Abstract
Technological developments, competitive economics climate and demanding competition have led the investment industry to experienced rapid and continuous development in the last few decades.
 Some of the rapid and continuous key developments are transformation in financial microstructures, development of investment strategies, the progression in computing capacity and the new trend of the investment performance of pioneers in algorithmic traders surpassing that of the human, discretionary investors (Jansen, Stefan 2017)
 These four key factors have driven the investment company and hedge fund to develop algorithmic trading methods even further to achieve a more stable and reliable profit over time.
 Therefore, to manifest aforementioned concerns, this research will conduct the process of building hybrid machine learning in Dow Jones Industrial Average stocks by using Long Short Term Memory (LSTM) Method to improve the investment profitability using phyton programming language.
 The Result of this research shows that the prediction made by the software has acceptable rate of errors. The several measurements of errors used are namely, Median Absolute Error, Mean Absolute Percentage Error and Median Absolute Percentage Error.
 
 Keywords: Artificial Intelligence, Stock Market, Data Science , LSTM, Phyton
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