Results 311 to 320 of about 6,744,718 (372)
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Algorithmic trading and firm value
Journal of Banking and Finance, 2021Using data from 2002 to 2013, we show that algorithmic trading has a positive impact on firm value. Most of this positive impact flows through the channels of stock liquidity, idiosyncratic volatility, and idiosyncratic skewness, but algorithmic trading ...
Brian C. Hatch +3 more
semanticscholar +3 more sources
A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets
Expert Systems With Applications, 2022Ali Shavandi, Majid Khedmati
semanticscholar +3 more sources
Algorithmic Trading Methods, 2021
Research, no doubt, plays an important role in formulating any policy. Algorithmic Trading and, in particular, High Frequency Trading and Colocation, are some of the most controversial issues affecting how global security transactions are conducted ...
Robert L. Kissell
semanticscholar +1 more source
Research, no doubt, plays an important role in formulating any policy. Algorithmic Trading and, in particular, High Frequency Trading and Colocation, are some of the most controversial issues affecting how global security transactions are conducted ...
Robert L. Kissell
semanticscholar +1 more source
Literature Review on Algorithmic Trading in Financial Markets
2023 International Conference on Sustainable Islamic Business and Finance (SIBF), 2023The financial market has drastically changed because of technological development. One of the most critical developments in this area is algorithmic trading, which refers to using computers to execute transactions.
Vaibhav Aggarwal +3 more
semanticscholar +1 more source
How Machine Learning Can Drive High Frequency Algorithmic Trading for Technology Stocks
International Journal of Data Science and Advanced Analytics, 2022The objective of this paper is to present an innovative method, based on deep machine learning (DRL), to resolve the algorithmic trading issue of figuring out the perfect trading place at any time during a trading activity on the stock market.
M. Sazu
semanticscholar +1 more source
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
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
Computer Law and Security Review, 2022
: An important part of the criticism raised against the adoption of advanced contract automation relates to the inflexibility of automated contracts.
M. J. Schmidt-Kessen +2 more
semanticscholar +1 more source
: An important part of the criticism raised against the adoption of advanced contract automation relates to the inflexibility of automated contracts.
M. J. Schmidt-Kessen +2 more
semanticscholar +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