  We summarized both common and novel predictive models used for stock price
prediction and combined them with technical indices, fundamental
characteristics and text-based sentiment data to predict S&P stock prices. A
66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in
individual stock directional prediction was achieved by combining different
machine learning models such as Random Forest and LSTM together into
state-of-the-art ensemble models. The data we use contains weekly historical
prices, finance reports, and text information from news items associated with
518 different common stocks issued by current and former S&P 500 large-cap
companies, from January 1, 2000 to December 31, 2019. Our study's innovation
includes utilizing deep language models to categorize and infer financial news
item sentiment; fusing different models containing different combinations of
variables and stocks to jointly make predictions; and overcoming the
insufficient data problem for machine learning models in time series by using
data across different stocks.
