Sentiment, Price & Valuation

Even if we grant that reflexivity propagates sentiment, one might object that exogenous constraints or physical realities serve as objective anchors for price. Yet the nature of these elements remains inert until understood. Fundamental constraints (such as fixed supply caps) consistently fail to yield determinate valuations, illustrating that market complexity arises not from objective reality, but from the subjective understanding that gives it meaning.

Conventional theory treats sentiment, price, and valuation as distinct entities, with sentiment as a potential distortion, price as output, and valuation as a principled correction. This framing assumes that, somewhere beneath market noise, there is an objective truth that disciplined analysis uncovers. Since sentiment acts as the spark for all market action, and reflexivity transmits that action through the system, these concepts cannot occupy the roles traditional financial theories assign to them. Consequently, as that spark, sentiment is constitutive not peripheral, leaving valuation as a disciplined (but speculative) process by which analysts weigh assumptions and argue interpretations into place.

Price therefore is not a revelation of what an asset is, but a consensus on what participants believe it will become. These distinctions matter, and if we conflate them, we obscure the importance of the causal relationship between sentiment, price and valuation this paper seeks to establish. Taken together, these delineate a lifecycle of market belief, from formation, through settlement, to its ongoing justification. The market functions not as an arbiter of objectivity, but as an arena where competing convictions vie for hegemony and the most potent narrative is the one that commands the greatest concentration of capital.

Although the sequence this essay establishes runs from sentiment through price to valuation, the argument that follows deliberately proceeds in a different order. We begin with an accepted definition of price, then deconstruct that definition to expose the subjective architecture beneath it. This leads to a reconstruction of what we know about price and valuation within the framework that deconstruction reveals. This define, deconstruct, reconstruct approach allows us to be intellectually honest and meticulous with our assumptions.

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Defining Price

To begin the deconstruction of price, we must first be precise about what it actually is. The standard economic definition is straightforward: price is the equilibrium where supply meets demand, the margin at which what sellers will accept meets what buyers will pay.¹ This definition (while accurate) is incomplete because it tells us how prices form, but not what supply and demand depend on.

When arguing that sentiment is requisite to shaping price, many appeal to the primacy of price objectivity, arguing that since price is where supply meets demand, it is a mechanical consequence and not a market construct. But what determines supply and demand? Supply is often more obviously constrained by physical realities, given costs of production, scarcity, and inventory. But even supply depends on expectations about prices and marketplace conditions. Demand is where this dependence becomes explicit, because the willingness to pay is inseparable from assumed value.

Many supply-and-demand arguments treat the relationship as given or self-evident: people desire something; therefore, demand exists. But this raises fundamental questions: why do people want it, at what price, and for how long? We cannot answer these questions without reference to expectations, narratives, and beliefs, all of which are sentiment-laden constructs. Sentiment operates as a collective lens of perception, which makes human judgment an essential driver of demand. Whether by inflating perceived value or amplifying perceived risk, it precedes and permeates demand, rendering the latter closer to a proxy than a distinct objective force, making clean separations between the two illusory.

Although this reasoning has primarily been applied to equities and other securitized assets, commodities also exhibit forward-looking prices that reflect expectations based on supply and demand conditions. Measurable production costs, physical constraints, supply inelasticity, and narrower valuation ranges do not absolve commodities from this quality. In fact, the physical constraints associated with commodities function more as contingent constraints, reliant on the continued dominance of production and consumption narratives.

For example, demand for Silver, Copper, or Lithium is influenced by expectations about technological adoption, and in the case of Silver, by its dual role as a monetary metal. Gold demand is based on the narrative of over 5,000 years of monetary utility, and is heavily influenced by geopolitical dynamics and macro-economic utility as a reserve asset or inflation hedge. In both cases, even physical demand is constrained by and contingent upon expectations. When juxtaposed against a digital commodity such as Bitcoin, the limits of scarcity as a price determinant become even clearer. With a fixed supply cap of 21 million, it has a supply limitation that tells us nothing about whether it should trade at $10,000, $100,000, or $1,000,000. The collective beliefs about Bitcoin’s monetary utility, adoption, and store-of-value properties drive demand; its fixed supply limits quantity, but neither determines where the price settles. The demand curve does not exist independently of perception; it is the aggregation of the market’s perception.

