Overfitting Probability Evaluation via Testing Trading Strategies in Financial Markets

Student: Polenova Yulia

Supervisor: Mikhail Kamrotov

Faculty: Faculty of World Economy and International Affairs

Educational Programme: World Economy (Bachelor)

Year of Graduation: 2017

The aim of this research is to outline major reasons why overfitting takes place in trading strategies on financial markets. CSCV-method is used to identify probability of backtest overfitting, which is then compared with the market efficiency coefficient and permutation entropy indicators to identify patterns between overfitting and market efficiency. At the same time, standard deviation helps to determine the relation between overfitting and market volatility. As a result, some connections can be presented. Besides, the paper contributes to the evaluation of possible losses that an investor can suffer from in case of using an overfitted model. As a result, a ‘risk-averse’ moving average strategy has been designed.

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