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Jan 18 (Reuters) - Crypto outlet CoinDesk Inc is exploring a full or partial sale of its business and has hired investment bank Lazard Ltd (LAZ.N) to lead the process, the media company's chief executive said on Wednesday.
The crypto industry is going through one of its worst phases, with prices of major tokens at two-year lows and a string of bankruptcy filings from top players.
"My goal in hiring Lazard is to explore various options to attract growth capital to the CoinDesk business, which may include a partial or full sale," CEO Kevin Worth told Reuters in a statement.
The development comes amid an industry turmoil, which began with the crash of stable coins TerraUSD and Luna early last year.
More recently, top crypto exchange FTX filed for bankruptcy, while publicly traded Coinbase Global Inc (COIN.O) laid off a fifth of its workforce after slashing over 1,000 jobs last year.
New York-based CoinDesk launched in 2013 to track Bitcoin, but the platform has emerged as a key source of news and pricing benchmarks for the entire range of crypto currencies.
The company is wholly owned by crypto-focused venture capital firm Digital Currency Group, which also has interest in Coinbase, according to its website.
Reporting by Yuvraj Malik in Bengaluru; Editing by Subhranshu Sahu
Our Standards: The Thomson Reuters Trust Principles.
AI robot trading is the use of artificial intelligence (AI) and machine learning algorithms to analyze financial markets and execute trades automatically.
AI robots are able to analyze vast amounts of data and make decisions based on that analysis, allowing them to identify trading opportunities that might be missed by human traders.
One key aspect of AI robot trading is machine learning, which is a type of AI that enables systems to learn and improve their performance over time. Machine learning algorithms are able to analyze data and use it to make predictions or decisions without being explicitly programmed to do so. This allows them to adapt and improve their performance as they are exposed to more data.
There are several techniques that can be used in AI robot trading, including:
Decision trees: These are algorithms that use a tree-like structure to make decisions based on multiple conditions.
Neural networks: These are algorithms that are inspired by the structure and function of the human brain, and are capable of learning and adapting based on input data.
Genetic algorithms: These are algorithms that use principles of genetics and natural selection to optimize and improve their performance over time.
Reinforcement learning: This is a type of machine learning that involves an AI agent receiving rewards or punishments for certain actions, and using that feedback to learn and adapt its behavior.
By using these and other techniques, AI robot traders are able to analyze financial data and make informed decisions about when to buy and sell assets. This can help them achieve better returns and outperform human traders in some cases.
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