Capital Markets do not discover price in the way a scale displays a weight; supply and demand are not objective constants, they are reflexive outcomes shaped by sentiment. The aggregation across many actors gives the appearance of objectivity, which in effect amplifies, not abolishes, their subjectivity. Prices may aggregate distributed knowledge across participants, as Hayek observed², but the aggregated knowledge is an interpretation being filtered through the perception, judgment, and conviction of each actor before it crystallizes through price. Furthermore, any claim that this knowledge aggregation converges upon a “correct” price invites the question: correct as compared to what? Verifying convergence presupposes an independent standard, yet no such standard exists against which to assess whether the process has arrived at the “correct” value.

Since market participants base their actions on expectations, and not solely on empirical findings, sentiment will contextualize market conditions before any fundamental assessments take place. While an essential aspect of price formation, supply and demand manifest downstream of these expectations and drive short-term volatility.³ Price is not a neutral reflection of reality, but the resolution of competing beliefs by means of an observable number after the market has actualized its verdict.

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Contextualizing Sentiment

What exactly is sentiment? Behavioral finance commonly defines investor sentiment as a belief about future cash flows and investment risks that present details do not justify.⁴ This definition is helpful because it isolates a real phenomenon where markets express attitudes that data cannot explain. However, this framing is still incomplete because it treats sentiment as a deviation from a rational baseline, implying that there exists a set of “facts” capable of justifying belief in a particular outcome.

But facts do not interpret themselves, and something must support the transition from belief to conviction. Sentiment serves this purpose, acting as the foundation upon which participants form and sustain beliefs about market outcomes. To understand why sentiment is a central part of this process as opposed to peripheral, consider that market action requires not one, but three subjective processes, all operating in tandem.

Sentiment As Perception

Before evaluating information, an investor must decide what qualifies as relevant and where to direct their attention. The decision of where to direct cognitive resources is itself shaped by narrative environment, professional orientation, personal biases, and prevailing market themes. Even then, two analysts reviewing the same quarterly report may focus on different line items, assign varying importance to specific metrics, and absorb identical figures within distinct contexts.

One analyst may see rapid revenue growth as evidence of a durable competitive advantage, while another may view it as a sign of an unsustainable trend. The facts remain unchanged; the only differences are the assumptions guiding each analyst in identifying “signals”, “noise”, and areas for further investigation. This is perception; an active process shaped by individual experience, introducing subjectivity even before formal analysis begins.

Sentiment As Judgment

After perception filters and frames information, the investor must evaluate it against their expectations, and actively assign probabilities to potential outcomes. This involves decisions on justifiable premiums or discounts, growth projections, risk, and other related factors. This act of judgment is informed by models and expertise, but builds upon the irreducibly subjective foundations of perception. Every decision embeds an assumption about the future, and since the future remains unknowable, the gap between what is known and what must be assumed can never be closed by analysis alone. Nevertheless, without capital at stake, judgment has no real effect on the market and exists only in the mind of the investor.

Sentiment As Conviction

Perception and judgment do not move markets in and of themselves; an investor can perceive, interpret, and evaluate without taking action. Markets move only when judgment leads to a commitment of capital and when the perceived reward justifies the perceived risk. Investors commit capital and express their conviction through the allocation of that capital. The intensity and distribution of conviction across participants is where individual sentiment enters the aggregate record we call price, determining not only its direction but also its stability.

Sentiment: The Medium of Investor Experience

Sentiment is not a singular phenomenon, it embodies the apparatus with which individuals relate to the unknown, and the medium through which investors experience the market. The market then aggregates these layered subjective acts across millions of participants and records their net settlement as a price. For the investor, what we call “market sentiment” is the observable result of this architecture operating at scale.

Critics may argue that including perception, judgment, and conviction in the definition of sentiment makes the thesis redundant or overly inclusive. However broad applicability is the strength of the thesis, and the definition of sentiment is seemingly all-encompassing because of the breadth of its domain. An expansive definition does not imply an absence of standards; although sentiment is inherently subjective, it is possible to differentiate between informationally dense opinions and those that are superficial, as well as between narratives that are internally coherent and those that are self-contradictory. Such distinctions enable the application of analytical and definitional rigor, even when the attainment of an objective truth remains elusive.

It is important to acknowledge the structural differences between sentiment (as perception, judgment and conviction) and thinking, since conflating them would incorrectly classify all cognition as sentiment. Consider that cognition refers to reasoning and understanding, while sentiment guides our approach to what we can classify as “open-ended” problems. For instance, an engineer calculating a steel beam’s load-bearing capacity uses cognition with objective, “closed” data. Contrast this example with an analyst estimating a growth company’s value that relies on “open-ended”, narrative contingent information.

