Intelligent Investment Approaches for Mutual Funds

2021 ◽  
pp. 267-281
Author(s):  
Dipankar Majumdar ◽  
Arup Kumar Bhattacharjee ◽  
Soumen Mukherjee
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stock Market ◽  
Mutual Funds ◽  
Learning Tools ◽  
Stock Trading ◽  
Strategic Investment ◽  
The Past ◽  
The Right ◽  
Selection Of

Investment in the right fund at the right time happens to be the key to success in the stock trading business. Therefore, for strategic investment, the selection of the right opportunity has to be executed crucially so as to reap the maximum returns from the market. Predicting the stock market has always been known to be very critical and needs years of experience as it involves lots of interleaving parameters and constraints. Intelligent investment in mutual funds (MF) can be done when various machine learning tools are used to predict future fund value using the past fund value. In this chapter, an elaborate discussion is presented on the different types of mutual funds and how these data can be used in prediction by machine learning in different literature. In this work, the NAV of a total of 17 different mutual funds have been extracted from the website of AMFI, and thereafter, ANFIS is used to forecast the time series of the NAV of the MF. They have been trained using ANFIS and thereafter tested for prediction with satisfactory results.

scholarly journals Solving the "right" problems for effective machine learning driven in vitro fertilization

2021 ◽  
Author(s):  
Itay Erlich ◽  
Assaf Ben-Meir ◽  
Iris Har-Vardi ◽  
James A Grifo ◽  
Assaf Zaritsky
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Clinical Factors ◽  
Vitro Fertilization ◽  
Data Intensive ◽  
Live Embryo ◽  
The Right ◽  
Visual Properties ◽  
Selection Of

Automated live embryo imaging has transformed in-vitro fertilization (IVF) into a data-intensive field. Unlike clinicians who rank embryos from the same IVF cycle cohort based on the embryos visual quality and determine how many embryos to transfer based on clinical factors, machine learning solutions usually combine these steps by optimizing for implantation prediction and using the same model for ranking the embryos within a cohort. Here we establish that this strategy can lead to sub-optimal selection of embryos. We reveal that despite enhancing implantation prediction, inclusion of clinical properties hampers ranking. Moreover, we find that ambiguous labels of failed implantations, due to either low quality embryos or poor clinical factors, confound both the optimal ranking and even implantation prediction. To overcome these limitations, we propose conceptual and practical steps to enhance machine-learning driven IVF solutions. These consist of separating the optimizing of implantation from ranking by focusing on visual properties for ranking, and reducing label ambiguity.

scholarly journals Sustainability of ijtihad and fatwa to respond the issue of indirect contributions in matrimonial property claim

2021 ◽  
Vol 5 (S4) ◽  
Author(s):  
Mus’ab Yusoff ◽  
Nur Sarah Tajul Urus ◽  
Umair Yusoff ◽  
Mohamad Fauzi Md Thahir ◽  
Siti Nurul Husna Othman ◽  
...  
Keyword(s):  
The Past ◽  
Outdoor Work ◽  
The Right ◽  
Narrative Evaluation ◽  
Collection Methods ◽  
Selection Of

The issue of sustainability of ijtihad and fatwas to elaborate on polemics of indirect contributions in matrimonial property claims has become an important issue today. The selection of the right ijtihad and fatwa will ensure that critical discussions in this matter can be photographed to a knowledgeable society. There are many findings of jurisprudence writing that stated the main methods in ijtihad and fatwas used by fuqahak of the past and contemporary to draw interesting discussions on this issue, but in the context of indirect contributions in matrimonial property claims, there is still much to be clarified. The main objective of this study is to find out the method of ijtihad and fatwa in the book of fiqh applied by the jurists in this issue. This study is qualitative in which a total of 20 books of turath have been researched and understood descriptively. The main collection methods used were in-depth reading and analysis and narrative evaluation was used to analyze the findings obtained. The results of the analysis prove that the indirect contribution related to housework and outdoor work, can be done Akad al-Ijarah for the wife is eligible to take a certain wage according to the jumhur view.

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

A Survey on Intelligence Tools for Data Analytics

2021 ◽  
pp. 73-95
Author(s):  
Shatakshi Singh ◽  
Kanika Gautam ◽  
Prachi Singhal ◽  
Sunil Kumar Jangir ◽  
Manish Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Real Life ◽  
Learning Tools ◽  
The Core ◽  
Training Mode ◽  
Real Life Situation ◽  
Selection Of

The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.

