scholarly journals Recommending System for Penny Stock Trading

2019 ◽  
Vol 9 (1S5) ◽  
pp. 85-91
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Reinforcement Learning ◽  
Test Data ◽  
Stock Price ◽  
Prediction Models ◽  
Poor Performance ◽  
Stock Trading ◽  
Financial Metrics ◽  
Penny Stocks

Penny stocks at times makes the investors wealthy by turning to be a multi-bagger stocks or erode the wealth of the investors with poor performance in volatile conditions. While there are many machine learning-based prediction models that are used for stock price evaluation, very few studies have focused on the dynamics to be considered in penny stock conditions. Though the pattern might remain the same for normal stocks and the penny stock classification, still some of the parameters to be evaluated in the process needs changes. The model discussed in this report is a comprehensive solution discussed as scope for evaluation of the penny stock pick, using trading and reporting financial metrics. Experimental study of the test data indicates that the model is potential and if can be used effectively with reinforcement learning pattern, it can turn to be sustainable solution.

Prediction of the voluntary intake of grass silages by beef cattle 3. Precision of alternative prediction models

1990 ◽  
Vol 50 (3) ◽  
pp. 455-466 ◽  
Author(s):  
A. J. Rook ◽  
M. S. Dhanoa ◽  
M. Gill
Keyword(s):  
Beef Cattle ◽  
Least Squares ◽  
Test Data ◽  
Ridge Regression ◽  
Prediction Models ◽  
Agricultural Research ◽  
Poor Performance ◽  
Least Squares Regression ◽  
Agricultural Research Council ◽  
New Models

ABSTRACTThe precision of a number of new models for predicting silage intake by beef cattle was investigated with independent data using the mean-square prediction error and compared with two previously published models (Agricultural Research Council, 1980; Lewis, 1981). The new models generally performed well relative to the previous models.The new models included a number constructed using the technique of ridge regression which were shown to be consistently better predictors than the models obtained from the same estimation data by stepwise least-squares regression. Better prediction was also obtained by reducing the number of variables in the least-squares models below that required to maximize R2 in the estimation data. The poor performance of the least-squares models with the best R2 may be attributed to collinearity between the independent variates in the estimation data.Most of the models considered overpredicted relative to observed intakes. This may have been the result of differences in breed type and management of the animals between the test data and the estimation data used to construct the models, that is the use of the models with the test data involved a degree of extrapolation.It is concluded that ridge regression and deletion of variables offer a positive step forward in intake prediction compared with models based on maximizing R2 in the estimation data. However, further work is needed to clarify the effect of factors such as breed and rearing system on intake and to clarify the usefulness of various fibre measures in intake prediction. A number of new models are proposed which utilize a range of input variables thus allowing flexibility in their use in practical situations.

scholarly journals Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events

2021 ◽  
Author(s):  
Michael Schrempf ◽  
Diether Kramer ◽  
Stefanie Jauk ◽  
Sai P. K. Veeranki ◽  
Werner Leodolter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Test Data ◽  
Cardiovascular Events ◽  
Prediction Models ◽  
Early Stage ◽  
Training Data ◽  
Major Adverse Cardiovascular Events ◽  
Future Research ◽  
Patients At Risk

Background: Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions. Objectives: The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE. Methods: The data used for modelling included electronic medical records of more than 128,000 patients including 29,262 patients with MACE. A feature selection based on filter and embedded methods resulted in 826 features for modelling. Different machine learning methods were used for modelling on the training data. Results: A random forest model achieved the best calibration and discriminative performance on a separate test data set with an AUROC of 0.88. Conclusion: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.

scholarly journals A study on handling intrinsic motivation for devising sample efficient actor-critic agents

2021 ◽  
Author(s):  
André Quadros ◽  
Roberto Xavier Junior ◽  
Kleber Souza ◽  
Bruno Gomes ◽  
Filipe Saraiva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Reinforcement Learning ◽  
Intrinsic Motivation ◽  
Sampling Efficiency ◽  
Practical Guidelines

Reinforcement learning has evolved in recent years,and overcoming challenges found in this field. This area, unlikeconventional machine learning, does not learn through a setof observational instances, but through interaction with anenvironment. The sampling efficiency of a reinforcement learningagent is a challenge. That is, how to make an agent learn withinan environment with as little interaction as possible. In this workwe perform an experimental study on the difficulties to integratea strategy of intrinsic motivation to an actor-critic agent toimprove the sampling efficiency. We found results that point to theeffectiveness of the intrinsic motivation as a approach to improvethe agent’s sampling efficiency, as well as its performance. Weshare practical guidelines to assist in the implementation of actor-critic agents to deal with sparse reward environments whilemaking use of intrinsic motivation feedback.

