Donald Trump has said that the launch of a chatbot by China's DeepSeek is a "wake-up call" for US tech firms in the global race to dominate artificial intelligence. The emergence of DeepSeek, which has built its R1 model chatbot at a fraction of the cost of competitors such as OpenAI's ChatGPT and Google's Gemini, wiped 1tn ( 800bn) in value from the leading US tech index on Monday. Nvidia, a leading maker of computer chips that has experienced explosive growth amid the AI boom, had 600bn wiped off its market value in the biggest one-day fall in US stock market history. "The release of DeepSeek, AI from a Chinese company, should be a wake-up call for our industries that we need to be laser-focused on competing to win," said Trump. He pointed to DeepSeek's ability to apparently deliver the same performance as existing AI models with far fewer resources, threatening the dominance of the US-led AI boom.

Investors punished global tech stocks on Monday after the emergence of a Chinese chatbot competitor to OpenAI's ChatGPT, DeepSeek, raised doubts about the sustainability of the US artificial intelligence boom. The tech-heavy Nasdaq index in New York opened lower after investors digested the implications of the latest AI model developed by the startup DeepSeek. Nvidia, the most valuable listed company in the US and a leading maker of the computer chips that power AI models, lost more than 400bn ( 321bn) in stock market value in early trading as its shares declined 13.6%, while Microsoft shed 130bn and Google's parent, Alphabet, declined by 80bn. Nvidia's fall – which wiped about 465bn off its value, was the biggest in US stock market history, according to Bloomberg. The DeepSeek AI assistant topped the Apple app store in the US and UK over the weekend, above OpenAI's ChatGPT.

On January 20, DeepSeek, a relatively unknown AI research lab from China, released an open source model that's quickly become the talk of the town in Silicon Valley. According to a paper authored by the company, DeepSeek-R1 beats the industry's leading models like OpenAI o1 on several math and reasoning benchmarks. In fact, on many metrics that matter--capability, cost, openness--DeepSeek is giving Western AI giants a run for their money. US export controls have severely curtailed the ability of Chinese tech firms to compete on AI in the Western way--that is, infinitely scaling up by buying more chips and training for a longer period of time. As a result, most Chinese companies have focused on downstream applications rather than building their own models.

A unit of Ryobi Holdings, a bus route operator in rural Japan, is setting up an artificial intelligence-powered hedge fund specializing in forex to survive a sharp drop in the country's population outside Tokyo. Ryobi has enlisted Kyosuke Suzuki, a former currency trader at Societe Generale, to set up the fund by the end of the year. The hedge fund, part of Ryobi's subsidiary, Ryobi Systems, will begin investing around the beginning of 2025. Ryobi was founded 114 years ago as a short railway connecting towns and villages in Okayama Prefecture. Today, it is at the front line of the country's demographic crisis, grappling with aging customers and shrinking communities.

When constructing portfolios, a key problem is that a lot of financial time series data are sparse, making it challenging to apply machine learning methods. Polymodel theory can solve this issue and demonstrate superiority in portfolio construction from various aspects. To implement the PolyModel theory for constructing a hedge fund portfolio, we begin by identifying an asset pool, utilizing over 10,000 hedge funds for the past 29 years' data. PolyModel theory also involves choosing a wide-ranging set of risk factors, which includes various financial indices, currencies, and commodity prices. This comprehensive selection mirrors the complexities of the real-world environment. Leveraging on the PolyModel theory, we create quantitative measures such as Long-term Alpha, Long-term Ratio, and SVaR. We also use more classical measures like the Sharpe ratio or Morningstar's MRAR. To enhance the performance of the constructed portfolio, we also employ the latest deep learning techniques (iTransformer) to capture the upward trend, while efficiently controlling the downside, using all the features. The iTransformer model is specifically designed to address the challenges in high-dimensional time series forecasting and could largely improve our strategies. More precisely, our strategies achieve better Sharpe ratio and annualized return. The above process enables us to create multiple portfolio strategies aiming for high returns and low risks when compared to various benchmarks.

How can firms optimally negotiate bilateral contracts with each other in a financial network? Every firm seeks to maximize the utility it gains from its portfolio of contracts. We focus on mean-variance utilities, where each firm has its own beliefs about the expected returns of the contracts and the covariances between them (Markowitz, J. Finance 7(11), 1952). Instead of revealing these beliefs, a firm may adopt a different negotiating position, seeking better contract terms. We formulate a contract negotiation process by which such strategic behavior leads to a network of contracts. In our formulation, any subset of firms can be strategic. The negotiating positions of these firms can form Nash equilibria, where each firm's position is optimal given the others' positions. We give a polynomial-time algorithm to find the Nash equilibria, if they exist, and certify their nonexistence otherwise. We explore the implications of such equilibria on several model networks. These illustrate that firms' utilities can be sensitive to their negotiating position. We then propose trade deadlines as a mechanism to reduce the need for strategic behavior. At the deadline, each firm can unilaterally cancel some or all of its contracts, for a penalty. In our model networks, we show that trade deadlines can reduce the loss of utility from being honest. We empirically verify our insights using data on international trade between 46 large economies.

Stanford Graduate School of Business lecturer Dave Dodson claims the Biden admin's handling of the economy is to'tinker' with it'like it's a video game' on'Your World with Neil Cavuto.' Following the 2008 global financial crisis, the Federal Reserve created trillions of dollars to ease financial conditions and keep banks afloat. Many economists predicted record inflation would result. But Fed Chairman Ben Bernanke pulled an ace out of his sleeve. He paid banks to park much of that money at the Fed and limit its inflationary effects.

Who are the earliest adopters of new technologies? Cutting-edge stuff tends to be expensive, meaning the answer is often the extremely rich. Early adopters also tend to be incentivised by cut-throat competition to look beyond the status quo. As such, there may be no group more likely to pick up new tools than the uber-rich and hyper-competitive hedge-fund industry. Your browser does not support the audio element.

Quantitative trading is the usage of mathematical models or algorithms to create trading strategies and trade them. Quant trading is usually employed by large institutional traders or hedge funds who employ large teams of PhDs and engineers. Historically the quantitative trading field has been very secretive and ideas which work tend to be guarded very closely by the firms but in the last few years the growth of openly available datasets and access to compute i.e ( in the form of GPUs and cloud) has made quant trading accessible to a larger audience. Each of the above steps involve lot of research and trial and error to get right. Quant trading is a complex field and requires careful and detailed study to be successful. The following are 10 such books which can help one get started on their Quant journey.

Event detection is a critical task for timely decision-making in graph analytics applications. Despite the recent progress towards deep learning on graphs, event detection on dynamic graphs presents particular challenges to existing architectures. Real-life events are often associated with sudden deviations of the normal behavior of the graph. However, existing approaches for dynamic node embedding are unable to capture the graph-level dynamics related to events. In this paper, we propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs. DyGED learns correlations between the graph macro dynamics -- i.e. a sequence of graph-level representations -- and labeled events. Moreover, our approach combines structural and temporal self-attention mechanisms to account for application-specific node and time importances effectively. Our experimental evaluation, using a representative set of datasets, demonstrates that DyGED outperforms competing solutions in terms of event detection accuracy by up to 8.5% while being more scalable than the top alternatives. We also present case studies illustrating key features of our model.