Financial economics

Template:Short description Template:Economics sidebar Financial economics is the branch of economics characterized by a "concentration on monetary activities", in which "money of one type or another is likely to appear on both sides of a trade".<ref name="stanford1">William F. Sharpe, "Financial Economics" Template:Webarchive, in {{#invoke:citation/CS1|citation |CitationClass=web }}</ref> Its concern is thus the interrelation of financial variables, such as share prices, interest rates and exchange rates, as opposed to those concerning the real economy. It has two main areas of focus:<ref name="Miller">Merton H. Miller, (1999). The History of Finance: An Eyewitness Account, Journal of Portfolio Management. Summer 1999.</ref> asset pricing and corporate finance; the first being the perspective of providers of capital, i.e. investors, and the second of users of capital. It thus provides the theoretical underpinning for much of finance.

The subject is concerned with "the allocation and deployment of economic resources, both spatially and across time, in an uncertain environment".<ref>Robert C. Merton {{#invoke:citation/CS1|citation |CitationClass=web }}</ref><ref name="Fama and Miller">See Fama and Miller (1972), The Theory of Finance, in Bibliography.</ref> It therefore centers on decision making under uncertainty in the context of the financial markets, and the resultant economic and financial models and principles, and is concerned with deriving testable or policy implications from acceptable assumptions. It thus also includes a formal study of the financial markets themselves, especially market microstructure and market regulation. It is built on the foundations of microeconomics and decision theory.

Financial econometrics is the branch of financial economics that uses econometric techniques to parameterise the relationships identified. Mathematical finance is related in that it will derive and extend the mathematical or numerical models suggested by financial economics. Whereas financial economics has a primarily microeconomic focus, monetary economics is primarily macroeconomic in nature.

Underlying economicsEdit

Fundamental valuation equation <ref name="Cochrane & Culp"/>
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Financial economics studies how rational investors would apply decision theory to investment management. The subject is thus built on the foundations of microeconomics and derives several key results for the application of decision making under uncertainty to the financial markets. The underlying economic logic yields the fundamental theorem of asset pricing, which gives the conditions for arbitrage-free asset pricing.<ref name="Rubinstein"/><ref name="Cochrane & Culp"/> The various "fundamental" valuation formulae result directly.

Present value, expectation and utilityEdit

Underlying all of financial economics are the concepts of present value and expectation.<ref name="Rubinstein"/>

Calculating their present value, <math>X_{sj}/r</math> in the first formula, allows the decision maker to aggregate the cashflows (or other returns) to be produced by the asset in the future to a single value at the date in question, and to thus more readily compare two opportunities; this concept is then the starting point for financial decision making.Template:NoteTag (Note that here, "<math>r</math>" represents a generic (or arbitrary) discount rate applied to the cash flows, whereas in the valuation formulae, the risk-free rate is applied once these have been "adjusted" for their riskiness; see below.)

An immediate extension is to combine probabilities with present value, leading to the expected value criterion which sets asset value as a function of the sizes of the expected payouts and the probabilities of their occurrence, <math>X_{s}</math> and <math>p_{s}</math> respectively.Template:NoteTag

This decision method, however, fails to consider risk aversion. In other words, since individuals receive greater utility from an extra dollar when they are poor and less utility when comparatively rich, the approach is therefore to "adjust" the weight assigned to the various outcomes, i.e. "states", correspondingly: <math>Y_{s}</math>. See indifference price. (Some investors may in fact be risk seeking as opposed to risk averse, but the same logic would apply.)

Choice under uncertainty here may then be defined as the maximization of expected utility. More formally, the resulting expected utility hypothesis states that, if certain axioms are satisfied, the subjective value associated with a gamble by an individual is that individualTemplate:'s statistical expectation of the valuations of the outcomes of that gamble.

The impetus for these ideas arises from various inconsistencies observed under the expected value framework, such as the St. Petersburg paradox and the Ellsberg paradox.Template:NoteTag

Arbitrage-free pricing and equilibriumEdit

JEL classification codes
In the Journal of Economic Literature classification codes, Financial Economics is one of the 19 primary classifications, at JEL: G. It follows Monetary and International Economics and precedes Public Economics. The New Palgrave Dictionary of Economics also uses the JEL codes to classify its entries. The primary and secondary JEL categories are: JEL: G – Financial Economics (archived link) JEL: G0 – General JEL: G1 – General Financial Markets JEL: G2 – Financial institutions and Services JEL: G3 – Corporate finance and Governance Each is further divided into its tertiary categories.

The concepts of arbitrage-free, "rational", pricing and equilibrium are then coupled <ref name="Varian">Template:Cite journal</ref> with the above to derive various of the "classical"<ref name="Rubinstein2">See Rubinstein (2006), under "Bibliography".</ref> (or "neo-classical"<ref name="Derman"/>) financial economics models.

Rational pricing is the assumption that asset prices (and hence asset pricing models) will reflect the arbitrage-free price of the asset, as any deviation from this price will be arbitraged away: the "law of one price". This assumption is useful in pricing fixed income securities, particularly bonds, and is fundamental to the pricing of derivative instruments.

Economic equilibrium is a state in which economic forces such as supply and demand are balanced, and in the absence of external influences these equilibrium values of economic variables will not change. General equilibrium deals with the behavior of supply, demand, and prices in a whole economy with several or many interacting markets, by seeking to prove that a set of prices exists that will result in an overall equilibrium. (This is in contrast to partial equilibrium, which only analyzes single markets.)

The two concepts are linked as follows: where market prices are complete and do not allow profitable arbitrage, i.e. they comprise an arbitrage-free market, then these prices are also said to constitute an "arbitrage equilibrium". Intuitively, this may be seen by considering that where an arbitrage opportunity does exist, then prices can be expected to change, and they are therefore not in equilibrium.<ref name="Delbaen_Schachermayer"/> An arbitrage equilibrium is thus a precondition for a general economic equilibrium.

"Complete" here means that there is a price for every asset in every possible state of the world, <math>s</math>, and that the complete set of possible bets on future states-of-the-world can therefore be constructed with existing assets (assuming no friction): essentially solving simultaneously for n (risk-neutral) probabilities, <math>q_{s}</math>, given n prices. For a simplified example see Template:Section link, where the economy has only two possible states – up and down – and where <math>q_{up}</math> and <math>q_{down}</math> (Template:Nowrap) are the two corresponding probabilities, and in turn, the derived distribution, or "measure".

The formal derivation will proceed by arbitrage arguments.<ref name="Rubinstein"/><ref name="Delbaen_Schachermayer">Freddy Delbaen and Walter Schachermayer. (2004). "What is... a Free Lunch?" Template:Webarchive (pdf). Notices of the AMS 51 (5): 526–528</ref><ref name="Varian"/> The analysis here is often undertaken to assume a representative agent,<ref name="Farmer_Geanakoplos"/> essentially treating all market participants, "agents", as identical (or, at least, assuming that they act in such a way that the sum of their choices is equivalent to the decision of one individual) with the effect that the problems are then mathematically tractable.

