Jaydip Sen

Jaydip Sen
Praxis Business School · Data Science and Artificial Intelligence

Master of Technology (Computer Science), Indian Statistical Institute, Kolkata, INDIA

About

433
Publications
285,796
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Introduction
I am a Professor of Data Science and Artificial Intelligence at Praxis Business School. My primary responsibilities include teaching, research, and consulting on broad areas of Data Science, Machine Learning, Artificial Intelligence. The particular interest of my research group is on times series analysis and forecasting using Deep Learning and Reinforcement Learning-based approaches. I have figured among the top 2% of scientists in the world in a study conducted by the Stanford University, USA.
Additional affiliations
December 2018 - June 2020
NSHM Knowledge Campus
Position
  • Professor (Full)
Description
  • I am a Professor and Head of School of Computing, Basic and Applied Sciences in NSHM College of Management and Technology, Kolkata. I am involved in planning, research, and teaching the areas of analytics, artificial intelligence and cyber security.
January 2017 - December 2018
Praxis Business School
Position
  • Professor (Full)
Description
  • I was involved in teaching and research in the broad areas of analytics and information technology applications in business, especially concentrating on Machine Learning and Deep Learning areas.
April 2014 - December 2016
Calcutta Business School
Position
  • Professor (Full)
Description
  • I am involved with research and development in the broad areas of analytic spanning over all the three areas of Data Management, Data Analytics and Data Visualization.
Education
July 1999 - July 2001
Indian Statistical Institute
Field of study
  • Computer Science
August 1984 - June 1988
Jadavpur University
Field of study
  • Mechanical Engineering

Publications

Publications (433)
Conference Paper
Full-text available
The term peer-to-peer (P2P) system encompasses a broad set of distributed applications which allow sharing of computer resources by direct exchange between systems. The goal of a P2P system is to aggregate resources available at the edge of Internet and to share it cooperatively among users. Specially, the file sharing P2P systems have become popul...
Chapter
Full-text available
This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. F...
Article
Full-text available
Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is mor...
Chapter
Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This paper presents a systematic approach to portfolio...
Presentation
Full-text available
This is the presentation of my paper (paper id: 267) which has been accepted for oral presentation and publication in the proceedings of the 2nd Asian International Conference on Innovation in Technology (ASIANCON). The conference will be organized in Pune, India, during August 26-27, 2022. The paper will be listed in the IEEE Xplore.
Preprint
Full-text available
This is the introduction to Chapter 2 in the volume titled "Analysis and Forecasting of Financial Time Series: Selected Cases", authored by Jaydip Sen, to be published in November 2022, by Cambridge Scholars Publishing Limited, Welbeck Road, Newcastle Upon Tyne, NE6 2PA, United Kingdom.
Preprint
Full-text available
This is the introduction to Chapter 4 in the volume titled "Analysis and Forecasting of Financial Time Series: Selected Cases", authored by Jaydip Sen, to be published in November 2022 by Cambridge Scholars Publishing Limited, Welbeck Road, Newcastle Upon Tyne, NE6 2PA, United Kingdom.
Preprint
Full-text available
This is the introduction to Chapter 3 in the volume titled "Analysis and Forecasting of Financial Time Series Analysis: Selected Cases", authored by Jaydip Sen, to be published in November 2022 by Cambridge Scholars Publishing Limited, Welbeck Road, Newcastle Upon Tyne, NE6 2PA, United Kingdom.
Preprint
Full-text available
This is the introduction to Chapter 5in the volume titled "Analysis and Forecasting of Financial Time Series: Selected Cases", authored by Jaydip Sen to be published in November 2022, by Cambridge Scholars Publishing Limited, Welbeck Road, Newcastle Upon Tyne, NE6 2PA, United Kingdom.
Preprint
Full-text available
This is the introduction to Chapter 6 in the volume titled "Analysis and Forecasting of Financial Time Series: Selected Cases", authored by Jaydip Sen to be published by Cambridge Scholars Publishing Limited, Welbeck Road, Newcastle Upon Tyne, NE6 2PA, United Kingdom. The volume will be published in November 2022.
Chapter
Although recurrent neural networks (RNNs) are effective in handling sequential data, they are poor in capturing the long‐term dependencies in the data due to a problem known as vanishing and exploding gradients. A variant of RNNs known as Long‐and‐Short‐Term Memory (LSTM) networks effectively gets rid of the problem, and hence these networks are pr...
