This article applies natural language processing (NLP) to extract and quantify textual information to predict stock performance. Using an extensive dataset of Chinese analyst reports and employing a customized BERT deep learning model for Chinese text, this study categorizes the sentiment of the reports as positive, neutral, or negative. The findings underscore the predictive capacity of this sentiment indicator for stock volatility, excess returns, and trading volume. Specifically, analyst reports with strong positive sentiment will increase excess return and intraday volatility, and vice versa, reports with strong negative sentiment also increase volatility and trading volume, but decrease future excess return. The magnitude of this effect is greater for positive sentiment reports than for negative sentiment reports. This article contributes to the empirical literature on sentiment analysis and the response of the stock market to news in the Chinese stock market.

We introduce FinTral, a suite of state-of-the-art multimodal large language models (LLMs) built upon the Mistral-7b model and tailored for financial analysis. FinTral integrates textual, numerical, tabular, and image data. We enhance FinTral with domain-specific pretraining, instruction fine-tuning, and RLAIF training by exploiting a large collection of textual and visual datasets we curate for this work. We also introduce an extensive benchmark featuring nine tasks and 25 datasets for evaluation, including hallucinations in the financial domain. Our FinTral model trained with direct preference optimization employing advanced Tools and Retrieval methods, dubbed FinTral-DPO-T&R, demonstrates an exceptional zero-shot performance. It outperforms ChatGPT-3.5 in all tasks and surpasses GPT-4 in five out of nine tasks, marking a significant advancement in AI-driven financial technology. We also demonstrate that FinTral has the potential to excel in real-time analysis and decision-making in diverse financial contexts. The GitHub repository for FinTral is available at \url{https://github.com/UBC-NLP/fintral}.

In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock performance is a critical challenge that has attracted both academics and investors. While previous studies have used deep learning-based models to obtain a general view of ECCs, they often fail to capture detailed, complex information. Our study introduces a novel framework: \textbf{ECC Analyzer}, combining Large Language Models (LLMs) and multi-modal techniques to extract richer, more predictive insights. The model begins by summarizing the transcript's structure and analyzing the speakers' mode and confidence level by detecting variations in tone and pitch for audio. This analysis helps investors form an overview perception of the ECCs. Moreover, this model uses the Retrieval-Augmented Generation (RAG) based methods to meticulously extract the focuses that have a significant impact on stock performance from an expert's perspective, providing a more targeted analysis. The model goes a step further by enriching these extracted focuses with additional layers of analysis, such as sentiment and audio segment features. By integrating these insights, the ECC Analyzer performs multi-task predictions of stock performance, including volatility, value-at-risk (VaR), and return for different intervals. The results show that our model outperforms traditional analytic benchmarks, confirming the effectiveness of using advanced LLM techniques in financial analytics.

The capital asset pricing model (CAPM) is very widely used and is considered to be a very fundamental concept in investing. It determines the link between the risk and expected return of assets, in particular stocks. According to CAPM, the value of α is expected to be zero and that it is very random and cannot be predicted. The equation seen above is in the form of y mx b and therefore it can be treated as a form of linear regression. The scipy package will be used. It has a function to calculate the linear regression.

Everyone is talking about artificial intelligence (AI) right now- with many predicting that AI will lead the next wave of economic growth and productivity for the next couple of decades at least. AI refers to the use of data to simulate human intelligence processes including learning, reasoning and self-correction by machines. AI is making its way into almost every industry. With IDC predicting that worldwide spending on AI will be nearly $98 Billion in 2023, the implications of this technology are massive. And this has not been ignored by Wall Street. Analysts say that plenty of compelling investments can be found within this space.

Ever since British economist John Maynard Keynes first declared that investors are prey to people's urge to act, however irrationally, the financial world has tried to quantify the impact of public sentiment on stock prices. Solving the puzzle would give investors in the know a huge advantage over the competition. Over the past decade, one vibrant corner of that still ongoing research has been data analysis. The goal has been to tease out clues about sentiment that are hidden in news articles, regulatory filings, transcripts, and press releases. With the rise of artificial intelligence, the sophistication of sentiment-measuring technology is increasing.

AI can be very strategic. Automation, from robotic process automation to artificial intelligence, is transforming every function of every business in every industry. In fact, according to research from PWC, AI's impact on business will be greater than the internet. The potential applications are limitless, from individualized customer marketing, to employee screening and selection, to smarter products that collect data, to automated customer support. AI has begun to change organizational processes on a scale that the re-engineering movement of thirty years ago could only imagine.