In the aforementioned example, the beam does not (and cannot) alter its properties based on what the engineer believes about it. Conversely, the analyst assessing a company is impacted by the expectations of other agents, and those expectations depend in turn on what they anticipate others will expect. The interdependence of expectations is what makes sentiment a fundamental aspect of market outcomes; distinct from engineering or mathematical problems that require only cognitive (not situational) insight. Any conventional theory that denies or obscures this reality implicitly carries the burden of proof and must show which part of price formation escapes sentiment.

There may be further resistance to granting sentiment primacy, stemming from a conceptual fallacy that conflates sentiment with emotion. But the nature of emotion is individual, episodic, and subjective, while sentiment, though experienced individually, becomes intersubjective and collective once it aggregates through market activity. Market sentiment is not a residue that remains after reason finishes its work, but a preceding structural condition that makes choice possible when reason cannot close the gap between what is known today and has to be decided on tomorrow. Keynes captured this nuance in the idea of “animal spirits” stating that when individuals cannot make rational calculations based on expected values, confidence, impulse, or conviction likely drives decisions.⁵

Sentiment in this sense is not some subjective qualia that obfuscates an unbiased process, but is a necessary bridge between what analysis can resolve and what judgment must still decide. This reframing matters because if sentiment were merely noise, or temporary oscillation around a knowable signal, then disciplined investors could filter it out or safely ignore it. But if the very act of pricing the unknowable embeds sentiment, then subjectivity is not just an occasional distortion of markets. Markets produce prices through the aggregation of beliefs; sentiment shapes which beliefs become durable enough to attract capital.

A remaining objection may be that sentiment dominates for traders in the short term, while investors with a long-term horizon focus on fundamentals. Yet fundamentals only become valuation inputs after we assign importance and meaning and a longer time horizon would increase, not remove this dependence. The long-term allocator does not bypass sentiment by waiting; they merely express it differently through their assumptions about outcomes and risk distributions. Even though markets can stabilize around a consensus for a time, that consensus remains a tradable agreement, not a discovered truth.

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Understanding Valuation

“Valuation is inherently uncertain, since it involves the future. As I often remind our analysts, 100% of the information you have about a company represents the past, and 100% of the value depends on the future.”⁶ - Bill Miller

The transition from discussing subjective expectations to understanding market value requires a systematic approach for comparison. Valuation provides this structure through the application of standardized metrics that allow market participants to categorize price relative to its underlying drivers.⁷ This normalization process relies on established ratios, such as earnings, book value, or replacement cost, to create a common language for appraisal. A proper discussion of valuation requires us to maintain conceptual rigor, and we must distinguish between two forms of “anchors” that exist in the appraisal of value: those rooted in mathematical identity and those rooted in market convention.

”The Anchor of 1” Framework

A common language of comparison naturally requires a reference point or generally accepted anchor; and in capital markets, valuations center around multiples of value. However, a “multiple”, by definition, is a quantity multiplied by a unit, which has some very specific implications for how value is derived. If we strip the multiplier away, what remains in every case is “1”: the base unit against which the market expresses its premium or discount. Whether the base unit is book value, replacement cost, net asset value, earnings, revenue, cash flow, or par value on a fixed-income instrument, the question that every valuation framework asks is universal: how many units of the reference metric are participants willing to pay? The answer is always expressed as a distance from the base unit of “1”.

The universality of this question is not happenstance, and reflects the mathematical identity of “1” as a zero-justification proof in every valuation methodology. It is the only value that validates itself through its own structure, without requiring any external inputs. Any deviation from 1, in either direction, demands a narrative basis that the underlying information cannot supply. The Anchor of 1 is therefore not a discovery, but an absolute structural feature of how valuation must operate, and acts as a natural point of origin in value assessment.

We should understand that the existence of an absolute anchor does not constitute a discovery of intrinsic value, and “1” as a zero-justification proof is not a representation of objective worth. We do not arrive at “1” because it reveals itself as the correct intrinsic valuation; we arrive at “1” because it is all that remains when we strip away extraneous factors and intersubjective content. Intrinsic value, as traditionally conceived, is where rigorous analysis supposedly ends, yet is often where idiosyncratic projections begin. The Anchor of 1 is diametrically opposite to this, both conceptually and practically, as a fixed construct where nothing beyond the reference data exerts influence. With a sentiment-attuned approach, it is an expression of fundamental value.

