Return Predictability of a Corporate Governance Neural Networks Trading System
Nor and Islam (2011) study machine learning return prediction using a feedforward neural network. The abstract mentions a Sharpe ratio, an information ratio, a benchmark, an out-of-sample test and transaction costs but not multiple testing or the size of the universe.
“The overall results indicate superior performance of the corporate governance trading system compared to the benchmark buy-and-hold strategy, with significantly better returns and much greater Sharpe and Sortino ratios.”
StrategyMachine learning
TechniqueFeedforward network
ClaimSharpe ratioinformation ratio
Long/shortLong-shortlong-only
Also stated: benchmark, out-of-sample test, costs
Missing from abstract: multiple testing, universe
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