Browsing by Author "Manawadu, I"

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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.
item: Conference-Full-text
A framework to detect sale forecasting with optimum batch size
(Faculty of Information Technology, University of Moratuwa., 2021-12) Saradha, RMS; Samadhi, MA; Manawadu, I; Ganegoda, GU; Ganegoda, GU; Mahadewa, KT
Today, sales forecasting plays a key role for each business. To maintain the sales process successfully, every manufacture focus on retaining optimum production batch size. Therefore, this study aims to develop a framework to detect sale forecasting with optimum batch size. This work focuses on predict future sales and optimum production batch size by using different machine learning techniques and trying to determine the best algorithm suited to the problem. Here, Auto-Regressive Integrated Moving Average (ARIMA) model is used to predict future sales and Artificial Neural Network (ANN) model is developed to determine the optimum level of production as a function of product unit, setup cost, and holding cost in our approach and have found these models have better result than other machine learning models.