Comparing the predictability of the next day stock trend between high volatile and low volatile stocks using a feedforward neural network

University essay from KTH/Skolan för datavetenskap och kommunikation (CSC); KTH/Skolan för datavetenskap och kommunikation (CSC)

Author: Jonathan Gyllenram; Fredrik Gisslém; [2017]

Keywords: ;

Abstract: An ongoing debate is whether it is possible to predict future price movements for stocks by analysing the historical stock data. Accord- ing to the Effective Market Hypothesis and the Random Walk Theory this should not be possible and according to the Non Random Walk Theory it should be possible. A large group who also believe that it is possible to predict the market are all those traders who often use technical analysis as support in their daily investment decisions. A commonly used recommendation among traders and technical an- alysts is to trade in stocks with high volatility to achieve maximum profit. If traders generally trade in high volatile stocks and also use similar analytical methods, then their analyzes and predictions may be self-fulfilling. This study investigate whether there is a difference between predicting tomorrow’s trend of high volatile stocks versus low volatile stocks. Machine learning and a feed forward artificial neural network was used as a tool for making the analyzes and predictions. Ten stocks was selected on the Stockholm Stock Exchange, the five most volatile stocks and the five least volatile stocks. For each stock, stock data from 2001-03-01 until 2017-03-01 was downloaded from Ya- hoo Finance, where 70% of the data was used for training, 15% for validation and 15% for tests. For each stock the tests were repeated ten times and then the average hit rate for each stock was calculated, and also the average hit rate for each test group. The high volatile test group achieves an average hit rate of 59,3% and the low volatile test group achieves an average hit rate of 54,1%. A difference over 5% indicates that our theory holds and that it is easier to predict fu- ture price movements for a stock with high volatility than for a low volatility stock. 

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