With this measure in place, the expected, i.e. required, return of any security (or portfolio) will then equal the risk-free return, plus an "adjustment for risk",<ref name="Rubinstein"/> i.e. a security-specific risk premium, compensating for the extent to which its cashflows are unpredictable. All pricing models are then essentially variants of this, given specific assumptions or conditions.<ref name="Rubinstein"/><ref name="Cochrane & Culp"/><ref name="Backus"/> This approach is consistent with the above, but with the expectation based on "the market" (i.e. arbitrage-free, and, per the theorem, therefore in equilibrium) as opposed to individual preferences.

Continuing the example, in pricing a derivative instrument, its forecasted cashflows in the abovementioned up- and down-states <math>X_{up}</math> and <math>X_{down}</math>, are multiplied through by <math>q_{up}</math> and <math>q_{down}</math>, and are then discounted at the risk-free interest rate; per the second equation above. In pricing a "fundamental", underlying, instrument (in equilibrium), on the other hand, a risk-appropriate premium over risk-free is required in the discounting, essentially employing the first equation with <math>Y</math> and <math>r</math> combined. This premium may be derived by the CAPM (or extensions) as will be seen under Template:Slink.

The difference is explained as follows: By construction, the value of the derivative will (must) grow at the risk free rate, and, by arbitrage arguments, its value must then be discounted correspondingly; in the case of an option, this is achieved by "manufacturing" the instrument as a combination of the underlying and a risk free "bond"; see Template:Section link (and Template:Slink below). Where the underlying is itself being priced, such "manufacturing" is of course not possible – the instrument being "fundamental", i.e. as opposed to "derivative" – and a premium is then required for risk.

(Correspondingly, mathematical finance separates into two analytic regimes: risk and portfolio management (generally) use physical- (or actual or actuarial) probability, denoted by "P"; while derivatives pricing uses risk-neutral probability (or arbitrage-pricing probability), denoted by "Q". In specific applications the lower case is used, as in the above equations.)

State pricesEdit

With the above relationship established, the further specialized Arrow–Debreu model may be derived. Template:NoteTag This result suggests that, under certain economic conditions, there must be a set of prices such that aggregate supplies will equal aggregate demands for every commodity in the economy. The Arrow–Debreu model applies to economies with maximally complete markets, in which there exists a market for every time period and forward prices for every commodity at all time periods.

A direct extension, then, is the concept of a state price security, also called an Arrow–Debreu security, a contract that agrees to pay one unit of a numeraire (a currency or a commodity) if a particular state occurs ("up" and "down" in the simplified example above) at a particular time in the future and pays zero numeraire in all the other states. The price of this security is the state price <math>\pi_{s}</math> of this particular state of the world; the collection of these is also referred to as a "Risk Neutral Density".<ref name="Figlewski"/>

In the above example, the state prices, <math>\pi_{up}</math>, <math>\pi_{down}</math>would equate to the present values of <math>$q_{up}</math> and <math>$q_{down}</math>: i.e. what one would pay today, respectively, for the up- and down-state securities; the state price vector is the vector of state prices for all states. Applied to derivative valuation, the price today would simply be Template:Nowrap: the fourth formula (see above regarding the absence of a risk premium here). For a continuous random variable indicating a continuum of possible states, the value is found by integrating over the state price "density".

State prices find immediate application as a conceptual tool ("contingent claim analysis");<ref name="Rubinstein"/> but can also be applied to valuation problems.<ref name="corp fin state prices">See de Matos, as well as Bossaerts and Ødegaard, under bibliography.</ref> Given the pricing mechanism described, one can decompose the derivative value – true in fact for "every security"<ref name="Miller"/> – as a linear combination of its state-prices; i.e. back-solve for the state-prices corresponding to observed derivative prices.<ref name="Chance2"/><ref name="corp fin state prices"/> <ref name="Figlewski">Template:Cite journal</ref> These recovered state-prices can then be used for valuation of other instruments with exposure to the underlyer, or for other decision making relating to the underlyer itself.

Using the related stochastic discount factor - SDF; also called the pricing kernel - the asset price is computed by "discounting" the future cash flow by the stochastic factor <math>\tilde{m}</math>, and then taking the expectation;<ref name="Backus">See: David K. Backus (2015). Fundamentals of Asset Pricing, Stern NYU</ref><ref>Lars Peter Hansen & Eric Renault (2020). "Pricing Kernels" in: Encyclopedia of Quantitative Finance. Template:ISBN</ref> the third equation above. Essentially, this factor divides expected utility at the relevant future period - a function of the possible asset values realized under each state - by the utility due to today's wealth, and is then also referred to as "the intertemporal marginal rate of substitution". Correspondingly, the SDF, <math>\tilde{m}_{s}</math>, may be thought of as the discounted value of Risk Aversion, <math>Y_{s}.</math> (The latter may be inferred via the ratio of risk neutral- to physical-probabilities, <math>q_{s} / p_{s}.</math> See Girsanov theorem and Radon-Nikodym derivative.)

Resultant modelsEdit

Applying the above economic concepts, we may then derive various economic- and financial models and principles. As above, the two usual areas of focus are Asset Pricing and Corporate Finance, the first being the perspective of providers of capital, the second of users of capital. Here, and for (almost) all other financial economics models, the questions addressed are typically framed in terms of "time, uncertainty, options, and information",<ref name="stanford1"/><ref name="Farmer_Geanakoplos"/> as will be seen below.

Time: money now is traded for money in the future.
Uncertainty (or risk): The amount of money to be transferred in the future is uncertain.
Options: one party to the transaction can make a decision at a later time that will affect subsequent transfers of money.
Information: knowledge of the future can reduce, or possibly eliminate, the uncertainty associated with future monetary value (FMV).

Applying this framework, with the above concepts, leads to the required models. This derivation begins with the assumption of "no uncertainty" and is then expanded to incorporate the other considerations.<ref name="Fama and Miller"/> (This division sometimes denoted "deterministic" and "random",<ref name="Luenberger"/> or "stochastic".)

CertaintyEdit

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File:MM2.png

Modigliani–Miller Proposition II with risky debt. Even if leverage (D/E) increases, the WACC (k0) stays constant.

