Multivariate forecasting on financial time series with transformer model - Poh, Jeanette Wen Jun (Nanyang Technological University, 2023)
The stock market provides an open platform for companies to raise capital for funding necessary operations and expanding their business, which is essential in driving world economic growth. Given its importance for making profitable trading decisions, stock price prediction has always been an active area of interest among investors and quantitative researchers. Although various machine learning models have been used extensively for stock prediction, these techniques suffer from several drawbacks which make them unsuitable for modelling financial time series. Another limitation of existing studies is that they focus primarily on single-stock prediction and did not consider the information of other stocks during forecasting. As the prices of multiple assets are often correlated, this approach is insufficient because it disregards the underlying relations between stocks.