Algorithmic Machine Learning for Prediction of Stock Prices

2019 ◽  
pp. 142-169
Author(s):  
Mirza O. Beg ◽  
Mubashar Nazar Awan ◽  
Syed Shahzaib Ali
Keyword(s):  
Machine Learning ◽  
Stock Prices ◽  
Prediction Models ◽  
Stock Exchange ◽  
Moving Average ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Daily News ◽  
Trading Decisions

Stock markets and relevant entities generate enormous amounts of data on a daily basis and are accessible from various channels such as stock exchange, economic reviews, and employer monetary reports. In recent times, machine learning techniques have proven to be very helpful in making better trading decisions. Machine learning algorithms use complex logic to observe and learn the behavior of stocks using historical data which can be used to predict future movements of the stock. Technical indicators such as rolling mean, momentum, and exponential moving average are calculated to convert the data into meaningful information. Furthermore, this information can be used to build machine learning prediction models that learn different patterns in the data and make future predictions for accurate financial forecasting. Additional factors that are being used for stock prediction include social media influences and daily news on trading stocks. Considering these qualitative and quantitative features at the same time result in improved prediction models.

Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques

2019 ◽  
Vol 6 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Jai Prakash Verma ◽  
Sudeep Tanwar ◽  
Sanjay Garg ◽  
Ishit Gandhi ◽  
Nikita H. Bachani
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Time Window ◽  
Stock Exchange ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Trend Prediction ◽  
Stock Market Forecasting ◽  
Learning Techniques

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. In order to conduct these processes, a real-time dataset has been obtained from the Indian stock market. This article learns the model from Indian National Stock Exchange (NSE) data obtained from Yahoo API to forecast stock prices and targets to make a profit over time. In this article, two separate algorithms and methodologies are analyzed to forecast stock market trends and iteratively improve the model to achieve higher accuracy. Results are showing that the proposed pattern-based customized algorithm is more accurate (10 to 15%) as compared to other two machine learning techniques, which are also increased as the time window increases.

scholarly journals Predicting Loan Approval of Bank Direct Marketing Data Using Ensemble Machine Learning Algorithms

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Ensemble Machine Learning ◽  
Marketing Data ◽  
Loan Approval

The Bank Marketing data set at Kaggle is mostly used in predicting if bank clients will subscribe a long-term deposit. We believe that this data set could provide more useful information such as predicting whether a bank client could be approved for a loan. This is a critical choice that has to be made by decision makers at the bank. Building a prediction model for such high-stakes decision does not only require high model prediction accuracy, but also needs a reasonable prediction interpretation. In this research, different ensemble machine learning techniques have been deployed such as Bagging and Boosting. Our research results showed that the loan approval prediction model has an accuracy of 83.97%, which is approximately 25% better than most state-of-the-art other loan prediction models found in the literature. As well, the model interpretation efforts done in this research was able to explain a few critical cases that the bank decision makers may encounter; therefore, the high accuracy of the designed models was accompanied with a trust in prediction. We believe that the achieved model accuracy accompanied with the provided interpretation information are vitally needed for decision makers to understand how to maintain balance between security and reliability of their financial lending system, while providing fair credit opportunities to their clients.

Application of Machine Learning Algorithms in Stock Market Prediction

2020 ◽  
pp. 153-180
Author(s):  
Sumit Kumar ◽  
Sanlap Acharya
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Better Than

The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM.

Prediction Models for Healthcare Using Machine Learning

2021 ◽  
pp. 70-91
Author(s):  
Ayushe Gangal ◽  
Peeyush Kumar ◽  
Sunita Kumari ◽  
Anu Saini
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Brain Diseases ◽  
Health Issues ◽  
Industry Study ◽  
Learning Techniques

Healthcare is always a sensitive issue for all of us, and it will always remain. Predicting various types of health issues in advance can lead us to a better life. Various types of health problems are there like cancer, heart diseases, diabetes, arthritis, pneumonia, lungs disease, liver disease, and brain disease, which all are at high risk. To reduce the risk of health issues, some suitable models are needed for prediction. Thus, it became as a motivational factor for the authors to survey the existing literature on this topic thoroughly and have consequently to identify suitable machine learning techniques so that improvement can be possible while selecting a prediction model. In this chapter, concept of survey is used to provide the prediction models for healthcare issues along with the challenges associated with each model. This chapter will broadly cover the following: machine learning algorithms used in health industry, study various prediction models for Cancer, Heart diseases, Diabetes and Brain diseases, comparative study of various machine learning algorithms used for prediction.

