This project focuses on analyzing retail market trends using historical sales data, search trends, and customer reviews. By identifying the patterns and trending products, the analysis provides actionable insights for retailers to optimize inventory management and marketing strategies, ultimately enhancing customer satisfaction and maximizing revenue.

Financial metrics like the Sharpe ratio are pivotal in evaluating investment performance by balancing risk and return. However, traditional metrics often struggle with robustness and generalization, particularly in dynamic and volatile market conditions. This paper introduces AlphaSharpe, a novel framework leveraging large language models (LLMs) to iteratively evolve and optimize financial metrics. AlphaSharpe generates enhanced risk-return metrics that outperform traditional approaches in robustness and correlation with future performance metrics by employing iterative crossover, mutation, and evaluation. Key contributions of this work include: (1) an innovative use of LLMs for generating and refining financial metrics inspired by domain-specific knowledge, (2) a scoring mechanism to ensure the evolved metrics generalize effectively to unseen data, and (3) an empirical demonstration of 3x predictive power for future risk-return forecasting. Experimental results on a real-world dataset highlight the superiority of AlphaSharpe metrics, making them highly relevant for portfolio managers and financial decision-makers. This framework not only addresses the limitations of existing metrics but also showcases the potential of LLMs in advancing financial analytics, paving the way for informed and robust investment strategies.

Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed.

We introduce the first general test of capacity-constrained learning models. Cognitive economic models of this type share the common feature that constraints on perception are exogenously fixed, as in the widely used fixed-capacity versions of rational inattention (Sims 2003) and efficient coding (Woodford 2012). We show that choice data are consistent with capacity-constrained learning if and only if they satisfy a No Improving (Action or Attention) Switches (NIS) condition. Based on existing experiments in which the incentives for being correct are varied, we find strong evidence that participants fail NIS for a wide range of standard perceptual tasks: identifying the proportion of ball colors, recognizing shapes, and counting the number of balls. However, we find that this is not true for all existing perceptual tasks in the literature, which offers insights into settings where we do or do not expect incentives to impact the extent of attention.

MarketSenseAI is a novel framework for holistic stock analysis which leverages Large Language Models (LLMs) to process financial news, historical prices, company fundamentals and the macroeconomic environment to support decision making in stock analysis and selection. In this paper, we present the latest advancements on MarketSenseAI, driven by rapid technological expansion in LLMs. Through a novel architecture combining Retrieval-Augmented Generation and LLM agents, the framework processes SEC filings and earnings calls, while enriching macroeconomic analysis through systematic processing of diverse institutional reports. We demonstrate a significant improvement in fundamental analysis accuracy over the previous version. Empirical evaluation on S\&P 100 stocks over two years (2023-2024) shows MarketSenseAI achieving cumulative returns of 125.9% compared to the index return of 73.5%, while maintaining comparable risk profiles. Further validation on S\&P 500 stocks during 2024 demonstrates the framework's scalability, delivering a 33.8% higher Sortino ratio than the market. This work marks a significant advancement in applying LLM technology to financial analysis, offering insights into the robustness of LLM-driven investment strategies.

This paper examines a semi-analytical approach for pricing American options in time-inhomogeneous models characterized by negative interest rates (for equity, FX) or negative convenience yields (for commodities, cryptocurrencies). Under such conditions, exercise boundaries may exhibit a "floating" structure - dynamically appearing and disappearing. For example, a second exercise boundary could emerge within the computational domain and subsequently both could collapse, demanding specialized pricing methodologies.

We explore a dynamic patent citation network model to explain the established link between network structure and technological improvement rate. This model, a type of survival model, posits that the *dynamic* network structure determines the *constant* improvement rate, requiring consistent structural reproduction over time. The model's hazard rate, the probability of a patent being cited, represents "knowledge production," reflecting the output of new patents given existing ones. Analyzing hydrogen technology patents, we find distinct subdomain knowledge production rates, but consistent development across subdomains. "Distribution" patents show the lowest production rate, suggesting dominant "distribution" costs in $H_2$ pricing. Further modeling shows Katz-centrality predicts knowledge production, outperforming subdomain classification. Lower Katz centrality in "distribution" suggests inherent organizational differences in invention. Exploitative learning (within-subdomain citations) correlates with higher patenting opportunity costs, potentially explaining slower "distribution" development, as high investment needs may incentivize monopolization over knowledge sharing.

