Smart grid is a platform that enables the participants of electricity market to adjust their bidding strategies based on Demand-Side Management (DSM) models. Responsiveness of the market participants can improve reliability of system operation as well as capital cost investments. In this regard, the accurate forecast of electricity price and demand in smart grids is an important challenge as their strong correlation makes a separate forecasting to be ineffective. Therefore, this paper proposes a novel hybrid algorithm for simultaneous forecast of price and demand that uses a set of effective tools in preprocessing part, forecast engine and tuned algorithm. To highlight our contributions, the proposed forecast algorithm classified into three main parts. The first part employs a new Flexible Wavelet Packet Transform (FWPT) to decompose a signal into multiple terms at different frequencies, and a new feature selection method that employs Conditional Mutual Information (CMI) and adjacent features in order to select valuable input data. The second part consists of a novel Multi-Input Multi-Output (MIMO) model based on Nonlinear Least Square Support Vector Machine (NLSSVM) and Autoregressive Integrated Moving Average (ARIMA) in order to model the linear and nonlinear correlation between price and load in two stages. The final part employs a modified version of Artificial Bee Colony (ABC) algorithm based on time-varying coefficients and stumble generation operator, called TV-SABC, in order to optimize NLSSVM parameters in a learning process. The proposed hybrid forecasting algorithm is evaluated on several real and well-known markets illustrating its high accuracy in simultaneous forecast of electricity price and demand. Moreover, the interactive effects of demand-side management programs on load factor (load curve) and price signal are investigated by numerical indices.