A Novel Approach of Weighted Support Vector Machine with Applied Chance Theory for Forecasting Air Pollution Phenomenon in Egypt

2018 ◽  
Vol 17 (01) ◽  
pp. 1850001 ◽  
Author(s):  
Nabil Mohamed Eldakhly ◽  
Magdy Aboul-Ela ◽  
Areeg Abdalla
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Management Control ◽  
Air Pollutant ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Chance Theory

The particulate matter air pollutant of diameter less than 10 micrometers (PM[Formula: see text]), a category of pollutants including solid and liquid particles, can be a health hazard for several reasons: it can harm lung tissues and throat, aggravate asthma and increase respiratory illness. Accurate prediction models of PM[Formula: see text] concentrations are essential for proper management, control, and making public warning strategies. Therefore, machine learning techniques have the capability to develop methods or tools that can be used to discover unseen patterns from given data to solve a particular task or problem. The chance theory has advanced concepts pertinent to treat cases where both randomness and fuzziness play simultaneous roles at one time. The main objective is to study the modification of a single machine learning algorithm — support vector machine (SVM) — applying the chance weight of the target variable, based on the chance theory, to the corresponding dataset point to be superior to the ensemble machine learning algorithms. The results of this study are outperforming of the SVM algorithms when modifying and combining with the right theory/technique, especially the chance theory over other modern ensemble learning algorithms.

scholarly journals An IoT-Based Smart Building Solution for Indoor Environment Management and Occupants Prediction

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2959
Author(s):  
Alessandro Floris ◽  
Simone Porcu ◽  
Roberto Girau ◽  
Luigi Atzori
Keyword(s):  
Machine Learning ◽  
Indoor Environment ◽  
Prediction Models ◽  
Pearson Correlation ◽  
Environmental Parameters ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Environment Management ◽  
Smart Building

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

scholarly journals Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1563
Author(s):  
Chi-Jie Lu ◽  
Tian-Shyug Lee ◽  
Chien-Chih Wang ◽  
Wei-Jen Chen
Keyword(s):  
Machine Learning ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Adaptive Weighting ◽  
Stochastic Gradient Boosting ◽  
Basketball Game ◽  
Extreme Gradient Boosting

Developing an effective sports performance analysis process is an attractive issue in sports team management. This study proposed an improved sports outcome prediction process by integrating adaptive weighted features and machine learning algorithms for basketball game score prediction. The feature engineering method is used to construct designed features based on game-lag information and adaptive weighting of variables in the proposed prediction process. These designed features are then applied to the five machine learning methods, including classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine (ELM) for constructing effective prediction models. The empirical results from National Basketball Association (NBA) data revealed that the proposed sports outcome prediction process could generate a promising prediction result compared to the competing models without adaptive weighting features. Our results also showed that the machine learning models with four game-lags information and adaptive weighting of power could generate better prediction performance.

scholarly journals Early Detection of Cardiovascular Disease using Machine learning Techniques an Experimental Study

2020 ◽  
Vol 9 (3) ◽  
pp. 635-641
Keyword(s):  
Machine Learning ◽  
Proper Time ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Machine Learning Techniques ◽  
Entire Body ◽  
Time Data ◽  
Learning Techniques ◽  
Health Related

Human body prioritizes the heart as the second most important organ after the brain. Any disruption in the heart ultimately leads to disruption of the entire body. Being the members of modern era, enormous changes are happening to us on a daily basis that impact our lives in one way or the other. A major disease among top five fatal diseases includes the heart disease which has been consuming lives worldwide. Therefore, the prediction of this disease is of prime importance as it will enable one to take a proper and needful approach at a proper time. Data mining and machine learning are taking out and refining of useful information from a massive amount of data. It is a basic and primary process in defining and discovering useful information and hidden patterns from databases. The flexibility and adaptability of optimization algorithms find its use in dealing with complex non -linear problems. Machine Learning techniques find its use in medical sciences in solving real health-related issues by early prediction and treatment of various diseases. In this paper, six machine learning algorithms are used and then compared accordingly based on the evaluation of performance. Among all classifiers, decision tree outperforms over the other algorithms with a testing accuracy of 97.29%.

scholarly journals Predicting Intraday Prices in the Frontier Stock Market of Romania Using Machine Learning Algorithms

2020 ◽  
pp. 170-179
Author(s):  
Dan Gabriel ANGHEL
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
State Of The Art ◽  
Predictive Ability ◽  
Weak Form ◽  
Machine Learning Algorithms ◽  
Forecasting Models

This paper investigates if forecasting models based on Machine Learning (ML) Algorithms are capable to predict intraday prices in the small, frontier stock market of Romania. The results show that this is indeed the case. Moreover, the prediction accuracy of the various models improves as the forecasting horizon increases. Overall, ML forecasting models are superior to the passive buy and hold strategy, as well as to a naïve strategy that always predicts the last known price action will continue. However, we also show that this superior predictive ability cannot be converted into “abnormal”, economically significant profits after considering transaction costs. This implies that intraday stock prices incorporate information within the accepted bounds of weak-form market efficiency, and cannot be “timed” even by sophisticated investors equipped with state of the art ML prediction models.

scholarly journals Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques

2019 ◽  
Vol 8 (9) ◽  
pp. 382 ◽  
Author(s):  
Marcos Ruiz-Álvarez ◽  
Francisco Alonso-Sarria ◽  
Francisco Gomariz-Castillo
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Air Temperature ◽  
Cross Validation ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machines and Random Forest, are compared with Multiple Linear Regression and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using two different approaches, a leave-one-out cross validation in the spatial domain and a spatio-temporal k-block cross-validation, and four different statistics on a daily basis, allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest produces the best results (R 2 = 0.888 ± 0.026, Root mean square error = 3.01 ± 0.325 using k-block cross-validation). Regression methods (Support Vector Machine, Random Forest and Multiple Linear Regression) are calibrated with MODIS data and several predictors easily calculated from a Digital Elevation Model. The most important variables in the Random Forest model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

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