A Comparison of Different Hybrid ARIMA - Neural Network Models for Stock Index Return Forecasting and Trading Strategy
Machine learning return prediction in equities, studied by Kumar and Thenmozhi in 2007 with a feedforward neural network. The abstract mentions a Sharpe ratio, profits and drawdowns but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“The performance of the models are evaluated in terms of widely used statistical metrics, correctness of sign and direction change, and various trading performance measures like annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio etc via a trading strategy.”
AssetEquitiesindices
StrategyMachine learning
TechniqueFeedforward networkclassical time series
ClaimSharpe ratioprofit
Also stated: Price and volume
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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