scholarly journals Prediction of Stock Index of Tata Steel using Hybrid Machine Learning based Optimization Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 3186-3193
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Prediction Models ◽  
Market Price ◽  
Optimization Techniques ◽  
Stock Index ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Stock Price Prediction ◽  
Hybrid Machine

The trend of stock price prediction has always been in the focal point of analytical activity in financial domain for both the researchers and investors. Prediction with accuracy is very essential for improved investment decisions that imbibe minimum risk factors. Due to this, majority of investors depend upon that intelligent trading system which generates better forecasting results. As forecasting stock market price with high accuracy is quite a challenging task for the analysts, machine learning has been adopted as one of the popular techniques to predict future trends. Even if there are many recognized analytical time series analysis that are categorized either under soft computing or under conventional statistical techniques like fuzzy logic, artificial neural networks and genetic algorithms, researchers have been looking for more appropriate techniques which can exhibit improved results. In this paper, we developed different hybrid machine learning based prediction models and compared their efficiency. Dimension reduction techniques such as orthogonal forward selection (OFS) and kernel principal component analysis (KPCA) are used separately with support vector regression (SVR) and teaching learning based optimization (TLBO) to predict the stock price of Tata Steel. The performance of both the proposed approach is evaluated with 4143days daily transactional data of Tata steels stocks price, which was collected from Bombay Stock Exchange (BSE). We compared the results of both OFS-SVR-TLBO and KPCA-SVR-TLBO hybrid models and concludes that by incorporating KPCA is more practicable and performs better results than OFS

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 Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 612
Author(s):  
Young Jae Kim ◽  
Ji Soo Jeon ◽  
Seo-Eun Cho ◽  
Kwang Gi Kim ◽  
Seung-Gul Kang
Keyword(s):  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Test Data ◽  
Prediction Models ◽  
Prediction Performance ◽  
Support Vector ◽  
Obstructive Sleep ◽  
Clinical Indices ◽  
Sensitivity Specificity

This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, n = 213; no OSA, n = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, n = 149; no OSA, n = 46) and test data (OSA, n = 64; no OSA, n = 20). Using the seven clinical indices, the OSA prediction models were trained using four types of machine learning models—logistic regression, support vector machine (SVM), random forest, and XGBoost (XGB)—and each model was validated using the test data. In the validation, the SVM showed the best OSA prediction result with a sensitivity, specificity, and area under curve (AUC) of 80.33%, 86.96%, and 0.87, respectively, while the XGB showed the lowest OSA prediction performance with a sensitivity, specificity, and AUC of 78.69%, 73.91%, and 0.80, respectively. The machine learning algorithms showed high OSA prediction performance using data from South Koreans with suspected OSA. Hence, machine learning will be helpful in clinical applications for OSA prediction in the Korean population.

scholarly journals Machine learning-based prediction of in-hospital mortality using admission laboratory data: A retrospective, single-site study using electronic health record data

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246640
Author(s):  
Tomohisa Seki ◽  
Yoshimasa Kawazoe ◽  
Kazuhiko Ohe
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Test Data ◽  
Random Forests ◽  
Prediction Models ◽  
Laboratory Data ◽  
Data Set ◽  
Prediction Capability

Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient’s severity. Because recent machine learning application in the clinical area has been shown to enhance prediction ability, applying this technique to this issue can lead to an accurate prediction model for in-hospital mortality prediction. In this study, we aimed to generate an accurate prediction model of in-hospital mortality using machine learning techniques. Patients 18 years of age or older admitted to the University of Tokyo Hospital between January 1, 2009 and December 26, 2017 were used in this study. The data were divided into a training/validation data set (n = 119,160) and a test data set (n = 33,970) according to the time of admission. The prediction target of the model was the in-hospital mortality within 14 days. To generate the prediction model, 25 variables (age, sex, 21 laboratory test items, length of stay, and mortality) were used to predict in-hospital mortality. Logistic regression, random forests, multilayer perceptron, and gradient boost decision trees were performed to generate the prediction models. To evaluate the prediction capability of the model, the model was tested using a test data set. Mean probabilities obtained from trained models with five-fold cross-validation were used to calculate the area under the receiver operating characteristic (AUROC) curve. In a test stage using the test data set, prediction models of in-hospital mortality within 14 days showed AUROC values of 0.936, 0.942, 0.942, and 0.938 for logistic regression, random forests, multilayer perceptron, and gradient boosting decision trees, respectively. Machine learning-based prediction of short-term in-hospital mortality using admission laboratory data showed outstanding prediction capability and, therefore, has the potential to be useful for the risk assessment of patients at the time of hospitalization.

