Abstract: Earning forecast plays an important role in the investment decision because it reflects future growth of business. Conventionally, the statistical tools, such as the Univariate Time Series Methods and Multivariate Methods, are broadly used in the earning forecast analysis. However, the inferred forecasting accuracy is seriously limited without considering sufficient critical financial variables, and influenced by their linear searching sequences. In this study, we propose a hybrid swarm intelligence based mechanism, which efficiently combines the advantages of statistical methodologies and Particle Swarm Optimization (PSO) method. Using the nonlinear searching methodology and evolutionary computation characteristics, the PSO algorithm adjusts the trajectories of a population of "particles" through a problem spaces on the basis of information about each particle`s previous best performance and the best previous performance of its neighbors. From the empirical results, our introduced mechanism is not only superior in the earning forecasting accuracy with minimal critical financial variables consideration, its convergence speed is also 10 times faster than using GA algorithm.