Meta learning is an advanced field of Artificial Intelligence (AI) where automatic learning algorithms are applied to acquire learning experience for a set of learning algorithms to improve learning performance. One of popular Meta learning methodologies is based on cross validation, especially for selection processes among different machine learning models. However, the challenge is that it is a very time-consuming to do cross validation among models in large data sets, especially in financial big data with high noise. This paper proposes two Asymptotic Meta learning algorithms (AML-lin and AML-xiang), which are ordinal optimization algorithms for Meta learning based on cross validation. The numerical experiments and real-world cases are conducted to illustrate its efficiency in cross validation of models in different scenarios, especially for financial data. The method proposed in this paper has significant improvement by comparing with those ones in existing algorithms OCBA and IAML (e.g., see reference [8] [9]), and it is new in dealing with financial data.

The joint uncertainties of wholesale price and end-user demand quantity often poses huge pricing challenges to energy retailers. However, the literature lacks analysis of such uncertainties’ impacts on retailer pricing behaviors and possible hedging behaviors. To study these impacts, this paper proposes four models: a risk-averse or a risk-neutral retailer deciding retail price with or without forward contract. We present closed-form solutions for these four models on optimal retail price, as well as optimal forward position (if allowed). We propose a novel approach of volatility decomposition to describe the relationship between behaviors and different volatility sources. Comparative statics gives detailed analysis of the pricing and hedging behaviors in both uncertainties, as well as their correlation. We obtain profit distributions using Monte Carlo simulations in the context of the California Electricity Market. Results show that the price and quantity uncertainties and their correlation create significant differences in the retailer’s behaviors, and the determinants of these differences are different. In addition, forward contract increases expected profit and decreases profit volatility for risk-averse retailers simultaneously. These results could serve as a benchmark for analyses of deregulated, imperfect energy markets coupled with contingent financial markets under both price and quantity uncertainties.