Valuation Anchors In Practice

Regardless of the asset class or valuation model, analysts and market participants repeatedly reach for anchors, and this tendency does not arise from any financial law that forces convergence to 1, but from a human preference for reference points.⁸ For companies that are valued based on their asset holdings, such as holding companies or REIT’s, this exists as a natural parity consistently anchored around “1”. Values above 1 indicate a premium to the anchor, and values below 1 indicate a discount. In this instance, a “multiple” of 1 means the market accepts (or defines) a 1-to-1 parity with Net Asset Value as the appropriate valuation.

Expanding this concept further, in the case of Bitcoin | Digital Asset Treasury Companies, mNAV (Multiple to Net Asset Value) conveys the same market signal: an mNAV of 1 means the equity trades at the market value of the underlying Digital Asset holdings, while a multiple above 1 represents a market premium or multiple (1.5, 2.0, etc.) to Net Asset Value. This concept extends effortlessly to a company with a Price-to-Book or Price-to-NAV (P/NAV) of 1, which implies the market values the firm at its net book value, while a multiple to P/NAV indicates a premium. Similarly, if we were to use Tobin’s Q, a value of 1 would signal uniformity between market value and replacement cost⁹, yet another example illustrating the universal reality of “1” as a fixture for valuation.

For earnings-based valuations, we find anchors on different reference points closer to historical parity or a psychological baseline for the average equity. In this case, a P/E Ratio of 1 (a near impossibility in modern markets) would mean investors are willing to pay $1 for every $1 of the company’s annual earnings. The mean P/E ratio or “fair value” for the overall market historically averages around 15 (a P/E multiple of 15 is still derived from a base unit of 1), but what’s “normal” varies significantly by industry and growth expectations.¹⁰ With earnings based valuations, the historical parity of ~15 exists as a normalized belief about valuation that has become so collectively entrenched that it is mistaken for a rational baseline.

Furthermore, the fact that a P/E of 1 is a near impossibility is concrete proof that earnings-based valuation has never operated at parity and serves as an indictment to any claim of objectivity in earnings-based valuation. If markets have never traded at mathematical parity to earnings, then the entire history of equity valuation has been conducted at varying distances from parity, with the historical parity of 15 being nothing more than a multiple we’ve collectively acknowledged as a market convention, and no longer question.

No matter the source of the anchor, parity benchmarks do not act to constrain sentiment but effectively expose sentiment via a mathematical identity that supplies a neutral target, or an agreed-upon reference point; with sentiment expressing the deviation from that reference point. This reliance on benchmarks does not make valuations objective; the process is still inherently subjective, this merely changes what we use as justification for the belief. The valuation multiple, therefore, is simply a measurement of sentiment drift: the distance which collective belief has drifted from the agreed-upon baseline. While the degree to which sentiment determines the premium can vary with tightly constrained assets that trade closer to parity or highly volatile assets that exhibit wider drift; the dependence on sentiment is absolute, and never zero.

Measuring Sentiment Through Valuation Anchors

The Anchor of 1 proves that every valuation multiple is a measurement of distance from a mathematical constant present in all valuations; providing us with a persistent unit of measurement that already exists in every multiple and valuation ratio. If we hold that distance as an indicator of sentiment drift, these instruments are not only a description of price relative to a numerical entity but are functionally a quantification of collective belief.

To conceptualize this, consider that each unit of distance from the anchor represents one unit of drift above or below parity. Here, a Price-to-Book or Price-to-NAV (P/NAV) of 3 reflects 2 units of drift from natural parity, while a P/E Ratio of 25 represents 10 units of drift from historical parity (~15), or 24 units from its mathematical anchor of 1. Adjusting our perception of valuation multiples helps identify avenues for more granular or novel analysis and this formulation gives sentiment a novel quantifiable expression through a conceptual reframing of existing metrics.

Other applications come to mind, for example, if we decompose sentiment drift into decimal increments and track it over time, the rate of drift change becomes a measurable signal of how fast a multiple is expanding or contracting relative to its anchor. This measure of the velocity of sentiment drift serves as a quantifiable record of the speed of past belief revision around an equity, similar to how realized volatility operates now. Taken further, if combined with sentiment rich data from social media, news and other sources, there is the potential for a multivariate look into sentiment and its trends over time.

Establishing the proper computational boundaries for measuring sentiment drift advances measurability to the source of the denominator itself. Conventional analysis distinguishes between trailing multiples (backward-looking) and forward multiples (forward-looking) in valuation, but this is conceptually misleading since both are forward-facing instruments. Would an investor pay 20 times trailing earnings to own the past? Certainly not. They are simply expressing their conviction about a potential future price using historical performance as the informational basis for an allocation of capital. Trailing and forward multiples are not a juxtaposition of objective reality and informed speculation. They only help distinguish which temporal layer of sentiment, backward-looking (relying on actual earnings) or forward-looking (relying on forecasted earnings), that will inform the denominator.

