to other companies, specifically financial institutions.
Its customer base consists of a broad network of banks and credit unions. Upstart's business model emphasizes diversifying its partnerships across many financial institutions. As such, Upstart states in its financial filings that it does not have significant revenue concentration with any single bank or credit union partner. Therefore, no single "major customer" (typically defined as accounting for 10% or more of its total revenue) is individually identified in its public disclosures.
However, their customer base comprises:
- Banks (ranging from large regional banks to community banks)
- Credit Unions
Examples of financial institutions that have partnered with Upstart to originate loans through its platform include:
- Cross River Bank (a privately held bank): A prominent bank in the fintech space that frequently partners with platforms like Upstart to originate loans.
- Customers Bank (symbol: CBAY): A publicly traded financial services company that announced a partnership with Upstart to use its platform for personal loans.
- Various other regional and community banks, as well as credit unions, across the United States.
AI Analysis | Feedback
- Amazon Web Services (subsidiary of Amazon: AMZN)
- Experian (EXPGY)
- Equifax (EFX)
- TransUnion (TRU)
AI Analysis | Feedback
Dave Girouard, Co-Founder & CEO
Dave Girouard is the Co-Founder and CEO of Upstart, which he co-founded in 2012. Prior to Upstart, he served as President of Google Enterprise, where he was instrumental in building Google's multi-billion dollar cloud applications business globally, overseeing product development, sales, marketing, and customer support. His career in Silicon Valley began as a Product Manager at Apple, and he later held the position of SVP of Product Management & Marketing at Virage, a video search technology company. He also worked in software development at Accenture and as an associate in Booz Allen's Information Technology practice. Upstart went public on Nasdaq in December 2020.
Sanjay Datta, Chief Financial Officer
Sanjay Datta serves as the Chief Financial Officer of Upstart, a role he assumed in December 2016. Before joining Upstart, Mr. Datta was the Vice President of Global Advertising & Sales Finance at Google Inc., where he led planning and financial analytics for Google's advertising business across Asia and Europe. He also gained experience with the private investment group Artisan Capital, focusing on sourcing small to mid-cap equity investment opportunities, and began his career at Deloitte Consulting specializing in financial valuation.
Paul Gu, Co-Founder & Chief Technology Officer
Paul Gu is a Co-Founder and the Chief Technology Officer at Upstart. In this role, he is responsible for overseeing the company's technological advancements and product development. Before co-founding Upstart in 2012, Mr. Gu was a Thiel Fellow in the 20 Under 20 Program and worked as a Summer Analyst at D. E. Shaw & Co.
Anna M. Counselman, Co-Founder & Head of People & Operations
Anna M. Counselman is a Co-Founder and the Head of People & Operations at Upstart. She plays a key role in ensuring operational efficiency and regulatory adherence within the company. Ms. Counselman previously worked at Google, where she met fellow Upstart co-founder Dave Girouard.
Alison Nicoll, General Counsel
Alison Nicoll serves as the General Counsel for Upstart. Her responsibilities include providing legal and security oversight for the company.
AI Analysis | Feedback
Here are the key risks to Upstart's business:- Macroeconomic Headwinds and Credit Cycle Deterioration: Upstart's business model is highly susceptible to macroeconomic fluctuations, including rising interest rates and economic slowdowns. Such conditions can lead to decreased loan demand, higher default rates, and a reduction in confidence from lending partners, who may then pull back from originating loans through the platform. This directly impacts Upstart's revenue and loan origination volumes.
- Reliance on Key Lending Partners and Funding Constraints: Upstart's model depends heavily on a limited number of bank and institutional partners to originate and purchase loans. A significant portion of Upstart's revenue has historically come from a few large partners, creating a concentration risk. Should these partners reduce their loan quotas or if funding in securitization markets becomes constrained, it could severely disrupt Upstart's business operations and financial performance.
- Regulatory Scrutiny of AI-Driven Lending Models: As a pioneer in AI-driven lending, Upstart faces ongoing scrutiny from regulatory bodies, such as the Securities and Exchange Commission (SEC) and the Consumer Financial Protection Bureau (CFPB). Regulators are increasingly examining AI models for issues related to fairness, transparency, and potential bias in lending practices. Stricter regulations or adverse findings could necessitate costly adjustments to Upstart's algorithms and operational processes, potentially impacting the effectiveness and competitive advantage of its AI models.
