Apple reported better-than-expected earnings in the third quarter of 2024, with buzz about its new AI features offsetting a continuing decline in its key China market. Earnings exceeded analyst predictions despite a year-over-year decline in iPhone sales, with revenue rising 4.9% to 85.78bn in the three months ending 29 June, beating the average analyst estimate of 84.53bn. The company maintained its cash dividend at 25 cents for each share. The strong report represented a bright spot in the tech space after difficult earnings reports from other tech giants like Amazon, Snap and Intel. The market saw a sell-off on Thursday amid disappointing results, including from Intel – which announced plans to cut more than 15,000 jobs as it tries to cut billions of dollars in costs to turn its business around.

Meta's shares rose in after-hours trading on Wednesday off the back of a strong earnings report that comes as the company is spending heavily on AI tools. The company's stock price grew around 5% following the report, which revealed the company outperformed analysts' expectations for its second quarter. Meta, which owns Facebook, Instagram and WhatsApp, reported 39.07bn in revenue and 5.16 earnings per share. Both results outpaced market predictions of around 38bn in revenue and 4.7 per share, while the company also reported 8.47bn in capital expenditures – lower than analysts expected. "We had a strong quarter, and Meta AI is on track to be the most used AI assistant in the world by the end of the year," Mark Zuckerberg, Meta's CEO, claimed in a statement.

Microsoft outperformed analyst predictions in its latest quarterly earnings report, revealing on Tuesday that its revenue was up 15% year-over-year. But growth of the company's closely watched Azure cloud computing services failed to meet expectations and shares in Microsoft fell as much as 7% in after-hours trading. The company was expected to report steady growth in its fourth quarter earnings report, mostly on the back of its cloud services. Revenue from those services grew 29%, lower than the 30% to 31% that analysts predicted, resulting in a sell-off that exacerbates big tech's recent market woes. In Microsoft's earnings report, Satya Nadella, the CEO, sought to bolster confidence in the company's performance. "Our strong performance this fiscal year speaks both to our innovation and to the trust customers continue to place in Microsoft," said Nadella in the earnings statement.

Shares in technology companies, especially those related to AI, have driven much of this year's stock market gains. AI chip giant Nvidia, which has been one of the main beneficiaries of the AI boom, saw its shares drop 6.8%. It has lost about 15% of its value in the last two weeks. The company is set to report financial results at the end of August. Shares in multi-billionaire Elon Musk's electric car maker Tesla dropped by more than 12% after its latest financial results disappointed investors.

Google's parent company, Alphabet, outperformed analysts' expectations on Tuesday, reporting second-quarter earnings of 1.89 per share, the same as its first quarter results. Alphabet's CEO, Sundar Pichai, touted the results as proof that the company's investments across different areas of its tech empire were seeing positive returns. "Our strong performance this quarter highlights ongoing strength in Search and momentum in Cloud. We are innovating at every layer of the AI stack," Pichai stated in the earnings report. "Our longstanding infrastructure leadership and in-house research teams position us well as technology evolves and as we pursue the many opportunities ahead."

Samsung Electronics expects its profits for the three months to June 2024 to jump 15-fold compared to the same period last year. An artificial intelligence (AI) boom has lifted the prices of advanced chips, driving up the firm's forecast for the second quarter. The South Korean tech giant is the world's largest maker of memory chips, smartphones and televisions. The announcement pushed Samsung shares up more than 2% during early trading hours in Seoul. The firm also reported a more than 10-fold jump in its profits for the first three months of this year.

Time series models, typically trained on numerical data, are designed to forecast future values. These models often rely on weighted averaging techniques over time intervals. However, real-world time series data is seldom isolated and is frequently influenced by non-numeric factors. For instance, stock price fluctuations are impacted by daily random events in the broader world, with each event exerting a unique influence on price signals. Previously, forecasts in financial markets have been approached in two main ways: either as time-series problems over price sequence or sentiment analysis tasks. The sentiment analysis tasks aim to determine whether news events will have a positive or negative impact on stock prices, often categorizing them into discrete labels. Recognizing the need for a more comprehensive approach to accurately model time series prediction, we propose a collaborative modeling framework that incorporates textual information about relevant events for predictions. Specifically, we leverage the intuition of large language models about future changes to update real number time series predictions. We evaluated the effectiveness of our approach on financial market data.

Stock volatility prediction is an important task in the financial industry. Recent advancements in multimodal methodologies, which integrate both textual and auditory data, have demonstrated significant improvements in this domain, such as earnings calls (Earnings calls are public available and often involve the management team of a public company and interested parties to discuss the company's earnings). However, these multimodal methods have faced two drawbacks. First, they often fail to yield reliable models and overfit the data due to their absorption of stochastic information from the stock market. Moreover, using multimodal models to predict stock volatility suffers from gender bias and lacks an efficient way to eliminate such bias. To address these aforementioned problems, we use adversarial training to generate perturbations that simulate the inherent stochasticity and bias, by creating areas resistant to random information around the input space to improve model robustness and fairness. Our comprehensive experiments on two real-world financial audio datasets reveal that this method exceeds the performance of current state-of-the-art solution. This confirms the value of adversarial training in reducing stochasticity and bias for stock volatility prediction tasks.

The scope for the accurate calculation of the Loss Given Default (LGD) parameter is comprehensive in terms of financial data. In this research, we aim to explore methods for improving the approximation of realized LGD in conditions of limited access to the cash-flow data. We enhance the performance of the method which relies on the differences between exposure values (delta outstanding approach) by employing machine learning (ML) techniques. The research utilizes the data from the mortgage portfolio of one of the European countries and assumes a close resemblance to similar economic contexts. It incorporates non-financial variables and macroeconomic data related to the housing market, improving the accuracy of loss severity approximation. The proposed methodology attempts to mitigate the country-specific (related to the local legal) or portfolio-specific factors in aim to show the general advantage of applying ML techniques, rather than case-specific relation. We developed an XGBoost model that does not rely on cash-flow data yet enhances the accuracy of realized LGD estimation compared to results obtained with the delta outstanding approach. A novel aspect of our work is the detailed exploration of the delta outstanding approach and the methodology for addressing conditions of limited access to cash-flow data through machine learning models.

Sharp said Friday that it has signed a basic agreement to grant telecommunications carrier SoftBank Corp. exclusive negotiating rights for the partial sale of its Sakai plant in Osaka Prefecture. Sharp will halt production at the Sakai plant by the end of September as it scales down its liquid crystal display business. SoftBank plans to take over about 440,000 square meters, or about 60%, of the plant site and build a large data center for the development of generative artificial intelligence. It aims to start construction this autumn and put the data center into full operation in 2025. The price for the part of the plant site will be decided later. SoftBank plans to operate the data center on its own, while allowing external organizations such as universities and research institutions to use it.