This new schema for operationalizing market sentiment requires an important qualification: units of sentiment drift are specific to their respective valuation ratios, and are not interchangeable across metrics. Just as we should not compare the magnitude of a P/E ratio directly to oscillation in mNAV, one unit of drift expressed via P/E is not conceptually or economically equivalent to one unit of Price-to-NAV drift. However, the drift that is expressed within a given metric is directly comparable across companies, sectors, and time periods. If two companies in the same sector with similar financial profiles exhibit materially different levels of drift from historical parity, the differential can serve as a direct measurement of how much collective belief one company commands over another, or their Narrative Premium.

Measuring sentiment through valuation anchors would not just reclassify or reinterpret existing data; it would also expand the statistical assumptions and methods that are valid for analysis. If valuation multiples are reclassified as sentiment data, the convergence assumptions that underpin traditional ratio analysis such as mean reversion, equilibrium pricing, and fundamental anchoring, are supplanted by the dynamics of belief formation via persistence, regime shifts, and contagion. The specific methodological consequences of this are explored in the “Empirical Implications & Future Research” section of this essay.

The “Blind Ledger” Experiment: A Proof of Concept

A skeptic might still object that sentiment is an ephemeral artifact they do not need to account for or measure: if they have financial information for a company, and they see 20% growth, the math alone would justify a premium or multiple. However, without narrative context, how does one assess the cause of that growth and, as a result, its durability? Is it due to a potential monopoly, like Nvidia’s GPU dominance, or is the growth a “one-off” event driven by liquidation? Furthermore, why would math, in and of itself, “justify” any multiple at all, and how high a multiple does the math justify? Without a market story, even if you grant that math supports some premium, it cannot specify how much. Market actors would have no choice but to assume a reversion to the mean or to a state of either natural or historical parity, proving that the multiple is the price of belief.

To demonstrate that valuation is a product of sentiment, consider the thought experiment of the “Blind Ledger”. Imagine that an analyst has the complete financial history of a company, including its balance sheet, income statements, and cash flows. However, there is no additional context; they do not know the company’s name, industry, management team, ticker symbol, or any other details. In this scenario, they possess all the informational structure but none of the interpretive context. Given these circumstances, how would someone value this entity? Without the story to explain the “how” and “why” of the growth, an analyst cannot assign a probability to its continuation, and when faced with ambiguity, any logical path must assume a reversion to the mean.

With asset holding or commodity-based valuations, the analyst would have no choice but to revert to the natural parity as they can verify that the assets exist, but cannot assign a premium for intangible value they cannot identify. For earnings-based models, the analyst must revert to historical parity (~15x the Anchor of 1) since they cannot apply a premium for quality, or a discount for distress, without context to inform the valuation. If we were to reveal the name of the company as a member of the Magnificent Seven or as a legacy software company in decline, the analyst would have narrative justification to assign a premium or assume a discount.

Critically, the numbers would not change; the only change would be the introduction of a narrative that enables the analyst to justify (or defend) the valuation. In both cases, the Blind Ledger illustrates that multiples are not inherent to the fundamentals; they are exogenous and derived from market narratives attached to the entity. Fundamentals and financials inform the anchor, but sentiment drives the valuation, providing us with specific structural consequences that arise from sentiment, not just claims that are arbitrarily or logically true.

Further Contextualizing “Multiples”

It is evident that not only are markets heavily influenced by the subjective, but that any attempt at objectivity in markets is structurally impossible without a narrative input. The previous examples are norms, not anomalies, proving that any valuation above parity must be sentiment-driven. If valuation were entirely objective, and price merely the result of mechanically processed information, we would expect equities to consistently trade at book value, replacement cost, or in line with earnings. Deviations from parity necessitate explanation, which cannot be derived solely from the data, as the data itself establishes the anchor.

The causal sequence that results from this is revealing. Textbook approaches suggest that valuation precedes price: analysts estimate “intrinsic value” (which is entirely subjective), compare it to the market price, and respond to any perceived discrepancy.¹¹ In reality, the market establishes price first, with the multiple calculated afterward, with analysts subsequently debating the justification for the multiple. In this context, valuation acts less as a method of truth-seeking and more as a structured approach towards contesting the market’s prior settlement.

