The adoption of artificial intelligence (AI) offers transformative potential for small and medium-sized enterprises (SMEs), particularly in enhancing financial decision-making processes. However, SMEs often face significant barriers to implementing AI technologies, including limited resources, technical expertise, and data management capabilities. This paper presents a conceptual model for the adoption of AI in financial decision-making for SMEs. The proposed model addresses key challenges faced by SMEs, including limited resources, technical expertise, and data management capabilities. The model is structured into layers: data sources, data processing and integration, AI model deployment, decision support and automation, and validation and risk management. By implementing AI incrementally, SMEs can optimize financial forecasting, budgeting, investment strategies, and risk management. This paper highlights the importance of data quality and continuous model validation, providing a practical roadmap for SMEs to integrate AI into their financial operations. The study concludes with implications for SMEs adopting AI-driven financial processes and suggests areas for future research in AI applications for SME finance.

We propose the Causal Sphere Hypergraph Transformer (CSHT), a novel architecture for interpretable financial time-series forecasting that unifies \emph{Granger-causal hypergraph structure}, \emph{Riemannian geometry}, and \emph{causally masked Transformer attention}. CSHT models the directional influence of financial news and sentiment on asset returns by extracting multivariate Granger-causal dependencies, which are encoded as directional hyperedges on the surface of a hypersphere. Attention is constrained via angular masks that preserve both temporal directionality and geometric consistency. Evaluated on S\&P 500 data from 2018 to 2023, including the 2020 COVID-19 shock, CSHT consistently outperforms baselines across return prediction, regime classification, and top-asset ranking tasks. By enforcing predictive causal structure and embedding variables in a Riemannian manifold, CSHT delivers both \emph{robust generalisation across market regimes} and \emph{transparent attribution pathways} from macroeconomic events to stock-level responses. These results suggest that CSHT is a principled and practical solution for trustworthy financial forecasting under uncertainty.

Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates 'base factors', such as financial metric growth and earnings transcripts, with 'external factors', including recent market indices performances and analyst grades, to create a rich, supervised dataset. This comprehensive dataset enables our models to achieve superior predictive performance in terms of accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially evident in the comparison with benchmarks such as GPT-4. We specifically highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases significant improvements over baseline models. The paper also discusses the potential of expanding the output capabilities to include a 'Hold' option and extending the prediction horizon, aiming to accommodate various investment styles and time frames. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.

Financial forecasting plays an important role in making informed decisions for financial stakeholders, specifically in the stock exchange market. In a traditional setting, investors commonly rely on the equity research department for valuable reports on market insights and investment recommendations. The equity research department, however, faces challenges in effectuating decision-making due to the demanding cognitive effort required for analyzing the inherently volatile nature of market dynamics. Furthermore, financial forecasting systems employed by analysts pose potential risks in terms of interpretability and gaining the trust of all stakeholders. This paper presents an interpretable decision-making model leveraging the SHAP-based explainability technique to forecast investment recommendations. The proposed solution not only provides valuable insights into the factors that influence forecasted recommendations but also caters to investors of varying types, including those interested in daily and short-term investment opportunities. To ascertain the efficacy of the proposed model, a case study is devised that demonstrates a notable enhancement in investor's portfolio value, employing our trading strategies. The results highlight the significance of incorporating interpretability in forecasting models to boost stakeholders' confidence and foster transparency in the stock exchange domain.

The Workshop Program of the Association for the Advancement of Artificial Intelligence's 37th Conference on Artificial Intelligence (AAAI-23) was held in Washington, DC, USA on February 13-14, 2023. There were 32 workshops in the program: AI for Agriculture and Food Systems, AI for Behavior Change, AI for Credible Elections: A Call to Action with Trusted AI, AI for Energy Innovation, AI for Web Advertising, AI to Accelerate Science and Engineering, AI4EDU: AI for Education, Artificial Intelligence and Diplomacy, Artificial Intelligence for Cyber Security (AICS), Artificial Intelligence for Social Good (AI4SG), Artificial Intelligence Safety (SafeAI), Creative AI Across Modalities, Deep Learning on Graphs: Methods and Applications (DLG-AAAI'23), DEFACTIFY: Multimodal Fact-Checking and Hate Speech Detection, Deployable AI (DAI), DL-Hardware Co-Design for AI Acceleration, Energy Efficient Training and Inference of Transformer Based Models, Graphs and More Complex Structures for Learning and Reasoning (GCLR), Health Intelligence (W3PHIAI-23), Knowledge-Augmented Methods for Natural Language Processing, Modelling Uncertainty in the Financial World (MUFin'23), Multi-Agent Path Finding, Multimodal AI for Financial Forecasting (Muffin), Multimodal AI for Financial Forecasting (Muffin), Privacy-Preserving Artificial Intelligence, Recent Trends in Human-Centric AI, Reinforcement Learning Ready for Production, Scientific Document Understanding, Systems Neuroscience Approach to General Intelligence, Uncertainty Reasoning and Quantification in Decision Making (UDM'23), User-Centric Artificial Intelligence for Assistance in At-Home Tasks, and When Machine Learning Meets Dynamical Systems: Theory and Applications. This report contains summaries of the workshops, which were submitted by some, but not all of the workshop chairs. An increasing world population, coupled with finite arable land, changing diets, and the growing expense of agricultural inputs, is poised to stretch our agricultural systems to their limits. By the end of this century, the earth's population is projected to increase by 45% with available arable land decreasing by 20% coupled with changes in what crops these arable lands can best support; this creates the urgent need to enhance agricultural productivity by 70% before 2050.

Neural networks are a type of machine learning algorithm that are modeled after the human brain. They have revolutionized the field of machine learning, and have become a key tool for solving complex problems in a wide range of domains. At their core, neural networks are a set of algorithms that are designed to recognize patterns. They can learn to recognize patterns in data by analyzing large amounts of information, and then use this knowledge to make predictions or classifications. One of the key features of neural networks is their ability to learn from data. This is accomplished through a process called training, where the algorithm is fed a set of input data and the corresponding output (or label) for that data.

Can we use convolutional neural networks for time series analysis? It seems like a strange use case of convolutional networks, since they are generally used for image related tasks. But in recent months, more and more papers have started using convolutional networks for sequence classification. And since stock prices are a sequence, we can use them to make predictions. I'll also talk about how recurrent networks work as background.