Academic journal article Quarterly Journal of Finance and Accounting

The Information Risk Drivers: A Long-Term Analysis to Support a Risk Premia Modeling

Academic journal article Quarterly Journal of Finance and Accounting

The Information Risk Drivers: A Long-Term Analysis to Support a Risk Premia Modeling

Article excerpt


Risk may be unbundled into payoff risk and information risk (Allen and Gale, 1994), both parts of risk requiring a risk premium depending on risk aversion grade and competences in risk management (Mantovani, 1998). For any level of market efficiency, information risk may arise from: (i) the timing of the information spreading in the market (i.e. risk of information timing); (ii) a bias in risk-return estimations (i.e. risk of information error); and (iii) the ways of information transmission to the market (i.e. risk of financial communication). The three sources of information risk originate both at systematic and idiosyncratic level, defined by six information-risk classes (Bertinetti et al., 2004). A basic model of proxy estimation of information risk premia at systematic and idiosyncratic level has been developed (Mantovani, 2004) and tested referring to several firm-specific facts (Bertinetti et al., 2004). Links between information risk and risk aversion have been identified in a behavioural finance framework (Gardenal, 2007).

Investment policies are mainly based on original ways of dealing with asset classes; should information risk be an economic driver, such policies could be affected by the opportunity of profitable alternative investment rules dealing with risk classes and related risk aversions. Is it possible to generate positive performance by managing assets through rules manipulating the information risk premium? The research question in this study is to investigate possible drivers of the information risk to be used to fix an information-risk-premia model.

In section 1 the economics of information risk is compared with the more traditional approach of lack of market efficiency arising from information asymmetries. Section 2 shows a possible theoretical approach for modelling information risk and thus investigating it. Section 3presents an empirical analysis of the level of the information risk in the industries included in the European Stock Exchanges between 1992 and 2010, 1st quarter. In section 4 tests are conducted to discover possible drivers of the information risk by testing their correlation with possible drivers as suggested by section 3's results. Section 5 discusses the empirical evidence and proposes some conclusions about a model for pricing the information risk premia.

Market Efficiency, Information Asymmetries and Information Risk

Market equilibriums are based on expectations. Higher quantity of information generates higher quality of expectations, thus making financial markets a good instrument to allocate capital allowances. In standard financial market models, the inner problem is concerned with the quantity of information that is incorporated in asset prices given a certain set of existing information. Another very important subject is the quantity of traders having information at their disposal, thus defining information asymmetries. Fully efficient markets exist when the entire set of information is considered in price setting, so that information is available for any trader. Several degrees of efficiency can be found empirically according to the kind of information that is actually included in asset prices: weak forms are found when historical-only information is considered; semi-strong forms are found in the case of partial information inclusion; strong forms can be found if the entire information set is included.

From the seminal work of Fama (1970) stating the above framework for market efficiency analysis, several studies try to verify both the levels of efficiency that can be achieved in real markets and the conditions for markets to reach higher efficiency. Studying degrees of efficiency is of interest for regulators aiming to protect market investors, while deeper knowledge of market dynamics between different states of efficiency can help market traders to gain excess-return, both in long and short term.

De Bondt and Thaler (1985) suggest that stock markets tend to have endogenous overreaction so that historical level of excess return may infer future price trends. …

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