Discovering Language of the Stocks. 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 u0026 Hold, MA and MACD according to the yield achieved. We first evaluated Word2Vec on three shares of Apple, Microsoft and CocaCola 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 u0026 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.

Enhancing Profit by Predicting Stock Prices using Deep Neural Networks Financial time series forecasting is a challenging task, which has attracted the interest of several researchers and is immensely important for investors. In this paper, we present a deep learning system, which uses a variety of data for a subset of the stocks on the NASDAQ exchange to forecast the stock price. The prediction model is trained on the minutely data for a specific stock ticker and predicts the closing price of that stock ticker for multistepahead. Our deep learning framework consists of a Variational Autoencoder for removing noise and uses timeseries data engineering to combine the higherlevel features with the original features. This new set of features is fed to a Stacked LSTM Autoencoder for multistepahead prediction of the stock closing price. Besides, this prediction is used by a profitmaximization strategy to provide advice on the appropriate time for buying and selling a specific stock. Results show that the proposed framework outperforms the stateoftheart time series forecasting approaches with respect to predictive accuracy and profitability.