The SLIC Framework: A Multi-Factor Approach to Stock Valuation for the Post-CAPM Era

Social Support · Liquidity · Imagination · Capital Asset Pricing

Developed by Brian French, former Portfolio Manager and Investment Analyst and CFA level III dropout.

---

Executive Summary: Why the Old Map No Longer Matches the Territory

For most of the last half-century, the professional investment establishment taught a single story about how stocks are valued. A company earns cash. Some of that cash is paid to shareholders.

The value of the stock is the present value of those future payments, discounted at a rate derived from the Capital Asset Pricing Model (CAPM): the risk-free rate plus beta times the equity risk premium. That is the intellectual backbone of the CFA curriculum, the discounted cash flow (DCF) template, and the dividend discount model.

It is elegant, it is teachable, and for a large and growing share of the stocks that actually generated wealth over the last twenty-five years, it has been almost useless as a predictive tool.

Consider four cases that any honest practitioner has to reckon with:

Amazon (1997–2015). On any earnings-based or dividend-based metric, Amazon was uninvestable for nearly two decades. It paid no dividend. It reported razor-thin or negative margins by choice. A DCF built on reported free cash flow in 2001, after the stock had fallen roughly 90% from its dot-com peak, would have produced a value that made the business look like a bookstore with a delivery problem.

The stock compounded at more than 30% annually for the next twenty years, because the market was pricing something a dividend model cannot see: the imagined total addressable market of global retail, cloud computing, and logistics, and a founder who told shareholders in his first annual letter that he would sacrifice near-term profit for long-term dominance.

Uber (2019–2024). Uber went public in May 2019 having lost billions of dollars, and continued losing billions for years afterward. Aswath Damodaran, the most respected valuation academic in the world, famously valued the company in 2014 at roughly $6 billion when private investors had marked it at $17 billion.

The stock traded, for most of its public life, at a valuation that no earnings model could justify. The bulls were pricing the imagined TAM of all urban transportation, food delivery, and freight. When profits finally arrived in 2023 and 2024, the earnings model “caught up” to a price that had been sustained for years by imagination and liquidity.

Tesla (2019–2021). In mid-2019 Tesla traded below $40 per share (split-adjusted). Eighteen months later it exceeded $400. Its earnings did not increase tenfold.

What changed was a combination of an enormous liquidity wave (the Federal Reserve’s balance sheet expanded by roughly $3 trillion in 2020), inclusion in the S&P 500 in December 2020 (which forced index funds to buy tens of billions of dollars of stock), and a social-media ecosystem of hundreds of YouTube channels, Twitter accounts, and Reddit communities that turned the company into a cultural movement. A CAPM-and-dividends model literally could not describe any of these three forces.

Altria / Philip Morris (1925–present). Now the counterexample that keeps the framework honest. Tobacco has been socially reviled for sixty years. It has no imagination story; the addressable market is shrinking by design.

Influencers do not make videos praising Marlboro. And yet Jeremy Siegel’s research in Stocks for the Long Run identified Philip Morris as the single best-performing stock in the U.S. market from 1925 to 2003, compounding at roughly 17% per year. Why? Because the CAPM/dividend pillar was doing all the work: the stock was perpetually cheap relative to its cash flows, it paid enormous dividends, and it relentlessly shrank its own share count. The conventional model was right about Altria. It was not wrong; it was incomplete.

That last example is the reason the SLIC framework does not throw away the Capital Asset Pricing Model. It demotes it from “the whole answer” to “one of four pillars,” and it assigns it a point score alongside three other forces that have demonstrably driven equity prices: Liquidity, Imagination, and Social Support.

The claim of this paper is simple. A stock’s price, over any horizon that matters to a portfolio manager, is the product of four things: what the business is worth on its cash flows (C), how much money is flowing toward its currency, asset class, index, and share count (L), how large the market believes the business could become (I), and how strongly the crowd is currently cheering for it or against it (S). A model that scores only the first of these will systematically miss the biggest winners of every decade and will systematically over-weight stagnant businesses whose cheapness is a trap rather than an opportunity.

---

Part One: The Single-Factor Illusion

What the CFA curriculum actually teaches, and what practitioners actually do

To be fair to the CFA Institute, its curriculum has never been purely CAPM. Candidates learn the dividend discount model, multi-stage DCF, residual income, relative valuation using multiples, and at Level II they encounter multi-factor models including the Fama-French three-factor model and arbitrage pricing theory. The problem is not that the curriculum is ignorant of other factors. The problem is what survives into practice.

What survives into practice is this: an analyst builds a spreadsheet, projects free cash flow for five to ten years, picks a terminal growth rate between 2% and 4%, and discounts everything at a cost of equity computed from CAPM. The result is a “fair value.” Every sell-side price target, every buy-side model, every fairness opinion in an M&A transaction is built on this scaffold. The three-factor model is treated as an academic curiosity for explaining portfolio returns after the fact, not as a tool for choosing stocks before the fact.

