I’ve spent 25 years watching, trading and investing in the stock market. The repetition of patterns is amazing. In every generation we see new bubbles, which form when a new innovation comes along and everyone gets excited about the future. The crowd gets swept away on a wave of madness, fueled by the recent gains they’ve seen for themselves (or for others) and all other considerations go out the window. Get me in, I don’t care how, I can’t miss out on this.

In December, ChatGPT began to spread like wildfire on social media. A handful of art-related AI programs like DALL-E 2 also began to proliferate on Instagram and some of the more image-oriented sites, but ChatGPT captured the imaginations (and nightmares) of the chattering class like nothing else we’ve ever seen.

Wall Street has begun to take notice of the AI theme for the stock market. It should be noted that trading programs based on earlier versions of AI have been around for decades, so the concept is a very comfortable one among analysts, traders and bankers at traditional firms. But now that there is retail investor interest in riding the wave, you’re going to see the assembly line lurch into action very rapidly. The switch has already been thrown. They’re pulling up their overalls and rolling up their sleeves. Funds, products, IPOs and strategies are being formulated in the dozens as we speak. This will hit the hundreds before we’re through. It’s merely stage one. Read More

The evaluation of the financial markets to predict their behaviour have been attempted using a number of approaches, to make smart and profitable investment decisions. Owing to the highly non-linear trends and inter-dependencies, it is often difficult to develop a statistical approach that elucidates the market behaviour entirely. To this end, we present a long-short term memory (LSTM) based model that leverages the sequential structure of the time-series data to provide an accurate market forecast. We then develop a decision making StockBot that buys/sells stocks at the end of the day with the goal of maximizing profits. We successfully demonstrate an accurate prediction model, as a result of which our StockBot can outpace the market and can strategize for gains that are 15 times higher than the most aggressive ETFs in the market. Read More

Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the experimental agents’ actions. In contrast, multi-agent simulation presents a natural bottom-up approach to emulating agent interaction in financial markets. It allows to set up pools of traders with diverse strategies to mimic the financial market trader population, and test the performance of new experimental strategies. Since individual agent-level historical data is typically proprietary and not available for public use, it is difficult to calibrate multiple market agents to obtain the realism required or testing trading strategies. To addresses this challenge we propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based “world” agent can generate meaningful orders in response to an experimental agent. We integrate our synthetic market generator into ABIDES, an open source simulator of financial markets. By means of extensive simulations we show that our proposal outperforms previous work in terms of stylized facts reflecting market responsiveness and realism. Read More

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Despite all the stories about big companies bailing out of CES 2022 amidst the latest surge in COVID-19 cases, the consumer electronics show in Las Vegas is still the place to be for robots, autonomous vehicles, smart gadgets, and their inventors — an opportunity to take stock of what’s required to build practical machine intelligence into a consumer product. Read More

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The State of AI Report analyses the most interesting developments in AI. We aim to trigger an informed conversation about the state of AI and its implication for the future. The Report is produced by AI investorsNathan Benaich and Ian Hogarth.

Now in its fourth year, the State of AI Report 2021 is reviewed by AI practioners in industry and research, and features invited contributions from a range of well-known and up-and-coming companies and research groups. The Report considers the following key dimensions:

• Research: Technology breakthroughs and capabilities.
• Talent: Supply, demand and concentration of AI talent.
• Industry: Areas of commercial application for AI and its business impact.
• Politics: Regulation of AI, its economic implications and the emerging geopolitics of AI.
• Predictions: What we believe will happen and a performance review to keep us honest.