Equity portfolio management problems require fund managers to make decisions about what portfolio to hold (ex-ante) without knowing what future equity returns will be. Though these returns are uncertain, market participants try to understand the nature of the uncertainty and make decisions based on their beliefs about the market environment. Traditionally, portfolio managers have used variants of Markowitz mean-variance analysis to determine the optimal portfolio to hold and this is still fairly standard practice in industry. Mean-variance portfolio decision models fall into the more general group of mean-risk models, where portfolio risk and expected return are traded-off when making asset choices. Variance and standard deviation both measure the spread of a distribution about its mean. Since the variance of a portfolio can be easily calculated from the co-variances of the pairs of asset returns and the asset weights used in the portfolio, variance is predominantly used in portfolio formation. In contrast to computing the asset variances and covariances directly using historical data, multifactor models provide an accurate and efficient way to provide these estimates. They decompose an assets return into returns derived from exposure to common factors and an asset-specific component. The common factors can be understood as representing different risk (uncertainty) aspects, which all the assets are exposed to in varying degrees (factor sensitivities). By describing a group of asset returns through a set of key common factors, the size of the estimation problem is significantly reduced. The new problem faced is to estimate the covariance matrix of common sources of risk, the variances of the specific returns and estimates of each security factor exposures. These models capture the natural intuition that firms with similar characteristics will behave similarly. Active portfolio managers seek to incorporate their investment insight to beat the market. An accurate description of asset price uncertainty is key to the ability to outperform the market. Tetlock et al. (2008) develop a fundamental factor model that incorporates news as a factor. Investors perceptions of the riskiness of an asset are determined by their knowledge about the company and its prospects, that is, by their information sets. They note that these are determined from three main sources: analysts forecasts, quantifiable publicly disclosed accounting variables and linguistic descriptions of the firms current and future profit generating activities. If the first two sources of information are incomplete or biased, the third may give us relevant information for equity prices. We seek to extract an improved understanding of equity price uncertainty using a quantified measure of market sentiment to update a traditional factor model. This may give us the tools to make improved portfolio (management) decisions.
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