AI Analysis | Feedback
1. Intensified Regulatory Scrutiny of AI Lending Models: Regulatory bodies worldwide, including the Consumer Financial Protection Bureau (CFPB) in the United States, are increasingly scrutinizing the use of artificial intelligence and machine learning in lending for potential biases, lack of transparency, explainability challenges, and compliance with fair lending laws. The emergence of new rules, guidelines, or enforcement actions specifically targeting AI underwriting models could directly impact Upstart's core technology, potentially requiring costly model adjustments, limiting their application, or increasing compliance burdens. Such actions could erode the efficiency and competitive advantage derived from their proprietary AI, similar to how new drug regulations could threaten a pharmaceutical company's key product.
2. Diminished Lender and Investor Confidence in AI Model Resilience: Upstart's business relies on its AI models consistently outperforming traditional credit underwriting, especially in identifying creditworthy borrowers overlooked by conventional scores. However, periods of economic volatility, such as recent spikes in interest rates and inflationary pressures, have led to increased default rates for some Upstart-originated loans. This has, at times, caused institutional investors and bank partners to scale back their appetite for loans originated through the Upstart platform. If there is a sustained perception that Upstart's AI models are less robust or accurate in predicting loan performance during diverse economic conditions compared to traditional methods, it could lead to a long-term reduction in demand for their platform from capital providers, directly impacting their ability to originate loans and grow revenue.
AI Analysis | Feedback
Upstart (UPST) targets a significant addressable market within the U.S. for its lending products and services.
- The total addressable market (TAM) for Upstart's loan originations, encompassing personal, auto, home, and small business loans, is estimated at $3 trillion annually in the U.S.. Another source indicates a $4 trillion annual opportunity in the U.S..
For its main products, the addressable market sizes are as follows (all market sizes are for the U.S. unless otherwise specified):
- Personal Loans: The personal loan market, where Upstart has historically been active, is estimated at $155 billion.
- Auto Loans (Retail and Refinance): Upstart offers auto retail and refinance loans as part of its platform. Specific market size for Upstart's segment of auto loans is not explicitly stated in a single figure, but it is included within the overall $3 trillion to $4 trillion TAM.
- Home Equity Lines of Credit (HELOC): Upstart includes home equity lines of credit among its offerings. Specific market size for Upstart's HELOC segment is not explicitly stated as a standalone figure but is part of the overall $3 trillion to $4 trillion TAM.
- Small Business Loans: Upstart has expanded into the small business lending market. Specific market size for Upstart's small business loans is not explicitly stated as a standalone figure but is part of the overall $3 trillion to $4 trillion TAM.
- Small Dollar "Relief" Loans: This product is also listed as part of Upstart's platform. Specific market size for small dollar "relief" loans is not explicitly stated as a standalone figure but is part of the overall $3 trillion to $4 trillion TAM.
AI Analysis | Feedback
Here are 3-5 expected drivers of future revenue growth for Upstart (UPST) over the next 2-3 years:- Expansion into new loan categories: Upstart is actively diversifying its product offerings beyond personal loans to include auto, home equity lines of credit (HELOCs), and small-dollar loans. These newer offerings are showing substantial growth and are expected to contribute significantly to future revenue, given their larger addressable markets.
- Advancements and automation of AI models: Continuous improvements to Upstart's AI underwriting models are designed to enhance loan approval rates, reduce default risks, and increase conversion rates. These technological advancements, including features like the Payment Transition Model (PTM), are expected to drive higher loan origination volumes and improve profitability.
- Growth in partnerships with banks and credit unions: Expanding its network of lending partners is crucial for Upstart's growth. Collaborating with more financial institutions helps diversify capital supply for loans and increases the reach of Upstart's platform, leading to higher loan origination volumes. The company aims to transition new product funding off its balance sheet by the end of 2025, further emphasizing the importance of these partnerships.
- Increased loan origination volumes: Fueled by improvements in its AI models and a strengthening and diversifying capital supply, Upstart anticipates continued growth in the number of loans originated through its platform. This includes a strategic approach to moderately adjust take rates to further boost origination volumes.
AI Analysis | Feedback
Share Repurchases
- Upstart authorized a share repurchase program of up to $400 million on February 15, 2022.
- The company repurchased approximately $25 million worth of common stock (931 thousand shares) in the third quarter of 2022.
- As of June 30, 2024, $222.1 million remained available for future purchases under the share repurchase program, with no repurchases made during the six months ended June 30, 2024.
Share Issuance
- Upstart completed its Initial Public Offering (IPO) on December 16, 2020, raising $240.31 million by selling 12,015,690 shares at $20 per share.
- A secondary public offering (SPO) was conducted on April 12, 2021, generating $240 million from the issuance of 2,000,000 shares at $120 per share.