The starting point here is "Investment under certainty", and usually framed in the context of a corporation. The Fisher separation theorem, asserts that the objective of the corporation will be the maximization of its present value, regardless of the preferences of its shareholders. Related is the Modigliani–Miller theorem, which shows that, under certain conditions, the value of a firm is unaffected by how that firm is financed, and depends neither on its dividend policy nor its decision to raise capital by issuing stock or selling debt. The proof here proceeds using arbitrage arguments, and acts as a benchmark <ref name="Varian"/> for evaluating the effects of factors outside the model that do affect value. Template:NoteTag

The mechanism for determining (corporate) value is provided by <ref name="New School">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> <ref name="Rubinstein_2">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> John Burr Williams' The Theory of Investment Value, which proposes that the value of an asset should be calculated using "evaluation by the rule of present worth". Thus, for a common stock, the "intrinsic", long-term worth is the present value of its future net cashflows, in the form of dividends; in the corporate context, "free cash flow" as aside. What remains to be determined is the appropriate discount rate. Later developments show that, "rationally", i.e. in the formal sense, the appropriate discount rate here will (should) depend on the asset's riskiness relative to the overall market, as opposed to its owners' preferences; see below. Net present value (NPV) is the direct extension of these ideas typically applied to Corporate Finance decisioning. For other results, as well as specific models developed here, see the list of "Equity valuation" topics under Template:Section link. Template:NoteTag

Bond valuation, in that cashflows (coupons and return of principal, or "Face value") are deterministic, may proceed in the same fashion.<ref name="Luenberger">See Luenberger's Investment Science, under Bibliography.</ref> An immediate extension, Arbitrage-free bond pricing, discounts each cashflow at the market derived rate – i.e. at each coupon's corresponding zero rate, and of equivalent credit worthiness – as opposed to an overall rate. In many treatments bond valuation precedes equity valuation, under which cashflows (dividends) are not "known" per se. Williams and onward allow for forecasting as to these – based on historic ratios or published dividend policy – and cashflows are then treated as essentially deterministic; see below under Template:Slink.

For both stocks and bonds, "under certainty, with the focus on cash flows from securities over time," valuation based on a term structure of interest rates is in fact consistent with arbitrage-free pricing.<ref>See footnote 3 under Rubinstein (2005). "The Fundamental Theorem (Part I)", refenced below.</ref> Indeed, a corollary of the above is that "the law of one price implies the existence of a discount factor";<ref>§ 4.1 "Law of one price and existence of a discount factor" in Cochrane (2005).</ref> correspondingly, as formulated, Template:Nowrap.

Whereas these "certainty" results are all commonly employed under corporate finance, uncertainty is the focus of "asset pricing models" as follows. Fisher's formulation of the theory here - developing an intertemporal equilibrium model - underpins also <ref name="New School"/> the below applications to uncertainty; Template:NoteTag see <ref>Gonçalo L. Fonseca (N.D.). Irving Fisher's Theory of Investment. History of Economic Thought series, The New School.</ref> for the development.

UncertaintyEdit

File:Markowitz frontier.jpg

Efficient Frontier. The hyperbola is sometimes referred to as the 'Markowitz Bullet', and its upward sloped portion is the efficient frontier if no risk-free asset is available. With a risk-free asset, the straight line is the efficient frontier. The graphic displays the CAL, Capital allocation line, formed when the risky asset is a single-asset rather than the market, in which case the line is the CML.

File:CML-plot.png

The Capital market line is the tangent line drawn from the point of the risk-free asset to the feasible region for risky assets. The tangency point M represents the market portfolio. The CML results from the combination of the market portfolio and the risk-free asset (the point L). Addition of leverage (the point R) creates levered portfolios that are also on the CML.

Template:Smalldiv Template:Small</math>)}}, the asset's correlated volatility relative to the overall market <math>m</math>.}}

File:SML-chart.png

Security market line: the representation of the CAPM displaying the expected rate of return of an individual security as a function of its systematic, non-diversifiable risk.

For "choice under uncertainty" the twin assumptions of rationality and market efficiency, as more closely defined, lead to modern portfolio theory (MPT) with its capital asset pricing model (CAPM) – an equilibrium-based result – and to the Black–Scholes–Merton theory (BSM; often, simply Black–Scholes) for option pricing – an arbitrage-free result. As above, the (intuitive) link between these, is that the latter derivative prices are calculated such that they are arbitrage-free with respect to the more fundamental, equilibrium determined, securities prices; see Template:Slink.

Briefly, and intuitively – and consistent with Template:Slink above – the relationship between rationality and efficiency is as follows.<ref>For a more formal treatment, see, for example: Eugene F. Fama. (1965). "Random Walks in Stock Market Prices". Financial Analysts Journal, September/October 1965, Vol. 21, No. 5: 55–59.</ref> Given the ability to profit from private information, self-interested traders are motivated to acquire and act on their private information. In doing so, traders contribute to more and more "correct", i.e. efficient, prices: the efficient-market hypothesis, or EMH. Thus, if prices of financial assets are (broadly) efficient, then deviations from these (equilibrium) values could not last for long. (See earnings response coefficient.) The EMH (implicitly) assumes that average expectations constitute an "optimal forecast", i.e. prices using all available information are identical to the best guess of the future: the assumption of rational expectations. The EMH does allow that when faced with new information, some investors may overreact and some may underreact, <ref name="affirmative case">Mark Rubinstein (2001). "Rational Markets: Yes or No? The Affirmative Case". Financial Analysts Journal, May - Jun., 2001, Vol. 57, No. 3: 15-29</ref> but what is required, however, is that investors' reactions follow a normal distribution – so that the net effect on market prices cannot be reliably exploited <ref name="affirmative case"/> to make an abnormal profit. In the competitive limit, then, market prices will reflect all available information and prices can only move in response to news:<ref name="Shiller"/> the random walk hypothesis. This news, of course, could be "good" or "bad", minor or, less common, major; and these moves are then, correspondingly, normally distributed; with the price therefore following a log-normal distribution. Template:NoteTag

Under these conditions, investors can then be assumed to act rationally: their investment decision must be calculated or a loss is sure to follow;<ref name="affirmative case"/> correspondingly, where an arbitrage opportunity presents itself, then arbitrageurs will exploit it, reinforcing this equilibrium. Here, as under the certainty-case above, the specific assumption as to pricing is that prices are calculated as the present value of expected future dividends, <ref name="Cochrane & Culp">Christopher L. Culp and John H. Cochrane. (2003). ""Equilibrium Asset Pricing and Discount Factors: Overview and Implications for Derivatives Valuation and Risk Management" Template:Webarchive, in Modern Risk Management: A History. Peter Field, ed. London: Risk Books, 2003. Template:ISBN</ref> <ref name="Shiller">Template:Cite journal</ref> <ref name="Farmer_Geanakoplos"/> as based on currently available information. What is required though, is a theory for determining the appropriate discount rate, i.e. "required return", given this uncertainty: this is provided by the MPT and its CAPM. Relatedly, rationality – in the sense of arbitrage-exploitation – gives rise to Black–Scholes; option values here ultimately consistent with the CAPM.

