Global energy prices have spiked alarmingly again, driven largely in recent days by the escalating conflict between Yemen's Houthis and Saudi Arabia causing chaos for the oil industry. Petrol and diesel costs have risen rapidly in most countries while the wholesale price of natural gas - used for heating homes and generating electricity - has almost doubled in the UK and Europe since July. There are growing concerns of another inflationary shock on the world economy, which could cause interest rates to rise, increase mortgage costs and raise the price of almost everything in the shops including food. So what is behind this latest surge - and what are the implications for the world economy? The global oil price is currently above $108 (£80) per barrel, up from $70 (£52) in June 2026 - a roughly 50% increase.

Rises in petrol, diesel and airfares pushed UK inflation up to its highest level in five months in the year to August. Inflation accelerated to 3.1% from 2.9%, according to the Office for National Statistics (ONS). The cost of filling up a vehicle soared in August as the conflict in the Middle East continued to disrupt global oil supplies. Petrol prices jumped to their highest for nearly four years, the ONS said, while diesel also rocketed. Meanwhile, the cost of flying jumped during the key month for summer getaways.

US government borrowing costs climbed to their highest level since 2007 after a jump in oil prices further fuelled concerns about inflation. The effective interest rate on US government bonds over 10 years, known as the 10-year Treasury yield, rose as high as 5.04% but has eased back since. Government bond yields have been rising globally for months, driven by worries that inflation caused by the oil price surge since the start of the US-Israel war with Iran will lead to higher interest rates. The US has been buying back bonds back in a bid to drive the Treasury yield down, with Treasury Secretary Scott Bessent calling the intervention successful. The global benchmark wholesale oil price rose to over $109 a barrel on Tuesday, up from around $86 at the end of August, after renewed concerns about Saudi Arabia's ability to export oil following rising tensions in the region .

Brent crude has risen above $100 a barrel as war in Iran disrupts shipping through the Strait of Hormuz, while the Houthis' capture of Mayun Island raises concerns over another major oil route through the Bab al-Mandeb. JD Vance insists US is'on top of' Houthi advance in Red Sea China rejects AI'threat narratives', urges global cooperation US may be'forced to intervene' in the Red Sea

Long-term borrowing costs across some of the word's biggest economies hit fresh highs because of concerns over inflation, government debt levels and spending on Artificial Intelligence (AI). The interest rate on US borrowing over 30 years hit 5.33% on Tuesday, the highest since June 2007, meanwhile UK long-term debt reached 5.85%. There were similar moves in Germany and Japan. Interest rates on bonds - which are a type of debt - are known as yields and can directly affect the borrowing costs consumers pay on mortgages, car loans and credit cards. Rising oil prices are the main driver behind this recent surge in bond yields, as investors fear inflation could spike again. If that happens, central banks may choose to raise interest rates to cool inflation.

The president's comments on the oil price came after he spoke to Saudi Crown Prince Mohammed bin Salman on Wednesday. According to Saudi State media Bin Salman pledged to invest as much as 600bn in the US over the next four years, however this figure was not mentioned in the White House statement after the call. Despite the cordial exchange, Trump said he would be asking "the Crown Prince, who's a fantastic guy, to round it out to around 1tn". The price of crude fell by 1% following Trump's comments. According to David Oxley, Chief Climate and Commodities Economist at Capital Economics these comments are in keeping with Trump's desire for lower gasoline prices.

Abstract: In the field of global energy and environment, crude oil is an important strategic resource, and its price fluctuation has a far-reaching impact on the global economy, financial market and the process of low-carbon development. In recent years, with the gradual promotion of green energy transformation and low-carbon development in various countries, the dynamics of crude oil market have become more complicated and changeable. The price of crude oil is not only influenced by traditional factors such as supply and demand, geopolitical conflict and production technology, but also faces the challenges of energy policy transformation, carbon emission control and new energy technology development. This diversified driving factor makes the prediction of crude oil price not only very important in economic decision-making and energy planning, but also a key issue in financial markets.In this paper, the spot price data of European Brent crude oil provided by us energy information administration are selected, and a deep learning model with three layers of LSTM units is constructed to predict the crude oil price in the next few days. The results show that the LSTM model performs well in capturing the overall price trend, although there is some deviation during the period of sharp price fluctuation. The research in this paper not only verifies the applicability of LSTM model in energy market forecasting, but also provides data support for policy makers and investors when facing the uncertainty of crude oil price.

However, the prediction of crude oil prices is renowned for its obscurity and complexity. The high degree of volatility, unpredictable, irregular events, and complex interconnections among market factors make it extremely challenging to accurately forecast the fluctuations in crude oil prices. The dynamic interplay of supply and demand and changes in oil prices are influenced by external factors such as economic growth, financial markets, geopolitical conflicts, warfare, and political considerations [1, 2, 3]. A variety of methodologies have been utilised for predicting crude oil prices, involving the application of econometric and statistical time series analysis techniques such as VAR [4], ARIMA, GARCH [5], VMD [6], and Walvet decomposition [7]. In more recent studies, there has been a prevalent use of machine learning models and hybrid approaches [2, 8, 9] in the literature. Nevertheless, achieving accurate oil price forecasting remains a challenging task, particularly in terms of multi-step forecasting. Traditional econometric and statistical methods are often inadequate for forecasting oil prices due to many challenges related to the irregular characteristics of energy markets, such as non-stationarity, multi-frequency, non-linearity, and chaotic properties [10].

Accurate crude oil price forecasting is crucial for various economic activities, including energy trading, risk management, and investment planning. Although deep learning models have emerged as powerful tools for crude oil price forecasting, achieving accurate forecasts remains challenging. Deep learning models' performance is heavily influenced by hyperparameters tuning, and they are expected to perform differently under various circumstances. Furthermore, price volatility is also sensitive to external factors such as world events. To address these limitations, we propose a hybrid approach combining metaheuristic optimisation and an ensemble of five popular neural network architectures used in time series forecasting. Unlike existing methods that apply metaheuristics to optimise hyperparameters within the neural network architecture, we exploit the GWO metaheuristic optimiser at four levels: feature selection, data preparation, model training, and forecast blending. The proposed approach has been evaluated for forecasting three-ahead days using real-world Brent crude oil price data, and the obtained results demonstrate that the proposed approach improves the forecasting performance measured using various benchmarks, achieving 0.000127 of MSE.

This report outlines a transformative initiative in the financial investment industry, where the conventional decision-making process, laden with labor-intensive tasks such as sifting through voluminous documents, is being reimagined. Leveraging language models, our experiments aim to automate information summarization and investment idea generation. We seek to evaluate the effectiveness of fine-tuning methods on a base model (Llama2) to achieve specific application-level goals, including providing insights into the impact of events on companies and sectors, understanding market condition relationships, generating investor-aligned investment ideas, and formatting results with stock recommendations and detailed explanations. Through state-of-the-art generative modeling techniques, the ultimate objective is to develop an AI agent prototype, liberating human investors from repetitive tasks and allowing a focus on high-level strategic thinking. The project encompasses a diverse corpus dataset, including research reports, investment memos, market news, and extensive time-series market data. We conducted three experiments applying unsupervised and supervised LoRA fine-tuning on the llama2_7b_hf_chat as the base model, as well as instruction fine-tuning on the GPT3.5 model. Statistical and human evaluations both show that the fine-tuned versions perform better in solving text modeling, summarization, reasoning, and finance domain questions, demonstrating a pivotal step towards enhancing decision-making processes in the financial domain. Code implementation for the project can be found on GitHub: https://github.com/Firenze11/finance_lm.