- Upstart announced an offering of $600 million of 0% Convertible Senior Notes due 2032, with estimated net proceeds of approximately $587.3 million, expected to settle on August 14, 2025. Approximately $224.4 million of these proceeds are intended to repurchase existing 0.25% Convertible Senior Notes due 2026.
Outbound Investments
- Upstart acquired Prodigy Software Inc., a cloud-based automotive retail software provider, for $58 million on April 8, 2021. The acquisition consideration included $18 million in cash and 650,767 shares of common stock.
Capital Expenditures
- Capital expenditures were $5.61 million in 2020, $15 million in 2021, $23 million in 2022, and $12 million in 2023.
- Expected capital expenditures for 2024 were $9.99 million.
- The primary focus of capital expenditures includes investments to enhance borrowing and lending efficiency, expand market footprint (especially in auto and mortgage lending), and develop AI models and cloud applications.
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Peer Comparisons for Upstart
| Peers to compare with: |
Financials
| Median | |
|---|---|
| Name | |
| Mkt Price | 62.81 |
| Mkt Cap | 158.8 |
| Rev LTM | 56,496 |
| Op Inc LTM | 11,544 |
| FCF LTM | 7,327 |
| FCF 3Y Avg | 7,366 |
| CFO LTM | 8,590 |
| CFO 3Y Avg | 8,697 |
Growth & Margins
| Median | |
|---|---|
| Name | |
| Rev Chg LTM | 7.4% |
| Rev Chg 3Y Avg | 3.2% |
| Rev Chg Q | 9.4% |
| QoQ Delta Rev Chg LTM | 2.1% |
| Op Mgn LTM | 17.7% |
| Op Mgn 3Y Avg | 16.4% |
| QoQ Delta Op Mgn LTM | 0.1% |
| CFO/Rev LTM | 14.6% |
| CFO/Rev 3Y Avg | 17.1% |
| FCF/Rev LTM | 11.6% |
| FCF/Rev 3Y Avg | 12.1% |
Price Behavior
| Market Price | $47.47 | |
| Market Cap ($ Bil) | 4.6 | |
| First Trading Date | 12/16/2020 | |
| Distance from 52W High | -46.5% | |
| 50 Days | 200 Days | |
| DMA Price | $44.93 | $54.90 |
| DMA Trend | down | down |
| Distance from DMA | 5.7% | -13.5% |
| 3M | 1YR | |
| Volatility | 66.0% | 82.9% |
| Downside Capture | 339.58 | 255.62 |
| Upside Capture | 182.50 | 181.65 |
| Correlation (SPY) | 55.3% | 57.8% |
| 1M | 2M | 3M | 6M | 1Y | 3Y | |
|---|---|---|---|---|---|---|
| Beta | 3.19 | 3.09 | 2.93 | 2.81 | 2.47 | 2.87 |
| Up Beta | 2.54 | 2.83 | 3.67 | 3.61 | 2.31 | 2.22 |
| Down Beta | 6.27 | 4.01 | 4.06 | 4.30 | 3.15 | 2.83 |
| Up Capture | 303% | 224% | 43% | 157% | 259% | 37527% |
| Bmk +ve Days | 13 | 26 | 39 | 74 | 142 | 427 |
| Stock +ve Days | 13 | 25 | 34 | 70 | 122 | 367 |
| Down Capture | 281% | 292% | 304% | 204% | 153% | 112% |
| Bmk -ve Days | 7 | 16 | 24 | 52 | 107 | 323 |
| Stock -ve Days | 7 | 17 | 29 | 55 | 126 | 380 |
[1] Upside and downside betas calculated using positive and negative benchmark daily returns respectively
Based On 1-Year Data
| Comparison of UPST With Other Asset Classes (Last 1Y) | |||||||
|---|---|---|---|---|---|---|---|
| UPST | Sector ETF | Equity | Gold | Commodities | Real Estate | Bitcoin | |
| Annualized Return | -30.6% | 16.3% | 17.8% | 72.1% | 8.6% | 4.4% | -8.2% |
| Annualized Volatility | 82.6% | 19.0% | 19.4% | 19.3% | 15.2% | 17.0% | 35.0% |
| Sharpe Ratio | -0.09 | 0.67 | 0.72 | 2.70 | 0.34 | 0.09 | -0.08 |
| Correlation With Other Assets | 51.4% | 57.6% | -3.1% | 20.3% | 37.2% | 31.6% | |
