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WisdomTree has announced the expansion of its institutional tokenized funds solution to five more blockchains. The asset management firm, which initially launched its WisdomTree Connect on Ethereum (ETH) , is now looking to increase its presence across the rapidly expanding tokenization market with deployment on four more blockchain networks. WisdomTree Connect went live in September 2024. As well as Ethereum, the asset manager’s suite of 13 tokenized funds will now be available on Arbitrum, Avalanche, Base and Optimism. It’s a milestone that could see WisdomTree increase its assets under management significantly. Currently, WisdomTree Connect offers access to tokenized money market funds, asset allocation funds, equity index funds and asset allocation funds. The company’s money market fund, the WisdomTree Government Money Market Digital Fund, with ticker symbol WTGXX, allows eligible investors access to a product that provides exposure to short-term government securities. Meanwhile, the suite of equity index funds, including WisdomTree 500 Digital Fund and WisdomTree Technology & Innovation 100 Digital Fund, allow institutional investors to look for opportunities in funds that track leading stock market indices. According to Maredith Hannon, head of business development, digital assets at WisdomTree, providing access to several tokenized funds offers greater choice for investors. Adding support for the new blockchain networks bolsters this outlook, with flexibility key to adoption. Hannon added that users can access WisdomTree’s tokens directly on the supported blockchains, with the platform also integrated with self-custodial wallets. The company also supports major third-party wallets. WisdomTree is seeing notable adoption for its real world assets offering at a time major players such as BlackRock and Fidelity Investments are also quickly expanding across the market. Similar scenarios can be seen for blockchain-based platform like ONDO Finance, MANTRA and Injective. Ondo Finance taps into WisdomTree’s money market fund to collateralize it’s tokenized asset OUSG. The blockchain platform also invests in BlackRock’s and Franklin Templeton’s tokenized funds.

AI has reshaped a lot of industries since it appeared and it’s continuing to do so. The financial market is one of them, which particularly saw a big change with the introduction of AI-powered trading bots. These bots leverage machine learning, deep learning, and predictive analytics to identify trading opportunities and execute trades at blazing speed (one could say it’s even ludicrous speed). Unlike traditional algorithmic trading, AI-based systems continuously learn from new data and adapt to changing market conditions, making them powerful tools for traders. However, using AI for market prediction faces challenges and limitations. Predicting price movements with certainty remains difficult due to the inherent complexity of financial markets, external economic influences, and sudden, unpredictable events (which, considering human nature, is quite often). Let’s just say, the technology just isn’t quite there yet, or rather, people haven’t figured out all the kinks and nuances. As one might have gathered by now, predicting financial markets is far from straightforward, probably even more so today with the crypto industry in the mix. Multiple hurdles limit the effectiveness of AI-powered trading systems, starting with inherent complexity. Financial markets are complicated by nature and are influenced by a combination of several elements, that is, macroeconomic factors, geopolitical events, investor psychology, market sentiment, high-frequency trading, and institutional manipulation. A key issue is the lack of structured rules; markets lack fixed patterns and are often swayed by unforeseeable events. Artificial intelligence struggles to account for unexpected shifts, like regulatory crackdowns or economic crises, making accurate predictions challenging. The next set of challenges are data limitations and bias. AI models require vast amounts of high-quality data for precise predictions. Sounds simple enough, but the problem is that financial data often contains biases, missing information, or manipulated data that can mislead models. To give you an example, an AI model trained only on bull market data might perform poorly during a sudden market downturn because it has never encountered such conditions before. Similarly, historical data may not always reflect current market realities due to evolving economic policies and investor behaviors. Then, there are overfitting and model risks. At first glance, this doesn’t sound like an issue, but overfitting is a common problem in AI trading. It refers to a situation when an AI model performs exceptionally well on historical data but fails in live trading. Overfitting occurs when models memorize past trends rather than recognizing generalizable patterns. On top of that, large institutional traders actively adapt their strategies to counteract AI-driven retail trading, further diminishing the reliability of predictive models. Despite the challenges above, AI trading bots can still be useful as they use various techniques to generate market predictions. To name a few: Core AI components like supervised learning, reinforcement learning, and neural networks allow AI to learn from labeled past trading data for future predictions. Through a combination of these, AI learns from labeled past trading data and applies it to future predictions, all the while it continuously improves upon strategies via feedback from simulated trading. In addition, deep learning techniques recognize price patterns, helping AI detect trends. In summary, these models analyze historical price movements, trading volume, and volatility to forecast potential price actions. The name perhaps sounds complicated, but it basically involves AI bots scanning news articles, financial reports, and social media to assess market sentiment. Then, by analyzing text data, NLP models gauge investor outlook (bullish or bearish). For instance, an out-of-the-blue increase in positive sentiment about Bitcoin on social media might indicate an impending price surge. On the other hand, panic-driven discussions may signal a market downturn. NLP understands the context of these conversations, analyzing word relationships between words in a sentence across paragraphs to get the meaning. This is more technical in nature and is a bit more complicated as AI-powered trading bots rely on a bunch of technical indicators. These include moving averages (MA, EMA), relative strength index (RSI), moving average convergence divergence (MACD), Bollinger Bands, and liquidity analysis. If you’re not familiar with the terms, you’ve likely read a bunch of gibberish now. Put simply, these signals help AI determine potential entry and exit points for trades by: Last but not least, AI bots use and analyze alternative data sources to speculate. This could be blockchain data with on-chain transactions, whale movements, and DeFi activity for crypto markets. Also, it employs options market data where open interest and trading volumes help predict investor sentiment. Moreover, AI even uses Google, specifically Google Trends and web traffic data. It can look for spikes in searches for specific cryptocurrencies or stocks that may indicate upcoming market movements. It’s worth remembering that AI indeed is a powerful tool, but it’s not foolproof since it has its limitations. Impressive and at times unbelievable, it isn’t magical or a crystal ball where you can see your future. Who knows, that might be true in the next few years, but it certainly isn’t true today, as many people overestimate AI’s ability to predict price movements with absolute certainty (which is wrong on many levels). To help you avoid making these mistakes putting all your hopes in AI, it’s best to remember several things, such as: Keep in mind that AI can offer you an edge, but can’t guarantee you a profit. Though AI currently struggles with predictive certainty, there are likely several advancements coming in the future. Some are speculation, some more grounded, but sooner or later, at least a few improvements are bound to happen. We may get more advanced deep learning models, which would make AI models better at adapting to unexpected market conditions, thus improving predictive accuracy. Potentially, with the rise of decentralized finance (DeFi), AI trading bots could integrate directly into smart contracts, enabling autonomous trading without intermediaries. In addition, with the increasing regulatory issues regarding AI and ethical concerns over its impact on retail traders (or in general), we may also get new laws governing AI trading. Whatever happens in the years to come, it’s a fact that AI-powered trading bots have transformed financial markets by making trading faster, more efficient, and data-driven. The technology isn’t know-it-all, and it works best alongside human expertise, fundamental analysis, and strong risk management. As AI evolves, traders should stay informed, adhere to strategies, and above all, set realistic expectations about AI’s capabilities. The future of AI in trading is promising, but it remains a tool that requires careful application with oversight. Disclaimer: The information presented in this article is for informational and educational purposes only. The article does not constitute financial advice or advice of any kind. Coin Edition is not responsible for any losses incurred as a result of the utilization of content, products, or services mentioned. Readers are advised to exercise caution before taking any action related to the company.

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