scholarly journals Building Prediction Models for 30-Day Readmissions Among ICU Patients Using Both Structured and Unstructured Data in Electronic Health Records

2021 ◽  
Author(s):  
Alex Moerschbacher ◽  
Zhe He
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Poor Performance ◽  
Readmission Rates ◽  
Icu Patients ◽  
Increased Risk ◽  
Hospital Deaths ◽  
Poor Outcomes ◽  
Mimic Iii ◽  
Discharge Summaries

ICU readmissions are associated with poor outcomes for patients and poor performance of hospitals. Patients who are re-admitted have an increased risk of in-hospital deaths; hospitals with a higher readmission rate have reduced profitability, due to an increase in cost and reduced payments from Medicare and Medicaid programs. Predicting a patient's likelihood of being readmitted to the ICU can help reduce early discharges, the risk of in-hospital deaths, and help increase profitability. In this study, we built and evaluated multiple machine learning models to predict 30-day readmission rates of ICU patients in the MIMIC-III database. We used both the structured data including demographics, laboratory tests, comorbidities, and unstructured discharge summaries as the predictors and evaluated different combinations of features. The best performing model in this study Logistic Regression achieved an AUROC of 75.7%. This study shows the potential of leveraging machine learning and deep learning for predicting ICU readmissions.

scholarly journals Stock Trading System Based on Machine Learning and Kelly Criterion in Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lili Chen ◽  
Lingyun Sun ◽  
Chien-Ming Chen ◽  
Mu-En Wu ◽  
Jimmy Ming-Tai Wu
Keyword(s):  
Genetic Algorithm ◽  
Internet Of Things ◽  
Financial Services ◽  
Stock Price ◽  
Prediction Models ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Financial Industry ◽  
Stock Trading ◽  
Kelly Criterion

The evolution of the Internet of Things (IoT) has promoted the prevalence of the financial industry as a variety of stock prediction models have been able to accurately predict various IoT-based financial services. In practice, it is crucial to obtain relatively accurate stock trading signals. Considering various factors, finding profitable stock trading signals is very attractive to investors, but it is also not easy. In the past, researchers have been devoted to the study of trading signals. A genetic algorithm (GA) is often used to find the optimal solution. In this study, a long short-term (LSTM) memory neural network is used to study stock price fluctuations, and then, genetic algorithms are used to obtain appropriate trading signals. A genetic algorithm is a search algorithm that solves optimization. In this paper, the optimal threshold is found to determine the trading signal. In addition to trading signals, a suitable trading strategy is also crucial. In addition, this research uses the Kelly criterion for fund management; that is, the Kelly criterion is used to calculate the optimal investment score. Effective capital management can not only help investors increase their returns but also help investors reduce their losses.

scholarly journals Predicting Nigerian Stock Returns using Technical Analysis and Machine Learning

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
D. O. Oyewola ◽  
Emmanuel Gbenga Gbenga Dada ◽  
Omole Ezekiel Olaoluwa ◽  
K.A. Al-Mustapha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Stock Returns ◽  
Stock Price ◽  
Prediction Models ◽  
Technical Analysis ◽  
Classification Problem ◽  
Support Vector ◽  
Stock Market Returns ◽  
Stock Price Prediction

Models of stock price prediction have customarily utilized technical indicators alone to produce trading signals. In this paper, we construct trading techniques by applying machine-learning methods to technical analysis indicators and stock market returns data. The resulting prediction models can be utilized as an artificial trader used to trade on any given stock trade. Here the issue of stock trading decision prediction is enunciated as a classification problem with two class values representing the buy and sell signals. The stacking technique utilized in this paper is to assist trader with applying the proposed algorithms in their trading using random forest which was staked with different algorithms which incorporates Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN). The experimental results indicated that Top Layer of Random Forest (TRF) produced the best performance among all the algorithms compared. This is an indication that it is a promising strategy for forecasting Nigerian stock returns.

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