Philip Genetic Programming In Statistical Arbitrage

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Close range photogrammetry network design is referred to the process of placing a set of cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find the best location of two/three camera stations. The genetic algorithm optimization and Particle Swarm Optimization are developed to determine the optimal camera stations for computing the three dimensional coordinates. In this research, a mathematical model representing the genetic algorithm optimization and Particle Swarm Optimization for the close range photogrammetry network is developed. This paper gives also the sequence of the field operations and computational steps for this task. A test field is included to reinforce the theoretical aspects.

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The Stock Market is known for its volatile and unstable nature. A particular stock could be thriving in one period and declining in the next. Stock traders make money from buying equity when they are at their lowest and selling when they are at their highest. The logical question would be: "What Causes Stock Prices To Change?". At the most fundamental level, the answer to this would be the demand and supply. In reality, there are many theories as to why stock prices fluctuate, but there is no generic theory that explains all, simply because not all stocks are identical, and one theory that may apply for today, may not necessarily apply for tomorrow. This paper covers various approaches taken to attempt to predict the stock market without extensive prior knowledge or experience in the subject area, highlighting the advantages and limitations of the different techniques such as regression and classification. We formulate both short term and long term predictions. Through experimentation we achieve 81% accuracy for future trend direction using classification, 0.0117 RMSE for next day price and 0.0613 RMSE for next day change in price using regression techniques. The results obtained in this paper are achieved using only historic prices and technical indicators. Various methods, tools and evaluation techniques will be assessed throughout the course of this paper, the result of this contributes as to which techniques will be selected and enhanced in the final artefact of a stock prediction model. Further work will be conducted utilising deep learning techniques to approach the problem. This paper will serve as a preliminary guide to researchers wishing to expose themselves to this area.

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International Journal of Engineering Research and Development (IJERD)

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Earnings forecasting

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Analysis of Trends in Stock Market.pdf

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We are grateful to all JOSCM collaborators, associate editors, authors, reviewers and readers from this journal that help us, in many ways, to deliver the second edition of 2018. These eight papers illustrate from different theoretical and contextual perspectives some current topics in Operations Management and Supply Chain Management research such as risk management, international freight prices and health supply chain, among others. The last two papers are part of the Forum SIMPOI, which publishes the best papers from SIMPOI/2018 conference. I hope these papers bring relevant insights and advances for the development of our research area.

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IRJET- Stock Market Prediction using Machine Learning

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Abstract Mining profit oriented patterns is a novel technique of association rule mining in data mining, which basically focuses on important issues related with business. As it is well known that every business aims to generate the profit and find the ways to improve the same. In earlier days association rule mining was used for market basket analysis and targeted only some of the business and commercial aspects. Afterwards the researchers started to aim the most prominent element of any business i.e. Profit, and determined the innovative way to generate the association rules based on profit. Profit oriented patterns mining approach combines the statistic based pattern mining with value-based decision making to generate those patterns with the maximum profit and some ways to generate recommenders for future strategy. To achieve the desired goal the traditional association rule mining alone is not effectual, so we combine the strength of genetic algorithm with association rule mining to enhance its capability. The study shows that Genetic Algorithm improves the effectiveness and efficiency of association rule mining outcome, since genetic algorithms are competent to handle the problems related with the uncertainty, multi-dimensional, non-differential, non-continuous, and non-parametrical, non-linearity constraint and multi-objective optimization problems. In this paper we apply the concept of profit pattern mining with genetic algorithm to generate profit oriented pattern which help out in future business expansion and fulfill the business objective. Keywords: Data Mining, Association Rule Mining, Profit Pattern Mining, Genetic Algorithm

A model for profit pattern mining based on genetic algorithm

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