In general, then, while portfolio theory studies how investors should balance risk and return when investing in many assets or securities, the CAPM is more focused, describing how, in equilibrium, markets set the prices of assets in relation to how risky they are. Template:NoteTag This result will be independent of the investor's level of risk aversion and assumed utility function, thus providing a readily determined discount rate for corporate finance decision makers as above,<ref name="Jensen&Smith">Jensen, Michael C. and Smith, Clifford W., "The Theory of Corporate Finance: A Historical Overview". In: The Modern Theory of Corporate Finance, New York: McGraw-Hill Inc., pp. 2–20, 1984.</ref> and for other investors. The argument proceeds as follows: <ref name="Bollerslev">See, e.g., Tim Bollerslev (2019). "Risk and Return in Equilibrium: The Capital Asset Pricing Model (CAPM)"</ref> If one can construct an efficient frontier – i.e. each combination of assets offering the best possible expected level of return for its level of risk, see diagram – then mean-variance efficient portfolios can be formed simply as a combination of holdings of the risk-free asset and the "market portfolio" (the Mutual fund separation theorem), with the combinations here plotting as the capital market line, or CML. Then, given this CML, the required return on a risky security will be independent of the investor's utility function, and solely determined by its covariance ("beta") with aggregate, i.e. market, risk. This is because investors here can then maximize utility through leverage as opposed to stock selection; see Separation property (finance), Template:Section link and CML diagram aside. As can be seen in the formula aside, this result is consistent with the preceding, equaling the riskless return plus an adjustment for risk.<ref name="Cochrane & Culp"/> A more modern, direct, derivation is as described at the bottom of this section; which can be generalized to derive other equilibrium-pricing models.

File:Stockpricesimulation.jpg

Simulated geometric Brownian motions with parameters from market data

Template:Smalldiv

Template:Smalldiv\left[\ln\left(\frac{S}{K}\right) + \left(r + \frac{\sigma^2}{2}\right)(T - t)\right] \\ d_2 &= d_1 - \sigma\sqrt{T - t} \\ \end{align}</math> Interpretation: The value of a call is the risk free rated present value of its expected in the money value - i.e. a specific formulation of the fundamental valuation result. <math>N(d_2)</math> is the standard normal probability that the call will be exercised; <math>N(d_1)S</math> is the present value of the expected asset price at expiration, given that the asset price at expiration is above the exercise price.}}

Black–Scholes provides a mathematical model of a financial market containing derivative instruments, and the resultant formula for the price of European-styled options. Template:NoteTag The model is expressed as the Black–Scholes equation, a partial differential equation describing the changing price of the option over time; it is derived assuming log-normal, geometric Brownian motion (see Brownian model of financial markets). The key financial insight behind the model is that one can perfectly hedge the option by buying and selling the underlying asset in just the right way and consequently "eliminate risk", absenting the risk adjustment from the pricing (<math>V</math>, the value, or price, of the option, grows at <math>r</math>, the risk-free rate).<ref name="Rubinstein"/><ref name="Cochrane & Culp"/> This hedge, in turn, implies that there is only one right price – in an arbitrage-free sense – for the option. And this price is returned by the Black–Scholes option pricing formula. (The formula, and hence the price, is consistent with the equation, as the formula is the solution to the equation.) Since the formula is without reference to the share's expected return, Black–Scholes inheres risk neutrality; intuitively consistent with the "elimination of risk" here, and mathematically consistent with Template:Slink above. Relatedly, therefore, the pricing formula may also be derived directly via risk neutral expectation. Itô's lemma provides the underlying mathematics, and, with Itô calculus more generally, remains fundamental in quantitative finance. Template:NoteTag

As implied by the Fundamental Theorem, the two major results are consistent. Here, the Black-Scholes equation can alternatively be derived from the CAPM, and the price obtained from the Black–Scholes model is thus consistent with the assumptions of the CAPM.<ref name="Chance1">Don M. Chance (2008). "Option Prices and Expected Returns" Template:Webarchive</ref><ref name="Derman">Emanuel Derman, A Scientific Approach to CAPM and Options Valuation Template:Webarchive</ref> The Black–Scholes theory, although built on Arbitrage-free pricing, is therefore consistent with the equilibrium based capital asset pricing. Both models, in turn, are ultimately consistent with the Arrow–Debreu theory, and can be derived via state-pricing – essentially, by expanding the above fundamental equations – further explaining, and if required demonstrating, this consistency.<ref name="Rubinstein">Rubinstein, Mark. (2005). "Great Moments in Financial Economics: IV. The Fundamental Theorem (Part I)", Journal of Investment Management, Vol. 3, No. 4, Fourth Quarter 2005;
~ (2006). Part II, Vol. 4, No. 1, First Quarter 2006. (See under "External links".)</ref> Here, the CAPM is derived <ref name="Backus"/> by linking <math>Y</math>, risk aversion, to overall market return, and setting the return on security <math>j</math> as <math>X_j/Price_j</math>; see Template:Section link. The Black–Scholes formula is found, in the limit,<ref>Gregory Gundersen (2023). Proof the Binomial Model Converges to Black–Scholes</ref> by attaching a binomial probability<ref name="Varian"/> to each of numerous possible spot-prices (i.e. states) and then rearranging for the terms corresponding to <math>N(d_1)</math> and <math>N(d_2)</math>, per the boxed description; see Template:Section link.

ExtensionsEdit

More recent work further generalizes and extends these models. As regards asset pricing, developments in equilibrium-based pricing are discussed under "Portfolio theory" below, while "Derivative pricing" relates to risk-neutral, i.e. arbitrage-free, pricing. As regards the use of capital, "Corporate finance theory" relates, mainly, to the application of these models.

Portfolio theoryEdit

Template:See also

File:Pareto Efficient Frontier for the Markowitz Portfolio selection problem..png

Plot of two criteria when maximizing return and minimizing risk in financial portfolios (Pareto-optimal points in red)

File:Four Correlations.svg

Examples of bivariate copulæ used in finance.

The majority of developments here relate to required return, i.e. pricing, extending the basic CAPM. Multi-factor models such as the Fama–French three-factor model and the Carhart four-factor model, propose factors other than market return as relevant in pricing. The intertemporal CAPM and consumption-based CAPM similarly extend the model. With intertemporal portfolio choice, the investor now repeatedly optimizes her portfolio; while the inclusion of consumption (in the economic sense) then incorporates all sources of wealth, and not just market-based investments, into the investor's calculation of required return.

Whereas the above extend the CAPM, the single-index model is a more simple model. It assumes, only, a correlation between security and market returns, without (numerous) other economic assumptions. It is useful in that it simplifies the estimation of correlation between securities, significantly reducing the inputs for building the correlation matrix required for portfolio optimization. The arbitrage pricing theory (APT) similarly differs as regards its assumptions. APT "gives up the notion that there is one right portfolio for everyone in the world, and ...replaces it with an explanatory model of what drives asset returns."<ref>The Arbitrage Pricing Theory, Chapter VI in Goetzmann, under External links.</ref> It returns the required (expected) return of a financial asset as a linear function of various macro-economic factors, and assumes that arbitrage should bring incorrectly priced assets back into line.Template:NoteTag The linear factor model structure of the APT is used as the basis for many of the commercial risk systems employed by asset managers.

