Descripción del asentamiento 8 de diciembre

Each security valuation represents a process that consists of few steps. First step assumes understanding and evaluation of an industry as a whole, assessment of company’s competitive position and its strategies. In order for us to be able to understand company, it is important to analyze corporate economic and industry context in combination with management strategic responses to it. Second step assume forecasting of company’s performance from two perspectives, company’s economic environment and its financial

characteristics. In this step macroeconomic analysis is integrated with financial statement analysis, and numerical forecasts are created. Besides quantitative factors it is important to create some qualitative forecasts as well, where assessment of quality of earnings is one of the essential ones. Forecasts represent inputs in value estimation. Third step is a step when appropriate valuation model is selected. There are several value perspectives that can be used as a base for different valuation models. The most important value concept is intrinsic value (Stowe et al., 2007).

Valuation is an integral part of an effort to produce positive excess risk-adjusted return or abnormal return or alpha. If there is a difference between estimates of intrinsic value and the market price of an asset, that means that any recognized mispricing will be part of the holding-period return estimate which represents forecast of the total return. An expected holding-period return represents the sum of the expected capital appreciation and investment income. Therefore, assets alpha in a forward sense represents, the difference between expected holding-period return and fair return on the asset given its risk. Mispricing raises questions about market efficiency. In the traditional efficient markets formulation, the best available estimate of an intrinsic value is assumed to be assets market price. The rational efficient markets formulation assumes that no rational investor will incur expenses of gathering information except if he expects to receive higher return. Furthermore, if trading costs are taken into consideration than market price can even more deviate from assets value. Moreover, rational investor assumption was often challenged and new valuation approaches are formulated. Forth step, of a security valuation process, is a conversion of forecasts into a valuation, and a fifth step is giving recommendation to the investors (Stowe et al., 2007).

2.1. Valuation models

Third step of security valuation process assumes model selection. Stowe et al. (2007) distinguish between two broad types of valuation models, absolute and relative valuation models. An absolute valuation models produce a point estimate of intrinsic value that can be compared with market price of an asset. Present value models, as a type of absolute valuation models, assume that the value of an asset must be related to the returns, or asset’s cash flows. Present value models based on dividends are dividend discount models. Besides dividends, cash flows can also be defined at company level. Hence, present value models used for valuation of corporate cash flows are free cash flow models and residual income models. The application of present value models for common stock valuation engages uncertainty, that stem from inputs in these models, mainly cash flows and discount rates. Corporations can also be valued with another type of absolute valuation models, called asset-based valuation models . These models assume that company can be valued based on the market value of its assets/resources. Relative valuation models give assets value relative to the value of an another asset. Rationale for these type of models is that similar assets should have similar prices. This type of valuation is implemented using different price multiples, like price-earnings (P/E) multiple (Stowe et al., 2007; Bogojevic, 2004). Bogojevic (2009) distingueshes two approaches for determining cost of equity capital, approach based on the dividend discount model (which can be used in fundamental analysis) and approach of the Capital market line. Also, basic models of risk and return are CAPM and APT. In time, many of their extensions were created in attempt to overcame to drawbacks of those basic one’s. Haugen (2001) distinguishes between different periods of evolution of academic finance, the old, the modern, and the new finance. Summary of Haugen’s view of the evolution of security analysis is presented in table 1. In the old finance period, security analysis was focused on accounting statements and law. Introduction of new finance era came with Harry Markowitz portfolio optimization and Capital Asset Pricing Model (CAPM) (Sharpe, 1964), and is completed with the efficient market hypothesis. These building blocks of the modern finance assume rational investor behaviour. Stock prices change randomly from one period to another, and they are responding instantly and accurately to the new information. Risk of an individual stock can be measured with beta, which represents the sensitivity of a security’s periodic return to changes in the periodic return to the market index. Modern finance thinking was challenged with discovery of myriad of market anomalies, such as January effect, or small stocks premium. The new finance paradigm dismisses rational investor’s behaviour, and assumes that markets are inefficient. It is necessary to measure the behaviour first, and then to try to find reasonable explanations for it. Hence, stocks with particular characteristics are more likely to yield premium returns.

Table 1: Summary of Haugen’s view of evolution of security analysis

Evolution of security analysis

Old finance Modern finance New finance

Financial statement analysis Rational investors’ Irrational investors’

Capital market theory extends portfolio theory and introduces CAPM which allows pricing of all risky assets. CAPM is used for calculating the required rate of return which can be compared with an estimate of the assets expected rate of return during specific investment horizon. Based on the result obtained it can be observed that asset is undervalued, properly valued or overvalued. CAPM gives required or expected rate of return when risk-free rate is enhanced for risk premium, and this transition is helpful when one wants to value an asset because it provides a discount rate which can be used in any valuation model. CAPM is a single risk factor model which takes into account volatility inherent in an individual security or portfolio of securities. Only relevant risk is a covariance of the asset with the market portfolio (Reilly & Brown, 2002). Due to unrealistic assumptions of CAPM there are several extensions of this model and one of them is inter- temporal capital asset pricing model.