This is where the single-factor illusion lives. CAPM says that the only priced risk is market risk, captured by beta. Everything else is diversifiable noise. If that were true, then two stocks with the same beta and the same expected cash flows should have the same price, and any divergence would be arbitraged away. Forty years of empirical finance says otherwise.

The academic evidence that CAPM alone fails

The evidence is not fringe. It comes from the very economists whose names are attached to modern portfolio theory.

Richard Roll’s 1977 critique argued that CAPM is essentially untestable because the “market portfolio” it requires cannot be observed, and any test with a proxy is a test of the proxy, not the model.

Eugene Fama and Kenneth French, in their 1992 paper “The Cross-Section of Expected Stock Returns,” found that once size and book-to-market were controlled for, beta had no explanatory power for average returns over 1963–1990. Their 1993 follow-up introduced the three-factor model. In 2004, in “The Capital Asset Pricing Model: Theory and Evidence,” they wrote plainly that the empirical failures of CAPM “invalidate most applications of the model,” and specifically called out its use in estimating cost of equity capital for valuation.

Mark Carhart in 1997 added momentum as a fourth factor. Fama and French themselves expanded to five factors in 2015, adding profitability and investment. Each expansion was an admission that the single-beta model left too much unexplained.

If the academic community that built CAPM has spent thirty years documenting its failures and bolting on additional factors, the practitioner who still discounts cash flows at “risk-free plus beta times 5.5%” and calls the result “intrinsic value” is engaged in a ritual rather than an analysis.

Calling it a joke may be impolite, but it is not inaccurate. The precision of a DCF output to the second decimal place, when the discount rate itself is built on a model that its own authors say is empirically invalid, is false precision of the highest order.

The deeper problem: CAPM models the discount rate, not the numerator

Even if CAPM perfectly estimated the cost of equity, the DCF would still fail on the growth stocks that drove the last twenty-five years. The reason is that the model’s sensitivity lies almost entirely in the numerator: the projected cash flows and, above all, the terminal value. In a typical ten-year DCF for a growth company, the terminal value represents 70% to 90% of the total. And the terminal value is a function of one input that no CAPM, no beta, and no historical financial statement can supply: how big can this business become?

That question is a question of imagination. It is a question of total addressable market, of whether the product is transformational or incremental, of whether the company can extend from one market into adjacent ones.

The DCF answers it with a single terminal growth rate, typically 2% to 3%, applied mechanically to every company in the coverage universe. That is the assumption that made Amazon look worthless in 2001 and Apple look fully valued in 2004. The framework does not fail because of bad arithmetic. It fails because the most important input has been reduced to a constant.

---

Part Two: The Four Pillars of SLIC

The SLIC framework keeps the conventional valuation engine (the C in the acronym, presented first here because everything else is measured against it) but surrounds it with three additional pillars that have historically explained the largest divergences between price and “fair value.” Each pillar is scored, each is defined operationally, and each has a body of academic and practitioner evidence behind it.

Pillar C: Capital Asset Pricing (the anchor)

The C pillar is the conventional toolkit: discounted cash flow, dividend discount, free-cash-flow yield, earnings yield relative to the cost of equity, return on invested capital relative to the cost of capital, and balance sheet quality. It asks the question the CFA curriculum asks: what is this business worth today on the cash it can plausibly generate?

SLIC retains C for two reasons.

First, it is the gravity of the system. Every stock, eventually, has to reconcile with its cash flows. Imagination and social support can lift a price for years, sometimes for a decade, but when the cash flows never arrive, the price collapses. Pets.com, Webvan, WeWork, Nikola, Peloton at $160, and hundreds of SPACs from 2021 are the graveyard of stocks that scored high on I and S and zero on C. The C pillar is what separates Amazon (which eventually generated enormous cash flow) from the dot-com companies that merely resembled it.

Second, C is the pillar that explains the Altria phenomenon. When a stock has no imagination story and negative social support, C is the only thing holding the price up, and a strong C score is sufficient, on its own, to produce excellent long-term returns. Siegel’s tobacco data, the extensive academic literature on value investing (Fama-French’s value factor, Lakonishok, Shleifer and Vishny’s 1994 “Contrarian Investment, Extrapolation, and Risk”), and the entire career of Warren Buffett are evidence that C alone can work. The framework’s contention is not that C is wrong but that C is one of four, and that a portfolio built on C alone will miss every Amazon while faithfully collecting every Altria.

How C is scored (0–25 points): Discount to a conservatively built DCF or sum-of-parts value; free cash flow yield relative to the 10-year Treasury; ROIC spread over WACC; balance sheet leverage and interest coverage; dividend and buyback yield. A stock trading at a 40% discount to conservative intrinsic value with a double-digit free cash flow yield and no debt would score near 25. A company burning cash with no visible path to profitability scores near zero on C, and must earn its keep on the other three pillars.

