South Korean chip giant SK Hynix has raised a record-breaking $26.5bn ahead of its Wall Street debut amid soaring demand for semiconductors used in AI. SK Hynix said on Friday that it had sold 177.9 million American depositary shares (ADS) at $149 each ahead of its listing on the New York-based Nasdaq stock exchange. SK Hynix's 177.9 million ADSs are equivalent to 18 million ordinary shares. SK Hynix's initial public offering (IPO) marks the largest-ever listing by a foreign company in the US, surpassing Chinese e-commerce giant Alibaba's $25bn debut in 2014. The listing also ranks as the second-largest globally, after SpaceX's record-breaking $85.7bn Nasdaq listing in June.

Octopus Energy to spin off $8.65bn tech arm Kraken Octopus Energy is set to spin off its Kraken Technologies arm as a standalone company after a deal to sell a stake in the platform valued it at $8.65bn (£6.4bn). The energy giant, Britain's biggest gas and electricity supplier, has sold a $1bn stake in the AI-based division to a group of investors led by New York-based D1 Capital Partners. The move paves the way for Kraken to be demerged from Octopus, and for a potential stock market flotation for the business in the future. Octopus founder and chief executive Greg Jackson told the BBC there was every chance Kraken would list its shares in the medium term, with the location of the flotation between London and the US. Kraken uses AI to automate customer service and billing for energy companies and can manage when customers use energy, rewarding them for reducing consumption at peak times. It was initially built for use by Octopus but has since picked up a raft of other utilities clients, including EDF, E.On Next, TalkTalk and National Grid US.

Much research has been done to analyze the stock market. After all, if one can determine a pattern in the chaotic frenzy of transactions, then they could make a hefty profit from capitalizing on these insights. As such, the goal of our project was to apply reinforcement learning (RL) to determine the best time to buy a stock within a given time frame. With only a few adjustments, our model can be extended to identify the best time to sell a stock as well. In order to use the format of free, real-world data to train the model, we define our own Markov Decision Process (MDP) problem. These two papers [5] [6] helped us in formulating the state space and the reward system of our MDP problem. We train a series of agents using Q-Learning, Q-Learning with linear function approximation, and deep Q-Learning. In addition, we try to predict the stock prices using machine learning regression and classification models. We then compare our agents to see if they converge on a policy, and if so, which one learned the best policy to maximize profit on the stock market.

The financial industry is increasingly seeking robust methods to address the challenges posed by data scarcity and low signal-to-noise ratios, which limit the application of deep learning techniques in stock market analysis. This paper presents two innovative generative model-based approaches to synthesize stock data, specifically tailored for different scenarios within the A-share market in China. The first method, a sector-based synthesis approach, enhances the signal-to-noise ratio of stock data by classifying the characteristics of stocks from various sectors in China's A-share market. This method employs an Approximate Non-Local Total Variation algorithm to smooth the generated data, a bandpass filtering method based on Fourier Transform to eliminate noise, and Denoising Diffusion Implicit Models to accelerate sampling speed. The second method, a recursive stock data synthesis approach based on pattern recognition, is designed to synthesize data for stocks with short listing periods and limited comparable companies. It leverages pattern recognition techniques and Markov models to learn and generate variable-length stock sequences, while introducing a sub-time-level data augmentation method to alleviate data scarcity issues.We validate the effectiveness of these methods through extensive experiments on various datasets, including those from the main board, STAR Market, Growth Enterprise Market Board, Beijing Stock Exchange, NASDAQ, NYSE, and AMEX. The results demonstrate that our synthesized data not only improve the performance of predictive models but also enhance the signal-to-noise ratio of individual stock signals in price trading strategies. Furthermore, the introduction of sub-time-level data significantly improves the quality of synthesized data.

Users often interact with large language models through black-box inference APIs, both for closed- and open-weight models (e.g., Llama models are popularly accessed via Amazon Bedrock and Azure AI Studio). In order to cut costs or add functionality, API providers may quantize, watermark, or finetune the underlying model, changing the output distribution -- often without notifying users. We formalize detecting such distortions as Model Equality Testing, a two-sample testing problem, where the user collects samples from the API and a reference distribution and conducts a statistical test to see if the two distributions are the same. We find that tests based on the Maximum Mean Discrepancy between distributions are powerful for this task: a test built on a simple string kernel achieves a median of 77.4% power against a range of distortions, using an average of just 10 samples per prompt. We then apply this test to commercial inference APIs for four Llama models, finding that 11 out of 31 endpoints serve different distributions than reference weights released by Meta.

We consider a trading marketplace that is populated by traders with diverse trading strategies and objectives. The marketplace allows the suppliers to list their goods and facilitates matching between buyers and sellers. In return, such a marketplace typically charges fees for facilitating trade. The goal of this work is to design a dynamic fee schedule for the marketplace that is equitable and profitable to all traders while being profitable to the marketplace at the same time (from charging fees). Since the traders adapt their strategies to the fee schedule, we present a reinforcement learning framework for simultaneously learning a marketplace fee schedule and trading strategies that adapt to this fee schedule using a weighted optimization objective of profits and equitability. We illustrate the use of the proposed approach in detail on a simulated stock exchange with different types of investors, specifically market makers and consumer investors. As we vary the equitability weights across different investor classes, we see that the learnt exchange fee schedule starts favoring the class of investors with the highest weight. We further discuss the observed insights from the simulated stock exchange in light of the general framework of equitable marketplace mechanism design.

WPP is the transformation company. We use the power of creativity to build better futures for our people, planet, clients, and communities. Working at WPP means being part of a global network of more than 100,000 accomplished people in 110 countries. We create transformative ideas and outcomes for its clients through an integrated offer of communications, experience, commerce, and technology. WPP and our award-winning agencies work with most of the world's biggest companies and organisations – from Ford, Unilever and P&G to Google, HSBC, and the UN.

Artificial Intelligence is an ever-growing profession and also one of the most impactful divisions as it works at the crossroads of machine learning, cognitive intelligence, mathematics, and last but not the least, statistics. So, the most fundamental question we need to ask ourselves is what exactly is algorithmic trading. Algorithmic trading generally uses a computer program that follows a series of predefined directions to make a deal. In theory, the trade generates profits at a rate and frequency that a manual trader cannot complement since the algorithm makes trades using functions from advanced mathematics and does that round the clock, something which manual trader cannot compete with. AI trading systems are now set to usher in the second wave of innovation, the most momentous in the history of finance.

This article is an on-site version of the #fintechFT newsletter. Using artificial intelligence to improve fraud detection is becoming one of the hottest trends in the insurance industry, but it is also one of the most controversial trends. US insurer Lemonade has become a case study of potential technology rewards and reputational risks. Lemonade has become one of the most successful large-scale IPOs in 2020, fulfilling its promise to speed up and simplify lessor insurance and home insurance with AI-powered apps. But earlier this year, it sparked a social media backlash in concerns about the behavior of the algorithm.

'Looks can be deceiving,' a wise person once said. The phrase holds true for Algorithmic Trading Strategies. The term'Algorithmic trading strategies' might sound very fancy or too complicated. However, the concept is very simple to understand, once the basics are clear. In this article, We will be telling you about algorithmic trading strategies with some interesting examples. If you look at it from the outside, an algorithm is just a set of instructions or rules. These set of rules are then used on a stock exchange to automate the execution of orders without human intervention. This concept is called Algorithmic Trading. Popular algorithmic trading strategies used in automated trading are covered in this article.