A lattice gas model of financial markets is presented that has the potential to explain previous empirical observations of the interplay of trends and reversion in the markets, including a Langevin equation for the time evolution of trends. In this model, the shares of an asset correspond to gas molecules that are distributed across a hidden social network of investors. Neighbors in the network tend to align their positions due to herding behavior, corresponding to an attractive force of the gas molecules.This model is equivalent to the Ising model on this network, with the magnetization in the role of the deviation of the market price of an asset from its long-term value. In efficient markets, it is argued that the system is driven to its critical temperature, where it undergoes a second-order phase transition. There, it is characterized by long-range correlations and universal critical exponents, in analogy with the phase transition between water and steam.Applying scalar field theory and the renormalization group, we show that these critical exponents imply predictions for the auto-correlations of financial market returns. For a network topology of R D , consistency with observation implies a fractal dimension of the network of D ≈ 3.3, and a correlation time of 10 years. However, while this simplest model agrees well with market data on long time scales, it does not explain the observed market trends over time horizons from one month to one year.In a next step, the approach should therefore be extended to the vicinity of the critical point, to other models of critical dynamics, and to general network topologies.It allows us to indirectly measure universal properties of the hidden social network of investors from the empirically observable interplay of trends and reversion.