Anomalii ale pieței financiare
The mispricing explanations are often contentious within academic finance, as academics do not agree on the proper benchmark theory see Unmeasured Risk, below.
This disagreement is closely related to the "joint-hypothesis problem" of the efficient market hypothesis. Main article: Risk factor finance Among anomalii ale pieței financiare, a common response to claims of mispricing was the idea that the anomaly captures a dimension of risk that is missing from the benchmark theory.
For example, the anomaly may generate expected returns beyond those measured using the CAPM regression because the time-series of its returns are correlated with labor income, which is not captured by standard proxies for the market return.
The [3-factor model] time-series regressions give direct evidence on this issue. Perhaps the most common critique of the CAPM is that it is derived in a single period setting, and thus is missing dynamic features like periods of high uncertainty.
Moreover, the ICAPM generally implies the expected returns vary over time, and thus time-series predictability is not clear evidence of mispricing. Indeed, since the CAPM cannot at all capture dynamic expected returns, evidence of time-series predictability is less often regarded as mispricing as compared to cross-sectional predictability.
Ele cad şi mai rău decât celelalte, care au mai multe condiţionări. Preţul pâinii nu poate scădea sub preţul de cost, de exemplu; în schimb, scăderea acţiunilor nu are nicio oprelişte şi de foarte multe ori nicio raţiune. Deşi nu era pe dezonorantul loc întâi, printre fruntaşe se număra "Impact". Dacă am face capitalizarea bursieră a "Impact", ceva mai greu de făcut acum, deoarece "Impact" a cumpărat foarte multe acţiuni pre care le anuleazărezultă o valoare care probabil este mai mică decât un sfert din valoarea terenurilor pe care le deţine, chiar dacă valoarea de piaţă a acestora a scăzut şi ea destul de mult. Dar peste terenuri, "Impact" mai are case terminate pe care o să le vândă cumva, cândva, utilaje, materiale, creanţe la vânzările în rate etc.
Empirical shortcomings primarily regard the difficulty in measuring wealth or marginal utility. Theoretically, wealth includes not only stock market wealth, but also non-tradable wealth like private assets and future labor income.
Empirical studies[ edit ] Research by Alfred Cowles in the s and s suggested that professional investors were in general unable to outperform the market. During the ss empirical studies focused on time-series properties, and found that US stock prices and related financial series followed a random walk model in the short-term. In their seminal paper, Fama, Fisher, Jensen, and Roll propose the event study methodology and show that stock prices on average react before a stock split, but have no movement afterwards. Weak, semi-strong, and strong-form tests[ edit ] In Fama's influential review paper, he categorized empirical tests of efficiency into "weak-form", "semi-strong-form", and "strong-form" tests. Semi-strong form tests study information beyond historical prices which is publicly available.
Despite the theoretical soundness of the unmeasured risk explanation, there is little consensus among academics about the proper risk model over and above the CAPM. Limits to arbitrage[ edit ] Main article: Limits to arbitrage Anomalies are almost always documented using closing prices from the CRSP dataset.
These prices do not reflect trading costs, which can prevent arbitrage and thus the elimination predictability. Moreover, almost all anomalies are documented using equally-weighted portfolios,  and thus require trading of illiquid costly-to-trade stocks.
The limits to arbitrage explanation can be thought of as a refinement of the mispricing framework.
A return pattern only offers profits if the returns it offers survives trading costs, and thus should not be considered mispricing unless trading costs are accounted for. A large literature documents that trading costs greatly reduce anomaly returns. This selection creates a bias and implies that estimates of the profitability of anomalies is overstated.
This explanation for anomalies is also known as data snooping, p-hacking, data mining, and data anomalii ale pieței financiareand is closely related to the multiple comparisons problem. Concerns about selection bias in anomalies goes back at least to Jensen and Bennington For example, Sullivan, Timmermann, and White show that calendar-based anomalies are no longer significant after adjusting for selection bias.
They refer to a factor as any variable that helps explain the cross-section of expected returns, and thus include many anomalies in their study.