Pillar L: Liquidity (the tide)

Liquidity is the pillar most neglected by bottom-up analysts and most obvious to macro traders. The question is not “is this stock liquid,” in the sense of daily trading volume. The question is: is money flowing toward this asset, or away from it? SLIC defines liquidity at five nested levels.

Level 1: Central bank liquidity. The single most important variable in asset prices from 2009 to 2021 was the size of the Federal Reserve’s balance sheet. Quantitative easing pushed roughly $4 trillion into the financial system between 2008 and 2014, and the S&P 500 tripled. The 2020 pandemic response added roughly $3 trillion in a matter of months, and the Nasdaq more than doubled from its March 2020 low in under a year. When the Fed reversed course in 2022, raising rates from zero to over 5% and beginning quantitative tightening, the Nasdaq fell 33% and the long-duration growth stocks that had been most lifted by liquidity fell 60% to 80%. Their businesses did not change. The tide went out. Any analyst with a CAPM model in 2022 who did not have a liquidity pillar was left explaining why “fair value” had somehow halved in nine months.

Level 2: Currency flows. Capital is global, and it flows toward currencies that are strong or expected to strengthen. A strong dollar from 2011 to 2024 pulled capital into U.S. equities and out of emerging markets; the MSCI Emerging Markets index went sideways for over a decade while the S&P 500 quadrupled. A company’s C-pillar merits are only as good as the currency its earnings are denominated in and the willingness of global capital to hold that currency. Japanese equities in 2023 and 2024 rallied not primarily on earnings revisions but on a combination of yen weakness, Berkshire Hathaway’s public endorsement, and Tokyo Stock Exchange governance reforms that attracted foreign flows.

Level 3: Asset class flows. Money rotates between cash, bonds, equities, real estate, commodities, and crypto. When bond yields are near zero, as they were in 2020 and 2021, the phrase “TINA” (There Is No Alternative) captured the mechanical reality that trillions in savings had nowhere to go but equities. When money market funds began yielding 5% in 2023, over $6 trillion parked itself in cash and the equity market’s breadth narrowed dramatically. Bitcoin’s price history is the purest illustration of Level 3 liquidity: it has almost no C-pillar (no cash flows at all), and its price is almost entirely a function of global liquidity conditions and flows into the asset class.

Level 4: Index and passive flows. The rise of passive investing has created a structural, price-insensitive buyer. Roughly half of U.S. equity fund assets are now indexed. When a stock enters the S&P 500, index funds must buy it regardless of valuation. Tesla’s inclusion in December 2020 required an estimated $80 billion or more of forced buying. The Magnificent Seven’s dominance of the index from 2023 to 2025 became self-reinforcing: every dollar of passive inflow allocated over 30% of itself to seven stocks, which pushed their prices higher, which increased their index weight, which increased the next dollar’s allocation to them. This is a liquidity feedback loop that has nothing to do with discount rates.

Level 5: Share count and supply. The final and most company-specific level is whether the company is shrinking or expanding its own share count. Buybacks reduce supply; secondary offerings, stock-based compensation, and convertible issuance expand it. S&P 500 companies have repurchased on the order of $800 billion to $900 billion of stock annually in recent years, making corporations the single largest net buyer of U.S. equities. Apple alone has retired roughly 40% of its shares since 2013. Meanwhile, the companies with the weakest stock performance in 2021 and 2022 were disproportionately those that had just diluted shareholders through SPAC mergers, PIPEs, and at-the-market offerings. A stock with a strong buyback program has a permanent bid beneath it; a stock with a serial issuance program has a permanent offer above it.

Academic evidence for the liquidity pillar is substantial. Yakov Amihud’s 2002 paper showed that illiquidity is priced in expected returns. Pastor and Stambaugh (2003) documented a liquidity risk factor that explains cross-sectional returns beyond the Fama-French factors. Baker and Wurgler’s work on catering and equity issuance shows that corporate managers time share issuance to periods of high valuation, which is exactly the supply signal SLIC captures. And the entire literature on Fed policy and asset prices, from Bernanke and Kuttner (2005) onward, documents that unexpected easing raises equity prices far more than any earnings-based model would predict.

How L is scored (0–25 points): Direction of central bank balance sheet and policy rate (5 points); relative currency strength and foreign flows into the market (5 points); net flows into the asset class and sector, measured by fund flow data (5 points); index inclusion status and passive ownership trend (5 points); net share count change over trailing three years, buybacks net of issuance and dilution (5 points).

Pillar I: Imagination (the ceiling)

The I pillar measures the size of the story. Specifically: is the product or service transformational rather than incremental, how large is the plausible total addressable market, and is management credibly pursuing it?

The CFA framework treats growth as a number: 15% for five years, fading to 3%. SLIC treats growth as a narrative with a ceiling, and the ceiling is the variable that matters most.

