SURVEY EFFICIENCY OF GAS IN FUTURES MARKET BY GMDH APPROACH

If the gas futures market is not efficient in the Fama sense, profitable trading opportunities may exist. This paper uses a GMDH neural network model with moving average crossover inputs to predict price in the gasfutures market. The predictions of price are used to construct buy and sell signals for traders. Compared to those of benchmark models, cumulative returns, year-to-year returns, returns over a market cycle, and Sharpe ratios all favor the GMDH model by a large factor. The significant profitability of the GMDH model casts doubt on the efficiency of the gas futures market.