As regards portfolio optimization, the Black–Litterman model<ref>Black F. and Litterman R. (1991). "Asset Allocation Combining Investor Views with Market Equilibrium". Journal of Fixed Income. September 1991, Vol. 1, No. 2: pp. 7-18</ref> departs from the original Markowitz model approach to constructing efficient portfolios. Black–Litterman starts with an equilibrium assumption, as for the latter, but this is then modified to take into account the "views" (i.e., the specific opinions about asset returns) of the investor in question to arrive at a bespoke <ref>Guangliang He and Robert Litterman (1999). "The Intuition Behind Black-Litterman Model Portfolios". Goldman Sachs Quantitative Resources Group</ref> asset allocation. Where factors additional to volatility are considered (kurtosis, skew...) then multiple-criteria decision analysis can be applied; here deriving a Pareto efficient portfolio. The universal portfolio algorithm applies information theory to asset selection, learning adaptively from historical data. Behavioral portfolio theory recognizes that investors have varied aims and create an investment portfolio that meets a broad range of goals. Copulas have lately been applied here; recently this is the case also for genetic algorithms and Machine learning, more generally<ref name="Bagnara">Bagnara, Matteo (2021). "Asset Pricing and Machine Learning: A Critical Review". Template:SSRN</ref> (see below).

Derivative pricingEdit

Template:Further Template:See also

File:Arbre Binomial Options Reelles.png

Binomial Lattice with CRR formulae

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File:Volatility smile.svg

Stylized volatility smile: showing the (implied) volatility by strike-price, for which the Black–Scholes formula returns market prices.

In pricing derivatives, the binomial options pricing model provides a discretized version of Black–Scholes, useful for the valuation of American styled options. Discretized models of this type are built – at least implicitly – using state-prices (as above); relatedly, a large number of researchers have used options to extract state-prices for a variety of other applications in financial economics.<ref name="Rubinstein"/><ref name="Chance1"/><ref name="Chance2">Don M. Chance (2008). "Option Prices and State Prices" Template:Webarchive</ref> For path dependent derivatives, Monte Carlo methods for option pricing are employed; here the modelling is in continuous time, but similarly uses risk neutral expected value. Various other numeric techniques have also been developed. The theoretical framework too has been extended such that martingale pricing is now the standard approach. Template:NoteTag

Drawing on these techniques, models for various other underlyings and applications have also been developed, all based on the same logic (using "contingent claim analysis"). Real options valuation allows that option holders can influence the option's underlying; models for employee stock option valuation explicitly assume non-rationality on the part of option holders; Credit derivatives allow that payment obligations or delivery requirements might not be honored. Exotic derivatives are now routinely valued. Multi-asset underlyers are handled via simulation or copula based analysis.

Similarly, the various short-rate models allow for an extension of these techniques to fixed income- and interest rate derivatives. (The Vasicek and CIR models are equilibrium-based, while Ho–Lee and subsequent models are based on arbitrage-free pricing.) The more general HJM Framework describes the dynamics of the full forward-rate curve – as opposed to working with short rates – and is then more widely applied. The valuation of the underlying instrument – additional to its derivatives – is relatedly extended, particularly for hybrid securities, where credit risk is combined with uncertainty re future rates; see Template:Section link and Template:Section link. Template:NoteTag

Following the Crash of 1987, equity options traded in American markets began to exhibit what is known as a "volatility smile"; that is, for a given expiration, options whose strike price differs substantially from the underlying asset's price command higher prices, and thus implied volatilities, than what is suggested by BSM. (The pattern differs across various markets.) Modelling the volatility smile is an active area of research, and developments here – as well as implications re the standard theory – are discussed in the next section.

After the 2008 financial crisis, a further development:<ref name="Youmbi">Didier Kouokap Youmbi (2017). "Derivatives Pricing after the 2007-2008 Crisis: How the Crisis Changed the Pricing Approach". Bank of England – Prudential Regulation Authority</ref> as outlined, (over the counter) derivative pricing had relied on the BSM risk neutral pricing framework, under the assumptions of funding at the risk free rate and the ability to perfectly replicate cashflows so as to fully hedge. This, in turn, is built on the assumption of a credit-risk-free environment – called into question during the crisis. Addressing this, therefore, issues such as counterparty credit risk, funding costs and costs of capital are now additionally considered when pricing,<ref>"Post-Crisis Pricing of Swaps using xVAs" Template:Webarchive, Christian Kjølhede & Anders Bech, Master thesis, Aarhus University</ref> and a credit valuation adjustment, or CVA – and potentially other valuation adjustments, collectively xVA – is generally added to the risk-neutral derivative value. The standard economic arguments can be extended to incorporate these various adjustments.<ref name="Hull_White_2">John C. Hull and Alan White (2014). Collateral and Credit Issues in Derivatives Pricing. Rotman School of Management Working Paper No. 2212953</ref>

A related, and perhaps more fundamental change, is that discounting is now on the Overnight Index Swap (OIS) curve, as opposed to LIBOR as used previously.<ref name="Youmbi"/> This is because post-crisis, the overnight rate is considered a better proxy for the "risk-free rate".<ref>Template:Cite journal</ref> (Also, practically, the interest paid on cash collateral is usually the overnight rate; OIS discounting is then, sometimes, referred to as "CSA discounting".) Swap pricing – and, therefore, yield curve construction – is further modified: previously, swaps were valued off a single "self discounting" interest rate curve; whereas post crisis, to accommodate OIS discounting, valuation is now under a "multi-curve framework" where "forecast curves" are constructed for each floating-leg LIBOR tenor, with discounting on the common OIS curve.

Corporate finance theoryEdit

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File:Manual decision tree.jpg

Project valuation via decision tree.

Mirroring the above developments, corporate finance valuations and decisioning no longer need assume "certainty". Monte Carlo methods in finance allow financial analysts to construct "stochastic" or probabilistic corporate finance models, as opposed to the traditional static and deterministic models;<ref name="Damodaran_Risk"/> see Template:Section link. Relatedly, Real Options theory allows for owner – i.e. managerial – actions that impact underlying value: by incorporating option pricing logic, these actions are then applied to a distribution of future outcomes, changing with time, which then determine the "project's" valuation today.<ref name="Damodaran"/> More traditionally, decision trees – which are complementary – have been used to evaluate projects, by incorporating in the valuation (all) possible events (or states) and consequent management decisions;<ref>Template:Cite journal</ref><ref name="Damodaran_Risk">Aswath Damodaran (2007). "Probabilistic Approaches: Scenario Analysis, Decision Trees and Simulations". In Strategic Risk Taking: A Framework for Risk Management. Prentice Hall. Template:ISBN</ref> the correct discount rate here reflecting each decision-point's "non-diversifiable risk looking forward."<ref name="Damodaran_Risk"/> Template:NoteTag