Technical Report
Full-text available
Stock market offers platform where people buy and sell shares of publicly listed companies. Generally stock prices are quite volatile; hence predicting them is daunting task. There are still many researches going to develop more accuracy in stock price prediction. Portfolio construction refers to allocation of different sector stocks optimally to a...
Preprint
Full-text available
The stock market offers a platform where people buy and sell shares of publicly listed companies. Generally, stock prices are quite volatile; hence predicting them is a daunting task. There is still much research going to develop more accuracy in stock price prediction. Portfolio construction refers to the allocation of different sector stocks opti...
Book
With this rapid transformation of the computing and communication world, information-system security has moved from a largely self-contained bounded environment interacting with a generally known and disciplined user community to a worldwide scope with a body of users that may not be known and are not necessarily trusted. Importantly, security cont...
Preprint
Full-text available
This is the introduction to Chapter 1 in the volume titled "Analysis and Forecasting of Financial Time Series: Selected Cases", authored by Jaydip Sen, to be published in November 2022, by Cambridge Scholars Publishing Limited, Welbeck Road, Newcastle Upon Tyne, NE6 2PA, United Kingdom.
Book
Full-text available
This book is a collection of real-world cases, illustrating how to handle challenging and volatile financial time series data for a better understanding of their past behavior and robust forecasting of their future movement. It demonstrates how the concepts and techniques of statistical, econometric, machine learning, and deep learning are applied...
Preprint
Full-text available
The final version of this chapter will be published in the volume titled "Encyclopedia of Data Science and Machine Learning", editor: John Wang. The book will be published by IGI Global, USA, in June 2022.
Chapter
Full-text available
This chapter proposes several machine learning and deep learning-based predictive models for predicting the NIFTY 50 index movement in the National Stock Exchange (NSE) of India. We use the daily stock price values for the period January 4, 2010, to December 31, 2018, as the training dataset for building the models, and apply the models to predict...
Chapter
Full-text available
In this chapter, we study the causal relationship between the Indian IT sector index and the DJIA index of the USA using their historical records. We apply several statistical, machine learning, and deep learning models for examining their forecasting accuracy on the future values of the IT and the DJIA index. The learning-based models proposed in...
Chapter
Full-text available
This chapter proposes a granular approach to forecasting stock price and the price movement pattern by combining several statistical, machine learning, and deep learning methods. We present several methods for predicting the short-term stock price movement patterns using classification and regression techniques and compare their performance. We bel...
Chapter
Full-text available
This chapter proposes a granular approach to stock price prediction by combining several statistical and machine learning methods of prediction on technical analysis of stock prices. We present various approaches for short-term stock price movement forecasting using multiple classification approaches and regression techniques and compare their perf...
Chapter
Full-text available
This chapter studies the behavior of India’s information technology (IT) sector as it is one of the important sectors of the country’s economy. A gamut of statistical, machine learning, and deep learning models is proposed to analyze the close values of the IT sector index. Both regression and classification methods are used for predicting the futu...
Chapter
Full-text available
This chapter emphasizes that understanding the sectoral characteristics is an important task before making a practical portfolio choice. The work described in the chapter also demonstrates that the sectors are different in terms of their trend, seasonal and random components of their respective time series of historical index values. The R programm...
Chapter
Full-text available
This chapter proposes several machine learning and deep learning-based predictive models for predicting NIFTY 50 stock price movements in the NSE of India. We use daily stock index values from December 29, 2014, to December 28, 2018, as the training dataset to build the models and apply them to predict stock index movement and the stock index's clo...
Conference Paper
Portfolio optimization is a challenging problem that has attracted considerable attention and effort from researchers. The optimization of stock portfolios is a particularly hard problem since the stock prices are volatile and estimation of their future volatilities and values, in most cases, is very difficult, if not impossible. This work uses thr...
Preprint
Full-text available
This is the introduction of the paper entitled "A Forecasting Framework for the Indian Healthcare Sector Index " authored by Jaydip Sen. The paper has been accepted for publication in the International Journal of Business Forecasting and Marketing Intelligence (IJBFMI) - a journal published by the Inderscience Publishers.
Preprint
Full-text available
Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the optimized values of return and risk. We present optimized portfolios based on the seven sectors of the Indian economy. The past pric...
Preprint
Full-text available
Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio with weights allocated to the stocks in a way that optimizes its return and the risk. This paper presents a systematic approach towards building two types of portfolios, optimum risk, and eigen, for four critical...
Preprint
Full-text available
Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic...