This paper addresses the critical disconnect between prediction and decision quality in portfolio optimization by integrating Large Language Models (LLMs) with decision-focused learning. We demonstrate both theoretically and empirically that minimizing the prediction error alone leads to suboptimal portfolio decisions. We aim to exploit the representational power of LLMs for investment decisions. An attention mechanism processes asset relationships, temporal dependencies, and macro variables, which are then directly integrated into a portfolio optimization layer. This enables the model to capture complex market dynamics and align predictions with the decision objectives. Extensive experiments on S\&P100 and DOW30 datasets show that our model consistently outperforms state-of-the-art deep learning models. In addition, gradient-based analyses show that our model prioritizes the assets most crucial to decision making, thus mitigating the effects of prediction errors on portfolio performance. These findings underscore the value of integrating decision objectives into predictions for more robust and context-aware portfolio management.

This study investigates the inherently random structures of dry bulk shipping networks, often likened to a taxi service, and identifies the underlying trade dynamics that contribute to this randomness within individual cargo sub-networks. By analysing micro-level trade flow data from 2015 to 2023, we explore the evolution of dry commodity networks, including grain, coal, and iron ore, and suggest that the Giant Strongly Connected Components exhibit small-world phenomena, indicative of efficient bilateral trade. The significant heterogeneity of in-degree and out-degree within these sub-networks, primarily driven by importing ports, underscores the complexity of their dynamics. Our temporal analysis shows that while the Covid-19 pandemic profoundly impacted the coal network, the Ukraine conflict significantly altered the grain network, resulting in changes in community structures. Notably, grain sub-networks display periodic changes, suggesting distinct life cycles absent in coal and iron ore networks. These findings illustrate that the randomness in dry bulk shipping networks is a reflection of real-world trade dynamics, providing valuable insights for stakeholders in navigating and predicting network behaviours.

Emergent technologies such as solar power, electric vehicles, and artificial intelligence (AI) often exhibit exponential or power function price declines and various ``S-curves'' of adoption. We show that under CES and VES utility, such price and adoption curves are functionally linked. When price declines follow Moore's, Wright's and AI scaling "Laws,'' the S-curve of adoption is Logistic or Log-Logistic whose slope depends on the interaction between an experience parameter and the elasticity of substitution between the incumbent and emergent good. These functional relations can serve as a building block for more complex models and guide empirical specifications of technology adoption.

Two novel and direct quantum mechanical representations of the Black-Scholes model are constructed based on the (Wick-rotated) quantization of two specific mechanical systems. The quantum setup is achieved by means of the associated Laplace-Beltrami operator (one for each model), and not by merely applying the usual naive rule. Additionally, the clear identification of the geometric content of the underlying classical framework is exploited in order to arrive at a noncommutative quantum mechanics generalization of the Black-Scholes model. We also consider a system consisting of two degrees of freedom whose (Wick-rotated) quantization leads to a model which can be seen as related to the Merton-Garman family. This model is also generalized via noncommutative quantum mechanics.

The Basel Committee on Banking Supervision proposed replacing all approaches for operational risk capital, including the Advanced Measurement Approach (AMA), with a simplified formula called the Standardized Measurement Approach (SMA). This paper examines and criticizes the weaknesses and failures of SMA, such as instability, insensitivity to risk, superadditivity, and the implicit relationship between the SMA capital model and systemic risk in the banking sector. Furthermore, it discusses the issues of the proposed Operational Risk Capital (OpCar) model by the Basel Committee, a precursor to SMA. The paper concludes by advocating for the maintenance of the AMA internal model framework and suggests a series of standardization recommendations to unify internal operational risk modeling. The findings and viewpoints presented in this paper have been discussed and supported by numerous operational risk professionals and academics from various regions of the world.

The exchange of rebates for formulary positions is conceptualized as a multi-round combinatorial position auction. This paper develops a linear assignment model of the winners' determination equation of this auction where the bases are net unit prices after unit rebates and expected demand.

Preference heterogeneity massively increases the social cost of carbon. We call this the Weitzman premium. Uncertainty about an exponential discount rate implies a hyperbolic discount rate, which in the near term is equal to the average discount rate but in the long term falls to the minimum discount rate. We calibrate the pure rate of time preference and the inverse of the elasticity of intertemporal substitution of 79,273 individuals from 76 countries and compute the corresponding social cost of carbon. Compared to the social cost of carbon for average time preferences, the average social cost of carbon is 6 times as large in the base calibration, and up to 200 times as large in sensitivity analyses.

