The formation of Japanese candlesticks language and using NLP algorithm Word2Vec for shares trend forecasting

Boris Savić (2016) The formation of Japanese candlesticks language and using NLP algorithm Word2Vec for shares trend forecasting. MSc thesis.

Preview

PDF Download (1690Kb)

Abstract

Our primary objective is predicting the future trends for stock market data with machine learning. We present a new innovative prediction model, based on centuries old Japanese candlesticks and modern NLP algorithm Word2Vec. Suggested model constructs a simple language of Japanese candlesticks with a very limited vocabulary. In the following steps the prediction model uses Word2Vec to discover semantic context of each word within the language vocabulary. To test the model we develop a simulation tool that takes into account the most important aspect of stock market trading -- trade fees. To compare the success of the suggested prediction model we also develop simple TA models such as: Buy and Hold, Simple Moving Averages and MACD. Analysis of the results show the superiority of suggested prediction model over previously mentioned models in the test data-set. Additional testing is done with validation data-set in order to verify the results.

Actions (login required)