The market valuation process does not reflect “fundamentals catching up”, as popular parlance suggests. The multiple provides a conceptual basis for an ongoing valuation debate and should not serve as a verdict on the price’s correctness. Conceptually, to claim that fundamentals catch up to price implies a progression from subjectivity to objectivity; however, this transition never occurs.¹² The actual sequence is sentiment to price to valuation, where valuation remains sentiment expressed through the agreed-upon language of ratios and multiples. There is no point at which objectivity intervenes; and valuation helps to rationalize the extent to which belief influences price relative to an anchor.¹³

Pricing Preference

When observers describe an equity as “overvalued” or “cheap”, they are expressing a preference for a specific benchmark as opposed to invoking an unbiased law. As a consequence, debates about valuations continue indefinitely, and there is no empirical test that can determine whether a particular multiple is “correct” for a company, since any claim to correctness would require knowledge of the future. An analyst can argue that the current multiple is inconsistent with probable outcomes, yet “probable” itself remains a subjective judgment shaped by the same interpretive model that produced the premium. Understanding this, we see that every multiple represents a wager that eventual developments will justify a premium, while discounts reflect a judgment that perceived flaws or risks warrant pricing the asset below its anchor value. While these are not irrational speculation, and may be well-reasoned, rigorously modeled, or informed by substantial expertise, they remain bets on uncertain outcomes.

Value practitioners may claim that as information improves or analysis becomes more sophisticated, valuation will approach objectivity asymptotically. But rather than reducing interpretive burden, adding information increases the effort needed to process it; each new variable introduces further questions of relevance that can only be answered by making new assumptions. The variables that guide valuation exist as branching possibilities that we assign probabilities to, and do more than compare price to an anchor; they reveal how aggressively the market is willing to lean into one set of expectations over another. Valuation completes a structured negotiation articulated through mathematics, with the anchor providing a shared language, and a shared story, but not a shared conclusion.

Footnotes

1. Inoua, Sabiou, and Vernon Smith. 2023. “The Classical Theory of Supply and Demand.” Eprint arXiv:2307.00413 arXiv: Theoretical Economics. https://arxiv.org/abs/2307.00413. ↩

2. Hayek, F.A. 1996. Individualism and Economic Order. Reissue. University of Chicago Press. ↩

3. Baumeister, Christiane. 2021. “Measuring Market Expectations.” CESifo Working Paper No. 9305. https://dx.doi.org/10.2139/ssrn.3929199. ↩

4. Baker, Malcolm, and Jeffrey Wurgler. 2007. “Investor Sentiment in the Stock Market.” Journal of Economic Perspectives 21 (2): 129-52. https://doi.org/10.1257/jep.21.2.129. ↩

5. Keynes, John Maynard. 1936. The General Theory of Employment, Interest, and Money. Harcourt, Brace and Company. ↩

6. Bill Miller, “The Question Is Not Growth Or Value, But Where Is The Best Value?,” The Acquirer’s Multiple, April 6, 2017, https://acquirersmultiple.com/2017/04/bill-miller-the-question-is-not-growth-or-value-but-where-is-the-best-value/. ↩

7. Damodaran, Aswath. 2006. “Valuation Approaches and Metrics: A Survey of the Theory and Evidence.” November. https://pages.stern.nyu.edu/~adamodar/pdfiles/papers/valuesurvey.pdf. ↩

8. Baucells, Manel, Martin Weber, and Frank Welfens. 2011. “Reference-Point Formation and Updating.” Management Science 57 (3): 506-19. ↩

9. Lewellen, Wilbur G., and S.G. Badrinath. 1997. “On the Measurement of Tobin’s q.” Journal of Financial Economics 44 (1): 77-122. ↩

10. Ghaeli, Reza. 2017. “Price-To-Earnings Ratio: A State of Art Review.” Accounting 3: 131-36. https://doi.org/10.5267/j.ac.2016.7.002. ↩

11. Schaumburg, Ernst, and Zhi Da. 2011. “Relative Valuation & Analyst Target Price Forecasts.” Journal of Financial Markets 14 (1): 161-92. ↩

12. Shiller, Robert J. 2019. Narrative Economics: How Stories Go Viral and Drive Major Economic Events. Princeton University Press. ↩

13. Marsat, Sylvain, and Benjamin Williams. 2009. “Does the Price Influence the Assessment of Fundamental Value? Experimental Evidence.” SSRN: Social Science Research Network, June 1. https://dx.doi.org/10.2139/ssrn.1938927. ↩