| dc.description.abstract | This paper focuses on utilizing LSTM networks to predict stock price volatility of S&P 500 and Yahoo 100 component stocks. Traditional stock price prediction methods primarily rely on daily price data for individual companies, such as opening price, high price, low price, closing price, and volume. In contrast, our study expands the scope of data to encompass the union of S&P 500 and Yahoo 100, totaling data for 508 companies, thereby providing a more comprehensive market perspective.
In addition to standard daily price data, this paper incorporates fundamental company information (e.g., industry category, full-time employees, year of establishment, ex-dividend date, and market capitalization) as well as macroeconomic data for the year (such as GDP-based recession indicator index, GDPNow, actual personal consumption expenditures, and actual private domestic fixed investment real-time forecasts). Furthermore, we integrate various technical indicators, including MA, daily returns, price volatility, RSI, K-line, D-line, and J-line, to enhance prediction accuracy.
The research results indicate that the multidimensional approach, in contrast to methods relying solely on daily price data (such as opening price, high price, low price, closing price, and volume), demonstrates higher accuracy in predicting stock price movements. This finding underscores the importance of considering fundamental company information, macroeconomic data, and technical indicators in stock price prediction. In comparisons among different time-series segmentation schemes, the model that predicts the next 5 days based on the previous 30 days performs the best. This may be attributed to this time frame providing sufficient data to capture market dynamics while avoiding the randomness of short-term fluctuations and the uncertainty of long-term predictions. | en_US |