Stock market tweets annotated with emotions

Corpora ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. 343-354
Author(s):  
Fernando J. Vieira da Silva ◽  
Norton T. Roman ◽  
Ariadne M.B.R. Carvalho
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Brazilian Portuguese ◽  
Learning Models ◽  
Stock Trading ◽  
Emotion Identification ◽  
Price Movements ◽  
Machine Learning Models ◽  
Stock Price Movements

As stock trading became a popular topic on Twitter, many researchers have proposed different approaches to make predictions on it, relying on the emotions found in messages. However, detailed studies require a reasonably sized corpus with emotions properly annotated. In this work, we introduce a corpus of tweets in Brazilian Portuguese annotated with emotions. Comprising 4,277 tweets, this is, to the best of our knowledge, the largest annotated corpus available in the stock market domain for this language. Amongst its possible uses, the corpus lends itself to the application of machine learning models for automatic emotion identification, as well as to the study of correlations between emotions and stock price movements.

scholarly journals Multidimensional Liquidity: Evidences from Indian Stock Market

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Sharad Nath Bhattacharya ◽  
Pramit Sengupta ◽  
Mousumi Bhattacharya ◽  
Basav Roychoudhury
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Markets ◽  
Trading Volume ◽  
Stock Exchange ◽  
Market Liquidity ◽  
Percentage Error ◽  
Efficiency Coefficient ◽  
Learning Tools ◽  
Indian Stock Market

Various dimensions of liquidity including breadth, depth, resiliency, tightness, immediacy are examined using BSE 500 and NIFTY 500 indices from Indian Equity market. Liquidity dynamics of the stock markets were examined using trading volume, trading probability, spread, Market Efficiency coefficient, and turnover rate as they gauge different dimensions of market liquidity. We provide evidences on the order of importance of these liquidity measures in Indian stock market using machine learning tools like Artificial Neural Network (ANN) and Random Forest (RF). Findings reveal that liquidity variables collectively explains the movements of stock markets. Both these machine learning tools performs satisfactorily in terms of mean absolute percentage error. We also evidenced lower level of liquidity in Bombay Stock Exchange (BSE) than National Stock Exchange (NSE) and findings supports the liquidity enhancement program recently initiated by BSE.

scholarly journals Review of Machine Learning Technologies and Neural Networks in Drug Synergy Combination pharmacological research

2020 ◽  
Vol 6 (3) ◽  
pp. 27-32
Author(s):  
Artur S. Ter-Levonian ◽  
Konstantin A. Koshechkin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Technologies ◽  
Learning Technology ◽  
Preclinical Research ◽  
Processing Technologies ◽  
Drug Synergy ◽  
Positive Effects ◽  
The Past ◽  
The Right

Introduction: Nowadays an increase in the amount of information creates the need to replace and update data processing technologies. One of the tasks of clinical pharmacology is to create the right combination of drugs for the treatment of a particular disease. It takes months and even years to create a treatment regimen. Using machine learning (in silico) allows predicting how to get the right combination of drugs and skip the experimental steps in a study that take a lot of time and financial expenses. Gradual preparation is needed for the Deep Learning of Drug Synergy, starting from creating a base of drugs, their characteristics and ways of interacting. Aim: Our review aims to draw attention to the prospect of the introduction of Deep Learning technology to predict possible combinations of drugs for the treatment of various diseases. Materials and methods: Literary review of articles based on the PUBMED project and related bibliographic resources over the past 5 years (2015–2019). Results and discussion: In the analyzed articles, Machine or Deep Learning completed the assigned tasks. It was able to determine the most appropriate combinations for the treatment of certain diseases, select the necessary regimen and doses. In addition, using this technology, new combinations have been identified that may be further involved in preclinical studies. Conclusions: From the analysis of the articles, we obtained evidence of the positive effects of Deep Learning to select “key” combinations for further stages of preclinical research.

Sexuality information for older people in the technological age

2004 ◽  
Vol 10 (1) ◽  
pp. 96 ◽  
Author(s):  
Juliette DG Goldman ◽  
Graham L Bradley
Keyword(s):  
Older People ◽  
Personal Relationships ◽  
Older Person ◽  
On Demand ◽  
The Past ◽  
Educational Structures ◽  
Technological Age ◽  
The Right ◽  
Personal Understanding ◽  
Selection Of

In this new millennium, in response to increasing knowledge and technological change, life-long education is becoming important for everyone, including older people. Life-long education also includes sexuality information. Everyone has the right to access sexuality information. In the past, this has been available for older people from sources such as books, magazines, peers, and television. The technological age upon us now provides yet another source. The Internet has a growing number of sites specifically for sexuality information for the older person. Such information is technology-derived, personal, instantaneous, on demand, accessible anytime, individualised, and not controlled by social, institutional, or educational structures. The opportunities this promotes are almost limitless for enhanced personal understanding and improved inter-personal relationships for older people. Here, a selection of relevant sites is identified and presented for their developmental, psychological and sociological appropriateness.

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