Consider Apple in 2004. The company sold Macintosh computers to a niche of loyal customers and had recently introduced a music player. A DCF built on the Mac business, with a fade to terminal growth, valued Apple as a modestly successful hardware company. What that model could not contain was the possibility that Apple would take the iPod’s design philosophy and apply it to the mobile phone, a market of several billion units, and then extend that platform into an App Store that would collect a 30% toll on a trillion dollars of software revenue. Apple’s stock rose roughly 100-fold from 2004 to 2024. The imagination pillar was worth more than the entire C-pillar many times over.

Consider Amazon in 2001. Jeff Bezos’s 1997 shareholder letter, which was reprinted in every subsequent annual report, laid out the imagination thesis explicitly: “It’s all about the long term.” He told investors he would prioritize market leadership over near-term profitability, that he would invest aggressively when he saw an opportunity to gain a durable advantage, and that the TAM was every product that could be delivered to a doorstep. Investors who took the letter seriously and scored Amazon high on Imagination bought a business that was, on the C-pillar, almost worthless. Investors who scored it on C alone sold at $6 and watched it go to $3,000 (pre-split).

Consider Tesla and SpaceX. Tesla’s market capitalization in 2021 exceeded the combined value of Toyota, Volkswagen, Daimler, GM, Ford, BMW, and Honda, while producing a small fraction of their volume. No C-pillar analysis could support that. The price was supported by imagination: the belief that Tesla was not a car company but an energy, autonomy, robotics, and AI company with a TAM measured in the tens of trillions. SpaceX is the same phenomenon carried from the private market into the public one. While still private, it was reported to be valued at around $350 billion in late 2024 and higher in subsequent secondary transactions, on revenue that would make any traditional aerospace analyst blanch. Its public listing did not resolve that tension; it exposed it to daily marking. SpaceX now trades as a public company at a market capitalization that no launch-revenue or Starlink-subscriber DCF can reach, because the imagined TAM includes global satellite broadband, point-to-point earth transport, and, in the most expansive version of the story, a multi-planetary civilization. The IPO itself is a textbook L-and-I event: a scarce float, index-eligibility, and the largest retail-recognized brand in aerospace arriving at a moment when the imagination narrative was already fully formed. Whether those stories come true is beside the point for a valuation framework; the point is that the stories are the price, and any model that cannot score them cannot explain the price.

Aswath Damodaran, ironically the same academic who undervalued Uber in 2014, wrote the best book on this subject: Narrative and Numbers (2017). His thesis is that every valuation is a story, that the numbers are the story’s discipline, and that analysts who refuse to engage with the narrative will systematically misvalue transformational companies. He also, to his credit, revisited his Uber valuation and acknowledged that he had defined the addressable market too narrowly. That acknowledgment is, in effect, the founding insight of the I pillar.

Imagination cuts both ways, which is why it is scored rather than assumed. The dot-com bubble was an imagination bubble: the TAM stories were correct (the internet did eat the world) but the individual companies were mostly wrong. The 2021 SPAC wave was an imagination bubble in electric vehicles, space, and fintech; most of the companies scored 20+ on I and zero on C, and most lost 80% or more. The I pillar therefore must be scored on credibility as well as size: does management have a track record of execution, is there a plausible path from today’s product to the imagined TAM, and is the imagined market real or merely fashionable?

How I is scored (0–25 points): Size of plausible TAM relative to current revenue (up to 10 points; a company at 1% penetration of a trillion-dollar market scores higher than a company at 40% penetration of a $10 billion market); transformational versus incremental product (up to 5 points; does it create a new behavior, or improve an existing one); optionality into adjacent markets (up to 5 points; Amazon’s retail-to-cloud, Apple’s iPod-to-iPhone, Tesla’s cars-to-energy); management credibility and execution history (up to 5 points).

Pillar S: Social Support (the weather)

The S pillar is the most controversial and the most misunderstood. Traditional finance treats social enthusiasm as noise to be ignored, or as a contrarian signal to be faded. SLIC treats it as a measurable force that moves prices for periods long enough to matter, and that can be scored in both directions.

The evidence that attention and sentiment move prices is now overwhelming. Brad Barber and Terrance Odean (2008) showed that retail investors are net buyers of attention-grabbing stocks: those in the news, with high abnormal volume, and with extreme one-day returns. Malcolm Baker and Jeffrey Wurgler (2006, 2007) constructed an investor sentiment index and showed that when sentiment is high, subsequent returns are low for exactly the stocks that are hardest to value and hardest to arbitrage: young, unprofitable, high-volatility growth companies. Robert Shiller’s Irrational Exuberance (2000, updated 2015) and his later Narrative Economics (2019) argue that contagious stories, spreading through social networks, are a primary driver of major price movements, and that economists ignore them at their peril. Lasse Heje Pedersen’s 2022 paper “Game On: Social Networks and Markets” formalized how social-media-driven coordination among retail investors can produce price dynamics that traditional models cannot generate.

