Quantitative Investing

James H. Simons, PhD: Using Mathematics to Make Money ArXiv ID: ssrn-4668072 “View on arXiv” Authors: Unknown Abstract In September 2022, James Simons spoke with members of the Journal of Investment Consulting editorial board about how his experience as a mathematician prepared Keywords: Quantitative Investing, Asset Management, Mathematical Modeling, Hedge Funds Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper discusses advanced mathematical concepts like Chern-Simons invariants but focuses on philosophical and strategic insights from James Simons’ career, lacking specific formulas, code, or empirical backtesting details. flowchart TD A["Research Goal: How does mathematics<br>prepare for quantitative investing?"] --> B["Data/Inputs:<br>Simons Interview Data"] B --> C["Methodology:<br>Qualitative Content Analysis"] C --> D["Computational Process:<br>Identify Key Mathematical Concepts"] D --> E["Computational Process:<br>Map Concepts to Investment Strategies"] E --> F["Key Findings:<br>1. Pattern Recognition<br>2. Data Modeling<br>3. Algorithmic Optimization<br>4. Risk Management"]

The New Quant: A Survey of Large Language Models in Financial Prediction and Trading ArXiv ID: 2510.05533 “View on arXiv” Authors: Weilong Fu Abstract Large language models are reshaping quantitative investing by turning unstructured financial information into evidence-grounded signals and executable decisions. This survey synthesizes research with a focus on equity return prediction and trading, consolidating insights from domain surveys and more than fifty primary studies. We propose a task-centered taxonomy that spans sentiment and event extraction, numerical and economic reasoning, multimodal understanding, retrieval-augmented generation, time series prompting, and agentic systems that coordinate tools for research, backtesting, and execution. We review empirical evidence for predictability, highlight design patterns that improve faithfulness such as retrieval first prompting and tool-verified numerics, and explain how signals feed portfolio construction under exposure, turnover, and capacity controls. We assess benchmarks and datasets for prediction and trading and outline desiderata-for time safe and economically meaningful evaluation that reports costs, latency, and capacity. We analyze challenges that matter in production, including temporal leakage, hallucination, data coverage and structure, deployment economics, interpretability, governance, and safety. The survey closes with recommendations for standardizing evaluation, building auditable pipelines, and advancing multilingual and cross-market research so that language-driven systems deliver robust and risk-controlled performance in practice. ...

Portfolio optimisation: bridging the gap between theory and practice ArXiv ID: 2407.00887 “View on arXiv” Authors: Unknown Abstract Portfolio optimisation is essential in quantitative investing, but its implementation faces several practical difficulties. One particular challenge is converting optimal portfolio weights into real-life trades in the presence of realistic features, such as transaction costs and integral lots. This is especially important in automated trading, where the entire process happens without human intervention. Several works in literature have extended portfolio optimisation models to account for these features. In this paper, we highlight and illustrate difficulties faced when employing the existing literature in a practical setting, such as computational intractability, numerical imprecision and modelling trade-offs. We then propose a two-stage framework as an alternative approach to address this issue. Its goal is to optimise portfolio weights in the first stage and to generate realistic trades in the second. Through extensive computational experiments, we show that our approach not only mitigates the difficulties discussed above but also can be successfully employed in a realistic scenario. By splitting the problem in two, we are able to incorporate new features without adding too much complexity to any single model. With this in mind we model two novel features that are critical to many investment strategies: first, we integrate two classes of assets, futures contracts and equities, into a single framework, with an example illustrating how this can help portfolio managers in enhancing investment strategies. Second, we account for borrowing costs in short positions, which have so far been neglected in literature but which significantly impact profits in long/short strategies. Even with these new features, our two-stage approach still effectively converts optimal portfolios into actionable trades. ...