Download or read book Application of Unsupervised Feature Selection Machine Learning and Evolutionary Algorithm in Predicting Stock Returns written by Tamal Chaudhuri and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Prediction of stock prices has become an important area of research in the field of financial analytics and has garnered a lot of attention among academicians. Drawing on the literature on application of econometric tools and also machine learning techniques, this paper presents a framework for predicting stock returns using three unsupervised feature selection techniques, four predictive modeling techniques and finally an ensemble combining the four predictive modeling techniques. To design the ensemble, evolutionary algorithm is applied. In order to assess the results of our study, four different performance measures, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Nash-Sutcliffe Efficiency (NSE) and Index of Agreement (IA) have been utilized. Our feature selection results indicate that all explanatory variables are not significant for different classes of companies and also for different time periods. This gives us insight into the fact that, for stock returns prediction, one has to be careful of the predictors to be chosen. Further, results indicate that for all the forecasting methods, namely, random forest, bagging, boosting and support vector regression, forecasting efficiency for large cap and mid-cap firms was better than that of small cap firms. Statistical analysis through Analysis of Variance (ANOVA) suggests that of all four predictive modeling techniques, boosting was the most efficient technique for forecasting the stock returns. We then proceeded to construct an ensemble of the above four methods. In terms of all four measurement metrics, performance of the proposed ensemble was better in both training and testing phase as compared to the efficiency of the individual predictive modeling techniques.

Download or read book Dissecting Characteristics via Machine Learning for Stock Selection written by David Dümig and published by GRIN Verlag. This book was released on 2020-01-31 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Academic Paper from the year 2019 in the subject Business economics - Investment and Finance, , language: English, abstract: We conduct a comparative analysis of methods in the machine learning repertoire, including penalized linear models, generalized linear models, boosted regression trees, random forests, and neural networks, that investors can deploy to forecast the cross-section of stock returns. Gaining more widespread use in economics, machine learning algorithms have demonstrated the ability to reveal complex, nonlinear patterns that are difficult or largely impossible to detect with conventional statistical methods and are often more robust to the effects of multi-collinearity among predictors. We provide new evidence that machine learning techniques can improve the economic value of cross-sectional return forecasts. The implications of machine learning for quantitative finance are becoming both increasingly apparent and controversial. There is a growing discussion over whether machine learning tools can and should be applied to predict stock returns with greater precision. Broadly speaking, models that can be used to explain the returns of individual stocks draw on stock and firm characteristics, such as the market price of financial instruments and companies' accounting data. These characteristics can also be used to predict expected returns out-of-sample.

Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Download or read book How can I get started Investing in the Stock Market written by Lokesh Badolia and published by Educreation Publishing. This book was released on 2016-10-27 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is well-researched by the author, in which he has shared the experience and knowledge of some very much experienced and renowned entities from stock market. We want that everybody should have the knowledge regarding the different aspects of stock market, which would encourage people to invest and earn without any fear. This book is just a step forward toward the knowledge of market.

Download or read book Predicting Stock Returns of Mid Cap Firms in India An Application of Random Forest and Dynamic Evolving Neural Fuzzy Inference System written by Tamal Datta Chaudhuri and published by . This book was released on 2016 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper examines the pattern of stock returns of mid cap Indian companies over a period of time and proposes frameworks for predictive modelling. Ten features are identified as predictors of stock returns. Subsequently two Machine Learning models, Random Forest and Dynamic Neural Fuzzy Inference System have been employed to check whether the returns can be predicted or not. Experimental setups have been designed and predictive accuracy of the respective models are evaluated using some standard measures. Further, investigation has also been made to recognize the key influential predictors by assessing their impact applying Genetic Algorithm. Our findings suggest that the returns of stocks of mid cap organizations in India can efficiently be forecasted using the frameworks discussed.

Download or read book Optimization for Decision Making II written by Víctor Yepes and published by MDPI. This book was released on 2020-11-25 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner.

Download or read book Application of Machine Learning Tools in Predictive Modeling of Pairs Trade in Indian Stock Market written by Indranil Ghosh and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper applies machine learning tools in pairs trading. Three different algorithms, namely, Support Vector Machine (SVM), Random Forest (RF) and Adaptive Neuro Fuzzy Inference System (ANFIS), have been used for predictive modeling of the value of the ratio of share prices of pairs of companies. The study considers nine different independent variables/features for forecasting. The analytical framework combines the mean reverting property of the movement of a pair of prices along with technical indicators. We also use feature selection algorithms for justification of the nine independent variables. The results support our methodology and also selection of the features for prediction.

