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2022 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), 2022
Stock price prediction with machine learning is an oft-studied area where numerous unsolved problems still abound owing to the high complexity and volatility that technical-factors and sentiment-analysis models are trying to capture.
Dakota Joiner +4 more
semanticscholar +1 more source
Stock price prediction with machine learning is an oft-studied area where numerous unsolved problems still abound owing to the high complexity and volatility that technical-factors and sentiment-analysis models are trying to capture.
Dakota Joiner +4 more
semanticscholar +1 more source
A New Hybrid VMD-ICSS-BiGRU Approach for Gold Futures Price Forecasting and Algorithmic Trading
IEEE Transactions on Computational Social Systems, 2021The gold market plays a vital role in the world economy. Due to its complex and nonstationary nature, predicting the price of gold is particularly challenging.
Yuze Li +3 more
semanticscholar +1 more source
Exploring the Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading
The Web Conference, 2021Quantitative trading and investment decision making are intricate financial tasks in the ever-increasing sixty trillion dollars global stock market. Despite advances in stock forecasting, a limitation of most existing neural methods is that they treat ...
Ramit Sawhney +3 more
semanticscholar +1 more source
Communications of the ACM, 2013
The competitive nature of AT, the scarcity of expertise, and the vast profits potential, makes for a secretive community where implementation details are difficult to find.
Philip Treleaven +2 more
openaire +1 more source
The competitive nature of AT, the scarcity of expertise, and the vast profits potential, makes for a secretive community where implementation details are difficult to find.
Philip Treleaven +2 more
openaire +1 more source
The Journal of Investment Strategies, 2012
The problem of universal sequential investment in stock markets is considered. We construct an algorithmic trading strategy that is asymptotically at least as good as any trading strategy that is not excessively complex and that computes the investment at each step using a fixed continuous function of the side information.
Vladimir V. V’yugin +1 more
openaire +1 more source
The problem of universal sequential investment in stock markets is considered. We construct an algorithmic trading strategy that is asymptotically at least as good as any trading strategy that is not excessively complex and that computes the investment at each step using a fixed continuous function of the side information.
Vladimir V. V’yugin +1 more
openaire +1 more source
Algorithmic Trading using Technical Indicators
International Conference on Communication, Information & Computing Technology, 2021Financial markets are volatile and dynamic. The uncertainties involved in the market and various economic factors affect the asset price. Predicting trends in asset prices and calculating future value of an asset is a very challenging task.
Tanishq Salkar +3 more
semanticscholar +1 more source
Regulation of Algorithmic Trading: Frameworks or Human Supervision and Direct Market Interventions
European Business Law Review, 2022This paper identifies the regulatory gaps that currently exist in algorithmic trading and provides a framework for machine learning regulation in finance.
Joseph Lee, Lukas Schu
semanticscholar +1 more source
Insider trading and the algorithmic trading environment
International Review of Finance, 2021AbstractWe examine how algorithmic trading (AT) changes the trading environment for corporate insiders, specifically in terms of motivation to trade and timing of trade. Using SEC Form 4 insider filings and AT computed from the limit order book, we find that AT affects insiders' decisions to buy or sell, depending on whether the trades are information ...
Millicent Chang +4 more
openaire +1 more source
Algorithmic Trading and AI: A Review of Strategies and Market Impact
World Journal of Advanced Engineering Technology and SciencesThis review explores the dynamic intersection of algorithmic trading and artificial intelligence (AI) within financial markets. It delves into the evolution, strategies, and broader market impact of algorithmic trading fueled by AI technologies ...
Wilhelmina Afua Addy +5 more
semanticscholar +1 more source
Machine learning in financial markets: A critical review of algorithmic trading and risk management
International Journal of Science and Research ArchiveThe integration of machine learning (ML) techniques in financial markets has revolutionized traditional trading and risk management strategies, offering unprecedented opportunities and challenges.
Wilhelmina Afua Addy +5 more
semanticscholar +1 more source