Results 31 to 40 of about 5,659 (253)

Optimal Markowitz portfolio using returns forecasted with time series and machine learning models

Journal of Big Data
We aim to answer whether using forecasted stock returns based on machine learning and time series models in a mean-variance portfolio framework yields better results than relying on historical returns.
Damian Ślusarczyk, Robert Ślepaczuk
doaj   +1 more source

Stock Market Prediction Using Deep Reinforcement Learning

Applied System Innovation, 2023
Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making.
Alamir Labib Awad, Saleh Mesbah Elkaffas, Mohammed Waleed Fakhr   +2 more
doaj   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Artificial intelligence applications for advancing sustainable green finance

Discover Sustainability
Artificial Intelligence (AI) is increasingly pivotal in sustainable green finance, supporting risk analytics, investment management, environmental, social, and governance (ESG) assessment, and sustainable reporting.
Nuraini Desty Nurmasari, Muhammad Roil Bilad   +1 more
doaj   +1 more source

Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

Working Papers
The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy ...
Filip Stefaniuk, Robert Slepaczuk
openaire   +2 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Grid Trading System Robot (GTSbot): A Novel Mathematical Algorithm for Trading FX Market

Applied Sciences, 2019
Grid algorithmic trading has become quite popular among traders because it shows several advantages with respect to similar approaches. Basically, a grid trading strategy is a method that seeks to make profit on the market movements of the underlying ...
Francesco Rundo, Francesca Trenta, Agatino Luigi di Stallo, Sebastiano Battiato   +3 more
doaj   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Research on the Investment Strategy of Quantitative Trading Based on Random Forest and Genetic Algorithm

BCP Business & Management, 2022
Gold and Bitcoin are two common investments in the market. Investors profit from the capital market through specific trading strategies. Based on previous price data, this paper established a prediction-decision model, which provides investors with an optimal trading strategy and effectively improves the return on investment.
Yuwei Chen, Yueyang Zhang, Yufeng Lan, Yujia Liu, Yaoxiang Lin   +4 more
openaire   +1 more source

Inverse Design of Nanoparticulate Materials

Advanced Materials, EarlyView.
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré, Annika Mauch, Michelle Berthold, Lukas Pflug, Wolfgang Peukert, Nicolas Vogel   +5 more
wiley   +1 more source