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International Journal of Engineering and Advanced Technology, 2019
Optimization problems are different from other mathematical problems in that they are able to dis... more Optimization problems are different from other mathematical problems in that they are able to discover solutions which are ideal or near ideal in accordance to the goals. Problems are not solved in one step, but we follow different sequence of steps to reach the solution. The steps could be to define problems, construct and solve models and evaluate and implement solutions. This paper presents an overall outlook of how a problem of optimization type can be solved
Finance and especially computational finance is one of the areas in which Artificial Intelligence... more Finance and especially computational finance is one of the areas in which Artificial Intelligence and machine learning has found a deep impact in the way financial problems are handled. As compared to traditional approaches the machine learning techniques have greater ease of use, higher degree of accuracy and the evaluation time to get the end result has been considerably reduced. In this paper, a general way of assessing risks and prediction of returns of different types of stocks using various machine learning techniques is reviewed and discussed and compared with the traditional methods. Earlier, more statistical and numerical methods were used for the same purpose. However, the nature of data in financial time series is nonlinear and chaotic. They do not follow linear characteristics, and are often a combination of white noise. To interpret this data meaningfully requires removal of noise and learning only the data that is actually suitable for analysis. Hence machine learning ...
Prediction tasks are often carried out efficiently by soft computing methods. This paper presents... more Prediction tasks are often carried out efficiently by soft computing methods. This paper presents how techniques in machine learning and soft computing areas can be easily applied to problems in computational finance. One such prevalent problems is that of portfolio allocation. The typical case is that of predicting stocks with high returns and allocating them to the basket of portfolios. This process is known as the stock selection in portfolio construction. It is about this problem, the paper is designed to address with the help of machine learning task especially one of the supervised learning methods, Artificial Neural Networks. Once this task is accomplished, to find out an efficient portfolio, among a basket of financial portfolios, applied various approaches. One such approach is to compute the minimum variance portfolio subject to the target return. This is the basis of Mean variance theory put forward by Markowitz. Based on this approach the neural network is trained to att...
International Journal of Engineering and Advanced Technology, 2019
Optimization problems are different from other mathematical problems in that they are able to dis... more Optimization problems are different from other mathematical problems in that they are able to discover solutions which are ideal or near ideal in accordance to the goals. Problems are not solved in one step, but we follow different sequence of steps to reach the solution. The steps could be to define problems, construct and solve models and evaluate and implement solutions. This paper presents an overall outlook of how a problem of optimization type can be solved
Finance and especially computational finance is one of the areas in which Artificial Intelligence... more Finance and especially computational finance is one of the areas in which Artificial Intelligence and machine learning has found a deep impact in the way financial problems are handled. As compared to traditional approaches the machine learning techniques have greater ease of use, higher degree of accuracy and the evaluation time to get the end result has been considerably reduced. In this paper, a general way of assessing risks and prediction of returns of different types of stocks using various machine learning techniques is reviewed and discussed and compared with the traditional methods. Earlier, more statistical and numerical methods were used for the same purpose. However, the nature of data in financial time series is nonlinear and chaotic. They do not follow linear characteristics, and are often a combination of white noise. To interpret this data meaningfully requires removal of noise and learning only the data that is actually suitable for analysis. Hence machine learning ...
Prediction tasks are often carried out efficiently by soft computing methods. This paper presents... more Prediction tasks are often carried out efficiently by soft computing methods. This paper presents how techniques in machine learning and soft computing areas can be easily applied to problems in computational finance. One such prevalent problems is that of portfolio allocation. The typical case is that of predicting stocks with high returns and allocating them to the basket of portfolios. This process is known as the stock selection in portfolio construction. It is about this problem, the paper is designed to address with the help of machine learning task especially one of the supervised learning methods, Artificial Neural Networks. Once this task is accomplished, to find out an efficient portfolio, among a basket of financial portfolios, applied various approaches. One such approach is to compute the minimum variance portfolio subject to the target return. This is the basis of Mean variance theory put forward by Markowitz. Based on this approach the neural network is trained to att...
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Papers by Nikhitha Pai