ETFs used for asset classes: Sector ETF = XLF, Equity = SPY, Gold = GLD, Commodities = DBC, Real Estate = VNQ, and Bitcoin = BTCUSD
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Based On 5-Year Data
| Comparison of UPST With Other Asset Classes (Last 5Y) | |||||||
|---|---|---|---|---|---|---|---|
| UPST | Sector ETF | Equity | Gold | Commodities | Real Estate | Bitcoin | |
| Annualized Return | 5.8% | 16.1% | 14.7% | 18.7% | 11.5% | 4.6% | 30.8% |
| Annualized Volatility | 116.6% | 18.9% | 17.1% | 15.5% | 18.7% | 18.9% | 48.6% |
| Sharpe Ratio | 0.58 | 0.71 | 0.70 | 0.97 | 0.50 | 0.16 | 0.57 |
| Correlation With Other Assets | 33.5% | 40.5% | 7.0% | 4.9% | 34.3% | 19.6% | |
ETFs used for asset classes: Sector ETF = XLF, Equity = SPY, Gold = GLD, Commodities = DBC, Real Estate = VNQ, and Bitcoin = BTCUSD
Smart multi-asset allocation framework can stack odds in your favor. Learn How
Based On 10-Year Data
| Comparison of UPST With Other Asset Classes (Last 10Y) | |||||||
|---|---|---|---|---|---|---|---|
| UPST | Sector ETF | Equity | Gold | Commodities | Real Estate | Bitcoin | |
| Annualized Return | 10.0% | 13.2% | 14.8% | 15.3% | 7.0% | 5.3% | 69.2% |
| Annualized Volatility | 116.9% | 22.3% | 18.0% | 14.7% | 17.6% | 20.8% | 55.8% |
| Sharpe Ratio | 0.62 | 0.55 | 0.71 | 0.86 | 0.32 | 0.22 | 0.90 |
| Correlation With Other Assets | 33.4% | 40.5% | 7.2% | 5.1% | 34.4% | 20.0% | |
ETFs used for asset classes: Sector ETF = XLF, Equity = SPY, Gold = GLD, Commodities = DBC, Real Estate = VNQ, and Bitcoin = BTCUSD
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Earnings Returns History
Expand for More| Forward Returns | |||
|---|---|---|---|
| Earnings Date | 1D Returns | 5D Returns | 21D Returns |
| 11/4/2025 | -9.7% | -15.8% | 1.1% |
| 8/5/2025 | -18.7% | -23.3% | -20.3% |
| 5/6/2025 | -9.6% | 4.4% | -0.0% |
| 2/11/2025 | 31.8% | 23.4% | -31.2% |
| 11/7/2024 | 46.0% | 21.5% | 37.7% |
| 8/6/2024 | 39.5% | 58.5% | 49.6% |
| 5/7/2024 | -5.6% | 16.8% | 9.8% |
| 2/13/2024 | -19.6% | -25.5% | -29.4% |
| ... | |||
| SUMMARY STATS | |||
| # Positive | 6 | 8 | 8 |
| # Negative | 8 | 6 | 6 |
| Median Positive | 37.1% | 22.4% | 28.4% |
| Median Negative | -14.0% | -19.5% | -24.8% |
| Max Positive | 89.3% | 84.7% | 132.5% |
| Max Negative | -27.3% | -25.5% | -42.7% |
SEC Filings
Expand for More| Report Date | Filing Date | Filing |
|---|---|---|
| 9302025 | 11042025 | 10-Q 9/30/2025 |
| 6302025 | 8052025 | 10-Q 6/30/2025 |
| 3312025 | 5062025 | 10-Q 3/31/2025 |
| 12312024 | 2142025 | 10-K 12/31/2024 |
| 9302024 | 11082024 | 10-Q 9/30/2024 |
| 6302024 | 8072024 | 10-Q 6/30/2024 |
| 3312024 | 5082024 | 10-Q 3/31/2024 |
| 12312023 | 2152024 | 10-K 12/31/2023 |
| 9302023 | 11082023 | 10-Q 9/30/2023 |
| 6302023 | 8092023 | 10-Q 6/30/2023 |
| 3312023 | 5102023 | 10-Q 3/31/2023 |
| 12312022 | 2162023 | 10-K 12/31/2022 |
| 9302022 | 11092022 | 10-Q 9/30/2022 |
| 6302022 | 8092022 | 10-Q 6/30/2022 |
| 3312022 | 5102022 | 10-Q 3/31/2022 |
| 12312021 | 2182022 | 10-K 12/31/2021 |
External Quote Links
| Y Finance | Barrons |
| TradingView | Morningstar |
| SeekingAlpha | ValueLine |
| Motley Fool | Robinhood |
| CNBC | Etrade |
| MarketWatch | Unusual Whales |
| YCharts | Perplexity Finance |
| FinViz |
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