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The integration of Artificial Intelligence, Machine Learning, and Neural Networks into trading, while offering numerous benefits, also comes with significant challenges and limitations that must be addressed.
(Mitchell, 2022, n.p.)
Market Volatility
Market Volatility
Market volatility poses another challenge to the application of AI, ML, and Neural Networks in trading. Financial markets are inherently unpredictable, and sudden changes in market conditions can render even the most sophisticated models ineffective. (Mitchell, 2022, n.p.)
"For example, an unforeseen geopolitical event or a sudden shift in investor sentiment can cause drastic market movements that were not anticipated by the models. While AI and ML models are designed to learn from historical data, they may struggle to adapt to unprecedented market scenarios that do not follow historical patterns." (Cuello, F., 2024, n.p.)
(Kramer, 2024, n.p.)
Limited Order
Limited Order
"A limit order is the use of a pre-specified price to buy or sell a security. For example, if a trader is looking to buy XYZ’s stock but has a limit of $14.50, they will only buy the stock at a price of $14.50 or lower. If the trader is looking to sell shares of XYZ’s stock with a $14.50 limit, the trader will not sell any shares until the price is $14.50 or higher." (Kramer, 2024, n.p.)
AI can’t predict this effectively, hence taking longer time to gain profits.
Data Dependency
Data Dependency
One of the primary challenges is data dependency. These technologies rely heavily on large and high-quality datasets to function effectively. In trading, the accuracy and reliability of AI and ML models are directly proportional to the quality of the data they are trained on.
"If the data is incomplete, biased, or outdated, the models can produce inaccurate predictions, leading to poor trading decisions. Moreover, financial markets are influenced by a wide range of factors, including economic indicators, geopolitical events, and market sentiment, all of which must be captured accurately in the datasets." (Cuello, F., 2024, n.p.)
Overfitting Risks
Overfitting Risks
Overfitting is another significant risk associated with the use of AI, ML, and Neural Networks in trading. Overfitting occurs when a model becomes too closely tailored to the historical data it was trained on, capturing noise and minor fluctuations rather than the underlying market patterns.
"While an overfitted model may perform exceptionally well on the training data, it is likely to perform poorly on new, unseen data because it fails to generalize to broader market conditions. This issue is particularly problematic in trading, where market conditions are constantly changing, and models must be able to adapt to new data." (Cuello, F., 2024, n.p.)
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