The GameStop episode of January 2021 is the cleanest laboratory experiment. A company with a declining business, a C-score in the low single digits, and an I-score that was generous at 5, rose from $17 to an intraday high above $480 in three weeks. The entire move was S-pillar: a Reddit community of several million members, a handful of charismatic posters, and a narrative of retail investors punishing hedge funds. The move was not sustainable, and that is precisely the lesson: S can produce enormous moves, but without C or I beneath it, the moves reverse. A framework that had scored GameStop at C=3, L=15 (stimulus checks and zero rates), I=5, S=25 would have correctly identified it as a stock that could triple on social momentum and would then collapse, which is exactly what happened.

Now the more important case: the ecosystem around Tesla and SpaceX. At any given time there are hundreds of YouTube channels, many with hundreds of thousands or millions of subscribers, devoted entirely to Tesla and SpaceX content. They cover every Starship launch, every Full Self-Driving software release, every factory expansion, every Elon Musk post. They produce daily content. Their audience skews toward retail investors who own the stocks and toward younger viewers who will become investors. With SpaceX now publicly traded, that audience can express its enthusiasm for both companies directly in the market, rather than through Tesla alone as a proxy, which makes the ecosystem’s influence on price more direct than at any time in its history. This ecosystem is not noise. It is a distribution channel for the imagination narrative, a source of persistent retail demand, a mechanism that recruits new shareholders daily, and a community that discourages selling through social reinforcement. When Tesla fell 65% in 2022, the ecosystem kept its retail base largely intact, which is one reason the stock recovered so quickly in 2023.

The S pillar also explains the ebb and flow around themes rather than single stocks. Artificial intelligence data centers are the current example. From late 2022 through 2024, the influencer echo chamber treated AI infrastructure as an unambiguous secular winner: Nvidia, the hyperscalers, the power utilities, the cooling and networking suppliers. The S-score for the entire theme was near maximum. Then in January 2025, the release of DeepSeek’s R1 model, trained at a reportedly small fraction of the cost of Western frontier models, sent Nvidia down 17% in a single session, a loss of roughly $590 billion in market capitalization, the largest single-day loss for any company in history. Nvidia’s business had not changed overnight. What changed was the social narrative: the echo chamber pivoted, within hours, from “AI demand is infinite” to “AI capital expenditure is a bubble.” The stock recovered as the narrative re-stabilized, but the episode demonstrates that the S-pillar has a volatility of its own that a CAPM beta does not capture. The social status of a theme can change faster than any fundamental, and the price will follow the social status first and the fundamental second.

How S is scored (0–25 points): Breadth of positive social coverage across YouTube, X, Reddit, TikTok, and podcasts, measured by channel count, subscriber reach, and posting frequency (up to 10 points); direction and momentum of that coverage over trailing 90 days, since a theme gaining popularity scores higher than one that is popular but fading (up to 5 points); retail ownership share and trend, from brokerage and 13F data (up to 5 points); analyst and institutional narrative alignment, since a stock that both Reddit and Goldman Sachs are bullish on has broader support than one owned by only one camp (up to 5 points). Actively negative social status, such as tobacco, oil in 2020, or defense contractors during peace dividends, scores at or near zero.

---

Part Three: The SLIC Point System

Why points rather than a formula

A single-number output invites false precision. A DCF that says a stock is worth $147.32 tells the reader nothing about which assumption is doing the work or how confident the analyst is. A point system, by contrast, forces the analyst to decompose the thesis. When a stock scores C=22, L=8, I=4, S=3 (total 37), the reader knows immediately that this is a cheap, unloved, out-of-favor business with a bounded upside and a dependence on a liquidity recovery. When a stock scores C=3, L=20, I=23, S=24 (total 70), the reader knows it is an expensive, beloved, story-driven stock riding a liquidity wave, with enormous upside if the story holds and catastrophic downside if the tide turns.

Both stocks can be good investments. They are good investments for different reasons, at different points in the cycle, and for different holding periods. The point system makes that visible. The single-factor DCF hides it.

The scoring template

Each pillar is scored from 0 to 25, for a maximum of 100. The suggested sub-components are described above. In practice the analyst should document the evidence for each sub-score in a one-page sheet so that scores can be audited and revisited quarterly.

Pillar	Weight	Key question	Primary data sources
C – Capital Asset Pricing	25	What is the business worth on cash flows today?	Financial statements, DCF, FCF yield, ROIC, balance sheet
L – Liquidity	25	Is money flowing toward this asset?	Fed balance sheet, DXY, fund flows, index weights, share count
I – Imagination	25	How big could this become, and is that credible?	TAM analysis, product roadmap, adjacencies, management record
S – Social Support	25	Is the crowd cheering, and in which direction is it moving?	Social platform metrics, retail ownership, analyst tone

Interpreting composite scores

The framework’s working thresholds, subject to refinement through backtesting:

• 75–100: Conviction. All four pillars are supportive. These are rare and typically occur early in a great company’s public life during a liquidity expansion (Apple 2009–2012, Nvidia 2023, Microsoft 2015–2019 after the cloud pivot). Position size can be large.
• 55–74: Constructive. Three pillars supportive, one weak. The analyst must identify which pillar is weak and decide whether it is likely to improve. A high-C, high-I, low-S stock (a great business the crowd has not noticed) is the ideal entry. A low-C, high-L, high-I, high-S stock is a momentum position that requires a stop-loss discipline.
• 35–54: Neutral or situational. Two pillars supportive. These stocks require a catalyst thesis. The classic deep-value position (high C, everything else low) lives here, and it works, but it works slowly and requires patience that most investors do not have.
• Below 35: Avoid or short. Fewer than two pillars supportive. This is where the SPAC graveyard lives, along with the melting ice cubes of dying industries during liquidity contractions.

