  Market indicators such as CPI and GDP have been widely used over decades to
identify the stage of business cycles and also investment attractiveness of
sectors given market conditions. In this paper, we propose a two-stage
methodology that consists of predicting ETF prices for each sector using market
indicators and ranking sectors based on their predicted rate of returns. We
initially start with choosing sector specific macroeconomic indicators and
implement Recursive Feature Elimination algorithm to select the most important
features for each sector. Using our prediction tool, we implement different
Recurrent Neural Networks models to predict the future ETF prices for each
sector. We then rank the sectors based on their predicted rate of returns. We
select the best performing model by evaluating the annualized return,
annualized Sharpe ratio, and Calmar ratio of the portfolios that includes the
top four ranked sectors chosen by the model. We also test the robustness of the
model performance with respect to lookback windows and look ahead windows. Our
empirical results show that our methodology beats the equally weighted
portfolio performance even in the long run. We also find that Echo State
Networks exhibits an outstanding performance compared to other models yet it is
faster to implement compared to other RNN models.
