We predict stock markets using information contained in articles pubilshed on the Web. Mostly textual artictes appearing in the leading and the most influential financial newspapers are taken as input. From those articles the daily closing values of major stock market indices In Asia, Europe and America are predicted. Textual statements contain not only the effect (e.g., stocks down) but also the possible causes of the event (e.g., stocks down because of weakness in the dollar and consequently a weakening of the treasury bonds). Exploiting textual information therefore increases the quality of the input.The forecasts are available real-time via www.cs.ust.hk/~beat/Predict dally at 7:45 am Hong Kong time. Hence all predictions are available before the major Asian markets, Tokyo, Hong Kong and Singapore, start trading. Several techniques, such as rule-based, k-NN algorithm and neural net have been employed to produce the forecast. Those techniques are compared with one another. A trading strategy based on the system’s forecast is suggested. This strategy is shown to potentially outperform stock fund managers. This suggests that it will be extremely difficult to further improve the system’s accuracy. Hence the performance is very close to what can be expected in the best case from a system or even from human beings.
|Number of pages||6|
|Journal||HKIE Transactions Hong Kong Institution of Engineers|
|Publication status||Published - 1 Jan 1998|
- Knowledge Discovery from the internet
- Stock Market Forecast
- Textual Mining
ASJC Scopus subject areas