Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies
Review of AI methodologies in algorithmic trading, focusing on deep learning, machine learning, and investor sentiment analysis.
What it examines
This paper reviews recent advances in AI-based algorithmic trading systems, integrating various methodologies and data sources to forecast financial asset trends. It aims to evaluate the effectiveness of these techniques in trading complex financial markets, using data from technical and fundamental analysis, and investor sentiment.
What it concludes
The study concludes that AI-based trading systems can outperform traditional methods, offering significant potential for future developments. Potential applications include more accurate financial forecasting and improved trading strategies. Future research should focus on adapting systems to changing market conditions and integrating more diverse data sources.
Evidence objects
The study concludes that AI-based trading systems can outperform traditional methods, offering significant potential for future developments. Potential applications include more accurate financial forecasting and improved trading strategies. Future research should focus on adapting systems to changing market conditions and integrating more diverse data sources.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Artificial Intelligence (AI) has been recently recognized as an essential aid for human traders. The advantages of the AI systems over human traders are that they can analyze an extensive data set from different sources in a fraction of a second and perform actual high-frequency trading (HFT) that can take advantage of market anomalies and price differences. This paper reviews the most important papers published in recent years that use the most advanced techniques to forecast financial asset trends and answer the question of whether those techniques can be used to successfully trade the complex financial markets. All systems use deep learning (DL) and machine learning (ML) protocols to explore nonobvious correlations and phenomena that influence the probability of trading success. Their predictions are based on linear or nonlinear models often combined with social media investors’ sentiment derivations or pattern recognitions. Most of the reviewed papers have proven the successful ability of their developed system to trade the financial markets.
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