  Stock prediction has always been attractive area for researchers and
investors since the financial gains can be substantial. However, stock
prediction can be a challenging task since stocks are influenced by a multitude
of factors whose influence vary rapidly through time. This paper proposes a
novel approach (Word2Vec) for stock trend prediction combining NLP and Japanese
candlesticks. First, we create a simple language of Japanese candlesticks from
the source OHLC data. Then, sentences of words are used to train the NLP
Word2Vec model where training data classification also takes into account
trading commissions. Finally, the model is used to predict trading actions. The
proposed approach was compared to three trading models Buy & Hold, MA and MACD
according to the yield achieved. We first evaluated Word2Vec on three shares of
Apple, Microsoft and Coca-Cola where it outperformed the comparative models.
Next we evaluated Word2Vec on stocks from Russell Top 50 Index where our
Word2Vec method was also very successful in test phase and only fall behind the
Buy & Hold method in validation phase. Word2Vec achieved positive results in
all scenarios while the average yields of MA and MACD were still lower compared
to Word2Vec.