Preprint
Full-text available
Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This chapter presents a systematic approach to portfoli...
Technical Report
Full-text available
This technical report is on the Capstone project done in partial fulfillment of the requirements for the Post Graduate Program in Data Science at Praxis Business School, Kolkata, Group-2 (Spring Batch 2021)
Preprint
Full-text available
Stock price prediction is a challenging task and a lot of propositions exist in the literature in this area. Portfolio construction is a process of choosing a group of stocks and investing in them optimally to maximize the return while minimizing the risk. Since the time when Markowitz proposed the Modern Portfolio Theory, several advancements have...
Presentation
Full-text available
This is the presentation of our paper titled "A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks" which has been accepted for oral presentation and publication in the proceedings of the International Conference ICCIDA'22, Jan 8-9, 2022, Hyderabad, India.
Preprint
Full-text available
Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and thei...
Chapter
Designing predictive models for forecasting future stock price has always been a very popular area of research. On the one hand, the proponents of the famous efficient market hypothesis believe that it is impossible to accurately predict stock prices, on the other hand, propositions exist in the literature that demonstrate that it is possible to ve...
Book
Full-text available
Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and thei...
Article
Forecasting of future stock prices is a complex and challenging research problem due to the random variations that the times series of these variables exhibit. In this work, we study the behaviour exhibited by the healthcare sector time series of India in the Bombay stock exchange (BSE). We collect the historical monthly index values of the BSE S&P...
Preprint
Full-text available
A recommender system, also known as a recommendation system, is a type of information filtering system that attempts to forecast a user's rating or preference for an item. This article designs and implements a complete movie recommendation system prototype based on the Genre, Pearson Correlation Coefficient, Cosine Similarity, KNN-Based, Content-Ba...
Presentation
Full-text available
This is the presentation of our paper titled "A Comparative Study of Portfolio Optimization Using Optimum Risk and Hierarchical Risk Parity Approaches" that has been accepted for oral presentation and publication in the proceedings of the 8th International Conference on Business Analytics and Intelligence (ICBAI’21).
Presentation
Full-text available
This is the presentation of our paper entitled "Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model" which has been accepted for oral presentation and publication in the proceedings of the International Conference IEEE INDICON'21, Guwahati, India.
Presentation
Full-text available
This is the presentation of our paper titled "Risk-Based Portfolio Optimization on Some Selected Sectors of the Indian Stock Market", which is accepted for oral presentation and publication in the proceedings of the "International Conference On Big Data, Machine Learning and Applications (BigDML 2021), December 19-20, 2021, NIT, Silchar, India.
Conference Paper
Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio with weights allocated to the stocks in a way that optimizes its return and the risk. This paper presents a systematic approach towards building two types of portfolios, optimum risk, and eigen, for four critical...
Presentation
Full-text available
This is the presentation of our work on portfolio optimization titled "Portfolio Optimization Using Deep Learning Models – A Comparative Study of Risk-Based Portfolio Design Approaches".
Presentation
Full-text available
This is the presentation of our paper titled "Precise Stock Price Prediction for Robust and Optimized Portfolio Design Using an LSTM Model" that has been accepted for oral presentation and publication in the proceedings of the 19th OTIS International Conference on Information Technology (IEEE OCIT'21). The paper will be listed in the IEEE Xplore di...
Presentation
Full-text available
This is the presentation of our paper entitled "Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks ", which has been accepted for oral presentation and publication in the proceedings of the "2021 IEEE International Conference on Decision Aid Sciences and Application (DASA'21)". The conference will be organized online...
Preprint
Full-text available
This is the abstract and the introduction of the paper that was presented in the International Conference IEEE DASA'21. The conference was organized online by the University of Bahrain and the IEEE Bahrain Chapter during December 7 - 8, 2021. The full paper will be available on the IEEE Xplore online repository.
Conference Paper
Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic...
Conference Paper
Full-text available
Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the optimized values of return and risk. We present optimized portfolios based on the seven sectors of the Indian economy. The past pric...
Preprint
div>Predictive model design for accurately predicting future stock prices has always been considered an interesting and challenging research problem. The task becomes complex due to the volatile and stochastic nature of the stock prices in the real world which is affected by numerous controllable and uncontrollable variables. This paper presents an...
Preprint
div>Predictive model design for accurately predicting future stock prices has always been considered an interesting and challenging research problem. The task becomes complex due to the volatile and stochastic nature of the stock prices in the real world which is affected by numerous controllable and uncontrollable variables. This paper presents an...
