How Good Are AI Crypto Price Predictions?

Price predictions from AI chatbots are gaining more following recently. With promises of accurate price forecasts and potential profitability, many traders are turning to AI-driven platforms for guidance in navigating the volatile crypto markets. However, amid the hype surrounding AI predictions, it’s crucial to delve deeper into their efficacy and consider the potential dangers associated with their widespread adoption.

How do they work?

AI works for crypto the same way it works in any other field: you feed large datasets to your trained model that otherwise no human could possibly operate with. In this case, the data is historical price movement as well as trading volumes and some additional indicators.

By analyzing vast amounts of data and leveraging advanced algorithms, AI-driven platforms aim to forecast crypto prices with precision. These predictions are often based on technical analysis indicators, historical trends, and social media sentiment.

One recent study explores Ethereum price forecasting using two methods: Genetic Algorithms (GA) and econometric models. Economic indicators and global indices serve as input variables. A hybrid algorithm combining GA and Artificial Neural Networks (ANN) is developed for accurate predictions, alongside regression analysis and Autoregressive Moving Average (ARMA) models. Historical data from 2019 to 2021 is utilized for evaluation, showcasing AI’s superiority in predictability and computational speed compared to econometric methods, maintaining accuracy and minimizing errors.

Traders often draw parallels between trained AI models and algorithmic trading. While algo bots operate based on real-time data in a matter of a millisecond, chatbots such as ChatGPT or Elon Musk’s Grok have limited access to current data. But common ground is usually described as ‘deprived of human emotions’. But what if human emotions is what differentiates the crypto world from traditional finance?

How accurate are the predictions?

Cryptocurrency prices are predominantly influenced by traders, with market sentiment dictating price movements. While events triggering exuberant or panic investing can cause significant fluctuations, the day-to-day trading activity largely shapes the market. In other words, if the BTC price is mostly defined by demand and supply, should there even be complex mathematical models to predict the price? More importantly, are AI chatbots capable of making precise market predictions?

This won’t come as a surprise to anyone who has ever used ChatGPT: it often makes mistakes. An honest mistake is easily recognizable, but deeper lies the more crucial bug of Language Models: making superficial connections between different subject matters. In other words, before asking for a price forecast from a magic ball, it’s better to understand how it works first. One of the major issues with Bitcoin price is the lack of fundamentals to base forecasts on.

A strikingly high price forecast, especially when it points upwards, often entices investors. For instance, an individual holding a cryptocurrency valued at $100 might easily envision it soaring to $10,000, driven by optimism and past precedents. However, the challenge lies in the lack of substantiated evidence and thorough analysis accompanying many of these predictions. Sure, you can call $1 million BTC price prediction merely ‘stupid’ but there’s always context behind those statements.

Trading behaviors are predominantly shaped by speculative pricing among traders. Transactions involving bitcoins typically don’t exert significant influence on prices due to insufficient buying volume. Consequently, analysts rely on price data influenced by traders and investors to formulate their forecasts.

To gauge the accuracy of AI price predictions, let’s examine a case study conducted by the GNY Range Report team. Using a machine-learning LSTM model, the team generated price range predictions for Bitcoin (BTC). Traders also participated in a prediction competition, offering insights into human vs. AI forecasting capabilities.

While AI predictions showcased a 3% accuracy rate, surpassing that of many traders, there were instances where human intuition outperformed the AI model.

The dangers of AI market domination

AI chatbots give huge influence to market participants crypto research. As one of DeFi developers put it in regard to Grok model:

GrokAI by X is a helpful tool for crypto research.

It can help find new airdrops, explain how protocols function, and roast you vulgarly based on your posts on X.

But I have issues finding trending tokens, and it often includes irrelevant info.

Overall, it’s not perfect yet. pic.twitter.com/FOKnJQqvgw

— Ignas | DeFi Research (@DefiIgnas) December 8, 2023

The potential dominance of AI in price forecasts poses several dangers for financial markets. Firstly, reliance on AI algorithms could lead to increased market volatility and instability if these systems misinterpret or react poorly to market conditions.

Secondly, the opaque nature of AI decision-making processes may exacerbate market manipulation and insider trading, as it becomes more challenging to detect and regulate illicit activities.

Another important issue is the ‘self-fulfilling prophecy’ problem if AI starts to play a bigger role. The widespread adoption of AI-driven trading strategies could result in herd behavior and systemic risks, where market participants react similarly to AI-generated signals, leading to exaggerated market movements

Finally, there’s a risk of overreliance on AI technology, potentially reducing human oversight and accountability, which could amplify the impact of any errors or biases inherent in the algorithms. Overall, while AI offers significant benefits in price forecasting, its unchecked dominance poses substantial risks to financial market stability and integrity.

AI predictions don’t matter

If we’re being completely honest, one must add that AI is not particularly worse in price predictions than its human counterparts. Precise price prediction doesn’t exist and false forecasts are seen more often than accurate.

Metrics for identifying effective AI trading models focus on profitability rather than predicting the future. While hedge funds integrate AI for data analysis and market forecasting, innovative approaches like AI-driven hedge funds are emerging, aiming to assist human decision-making rather than replace it.

While AI holds immense potential in crypto, it’s essential to approach its adoption with caution. Traders must weigh the benefits against the risks, ensuring that human judgment remains a critical component of decision-making processes. By balancing AI-driven insights and human expertise, traders can navigate the complex crypto markets more effectively, mitigating potential dangers while capitalizing on opportunities for profit

.Market prices, a culmination of countless judgments, reflect vast information. While AI aids trade execution, it struggles to predict future outcomes like markets do. The market, an intricate system, establishes prices with unparalleled accuracy. Despite AI’s allure, it lacks a nuanced understanding of real-world complexities. Evidence supports the efficacy of market pricing over AI predictions.

Next time you’re eager to ask the magic chatbot for trading advice, maybe try tossing a coin instead.

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