Related to this, is the treatment of forecasted cashflows in equity valuation. In many cases, following Williams above, the average (or most likely) cash-flows were discounted,<ref name="Markowitz_interview">Template:Cite journal</ref> as opposed to a theoretically correct state-by-state treatment under uncertainty; see comments under Financial modeling § Accounting. In more modern treatments, then, it is the expected cashflows (in the mathematical sense: <math display=inline>\sum_{s}p_{s}X_{sj}</math>) combined into an overall value per forecast period which are discounted. <ref name="Kruschwitz and Löffler"/> <ref name="welch">"Capital Budgeting Applications and Pitfalls" Template:Webarchive. Ch 13 in Ivo Welch (2017). Corporate Finance: 4th Edition</ref> <ref>George Chacko and Carolyn Evans (2014). Valuation: Methods and Models in Applied Corporate Finance. FT Press. Template:ISBN</ref> <ref name="Damodaran_Risk"/> And using the CAPM – or extensions – the discounting here is at the risk-free rate plus a premium linked to the uncertainty of the entity or project cash flows <ref name="Damodaran_Risk"/> (essentially, <math>Y</math> and <math>r</math> combined).

Other developments here include<ref>See Jensen and Smith under "External links", as well as Rubinstein under "Bibliography".</ref> agency theory, which analyses the difficulties in motivating corporate management (the "agent"; in a different sense to the above) to act in the best interests of shareholders (the "principal"), rather than in their own interests; here emphasizing the issues interrelated with capital structure. <ref>Template:Cite journal</ref> Clean surplus accounting and the related residual income valuation provide a model that returns price as a function of earnings, expected returns, and change in book value, as opposed to dividends. This approach, to some extent, arises due to the implicit contradiction of seeing value as a function of dividends, while also holding that dividend policy cannot influence value per Modigliani and Miller's "Irrelevance principle"; see Template:Section link.

"Corporate finance" as a discipline more generally, building on Fisher above, relates to the long term objective of maximizing the value of the firm - and its return to shareholders - and thus also incorporates the areas of capital structure and dividend policy. <ref>Corporate Finance: First Principles, from Aswath Damodaran (2022). Applied Corporate Finance: A User's Manual. Wiley. Template:ISBN</ref> Extensions of the theory here then also consider these latter, as follows: (i) optimization re capitalization structure, and theories here as to corporate choices and behavior: Capital structure substitution theory, Pecking order theory, Market timing hypothesis, Trade-off theory; (ii) considerations and analysis re dividend policy, additional to - and sometimes contrasting with - Modigliani-Miller, include: the Walter model, Lintner model, Residuals theory and signaling hypothesis, as well as discussion re the observed clientele effect and dividend puzzle.

As described, the typical application of real options is to capital budgeting type problems. However, here, they are also applied to problems of capital structure and dividend policy, and to the related design of corporate securities; <ref name="Garbade">Kenneth D. Garbade (2001). Pricing Corporate Securities as Contingent Claims. MIT Press. Template:ISBN</ref> and since stockholder and bondholders have different objective functions, in the analysis of the related agency problems. <ref name="Damodaran">Template:Cite journal</ref> In all of these cases, state-prices can provide the market-implied information relating to the corporate, as above, which is then applied to the analysis. For example, convertible bonds can (must) be priced consistent with the (recovered) state-prices of the corporate's equity.<ref name="corp fin state prices"/><ref name="Kruschwitz and Löffler">See Kruschwitz and Löffler under Bibliography.</ref>

Financial marketsEdit

The discipline, as outlined, also includes a formal study of financial markets. Of interest especially are market regulation and market microstructure, and their relationship to price efficiency.

Regulatory economics studies, in general, the economics of regulation. In the context of finance, it will address the impact of financial regulation on the functioning of markets and the efficiency of prices, while also weighing the corresponding increases in market confidence and financial stability. Research here considers how, and to what extent, regulations relating to disclosure (earnings guidance, annual reports), insider trading, and short-selling will impact price efficiency, the cost of equity, and market liquidity.<ref>See for example: Hazem Daouk, Charles M.C. Lee, David Ng. (2006). "Capital Market Governance: How Do Security Laws Affect Market Performance?". Journal of Corporate Finance, Volume 12, Issue 3; Emilios Avgouleas (2010). "The Regulation of Short Sales and its Reform" DICE Report, Vol. 8, Iss. 1. </ref>

Market microstructure is concerned with the details of how exchange occurs in markets (with Walrasian-, matching-, Fisher-, and Arrow-Debreu markets as prototypes), and "analyzes how specific trading mechanisms affect the price formation process",<ref>O'Hara, Maureen, Market Microstructure Theory, Blackwell, Oxford, 1995, Template:ISBN, p.1.</ref> examining the ways in which the processes of a market affect determinants of transaction costs, prices, quotes, volume, and trading behavior. It has been used, for example, in providing explanations for long-standing exchange rate puzzles,<ref>King, Michael, Osler, Carol and Rime, Dagfinn (2013). "The market microstructure approach to foreign exchange: Looking back and looking forward", Journal of International Money and Finance. Volume 38, November 2013, Pages 95-119</ref> and for the equity premium puzzle.<ref>Randi Næs, Johannes Skjeltorp (2006). "Is the market microstructure of stock markets important?". Norges Bank Economic Bulletin 3/06 (Vol. 77)</ref> In contrast to the above classical approach, models here explicitly allow for (testing the impact of) market frictions and other imperfections; see also market design.

For both regulation <ref>See, e.g., Westerhoff, Frank H. (2008). "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies", Journal of Economics and Statistics</ref> and microstructure,<ref>See, e.g., Mizuta, Takanobu (2019). "An agent-based model for designing a financial market that works well". 2020 IEEE Symposium Series on Computational Intelligence (SSCI).</ref> and generally,<ref name="LeBaron"/> agent-based models can be developed <ref name="ERIM"/> to examine any impact due to a change in structure or policy - or to make inferences re market dynamics - by testing these in an artificial financial market, or AFM. Template:NoteTag This approach, essentially simulated trade between numerous agents, "typically uses artificial intelligence technologies [often genetic algorithms and neural nets] to represent the adaptive behaviour of market participants".<ref name="ERIM">Katalin Boer, Arie De Bruin, Uzay Kaymak (2005). "On the Design of Artificial Stock Markets". Research In Management ERIM Report Series</ref>

These 'bottom-up' models "start from first principals of agent behavior",<ref name="LeBaron2">LeBaron, B. (2002). "Building the Santa Fe artificial stock market". Physica A, 1, 20.</ref> with participants modifying their trading strategies having learned over time, and "are able to describe macro features [i.e. stylized facts] emerging from a soup of individual interacting strategies".<ref name="LeBaron2"/> Agent-based models depart further from the classical approach — the representative agent, as outlined — in that they introduce heterogeneity into the environment (thereby addressing, also, the aggregation problem).

