Browsing by Author "Syed, Shehran"

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    Estimating flood susceptibility of Bangladesh in the future year using machine learning
    (BRAC University, 2021-06) Alim, Sakib Bin; Lucky, Rakebun Islam; Ahmed, Aunindya Arif; Nahian, Prethu; Islam, MD Saiful; Syed, Shehran; Anik, Marum Monem
    Being a riverine country with more than 400 rivers, flood is a common phenomenon for Bangladesh. As, the land is less than five meters above sea level, and also due to heavy rainfall during monsoon season, it makes the country an easy target of flooding and about 30% of the total area is in danger level during this period. Additional to the yearly flooding, every 4 to 5 years there is a major flood occurs which covers more than 60% of the country. As of 22 July, 2020 alone, 102 upazila and 654 unions have been inundated in flood, affecting 3.3 million people, leaving 731,958 people water logged and a total of 93 deaths [2]. The aim of this research is to predict Bangladesh’s susceptibility to flooding so that the government as well as the people of this country can take necessary steps to lessen the effect. To predict the probability of flood we will be using some machine learning algorithm namely Linear Regression model, Random forest Regressor, Naive Bayes Theorem and Artificial Neural Network. This study is based on the data set from 1991-2013 water level and weather variables from Khulna districts Rupsa-Pasur station.
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    Modelling option prices using neural networks
    (BRAC University, 2019) Nasim, Ahmed Zohair; Syed, Shehran; Majumdar, Mahbub Alam
    In this research, modelling of the European option prices of S&P 500 index options was carried out using Multi-layer Perceptron Neural Networks. The goal was to train the neural networks using historical data to accurately determine option prices, given the index price, strike price and time to expiry as inputs. There is no hard and fast formula for pricing options, with the exception of the Black Scholes model, which is only a theoretical model and often under-performs in practical applications. Therefore, developing a model for pricing real options is of great importance, and Neural Networks have the potential to be vital vehicles to that end. That is what motivated this study. Di erent results with respect to accuracy are achieved by partitioning the data according to moneyness of options, with the Neural Network performing exceptionally for in-the-money options, but poorly for out-of-the-money options. This suggest that in a volatile market the neural network outperforms the Black Scholes model for in-the-money options, however the Black Scholes model is still better for at-the-money options.