5 research outputs found
VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning
Volatility-based trading strategies have attracted a lot of attention in
financial markets due to their ability to capture opportunities for profit from
market dynamics. In this article, we propose a new volatility-based trading
strategy that combines statistical analysis with machine learning techniques to
forecast stock markets trend.
The method consists of several steps including, data exploration, correlation
and autocorrelation analysis, technical indicator use, application of
hypothesis tests and statistical models, and use of variable selection
algorithms. In particular, we use the k-means++ clustering algorithm to group
the mean volatility of the nine largest stocks in the NYSE and NasdaqGS
markets. The resulting clusters are the basis for identifying relationships
between stocks based on their volatility behaviour. Next, we use the Granger
Causality Test on the clustered dataset with mid-volatility to determine the
predictive power of a stock over another stock. By identifying stocks with
strong predictive relationships, we establish a trading strategy in which the
stock acting as a reliable predictor becomes a trend indicator to determine the
buy, sell, and hold of target stock trades.
Through extensive backtesting and performance evaluation, we find the
reliability and robustness of our volatility-based trading strategy. The
results suggest that our approach effectively captures profitable trading
opportunities by leveraging the predictive power of volatility clusters, and
Granger causality relationships between stocks.
The proposed strategy offers valuable insights and practical implications to
investors and market participants who seek to improve their trading decisions
and capitalize on market trends. It provides valuable insights and practical
implications for market participants looking to
Dataset Optimization for Chronic Disease Prediction with Bio-Inspired Feature Selection
In this study, we investigated the application of bio-inspired optimization
algorithms, including Genetic Algorithm, Particle Swarm Optimization, and Whale
Optimization Algorithm, for feature selection in chronic disease prediction.
The primary goal was to enhance the predictive accuracy of models streamline
data dimensionality, and make predictions more interpretable and actionable.
The research encompassed a comparative analysis of the three bio-inspired
feature selection approaches across diverse chronic diseases, including
diabetes, cancer, kidney, and cardiovascular diseases. Performance metrics such
as accuracy, precision, recall, and f1 score are used to assess the
effectiveness of the algorithms in reducing the number of features needed for
accurate classification.
The results in general demonstrate that the bio-inspired optimization
algorithms are effective in reducing the number of features required for
accurate classification. However, there have been variations in the performance
of the algorithms on different datasets.
The study highlights the importance of data pre-processing and cleaning in
ensuring the reliability and effectiveness of the analysis.
This study contributes to the advancement of predictive analytics in the
realm of chronic diseases. The potential impact of this work extends to early
intervention, precision medicine, and improved patient outcomes, providing new
avenues for the delivery of healthcare services tailored to individual needs.
The findings underscore the potential benefits of using bio-inspired
optimization algorithms for feature selection in chronic disease prediction,
offering valuable insights for improving healthcare outcomes
A Logic-based Multi-agent System for Ethical Monitoring and Evaluation of Dialogues
In Proceedings ICLP 2021, arXiv:2109.0791
Logic-based Machine Learning for Transparent Ethical Agents
Autonomous intelligent agents are increasingly engaging in human
communities. Thus, they must be expected to follow social and ethical norms
of the community in which they are deployed in. In this work we present an
approach for developing such ethical agents which are able to develop ethical
decision making and judgment capabilities by learning from interactions with the
users. Our approach is a logic-based approach and the resulting ethical agents are
transparent by design