More recent research focuses on the potential impact of Machine Learning on market functioning and efficiency. As these methods become more prevalent in financial markets, economists would expect greater information acquisition and improved price efficiency.<ref name="Barbopoulos">Barbopoulos, Leonidas G. et al. (2023) "Market Efficiency When Machines Access Information". NYU Stern School of Business. Template:SSRN</ref> In fact, an apparent rejection of market efficiency (see below) might simply represent "the unsurprising consequence of investors not having precise knowledge of the parameters of a data-generating process that involves thousands of predictor variables". <ref>Ian W.R. Martin, and Stefan Nagel (2022). "Market efficiency in the age of big data". Journal of Financial Economics. Volume 145, Issue 1, July 2022, Pages 154-177</ref> At the same time, it is acknowledged that a potential downside of these methods, in this context, is their lack of interpretability "which translates into difficulties in attaching economic meaning to the results found."<ref name="Bagnara"/>

Challenges and criticismEdit

Template:See also As above, there is a very close link between: the random walk hypothesis, with the associated belief that price changes should follow a normal distribution, on the one hand; and market efficiency and rational expectations, on the other. Wide departures from these are commonly observed, and there are thus, respectively, two main sets of challenges.

Departures from normalityEdit

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File:Ivsrf.gif

Implied volatility surface. The Z-axis represents implied volatility in percent, and X and Y axes represent the option delta, and the days to maturity.

As discussed, the assumptions that market prices follow a random walk and that asset returns are normally distributed are fundamental. Empirical evidence, however, suggests that these assumptions may not hold, and that in practice, traders, analysts and risk managers frequently modify the "standard models" (see kurtosis risk, skewness risk, long tail, model risk). In fact, Benoit Mandelbrot had discovered already in the 1960s<ref>Template:Cite journal</ref> that changes in financial prices do not follow a normal distribution, the basis for much option pricing theory, although this observation was slow to find its way into mainstream financial economics. <ref name="Taleb_Mandelbrot">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Financial models with long-tailed distributions and volatility clustering have been introduced to overcome problems with the realism of the above "classical" financial models; while jump diffusion models allow for (option) pricing incorporating "jumps" in the spot price.<ref name="holes">Template:Cite journal</ref> Risk managers, similarly, complement (or substitute) the standard value at risk models with historical simulations, mixture models, principal component analysis, extreme value theory, as well as models for volatility clustering.<ref>See for example III.A.3, in Carol Alexander, ed. (January 2005). The Professional Risk Managers' Handbook. PRMIA Publications. Template:ISBN</ref> For further discussion see Template:Section link, and Template:Section link. Portfolio managers, likewise, have modified their optimization criteria and algorithms; see Template:Slink above.

Closely related is the volatility smile, where, as above, implied volatility – the volatility corresponding to the BSM price – is observed to differ as a function of strike price (i.e. moneyness), true only if the price-change distribution is non-normal, unlike that assumed by BSM (i.e. <math>N(d_1)</math> and <math>N(d_2)</math> above). The term structure of volatility describes how (implied) volatility differs for related options with different maturities. An implied volatility surface is then a three-dimensional surface plot of volatility smile and term structure. These empirical phenomena negate the assumption of constant volatility – and log-normality – upon which Black–Scholes is built.<ref name="Haug Taleb"/><ref name="holes"/> Within institutions, the function of Black–Scholes is now, largely, to communicate prices via implied volatilities, much like bond prices are communicated via YTM; see Template:Section link.

In consequence traders (and risk managers) now, instead, use "smile-consistent" models, firstly, when valuing derivatives not directly mapped to the surface, facilitating the pricing of other, i.e. non-quoted, strike/maturity combinations, or of non-European derivatives, and generally for hedging purposes. The two main approaches are local volatility and stochastic volatility. The first returns the volatility which is "local" to each spot-time point of the finite difference- or simulation-based valuation; i.e. as opposed to implied volatility, which holds overall. In this way calculated prices – and numeric structures – are market-consistent in an arbitrage-free sense. The second approach assumes that the volatility of the underlying price is a stochastic process rather than a constant. Models here are first calibrated to observed prices, and are then applied to the valuation or hedging in question; the most common are Heston, SABR and CEV. This approach addresses certain problems identified with hedging under local volatility.<ref>Template:Cite journal</ref>

Related to local volatility are the lattice-based implied-binomial and -trinomial trees – essentially a discretization of the approach – which are similarly, but less commonly,<ref name="Figlewski"/> used for pricing; these are built on state-prices recovered from the surface. Edgeworth binomial trees allow for a specified (i.e. non-Gaussian) skew and kurtosis in the spot price; priced here, options with differing strikes will return differing implied volatilities, and the tree can be calibrated to the smile as required.<ref>See for example Pg 217 of: Jackson, Mary; Mike Staunton (2001). Advanced modelling in finance using Excel and VBA. New Jersey: Wiley. Template:ISBN.</ref> Similarly purposed (and derived) closed-form models were also developed. <ref>These include: Jarrow and Rudd (1982); Corrado and Su (1996); Brown and Robinson (2002); Backus, Foresi, and Wu (2004). See, e.g.: E. Jurczenko, B. Maillet, and B. Negrea (2002). "Revisited multi-moment approximate option pricing models: a general comparison (Part 1)". Working paper, London School of Economics and Political Science.</ref>

As discussed, additional to assuming log-normality in returns, "classical" BSM-type models also (implicitly) assume the existence of a credit-risk-free environment, where one can perfectly replicate cashflows so as to fully hedge, and then discount at "the" risk-free-rate. And therefore, post crisis, the various x-value adjustments must be employed, effectively correcting the risk-neutral value for counterparty- and funding-related risk. These xVA are additional to any smile or surface effect: with the surface built on price data for fully-collateralized positions, there is therefore no "double counting" of credit risk (etc.) when appending xVA. (Were this not the case, then each counterparty would have its own surface...)

As mentioned at top, mathematical finance (and particularly financial engineering) is more concerned with mathematical consistency (and market realities) than compatibility with economic theory, and the above "extreme event" approaches, smile-consistent modeling, and valuation adjustments should then be seen in this light. Recognizing this, critics of financial economics - especially vocal since the 2008 financial crisis - suggest that instead, the theory needs revisiting almost entirely: Template:NoteTag

The current system, based on the idea that risk is distributed in the shape of a bell curve, is flawed... The problem is [that economists and practitioners] never abandon the bell curve. They are like medieval astronomers who believe the sun revolves around the earth and are furiously tweaking their geo-centric math in the face of contrary evidence. They will never get this right; they need their Copernicus.<ref>The Risks of Financial Modeling: VAR and the Economic Meltdown, Hearing before the Subcommittee on Investigations and Oversight, Committee on Science and Technology, House of Representatives, One Hundred Eleventh Congress, first session, September 10, 2009</ref>{{#if:|{{#if:|}}
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Departures from rationalityEdit

