Browsing by Author "Ganegoda, U"

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    item: Conference-Abstract
    Event driven share price forecasting based on change based impact analysis
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2022-12) Bombuwala, C; Kahatapitiya, K; Kumaranayaka, R; Weerasinghe, S; Ganegoda, U; Manawadu, I; Sumathipala, KASN; Ganegoda, GU; Piyathilake, ITS; Manawadu, IN
    Investing in stocks is considered one of the riskiest options to invest due to regular unpredictable market fluctuations. It is difficult to forecast stock price variations due to this reason which makes investment or divestment decisions extremely challenging. This paper proposes a mechanism for share price forecasting by quantifying the impact of market externalities such as news events. We propose a novel multivariate approach that forecasts the behavior of stock prices — a projection modified for investor psychology and market features, more reliably compared to existing work. Our mechanism employs a strategy that models stock variations using a physical metaphor employing first-order derivatives of historical stock price and sentiment with respect to time. We do an extended forecast based on the sentimental impact on stock prices in response to an event using Kalman filtering, similarly to a trajectory of a physical object that is subject to a force. The proposed methodology achieves a significant accuracy of up to 97% for two-three days forecasts, which exceeds the forecast accuracy of related work.
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    item: Conference-Full-text
    Forecasting stock price of a company considering macroeconomic effect from news events
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2018) Waduge, N; Ganegoda, U; Wijesiriwardana, CP
    Investment perspective, Stock Market is the most popular potential investment market around the globe currently, because of this reason the need of an effective stock prediction approach was a target of many researchers. Most of the previous approaches have adopted Artificial Neural Networks and Support Vector Machine, while some have got insights from other models as well. Above traditional approaches was lacking in precise predictions of stock price fluctuations. This paper reviews the previous approaches with different Machine Learning methods and suggests a predicting method using modified Artificial Neural Networks with consideration of macro-economic effects to promise better results in stock prediction.