Discovering Language of the Stocks

TL;DR

This paper introduces a novel NLP-based approach using Word2Vec and Japanese candlestick language for stock trend prediction, outperforming traditional models in multiple scenarios.

Contribution

It combines NLP techniques with financial data by creating a language of candlesticks, enabling improved stock trend prediction over existing models.

Findings

Word2Vec outperformed Buy & Hold, MA, and MACD in yield on tested stocks.

The approach was successful on individual stocks and index stocks.

Positive results were consistent across different market scenarios.

Abstract

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.