The pillar interaction rules

The pillars do not simply add. Several interaction rules improve the framework’s predictive power:

Rule 1: C is the floor, not the ceiling. A stock cannot fall below its C-value for long without becoming a buyout candidate, and it cannot rise far above its C-value permanently without eventually delivering the cash flows. C defines the range; the other pillars define where in that range the stock trades, and for how long.

Rule 2: L is the multiplier on I and S. Imagination and social support require liquidity to express themselves in price. The most beloved story stock in the world will fall during a liquidity contraction (Tesla in 2022, the ARK Innovation portfolio in 2022, every growth stock in 2000–2002). Conversely, when liquidity is abundant, I and S scores translate into price with extraordinary efficiency (2020–2021). The practical implication: I and S scores should be discounted when L is below 10, and given full weight when L is above 15.

Rule 3: S without I is a trade, not an investment. GameStop, AMC, and the meme stocks of 2021 scored high on S with no I beneath them. They produced spectacular short-term returns and then reverted. Social support that is not attached to a genuine imagination narrative has a half-life measured in weeks.

Rule 4: I without C requires a time limit. Amazon took seven years from IPO to its first annual profit and nearly twenty to become a cash machine. Uber took five years as a public company. The framework tolerates a low C-score when I is high, but the analyst must specify a date by which C must begin improving, and must exit if it does not. This is the discipline that separates Amazon from Webvan.

Rule 5: The C-only portfolio is valid and slow. The Altria case, the Buffett record, and the value-factor literature all confirm that a portfolio of high-C, low-everything-else stocks compounds well over decades. But it will underperform dramatically during liquidity expansions and imagination cycles (value’s lost decade of 2010–2020), and it requires an investor base that will tolerate that underperformance. Most cannot.

---

Part Four: Case Studies Scored

The following retrospective scores illustrate the framework. They are illustrative reconstructions, not backtested outputs, and reasonable analysts will disagree on individual components. The point is to demonstrate how the composite score anticipated outcomes that the C-pillar alone would have missed.

Amazon, 2001 (stock near $6 post-crash)

• C: 3. No profit, negative free cash flow, survival questioned by some analysts after the dot-com collapse.
• L: 6. Post-bubble liquidity contraction; Fed was cutting rates but equity flows were negative and tech funds were bleeding assets.
• I: 23. Bezos’s 1997 letter defined a TAM of all retail; the business was demonstrably scaling; management credibility was high despite losses.
• S: 8. Amazon was mocked (“Amazon.bomb” was a magazine cover) but retained a loyal customer and shareholder base.
• Total: 40. Neutral-to-constructive on the framework, driven entirely by Imagination. A C-only analyst would have scored it near zero. The framework says: this is a situational position with enormous upside if L recovers and I is credible. L recovered in 2003; the stock rose 20-fold over the following decade before the cloud business added a second imagination pillar.

Tesla, mid-2019 (stock near $40 split-adjusted)

• C: 4. Unprofitable, cash-burning, debt-laden, with a CEO under SEC scrutiny.
• L: 8. Fed had just begun cutting rates; the company had repeatedly diluted shareholders.
• I: 24. The TAM story (all vehicles, all energy storage, autonomy, robotics) was at its most expansive.
• S: 22. The YouTube and Twitter ecosystem was already enormous and growing, and short sellers were the villains in the narrative.
• Total: 58. Constructive, driven by I and S, with a critical dependency on L. Eighteen months later L had jumped to 24 (pandemic QE, S&P inclusion, zero rates), lifting the composite above 75, and the stock had risen tenfold. In 2022, L collapsed to 5, the composite fell below 55, and the stock lost 65%. The framework tracked the entire round trip; the C-pillar alone said “overvalued” at $40 and “overvalued” at $400 and “overvalued” at $110, which is not an analysis.

Altria, 2009 (stock near $15)

• C: 24. Free cash flow yield above 12%, dividend yield near 8%, enormous pricing power, relentless buybacks.
• L: 10. Post-crisis QE was beginning; dividend-seeking flows were rising as rates collapsed; share count was shrinking.
• I: 2. Declining volumes by design; no adjacent market of note at the time.
• S: 1. Socially reviled; ESG exclusion was becoming mainstream; no positive coverage anywhere.
• Total: 37. Neutral, driven entirely by C. And it worked: Altria returned roughly 20% annually including dividends from 2009 to 2017. This is the “consistently unpopular stock that still rises” case. The framework did not miss it; the framework scored it as a slow, C-driven compounder, which is exactly what it was. The C-only analyst and the SLIC analyst agree on Altria. They disagree on Amazon.