The arbitrage pricing theory (APT) assumes that there are several risk factors or indexes, but it does not specify how many of them are there or what precisely are they (Ross, 1976). The APT assumes that stochastic process that drives asset returns can be represented as K factor model. Risk factors used in APT that influence returns can be either macroeconomic or microeconomic (Reilly & Brown, 2002). There are several multifactor models used in practice such as Chen, Roll, and Ross (1986), Fama and French (1993), Carhart (1997) extension of Fama and French model, and BARRA model.

Models of new finance are behavioural pricing models. Behavioural finance presents mixture of behavioural and cognitive psychological theory and conventional economics and finance. This field of finance emerged due to inability of efficient market theory to explain different empirical patterns. The underlying assumption of behavioural finance is that individual investors’ decisions and market outcomes are systematically influenced by information structure and characteristics of market participants. Humans behave in irrational manner, and therefore make forecast errors and also, do not follow risk aversion concepts. Such behaviour has influence on the efficiency of capital markets and performance of corporations (Baker&Nofsinger, 2010). It is complex to incorporate investors’ behaviour into valuation of assets. Szyszka (2010) gives an overview of different classes of behavioral asset pricing models. They can be classified into belief-based models and preference- based models. Belief-based models are models of investor sentiment, DHS model, and Hong and Stein’s Model. Preference-based models are Models of Shifting Risk Attitude and Probability Misperception Model. Model of investor sentiment suggest that attitudes of investors match to two behavioral patterns. First pattern, suggest that investors believe that profitability of a corporation oscillate around some mean value. If company would report increased profitability, investors would react adversely in fear that such news would be eliminated in the next period. Hence, price adjusts to the new information with delay. Humans tend to change their beliefs slowly when encountered with new information; they would need confirmations by successive results. Barberis et al. (1998) associate such investor behaviours with cognitive conservatism. Second pattern, correspond to the belief that profitability of corporations follow specific trend. In this case investors put emphasis on the latest results (Barberis et al., 1998). Barberis et al. (1998) associate this attitude with the representativeness heuristic. This means that investors give probability to the specific event based on the similarity with the precise characteristics of the sample, which will lead to an error. The weight of individual characteristics, which comply with some pattern, will be overstated, and the real statistics will be understated. Thus, their conclusions are based on the short series error.

Daniel et al. (1998) model (DHS) is based on the assumption that investors can be either informed or underinformed. Underinformed traders do not have any influence on the market. Informed traders are overconfident and because of that, their decisions can have an impact on the market. These types of traders are subject to calibration bias. Investors assume that their analysis is more precise than regularly available market information. Model of investor sentiment suppose that investors overreaction is due to a sequence of signals of similar significance and underreact to the new information. DHS model distinct overreaction and underreaction depending on whether information is public or private. DHS model does a good job in explaining short-term continuations, long-term trend reversals, as well as long-term continuations. However, this model assumes that investors’ reaction is incomplete when new information is published for the first time (Szyszka, 2010).

Hong and Stein (1999) also assume that there are two types of investors in the market, and they have bounded rationality. First type of investors is the type who uses fundamental analysis, and they track new information which can affect the value of a company. Second type of investors is momentum trader, who favors short-term price trends. All investors analyze only certain subset of publically available information. Fundamental information reaches investors slowly, causing certain delay in their reaction. They are focused on forward looking information, ignoring historical trend. Momentum traders only care about price movements.

Barberis, Huang, and Santos (2001) propose preference-based model, based on three ideas. Investors are interested in the level of their wealth and they are concerned with fluctuations in its value, also they are more stressed with reduction in the level of their wealth than to its enhancement. Investors are risk takers after they have realized gains, and they are risk averse when they have experienced losses. This model is trying to explain investors’ behavior when capital markets are analyzed from an aggregate level. Dacey and Zielonka (2008) proposed another preference-based model. Investors want to maximize their personal utility, and therefore they are prone to two types of errors. An error may occur due to the investors’ faulty estimate of the probability of the events. Errors may arise when investors assign inaccurate weights to the estimated probability level in the weighing function of prospects theory. Furthermore, there are two types of investors rational and quasi-rational. There are fewer rational investors’ in the marketplace and they correctly assign probabilities to the potential changes in assets prices. Reversely, quasi-rational investors make wrong estimates.