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The model derived rate of return will then be used to price the asset correctly - the asset price should equal the expected end of period price discounted at the rate implied by model. If the price diverges, arbitrage should bring it back into line. The theory was initiated by the economist Stephen Ross in 1976.(Ross,1976) If APT holds, then a risky asset can be described as satisfying the following relation: Some commonly accepted factors are Business Cycle, Time Horizon, Confidence, Inflation, Market Timing, oil prices, term structure of interest rates, industrial production, default premiums etc.
It has been shown that the empirical specification of the APT need not be unique.( Otuteye,1991) In other words, no one set of economic factors constitutes "the factors" of the APT. Any set of factors that fulfills the requirements of the returns generating process and the resulting linear relationship between expected returns and factor sensitivities will be an equally valid set of APT factors. However, there is a gradual consensus towards the use of some common factors. (Brown, Weinstein, 1983) The CAPM does not appear to adequately explain the variation in stock returns.
Empirical studies show that low beta stocks may offer higher returns than the model would predict. Some data to this effect was presented as early as a 1969 conference in Buffalo, New York in a paper by Fischer Black, Michael Jensen, and Myron Scholes(Black et.al,1972). Either that fact is itself rational (which saves the efficient markets hypothesis(EMH) but makes CAPM wrong), or it is irrational (which saves CAPM, but makes EMH wrong - indeed, this possibility makes volatility arbitrage a strategy for reliably beating the market).
The CAPM assumes that investors demand higher returns in exchange for higher risk. It does not allow for investors who will accept lower returns for higher risk. The model also assumes that all investors agree about the risk and expected return of all assets(Homogeneous expectations assumption). The model assumes unrealistically that asset returns are lognormally distributed, random variables. As a result, large swings (3 to 6 standard deviations from the mean) occur in the market more frequently than the normal distribution assumption would expect.
These swings can greatly impact an asset's
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