Uber, 2019 IPO (stock near $42)

• C: 3. Losses of several billion dollars annually; no clear path to profitability at the time.
• L: 12. Large IPO created supply; lockup expiry loomed; but rates were low and growth fund flows were positive.
• I: 20. TAM of global mobility, delivery, and freight; network effects credible; management transition to Dara Khosrowshahi added credibility.
• S: 10. Mixed; the company was widely used but its reputation had been damaged by governance scandals.
• Total: 45. Situational. The stock fell to $14 in the 2020 crash (L collapse), then rose to $80+ by 2024 as C improved (first GAAP profit in 2023) and S turned positive. The framework says: a stock whose C is near zero but whose I is 20 is a call option on execution. Uber executed; Lyft, with a smaller I-score (no delivery, no freight, U.S.-only), did not, and its stock languished. The difference between the two was almost entirely the I pillar.

Nvidia, January 2025 (the DeepSeek shock)

• C: 12. Extraordinarily profitable but trading above 30 times forward earnings; the C-pillar was fair-to-expensive, not cheap.
• L: 20. Largest weight in every index; enormous passive inflows; aggressive buybacks.
• I: 25. The AI TAM story was the largest imagination narrative since the internet.
• S: 25 to 12 in one day. The influencer ecosystem pivoted from euphoria to “capex bubble” within hours of the DeepSeek release.
• Total: 82 to 69. The framework explains the 17% one-day drop as an S-pillar event, not a C-pillar event, which is why the recovery was fast: C, L, and I were untouched. A CAPM analyst, seeing a beta-adjusted expected move of perhaps 3%, would have no framework for a 17% move on a day when no earnings were reported. The S-pillar is the only instrument that measures narrative volatility.

---

Part Five: Why This Is Superior, and What It Cannot Do

The core claims

1. SLIC explains the winners. Every one of the ten largest wealth-creating stocks of the last twenty-five years (Apple, Microsoft, Amazon, Alphabet, Nvidia, Meta, Tesla, Berkshire, Broadcom, Eli Lilly) spent extended periods scoring low on C and high on I. A C-only framework would have underweighted or excluded nearly all of them for most of their ascent.
2. SLIC explains the losers. The SPAC wave, the meme stocks, the 2021 IPO class, and the ARK portfolio all scored high on I and S with negligible C, during a liquidity peak. The framework’s interaction rules (S without I is a trade; I without C needs a time limit; L is the multiplier) flagged the vulnerability that a pure growth narrative concealed.
3. SLIC explains the regime changes. The value-to-growth rotation of 2010–2020, the growth-to-value crash of 2022, and the AI concentration of 2023–2025 were all liquidity-and-narrative events. Beta did not change. Cash flows did not change fast enough. The L and S pillars moved, and prices followed.
4. SLIC preserves what works in the old model. The tobacco case, the value literature, and the Buffett record are not contradicted; they are contained as the high-C, low-everything-else quadrant, with an honest acknowledgment that this quadrant compounds slowly and underperforms during liquidity expansions.

The limitations

The framework is a scoring discipline, not an oracle. Its weaknesses should be stated plainly.

Scoring is subjective. Two analysts will assign different I-scores to the same company. This is a feature in the sense that it forces the disagreement into the open, but it means composite scores are not comparable across analysts without a shared rubric.

S is hard to measure and easy to manipulate. Social metrics are noisy, platform-dependent, and susceptible to bots and coordinated promotion. The S pillar should be scored with skepticism and should never exceed the I pillar by more than ten points without triggering a “trade, not investment” classification.

L is a macro variable that most stock analysts are not trained to assess. Getting L right requires following central bank policy, currency markets, and fund-flow data, disciplines that sit outside the traditional equity analyst’s toolkit.

The framework has not been formally backtested. The case studies above are retrospective reconstructions. A rigorous test would require constructing point-in-time scores for a large universe over multiple cycles, with no hindsight, and comparing forward returns across score deciles. That is the next research project, and the author invites collaboration on it.

It is not investment advice. Nothing in this paper is a recommendation to buy or sell any security. It is a framework for organizing analysis, and any application of it should be made with a qualified advisor and an understanding of one’s own risk tolerance.

---

About the Creator

Brian French is a portfolio manager and investment analyst who developed the SLIC framework over the course of a career spent reconciling the valuation models he was trained on with the market behavior he actually observed. His work has focused on equity selection, multi-factor analysis, and the intersection of fundamental valuation with liquidity conditions and market narrative. The SLIC framework grew out of a recurring frustration: the stocks that best fit the conventional models were rarely the stocks that produced the best returns, and the stocks that produced the best returns were routinely dismissed by those models as overvalued. SLIC is his attempt to build a framework that a practitioner can actually use, that retains the discipline of cash-flow valuation while giving structured weight to the forces that conventional models treat as noise.

