scholarly journals Forecasting stock market trend: a comparison of machine learning algorithms

2020 ◽  
Vol 6 (1) ◽  
pp. 37-49
Author(s):  
R. Cervelló-Royo ◽  
F. Guijarro
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Research Topic ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Average Accuracy ◽  
Mathematical Formulas ◽  
Market Trends ◽  
Chart Patterns ◽  
Past Trend

Forecasting the direction of stocks markets has become a popular research topic in recent years. Differentapproaches have been applied by researchers to address the prediction of market trends by consideringtechnical indicators and chart patterns from technical analysis. This paper compares the performanceof four machine learning algorithms to validate the forecasting ability of popular technical indicators inthe technological NASDAQ index. Since the mathematical formulas used in the calculation of technicalindicators comprise historical prices they will be related to the past trend of the market. We assume thatforecasting performance increases when the trend is computed on a longer time horizon. Our resultssuggest that the random forest outperforms the other machine learning algorithms considered in ourresearch, being able to forecast the 10-days ahead market trend, with an average accuracy of 80%.

scholarly journals Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Risks ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Christopher Blier-Wong ◽  
Hélène Cossette ◽  
Luc Lamontagne ◽  
Etienne Marceau
Keyword(s):  
Machine Learning ◽  
Insurance Industry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Actuarial Science ◽  
Science Field ◽  
The Past ◽  
Highly Nonlinear ◽  
Computer Scientists ◽  
Applications Of Machine Learning

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

scholarly journals Mapping the NFT revolution: market trends, trade networks, and visual features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthieu Nadini ◽  
Laura Alessandretti ◽  
Flavio Di Giacinto ◽  
Mauro Martino ◽  
Luca Maria Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Visual Features ◽  
Smart Contracts ◽  
Public Attention ◽  
Trade Networks ◽  
Market Trends ◽  
Analyse Data ◽  
Digital Assets

AbstractNon Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.

scholarly journals The ethics of algorithms: key problems and solutions

AI & Society ◽  
2021 ◽  
Author(s):  
Andreas Tsamados ◽  
Nikita Aggarwal ◽  
Josh Cowls ◽  
Jessica Morley ◽  
Huw Roberts ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Design Development ◽  
Ethical Problems ◽  
Ethical Implications ◽  
The Past ◽  
Problems And Solutions

AbstractResearch on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. Big Data Soc 3(2), 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms.

A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms

2020 ◽  
Vol 16 (6) ◽  
pp. 639-652
Author(s):  
Vaira Suganthi Gnanasekaran ◽  
Sutha Joypaul ◽  
Parvathy Meenakshi Sundaram
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Computer Aided Detection ◽  
Breast Masses ◽  
Cad System ◽  
The Past ◽  
Mammogram Images

Breast cancer is leading cancer among women for the past 60 years. There are no effective mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities available for capturing the abnormalities in breasts, mammography is the most commonly used technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms used in the detection of breast cancer developed in recent years. We begin the review with a concise introduction about the fundamental concepts related to mammograms and CAD systems. We then focus on the techniques used in the diagnosis of breast cancer with mammograms.

scholarly journals Anticipating Cryptocurrency Prices Using Machine Learning

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Laura Alessandretti ◽  
Abeer ElBahrawy ◽  
Luca Maria Aiello ◽  
Andrea Baronchelli
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Trading Strategies ◽  
Short Term ◽  
The Past ◽  
Term Evolution ◽  
Daily Data ◽  
Abnormal Profits

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for 1,681 cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.

scholarly journals A Hybrid Framework for Heart Disease Prediction Using Machine Learning Algorithms

2021 ◽  
Vol 309 ◽  
pp. 01043
Author(s):  
L. Chandrika ◽  
K. Madhavi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Prediction Model ◽  
Linear Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
The Past ◽  
Past Data

Cardiovascular Diseases (CVDs) are the primary cause for the sudden death in the world today from the past few years the disease has emerged greatly as a most unpredictable problem, not only in India the whole planet facing the criticality. So, there is a desperate need of valid, accurate and practical solution or application to diagnose the CVD problems in time for mandatory treatment. Predicting the CVD is a great challenge in the health care domain of clinical data analysis. Machine learning Algorithms (MLA) and Techniques has been vastly developed and proven to be effective and efficient in predicting the problems using the past data. Using these MLA techniques and taking the clinical dataset which provided by the healthcare industry. Different studies were takes place and tried only a small part into predicting CVD with ML Algorithms. In this thesis, we propose the different novel methodology which concentrates at finding appropriate features by using MLA techniques resulting at finding out the accurate model to predict CVD. In this prediction model we are trying to implement the models with different combinations of features and several known classification techniques such as Deep Learning, Random Forest, Generalised Linear Model, Naïve Bayes, Logistic Regression, Decision Tree, Gradient Boosted trees, Support Vector Machine, Vote and HRFLM and we have got an higher accuracy level and of 75.8%, 85.1%, 82.9%, 87.4%, 85%, 86.1%, 78.3%, 86.1%, 87.41%, and 88.4% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

scholarly journals Wireless AI-Powered IoT Sensors for Laboratory Mice Behavior Recognition

