Abstract.  A new framework for asset price dynamics is introduced in which the concept of noisy information about future cash flows is used to derive the corresponding price processes. In this framework an asset is defined by its cash-flow structure.  Each cash flow is modelled by a random variable that can be expressed as a function of a collection of independent random variables called market factors. With each such market factor or "X-factor'' we associate a so-called market information process, the values of which we assume are accessible to market participants. Each market information process consists of a sum of two terms; one contains true information about the value of the associated market factor, and the other represents  "noise''. The noise term is modelled by an independent Brownian bridge process that spans the time interval from the present to the time at which the value of the given market factor is revealed. The market filtration is assumed to be that generated by the aggregate of the independent market information processes. The price of an asset is given by the expectation of the discounted cash flows in the risk neutral measure, conditional on the information provided by the market filtration thus constructed. In the cash where the cash flows are the random dividend payments associated with equities, an explicit model is obtained for the share-price process.  Dividend growth is taken into account by introducing appropriate structure on the market factors. The prices of options on dividend-paying assets are derived. Remarkably, the resulting formula for the price of a European-style call option is of the Black-Scholes type. We consider both the case where the rate at which information is revealed to the market is constant, as well as the case where the information flow rate varies in time. Option pricing formulae are obtained for both cases. The information-based framework has another significant consequence: it generates a natural explanation for the origin of unhedgeable stochastic volatility in financial markets, without the need for specifying on an ad hoc basis the stochastic dynamics of the volatility.  Co-authors: Dorje C. Brody, Imperial College London, and Andrea Macrina, King's College London.