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https://sites.google.com/site/cdmurray80/machinelearningandmarkets
black_swans_and_market_timing_-_how_not_to_generate_alpha.pdf
learning_in_financial_markets.pdf
a_practical_guide_to_quantitative_portfolio_trading.pdf
efficiency_in_foreign_exchange_markets.pdf
technical_market_indicators_-_an_overview.pdf
Number of Pages in PDF File: 64
intraday_momentum_-_the_first_half-hour_return_predicts_the_last_half-hour_return.pdf
polynomial_variation_vix_decomposition_and_tail_risk_premium.pdf
uncloaking_cape_-_a_new_look_at_an_old_valuation_ratio.pdf
comparing_the_market_risk_premia_in_jse_and_nyse_equity_markets.pdf
The contribution of this paper is the inclusion of the South African market risk premium to the forecasting exercise and its direct comparison with US forecasting results. The market risk premium is defined as the expected rate of return on the market portfolio in excess of the short-term interest rate for each market. All data are taken from January 2007 till December 2014 on a daily basis.
Elman networks provide superior results among the tested models in both insample and out-of sample periods as well as among the tested markets. In general, neural networks beat the naive benchmark model and achieve to perform better than the rest of their linear tested counterparts.
The forecasting models successfully capture patterns in the data that improve the forecasting accuracy of the tested models. Therefore, they can be applied to trading and investment purposes.