Presentation
Full-text available
This is the video presentation of our paper entitled "Hierarchical Risk Parity and Minimum Variance Portfolio Design on �NIFTY 50 Stocks" which will be presented online in the International Conference on Decision Aid Sciences and Applications (DASA'21) on December 8, 2021. The conference will be organized by University of Bahrain and IEEE Bahrain i...
Preprint
Predictive model design for accurately predicting future stock prices has always been considered an interesting and challenging research problem. The task becomes complex due to the volatile and stochastic nature of the stock prices in the real world which is affected by numerous controllable and uncontrollable variables. This paper presents an opt...
Preprint
Full-text available
This is the abstract and the introduction of our paper that has been presented and due to be published in the proceedings of the International Conference IEEE DATA'21. The conference was organized virtually by the University of Bahrain during October 25-26, 2021. The paper will be available in IEEE Xplore.
Conference Paper
Full-text available
Predicting future stock prices and their movement patterns is a complex problem. Hence, building a portfolio of capital assets using the predicted prices to achieve the optimization between its return and risk is an even more difficult task. This work has carried out an analysis of the time series of the historical prices of the top five stocks fro...
Conference Paper
Full-text available
For a long-time, researchers have been developing a reliable and accurate predictive model for stock price prediction. According to the literature, if predictive models are correctly designed and refined, they can painstakingly and faithfully estimate future stock values. This paper demonstrates a set of time series, econometric, and various learni...
Presentation
Full-text available
This video presentation is based on our paper titled "Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model" that has been accepted for oral presentation and publication in the proceedings of the IEEE International Conference on Data Analytics for Business and Industry (IEEE DATA'21). The paper will be published in IEEE Xplore.
Presentation
Full-text available
This is the presentation of our paper titled "Precise Stock Price Prediction for Robust and Optimized Portfolio Design Using an LSTM Model" that has been accepted for oral presentation and publication in the 5th IEEE International Conference on Intelligent Computing and Data Sciences (ICDS'21) which will be organized during October 2-22, 2021, at F...
Presentation
Full-text available
This is the presentation for our paper titled "Stock Portfolio Optimization Using a Deep Learning LSTM Model " that has been accepted for oral presentation in IEEE MysuruCon 2021, and publication in the IEEE Xplore. The conference will be organized in Mysore during October 24 - 25, 2021.
Presentation
Full-text available
This presentation is based on our paper titled "Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model" that has been accepted for oral presentation and publication in the proceedings of the IEEE International Conference on Data Analytics for Business and Industry (IEEE DATA'21). The paper will be published in IEEE Xplore.
Preprint
Prediction of future movement of stock prices has been the subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted accurately. On the other hand, there are propositions that have shown that, if appropriately modelled, stock prices can be predicted fairl...
Preprint
Prediction of future movement of stock prices has been the subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted accurately. On the other hand, there are propositions that have shown that, if appropriately modelled, stock prices can be predicted fairl...
Chapter
Full-text available
The paradigm of machine learning and artificial intelligence has pervaded our everyday life in such a way that it is no longer an area for esoteric academics and scientists putting their effort to solve a challenging research problem. The evolution is quite natural rather than accidental. With the exponential growth in processing speed and with the...
Preprint
The paradigm of machine learning and artificial intelligence has pervaded our everyday life in such a way that it is no longer an area for esoteric academics and scientists putting their effort to solve a challenging research problem. The evolution is quite natural rather than accidental. With the exponential growth in processing speed and with the...
Preprint
p>Designing efficient and robust algorithms for accurate prediction of stock market prices is one of the most exciting challenges in the field of time series analysis and forecasting. With the exponential rate of development and evolution of sophisticated algorithms and with the availability of fast computing platforms, it has now become possible t...
Preprint
p>Prediction of stock prices using time series analysis is quite a difficult and challenging task since the stock prices usually depict random patterns of movement. However, the last decade has witnessed rapid development and evolution of sophisticated algorithms for complex statistical analysis. These algorithms are capable of processing a large v...
Preprint
Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modeled, stock prices can be predicted with a high level of a...
Preprint
Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modeled, stock prices can be predicted with a high level of a...
Preprint
p>Time series analysis and forecasting of stock market prices has been a very active area of research over the last two decades. Availability of extremely fast and parallel architecture of computing and sophisticated algorithms has made it possible to extract, store, process and analyze high volume stock market time series data very efficiently. In...