[Editor’s note: specific firms, dates, credentials, and performance history should be inserted by the author before publication.]

---

References and Supporting Literature

The following works support the individual pillars of the SLIC framework. Readers are encouraged to verify each citation directly, as bibliographic details should be confirmed against the original sources.

On the empirical failure of single-factor CAPM (Pillar C context)

• Roll, R. (1977). “A Critique of the Asset Pricing Theory’s Tests.” Journal of Financial Economics, 4(2), 129–176.
• Fama, E. F., & French, K. R. (1992). “The Cross-Section of Expected Stock Returns.” Journal of Finance, 47(2), 427–465.
• Fama, E. F., & French, K. R. (1993). “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics, 33(1), 3–56.
• Fama, E. F., & French, K. R. (2004). “The Capital Asset Pricing Model: Theory and Evidence.” Journal of Economic Perspectives, 18(3), 25–46.
• Fama, E. F., & French, K. R. (2015). “A Five-Factor Asset Pricing Model.” Journal of Financial Economics, 116(1), 1–22.
• Carhart, M. M. (1997). “On Persistence in Mutual Fund Performance.” Journal of Finance, 52(1), 57–82.
• Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). “Contrarian Investment, Extrapolation, and Risk.” Journal of Finance, 49(5), 1541–1578.
• Siegel, J. J. Stocks for the Long Run (various editions; McGraw-Hill). See the discussion of Philip Morris as the best-performing U.S. stock, 1925–2003.

On liquidity, flows, and supply (Pillar L)

• Amihud, Y. (2002). “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.” Journal of Financial Markets, 5(1), 31–56.
• Pástor, Ľ., & Stambaugh, R. F. (2003). “Liquidity Risk and Expected Stock Returns.” Journal of Political Economy, 111(3), 642–685.
• Bernanke, B. S., & Kuttner, K. N. (2005). “What Explains the Stock Market’s Reaction to Federal Reserve Policy?” Journal of Finance, 60(3), 1221–1257.
• Baker, M., & Wurgler, J. (2000). “The Equity Share in New Issues and Aggregate Stock Returns.” Journal of Finance, 55(5), 2219–2257.
• Howell, M. (2020). Capital Wars: The Rise of Global Liquidity. Palgrave Macmillan.
• Gabaix, X., & Koijen, R. S. J. (2021). “In Search of the Origins of Financial Fluctuations: The Inelastic Markets Hypothesis.” NBER Working Paper 28967. (Argues that flows move prices far more than classical theory allows.)

On imagination, narrative, and TAM (Pillar I)

• Damodaran, A. (2017). Narrative and Numbers: The Value of Stories in Business. Columbia Business School Publishing.
• Damodaran, A. (2014). “Uber Isn’t Worth $17 Billion.” Blog post, Musings on Markets, and subsequent revisions acknowledging the narrower TAM assumption.
• Bezos, J. (1997). Letter to Shareholders, Amazon.com Annual Report. (Reprinted in every subsequent annual report.)
• Christensen, C. M. (1997). The Innovator’s Dilemma. Harvard Business School Press.
• Mauboussin, M. J., & Callahan, D. (2015). “Total Addressable Market: Methods to Estimate a Company’s Sales.” Credit Suisse Global Financial Strategies.

On social support, attention, and sentiment (Pillar S)

• Shiller, R. J. (2000; 3rd ed. 2015). Irrational Exuberance. Princeton University Press.
• Shiller, R. J. (2019). Narrative Economics: How Stories Go Viral and Drive Major Economic Events. Princeton University Press.
• Baker, M., & Wurgler, J. (2006). “Investor Sentiment and the Cross-Section of Stock Returns.” Journal of Finance, 61(4), 1645–1680.
• Baker, M., & Wurgler, J. (2007). “Investor Sentiment in the Stock Market.” Journal of Economic Perspectives, 21(2), 129–151.
• Barber, B. M., & Odean, T. (2008). “All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors.” Review of Financial Studies, 21(2), 785–818.
• Pedersen, L. H. (2022). “Game On: Social Networks and Markets.” Journal of Financial Economics, 146(3), 1097–1119.
• Tetlock, P. C. (2007). “Giving Content to Investor Sentiment: The Role of Media in the Stock Market.” Journal of Finance, 62(3), 1139–1168.
• Cookson, J. A., & Niessner, M. (2020). “Why Don’t We Agree? Evidence from a Social Network of Investors.” Journal of Finance, 75(1), 173–228.

On the CFA curriculum context

• CFA Institute. CFA Program Curriculum, Level II, Equity Valuation readings (discounted dividend valuation, free cash flow valuation, multi-factor models). Various years.

---

This document is provided for educational and analytical purposes only. It does not constitute investment advice, an offer to buy or sell securities, or a guarantee of any investment outcome. Past performance of the companies discussed is not indicative of future results. Readers should consult a qualified financial advisor before making investment decisions.