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Artificial Intelligence (AI) is a multidisciplinary branch of science that focuses on creating systems capable of performing tasks that usually require human intelligence. These tasks include problem-solving, pattern recognition, decision-making, and language understanding.
At its core, AI is powered by algorithms and models that learn and evolve from data, allowing machines to make predictions or decisions without explicit programming. In the finance industry, AI is used for various applications such as algorithmic trading, fraud detection, credit scoring, and personalized financial planning. These technologies enable financial institutions to improve efficiency, reduce risks, and offer more tailored services to their clients.
Can AI Predict Forex Price?
No, AI can not predict future forex prices accurately because, except for time and price, AI does not have enough features and overall data to create a high-accuracy model. However, AI algorithms enhance trading by automating strategies, predicting partial market movements, trading classification, analyzing sentiments, managing portfolios, detecting fraud, optimizing routes, etc.
Artificial Intelligence (AI) is a broad field of study focused on creating machines that can perform tasks requiring human-like intelligence. In contrast, Machine Learning (ML) is a subset of AI that concentrates on building models that learn and make predictions or decisions without being explicitly programmed for the task.
While AI encompasses many cognitive tasks, ML explicitly teaches machines to learn from data. Traders often use ML for price prediction by employing regression methods to forecast continuous values (like future stock prices) or classification methods to categorize data (like predicting whether a stock will go up or down). By analyzing historical price data and other relevant factors, these ML models can assist traders in making more informed investment decisions.
So, we can say AI when explaining ML methods and pattern recognition algorithms.
Why is it hard to predict forex prices with AI (ML)?
To forecast future price movements, technical indicators primarily analyze past market data, notably price and time. While these indicators can be valuable in understanding historical trends and patterns, they inherently lack comprehensive insight into the factors influencing currency valuations.
Why is this problematic?
Forex is affected by both macroeconomic and microeconomic events – from changes in interest rates by central banks to political instabilities. Without incorporating these fundamental influences, models that rely solely on technical indicators can be blindsided by sudden market shifts.
Unlike stock markets, where trading volumes are meticulously documented, the decentralized nature of the forex market means there’s no single source of truth for actual transaction volumes.
Volume is an essential metric for traders as it signifies the strength of a price movement. A price change backed by substantial volume is generally considered more significant and likely to continue than one with sparse volume. Without reliable volume data, ML models might misjudge the momentum of a trend.
Forex rates are influenced by economic indicators: GDP growth rates, unemployment figures, manufacturing outputs, and more. Each affects a country’s economic health, affecting its currency’s value.
Incorporating these vast and varied datasets into an ML model can be cumbersome, and more importantly, the exact relationship between each of these indicators and a currency’s value can be intricate and dynamic.
Currencies don’t operate in isolation. An event in one country can ripple through and impact the forex rates of many countries. This interconnectedness means modeling for one currency pair might inadvertently require understanding several seemingly unrelated pairs.
The forex market is notorious for its volatility. News can break, governments can change, natural disasters can strike – and the repercussions on the forex market can be instantaneous and unpredictable. No ML or AI model, regardless of its sophistication, can predict unforeseen global events.
Given the noise in forex data and the multitude of patterns, there’s a significant risk of ML models overfitting past data. An overfitted model might perform exceptionally well on historical data but fail miserably in real-world predictions.