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Predicting stock returns with financial ratios: A new methodology incorporating machine learning techniques to beat the market

Zeynep İltüzer

This study proposes a methodology incorporating machine learning algorithms to predict stock returns and construct portfolios that beat the market. The performance evaluation is based on the statistical metrics as well as the return and Sharpe ratios of the portfolios. Additionally, a new performance evaluation metric, Safe-Side, is introduced to address the needs of conservative portfolio managers and investors. The results provide strong evidence that the machine learning algorithms can be used to predict the stock returns with approximately 86 classification accuracy. The proposed methodolo ...Daha fazlası

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Option pricing with neural networks vs. Black-Scholes under different volatility forecasting approaches for BIST 30 index options

Zeynep İltüzer

This study compares the performances of neural network and Black-Scholes models in pricing BIST30 (Borsa Istanbul) index call and put options with different volatility forecasting approaches. Since the volatility is the key parameter in pricing options, GARCH (Generalized Autoregressive Conditional Heteroskedasticity), implied volatility, historical volatility, and implied volatility index (VBI) are used to determine the best volatility approach for pricing options according to moneyness and time-to-maturity dimensions. The paper also includes a subsample analysis in which the pricing performa ...Daha fazlası

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