Download or read book Applied Soft Computing and Communication Networks written by Sabu M. Thampi and published by Springer Nature. This book was released on 2021-07-01 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes thoroughly refereed post-conference proceedings of the International Applied Soft Computing and Communication Networks (ACN 2020) held in VIT, Chennai, India, during October 14–17, 2020. The research papers presented were carefully reviewed and selected from several initial submissions. The book is directed to the researchers and scientists engaged in various fields of intelligent systems.

Download or read book Third Congress on Intelligent Systems written by Sandeep Kumar and published by Springer Nature. This book was released on 2023-03-11 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of selected papers presented at the Third Congress on Intelligent Systems (CIS 2022), organized by CHRIST (Deemed to be University), Bangalore, India, under the technical sponsorship of the Soft Computing Research Society, India, during September 5–6, 2022. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers topics such as the Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber-physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision-making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human-computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro-fuzzy systems.

Download or read book International Conference on Innovative Computing and Communications written by Ashish Khanna and published by Springer Nature. This book was released on 2020-02-28 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes high-quality research papers presented at the Second International Conference on Innovative Computing and Communication (ICICC 2019), which is held at the VŠB - Technical University of Ostrava, Czech Republic, on 21–22 March 2019. Introducing the innovative works of scientists, professors, research scholars, students, and industrial experts in the fields of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.

Download or read book Handbook of Research on Smart Technology Models for Business and Industry written by Thomas, J. Joshua and published by IGI Global. This book was released on 2020-06-19 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in machine learning techniques and ever-increasing computing power has helped create a new generation of hardware and software technologies with practical applications for nearly every industry. As the progress has, in turn, excited the interest of venture investors, technology firms, and a growing number of clients, implementing intelligent automation in both physical and information systems has become a must in business. Handbook of Research on Smart Technology Models for Business and Industry is an essential reference source that discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. Featuring research on topics such as digital security, renewable energy, and intelligence management, this book is ideally designed for machine learning specialists, industrial experts, data scientists, researchers, academicians, students, and business professionals seeking coverage on current smart technology models.

Download or read book Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model written by and published by . This book was released on 2018 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, a Prediction Accuracy Based Hill Climbing Feature Selection Algorithm (AHCFS) is created and compared with an Error Rate Based Sequential Feature Selection Algorithm (ERFS) which is an existing Matlab algorithm. The goal of the study is to create a new piece of an algorithm that has potential to outperform the existing Matlab sequential feature selection algorithm in predicting the movement of S&P 500 (^GSPC) prices under certain circumstances. The two algorithms are tested based on historical data of ^GSPC, and Support Vector Machine (SVM) is employed by both as the classifier. A prediction without feature selection algorithm implemented is carried out and used as a baseline for comparison between the two algorithms. The prediction horizon set in this study for both algorithms varies from one to 60 days. The study results show that AHCFS reaches higher prediction accuracy than ERFS in the majority of the cases.

Download or read book Machine Learning in Predicting Stock Returns written by Truong Ngo Xuan and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite the efficient market hypothesis, investors are always looking for a method to predict stock price movement. In this paper, several forecasting models were developed using machine learning algorithms and a set of fundamental and macro indicators. Although the predictability was moderate, it showed promising potential for the use of machine learning in stock selection. A number of strategies based on the forecast were constructed and benchmarked against the market portfolio. The constructed portfolios had a tendency to outperform the market during a short period of low volatility but struggled in a highly volatile market.

Download or read book Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and published by Springer Nature. This book was released on 2019-11-11 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.

Download or read book Asset Allocation and Machine Learning written by Steven Wolfseher and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper is to benchmark a large set of eight contemporary machine learning algorithms in order to identify the best model for the selection of outperforming stocks in Switzerland. The selection process is modelled as a one-year ahead direction-prediction of stocks' excess returns. 65 fundamental, macroeconomic and technical variables are applied to perform the predictions and characterise the analysed 255 publicly listed Swiss companies between 2001 and 2019. The algorithms are compared with regards to their predictive power in the Swiss Performance Index (SPI). Additionally, the models' feature selection is derived and the most significant variables are analysed and discussed. Ultimately, a backtest is performed to verify the profitability of the predictions. The results indicate that ensemble models, namely XGBoost and Random Forest, are the best performing algorithms, selecting outperforming stocks with an accuracy above 80%. Furthermore, the feature selection analysis shows that the most important variables are similar throughout the best performing algorithms, creating a defining effect on the performance. Lastly, when backtested, the best two models yield average excess returns above 30%. This study contributes to extant literature, as it is the first to make such an extensive benchmark in general and specifically on a country level basis in Switzerland.

Download or read book Prediction of Stock Market Returns and Direction written by Roselyn Dimingo and published by . This book was released on 2019 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Evolutionary Machine Learning written by Wolfgang Banzhaf and published by Springer Nature. This book was released on 2023-11-01 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.