We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formalism of quantum theory, to distance metric learning in corporate bond markets. Compared to equities, corporate bonds are relatively illiquid and both trade and quote data in these securities are relatively sparse. Thus, a measure of distance/similarity among corporate bonds is particularly useful for a variety of practical applications in the trading of illiquid bonds, including the identification of similar tradable alternatives, pricing securities with relatively few recent quotes or trades, and explaining the predictions and performance of ML models based on their training data. Previous research has explored supervised similarity learning based on classical tree-based models in this context; here, we explore the application of the QCML paradigm for supervised distance metric learning in the same context, showing that it outperforms classical tree-based models in high-yield (HY) markets, while giving comparable or better performance (depending on the evaluation metric) in investment grade (IG) markets.

This project introduces an end-to-end trading system that leverages Large Language Models (LLMs) for real-time market sentiment analysis. By synthesizing data from financial news and social media, the system integrates sentiment-driven insights with technical indicators to generate actionable trading signals. FinGPT serves as the primary model for sentiment analysis, ensuring domain-specific accuracy, while Kubernetes is used for scalable and efficient deployment.

This paper presents the AI Enabled Individual Entrepreneurship Theory (AIET), a theoretical framework explaining how artificial intelligence technologies transform individual entrepreneurial capability. The theory identifies two foundational premises: knowledge democratization and resource requirements evolution. Through three core mechanisms skill augmentation, capital structure transformation, and risk profile modification AIET explains how individuals can now undertake entrepreneurial activities at scales previously requiring significant organizational infrastructure. The theory presents five testable propositions addressing the changing relationship between organizational size and competitive advantage, the expansion of individual entrepreneurial capacity, the transformation of market entry barriers, the evolution of traditional firm advantages, and the modification of entrepreneurial risk profiles. Boundary conditions related to task characteristics and market conditions define the theory's scope and applicability. The framework suggests significant implications for entrepreneurship theory, organizational design, and market structure as AI capabilities continue to advance. This theory provides a foundation for understanding the evolving landscape of entrepreneurship in an AI-enabled world.

We investigate whether artificial intelligence can address the peer review crisis in economics by analyzing 27,090 evaluations of 9,030 unique submissions using a large language model (LLM). The experiment systematically varies author characteristics (e.g., affiliation, reputation, gender) and publication quality (e.g., top-tier, mid-tier, low-tier, AI generated papers). The results indicate that LLMs effectively distinguish paper quality but exhibit biases favoring prominent institutions, male authors, and renowned economists. Additionally, LLMs struggle to differentiate high-quality AI-generated papers from genuine top-tier submissions. While LLMs offer efficiency gains, their susceptibility to bias necessitates cautious integration and hybrid peer review models to balance equity and accuracy.

This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were evaluated. Key themes explored include the impact of data privacy frameworks and advancements in feature engineering and data preprocessing. The study emphasizes challenges such as imbalanced datasets, model interpretability, and ethical considerations, alongside opportunities for automation and privacy-preserving techniques such as blockchain integration and Principal Component Analysis. By examining trends over the past five years, this review identifies critical gaps and promising directions for advancing DL applications in financial fraud detection, offering actionable insights for researchers and practitioners.

We study expected utility maximization problem with constant relative risk aversion utility function in a complete market under the reinforcement learning framework. To induce exploration, we introduce the Tsallis entropy regularizer, which generalizes the commonly used Shannon entropy. Unlike the classical Merton's problem, which is always well-posed and admits closed-form solutions, we find that the utility maximization exploratory problem is ill-posed in certain cases, due to over-exploration. With a carefully selected primary temperature function, we investigate two specific examples, for which we fully characterize their well-posedness and provide semi-closed-form solutions. It is interesting to find that one example has the well-known Gaussian distribution as the optimal strategy, while the other features the rare Wigner semicircle distribution, which is equivalent to a scaled Beta distribution. The means of the two optimal exploratory policies coincide with that of the classical counterpart. In addition, we examine the convergence of the value function and optimal exploratory strategy as the exploration vanishes. Finally, we design a reinforcement learning algorithm and conduct numerical experiments to demonstrate the advantages of reinforcement learning.