In this paper, the performance of artificial neural networks in option pricing was analyzed and compared with the results obtained from the Black–Scholes–Merton model, based on the historical volatility. The results were compared based on various error metrics calculated separately between three moneyness ratios. The market data-driven approach was taken to train and test the neural network on the real-world options data from 2009 to 2019, quoted on the Warsaw Stock Exchange. The artificial neural network did not provide more accurate option prices, even though its hyperparameters were properly tuned. The Black–Scholes–Merton model turned out to be more precise and robust to various market conditions. In addition, the bias of the forecasts obtained from the neural network differed significantly between moneyness states. This study provides an initial insight into the application of deep learning methods to pricing options in emerging markets with low liquidity and high volatility.
Popular research methods in assessing the impact of macroeconomic and environmental variables on music preferences were psychological experiments and surveys with small groups or analyzing the effect of one or two variables in the whole population. Instead inspired by the article of The Economist about February being the gloomiest month in terms of music listened to, we have created a dataset with many variables. We used Spotify API to create a dataset with average valence for 26 countries for the period from January 1, 2018, to December 1, 2019. Then we applied the regression and machine learning models to them. Our study confirmed the effects of summer, December, and the number of Saturdays in a month and contradicted the February effect. The influence of GDP per capita on the valence was confirmed, while the impact of the happiness index was disproved. All models partially confirmed the influence of the music genre on the valence. Among the weather variables, two models confirmed the significance of the temperature variable. Macroeconomic variables turned out to have non-linear relationships that made interpretations difficult, while the environmental ones clearly indicated a linear relationship with valence.
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