Preprint
p>Prediction of stock prices using time series analysis is quite a difficult and challenging task since the stock prices usually depict random patterns of movement. However, the last decade has witnessed rapid development and evolution of sophisticated algorithms for complex statistical analysis. These algorithms are capable of processing a large v...
Preprint
p>Designing efficient and robust algorithms for accurate prediction of stock market prices is one of the most exciting challenges in the field of time series analysis and forecasting. With the exponential rate of development and evolution of sophisticated algorithms and with the availability of fast computing platforms, it has now become possible t...
Preprint
p>Time series analysis and forecasting of stock market prices has been a very active area of research over the last two decades. Availability of extremely fast and parallel architecture of computing and sophisticated algorithms has made it possible to extract, store, process and analyze high volume stock market time series data very efficiently. In...
Conference Paper
Full-text available
Predictive model design for accurately predicting future stock prices has always been considered an interesting and challenging research problem. The task becomes complex due to the volatile and stochastic nature of the stock prices in the real world which is affected by numerous controllable and uncontrollable variables. This paper presents an opt...
Presentation
Full-text available
This is the presentation of our paper in the 2nd Deep Learning Developers' Conference (DLDC'21) organized by the Association of Data Scientists. The presentation will be made online on September 24, 2021.
Conference Paper
Full-text available
Volatility clustering is an important characteristic that has a significant effect on the behavior of stock markets. However, designing robust models for the accurate prediction of future volatilities of stock prices is a very challenging research problem. We present several volatility models based on generalized autoregressive conditional heterosc...
Presentation
Full-text available
This is the presentation of our paper titled "Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH ". The paper will be presented in the IEEE International Conference ASIANCON'21 on August 28, 2021 in Pune, INDIA.
Preprint
Full-text available
For a long-time, researchers have been developing a reliable and accurate predictive model for stock price prediction. According to the literature, if predictive models are correctly designed and refined, they can painstakingly and faithfully estimate future stock values. This paper demonstrates a set of time series, econometric, and various learni...
Preprint
Prediction of stock prices using econometrics and machine learning based approaches poses significant challenges to the research community since the movement of stock prices are essentially random in its nature. However, significant development and rapid evolution of sophisticated and complex algorithms which are capable of analyzing large volume o...
Preprint
Full-text available
Prediction of stock prices using econometrics and machine learning based approaches poses significant challenges to the research community since the movement of stock prices are essentially random in its nature. However, significant development and rapid evolution of sophisticated and complex algorithms which are capable of analyzing large volume o...
Preprint
Full-text available
This is a selected part of our paper entitled "Stock Portfolio Optimization Using a Deep Learning LSTM Model ". The paper has been accepted for oral presentation and publication in the proceedings of IEEE MYSURUCON 2021 which will be organized in Mysore, India, during October 24-25, 2021.
Research Proposal
Full-text available
This is the proposal for our work to be submitted in the 8th International Conference on Business Analytics and Intelligence (ICBAI) which will be organized at the Indian Institute of Science (IISc), Bangalore, INDIA during December 20-22, 2021.
Research Proposal
Full-text available
This is a proposal for a chapter for inclusion in the edited volume titled "Encyclopedia of Data Science and Machine Learning", editor: John Wang, Montclair State University, United States. The book will be published by IGI Global, USA in 2022.
Preprint
Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices...
Preprint
Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices...
Preprint
Full-text available
This chapter will appear in the following forthcoming edited volume which will be published by Wiley International in 2021. Seth, A. and Murugesan, S (eds). Emerging Computing Paradigms: Principles, Advances and Applications” (ECPPAA)
Preprint
Full-text available
Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of resear...
Preprint
Full-text available
Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of resear...
Chapter
Full-text available
Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. The well-known efficient market hypothesis believes in the impossibility of accurate prediction of future stock prices in an efficient stock market as the stock prices are assumed to be purely stochastic...
Preprint
Designing an optimum portfolio that allocates weights to its constituent stocks in a way that achieves the best trade-off between the return and the risk is a challenging research problem. The classical mean-variance theory of portfolio proposed by Markowitz is found to perform sub-optimally on the real-world stock market data since the error in es...
Presentation
Full-text available
This presentation is based on our paper that has been accepted for oral presentation and publication in the proceedings of the 2011 IEEE International Conference on Intelligent Technologies (IEEE CONIT'21). The conference will be organized in Hubli, Karnataka, India from June 25 - 27, 2021.
Article
Full-text available
Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demon...