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Market anomalies and economic puzzles
Calendar effect January effect Sell in May Mark Twain effect Santa Claus rally Closed-end fund puzzle Dividend puzzle Equity home bias puzzle Equity premium puzzle Excess volatility puzzle Forward premium anomaly Low-volatility anomaly Momentum anomaly Neglected firm effect Post-earnings-announcement drift Real exchange-rate puzzles

As seen, a common assumption is that financial decision makers act rationally; see Homo economicus. Recently, however, researchers in experimental economics and experimental finance have challenged this assumption empirically. These assumptions are also challenged theoretically, by behavioral finance, a discipline primarily concerned with the limits to rationality of economic agents. Template:NoteTag For related criticisms re corporate finance theory vs its practice see:.<ref>Template:Cite SSRN</ref>

Various persistent market anomalies have also been documented as consistent with and complementary to price or return distortions – e.g. size premiums – which appear to contradict the efficient-market hypothesis. Within these market anomalies, calendar effects are the most commonly referenced group. Related to these are various of the economic puzzles, concerning phenomena similarly contradicting the theory. The equity premium puzzle, as one example, arises in that the difference between the observed returns on stocks as compared to government bonds is consistently higher than the risk premium rational equity investors should demand, an "abnormal return". For further context see Random walk hypothesis § A non-random walk hypothesis, and sidebar for specific instances.

More generally, and, again, particularly following the 2008 financial crisis, financial economics (and mathematical finance) has been subjected to deeper criticism. Notable here is Nassim Taleb, whose critique overlaps the above, but extends <ref>See, e.g., this opinion piece: "The pseudo-science hurting markets" (Financial Times, November 2007).</ref> also to the institutional <ref name="Taleb_crisis"/> <ref name="Taleb_Goldstein_Spitznagel">Nassim N. Taleb, Daniel G. Goldstein, and Mark W. Spitznagel (2009). "The Six Mistakes Executives Make in Risk Management", Harvard Business Review</ref> aspects of finance - including academic.<ref>Nassim Taleb (2009). "History Written By The Losers", Foreword to Pablo Triana's Lecturing Birds How to Fly Template:ISBN</ref><ref name="Haug Taleb"/> His Black swan theory posits that although events of large magnitude and consequence play a major role in finance, since these are (statistically) unexpected, they are "ignored" by economists and traders. Thus, although a "Taleb distribution" - which normally provides a payoff of small positive returns, while carrying a small but significant risk of catastrophic losses - more realistically describes markets than current models, the latter continue to be preferred (even with professionals here acknowledging that it only "generally works" or only "works on average"). <ref>Against Value-at-Risk: Nassim Taleb Replies to Philippe Jorion, fooledbyrandomness.com</ref>

Here,<ref name="Taleb_crisis">Nassim Taleb (2011). “Why Did the Crisis of 2008 Happen?”</ref> financial crises have been a topic of interest <ref>From The New Palgrave Dictionary of Economics, Online Editions, 2011, 2012, with abstract links:
   • "regulatory responses to the financial crisis: an interim assessment" Template:Webarchive by Howard Davies
   • "Credit Crunch Chronology: April 2007–September 2009" Template:Webarchive by The Statesman's Yearbook team
   • "Minsky crisis" Template:Webarchive by L. Randall Wray
   • "euro zone crisis 2010" Template:Webarchive by Daniel Gros and Cinzia Alcidi.
   • Carmen M. Reinhart and Kenneth S. Rogoff, 2009. This Time Is Different: Eight Centuries of Financial Folly, Princeton. Description Template:Webarchive, ch. 1 ("Varieties of Crises and their Dates". pp. 3-20) Template:Webarchive, and chapter-preview links.</ref> and, in particular, the failure<ref name="Taleb_Goldstein_Spitznagel"/> of (financial) economists - as well as <ref name="Taleb_crisis"/> bankers and regulators - to model and predict these. See Template:Slink. The related problem of systemic risk, has also received attention. Where companies hold securities in each other, then this interconnectedness may entail a "valuation chain" – and the performance of one company, or security, here will impact all, a phenomenon not easily modeled, regardless of whether the individual models are correct. See: Systemic risk § Inadequacy of classic valuation models; Cascades in financial networks; Flight-to-quality.

Areas of research attempting to explain (or at least model) these phenomena, and crises, include <ref name="Farmer_Geanakoplos">Template:Cite journal</ref> market microstructure and Heterogeneous agent models, as above. The latter is extended to agent-based computational models; here,<ref name="LeBaron">For a survey see: LeBaron, Blake (2006). "Agent-based Computational Finance". Handbook of Computational Economics. Elsevier</ref> as mentioned, price is treated as an emergent phenomenon, resulting from the interaction of the various market participants (agents). The noisy market hypothesis argues that prices can be influenced by speculators and momentum traders, as well as by insiders and institutions that often buy and sell stocks for reasons unrelated to fundamental value; see Noise (economic) and Noise trader. The adaptive market hypothesis is an attempt to reconcile the efficient market hypothesis with behavioral economics, by applying the principles of evolution to financial interactions. An information cascade, alternatively, shows market participants engaging in the same acts as others ("herd behavior"), despite contradictions with their private information. Copula-based modelling has similarly been applied. See also Hyman Minsky's "financial instability hypothesis", as well as George Soros' application of "reflexivity". In the alternative, institutionally inherent limits to arbitrage - i.e. as opposed to factors directly contradictory to the theory - are sometimes referenced.

Note however, that despite the above inefficiencies, asset prices do effectively <ref name="affirmative case"/> follow a random walk - i.e. (at least) in the sense that "changes in the stock market are unpredictable, lacking any pattern that can be used by an investor to beat the overall market". <ref>Albert Phung (2024). How Can Random Walk Theory Be Applied to Investing?, investopedia</ref> Thus after fund costs - and given other considerations - it is difficult to consistently outperform market averages <ref>William F. Sharpe (1991). "The Arithmetic of Active Management" Template:Webarchive. Financial Analysts Journal Vol. 47, No. 1, January/February</ref> and achieve "alpha". The practical implication <ref name="Prosaic">William F. Sharpe (2002). Indexed Investing: A Prosaic Way to Beat the Average Investor Template:Webarchive. Presentation: Monterey Institute of International Studies. Retrieved May 20, 2010.</ref> is that passive investing, i.e. via low-cost index funds, should, on average, serve better than any other active strategy - and, in fact, this practice is now widely adopted. Template:NoteTag Here, however, the following concern is posited: although in concept, it is "the research undertaken by active managers [that] keeps prices closer to value... [and] thus there is a fragile equilibrium in which some investors choose to index while the rest continue to search for mispriced securities";<ref name="Prosaic"/> in practice, as more investors "pour money into index funds tracking the same stocks, valuations for those companies become inflated",<ref>James Faris (2025). A troubling 'self-fulfilling prophecy' may be forming a market bubble, Business Insider</ref> potentially leading to asset bubbles.