2020 ◽  
Author(s):  
Meng Chen ◽  
Yifan Liu ◽  
John Chung Tam ◽  
Ho-yin Chan ◽  
Xinyue Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Video Tracking ◽  
Machine Learning Algorithms ◽  
Laboratory Animals ◽  
Laboratory Mice ◽  
Motion Data ◽  
Average Accuracy ◽  
Sample Set

AbstractAccording to the U.S. Department of Agriculture in 2018, there are more than 100 million animals used in research, education, and testing per year. Of the laboratory animals used for research, 95 percent are mice and rats as reported by the Foundation for Biomedical Research (FBR). We present here our work in developing wireless Artificial Intelligent (AI)-powered IoT Sensors (AIIS) for laboratory mice motion recognition utilizing embedded micro-inertial measurement units (uIMUs). Based on the AIIS, we have demonstrated a small-animal motion tracking and recognition system that could recognize 5 common behaviors of mice in cages with accuracy of ~76.23%. The key advantage of this AIIS-based system is to enable high throughput behavioral monitoring of multiple to a large group of laboratory animals, in contrast to traditional video tracking systems that usually track only single or a few animals at a time. The system collects motion data (i.e., three axes linear accelerations and three axes angular velocities) from the IoT sensors attached to different mice, and classifies these data into different behaviors using machine learning algorithms. One of the challenging problems for data analysis is that the distribution of behavior samples is extremely imbalanced. Behaviors such as sleeping and walking dominate the entire sample set from different mice. However, machine learning algorithms often require a balanced sample set to achieve optimal performance. Thus, several methods are proposed to solve the imbalanced sample problem. Data processing methods for data segmentation, feature extraction, feature selection, imbalanced learning, and machine learning are explored to process motion data including sleeping, walking, rearing, digging, shaking, grooming, drinking and scratching. For example, by tuning the parameters of a machine-learning algorithm (i.e., Support Vector Machine (SVM)), the average accuracy of classifying five behaviors (i.e., sleeping, walking, rearing, digging and shaking) is 48.07% before solving the imbalance sample issue. To address this problem, an iteration of sample and feature selection is applied to improve the SVM performance. A combination of oversampling and undersampling is used to handle imbalanced classes, and feature selection provides the optimal number of features. The accuracy increases from 48.07% to 76.23% when the optimized combination is used. We further obtained an average accuracy of 86.46% by removing shaking, which is proved to have a negative effect on the overall performance, out of these five behaviors. Furthermore, we were able to classify less frequent behaviors including rearing, digging, grooming, drinking and scratching at an average accuracy of 96.35%.

scholarly journals A survey of Emerging Techniques in Detecting SMS Spam

2019 ◽  
Vol 7 (5) ◽  
pp. 23-35
Author(s):  
Sahar Alqahtani ◽  
Daniyal Alghazzawi
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Mobile Users ◽  
Topic Modelling ◽  
The Past ◽  
Machine Learning Methods ◽  
Effective Performance ◽  
Modelling Techniques

In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.

Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis

2019 ◽  
Vol 24 (15) ◽  
pp. 11019-11043 ◽  
Author(s):  
Wasiat Khan ◽  
Usman Malik ◽  
Mustansar Ali Ghazanfar ◽  
Muhammad Awais Azam ◽  
Khaled H. Alyoubi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Situation Analysis ◽  
Political Situation ◽  
Public Sentiment ◽  
Market Trends

Machine Learning for Smart Tourism and Retail

2020 ◽  
pp. 311-333
Author(s):  
Carlos Rodríguez-Pardo ◽  
Miguel A. Patricio ◽  
Antonio Berlanga ◽  
José M. Molina
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Customer Behaviour ◽  
Contextual Dependence ◽  
Smart Tourism

The unprecedented growth in the amount and variety of data we can store about the behaviour of customers has been parallel to the popularization and development of machine learning algorithms. This confluence of factors has created the opportunity of understanding customer behaviour and preferences in ways that were undreamt of in the past. In this chapter, the authors study the possibilities of different state-of-the-art machine learning algorithms for retail and smart tourism applications, which are domains that share common characteristics, such as contextual dependence and the kind of data that can be used to understand customers. They explore how supervised, unsupervised, and recommender systems can be used to profile, segment, and create value for customers.

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