Quantitative Finance

From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing ArXiv ID: 2403.06779 “View on arXiv” Authors: Unknown Abstract This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how 1) ML models, including supervised, unsupervised, semi-supervised, and reinforcement learning, provide versatile frameworks to address these complexities, and 2) the incorporation of advanced ML algorithms into traditional financial models enhances return prediction and portfolio optimization. These methods can adapt to changing market dynamics by modeling structural changes and incorporating heterogeneous data sources, such as text and images. In addition, this paper explores challenges in applying ML in asset pricing, addressing the growing demand for explainability in decision-making and mitigating overfitting in complex models. This paper aims to provide insights into novel methodologies showcasing the potential of ML to reshape the future of quantitative finance. ...

Ploutos: Towards interpretable stock movement prediction with financial large language model ArXiv ID: 2403.00782 “View on arXiv” Authors: Unknown Abstract Recent advancements in large language models (LLMs) have opened new pathways for many domains. However, the full potential of LLMs in financial investments remains largely untapped. There are two main challenges for typical deep learning-based methods for quantitative finance. First, they struggle to fuse textual and numerical information flexibly for stock movement prediction. Second, traditional methods lack clarity and interpretability, which impedes their application in scenarios where the justification for predictions is essential. To solve the above challenges, we propose Ploutos, a novel financial LLM framework that consists of PloutosGen and PloutosGPT. The PloutosGen contains multiple primary experts that can analyze different modal data, such as text and numbers, and provide quantitative strategies from different perspectives. Then PloutosGPT combines their insights and predictions and generates interpretable rationales. To generate accurate and faithful rationales, the training strategy of PloutosGPT leverage rearview-mirror prompting mechanism to guide GPT-4 to generate rationales, and a dynamic token weighting mechanism to finetune LLM by increasing key tokens weight. Extensive experiments show our framework outperforms the state-of-the-art methods on both prediction accuracy and interpretability. ...

Trends and Applications of Machine Learning in QuantitativeFinance ArXiv ID: ssrn-3397005 “View on arXiv” Authors: Unknown Abstract Recent advances in machine learning are finding commercial applications across many industries, not least the finance industry. This paper focuses on applicatio Keywords: machine learning, algorithmic trading, predictive analytics, quantitative finance, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a broad literature review of ML applications in finance, focusing on conceptual categorization rather than novel mathematical derivations or empirical backtesting. It outlines common algorithms and use cases but lacks implementation details, statistical metrics, or specific experimental results. flowchart TD G["Research Goal: Evaluate ML in Quant Finance"] --> D["Data Sources"] D --> M["Key Methodology"] D --> C["Computational Processes"] M --> F["Key Findings/Outcomes"] C --> F subgraph D ["Data/Inputs"] D1["Multi-Asset Market Data"] D2["Historical Price & Volatility"] end subgraph M ["Methodology Steps"] M1["Algorithmic Trading Strategies"] M2["Predictive Analytics"] end subgraph C ["Computational Processes"] C1["Deep Learning Models"] C2["Reinforcement Learning"] end subgraph F ["Outcomes"] F1["Enhanced Portfolio Optimization"] F2["Improved Risk Management"] F3["Commercial Applications in Finance"] end

Machine Learning for Stock Selection ArXiv ID: ssrn-3330946 “View on arXiv” Authors: Unknown Abstract Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning technique Keywords: Machine learning, Quantitative finance, Predictive accuracy, Quantitative Strategies Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper provides a conceptual overview of machine learning techniques in finance with minimal advanced mathematical derivations, focusing more on the debate and methodology rather than deep theoretical proofs. Empirical rigor is limited as it discusses general challenges like overfitting and proposes forecast combinations without presenting detailed backtest results, code, or specific implementation datasets. flowchart TD A["Research Goal: Evaluate ML for Stock Selection"] --> B["Data: Historical Prices, Fundamentals, Sentiment"] B --> C["Methodology: Train ML Models e.g., Gradient Boosting, Neural Networks"] C --> D{"Computational Process: Backtest on Out-of-Sample Data"} D --> E["Key Finding: ML Models Achieve High Predictive Accuracy"] D --> F["Key Finding: Significant Risk of Overfitting"] E & F --> G["Outcome: Mixed Results; Strategy Viability Depends on Rigorous Validation"] style A fill:#e1f5fe style G fill:#fff3e0

A Backtesting Protocol in the Era of Machine Learning ArXiv ID: ssrn-3275654 “View on arXiv” Authors: Unknown Abstract Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, th Keywords: Machine Learning, Investment Management, Quantitative Finance, Asset Pricing, Algorithmic Trading Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper focuses on a research protocol for backtesting and data mining, with moderate empirical rigor involving practical concerns like overfitting and data scarcity, but lacks advanced mathematical derivations, centering instead on statistical concepts and real-world data challenges. flowchart TD A["Research Goal: Develop robust backtesting protocol for ML in finance"] --> B["Data: Cross-sectional stock data & fundamental features"] B --> C["Methodology: ML pipelines with walk-forward validation"] C --> D["Computation: Model training, hyperparameter tuning, & signal generation"] D --> E["Risk Controls: Transaction costs, liquidity constraints, & overfitting tests"] E --> F["Key Outcomes: Generalizable signals & realistic performance metrics"] F --> G["Implication: ML requires rigorous validation to avoid false discoveries"]

Detection of False Investment Strategies Using Unsupervised Learning Methods ArXiv ID: ssrn-3167017 “View on arXiv” Authors: Unknown Abstract Most investment strategies uncovered by practitioners and academics are false. This partially explains the high rate of failure, especially among quantitative h Keywords: quantitative finance, investment strategies, backtesting bias, market efficiency, quantitative strategies Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 2.0/10 Quadrant: Lab Rats Why: The paper introduces a complex unsupervised learning algorithm involving probability distributions and multiple testing corrections, but lacks specific implementation details, code, or detailed backtesting results, focusing more on theoretical and statistical methodology. flowchart TD A["Research Goal:<br>Detect false quantitative investment strategies"] --> B["Methodology:<br>Unsupervised Learning (e.g., Clustering)"] B --> C["Data Inputs:<br>Strategy Returns, Factor Loadings, Backtest Metrics"] C --> D["Computational Process:<br>Identify Outliers & Anomalies in Strategy Space"] D --> E["Key Findings:<br>Strategies are often noise; high failure rate due to backtesting bias"]

The 10 Reasons Most Machine Learning Funds Fail ArXiv ID: ssrn-3104816 “View on arXiv” Authors: Unknown Abstract The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of ass Keywords: Financial Machine Learning, Quantitative Finance, Asset Management, Predictive Analytics, Trading Strategy, Quantitative Finance / Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper focuses on high-level methodological pitfalls and organizational paradigms in financial machine learning, with minimal advanced mathematical formalism. It lacks empirical backtests, statistical code, or implementation-heavy data analysis, making it more of a conceptual framework than a backtest-ready study. flowchart TD Q["Research Question:<br>Why do ML funds fail?"] --> D["Data: Financial ML<br>papers & strategies"] D --> M["Methodology: Cross-sectional<br>analysis of failures"] M --> C["Computational Process:<br>Identify recurring pitfalls"] C --> F["Findings: 10 systemic reasons<br>e.g., overfitting, data snooping"] F --> O["Outcome: Risk management<br>framework for ML funds"]

Stata forFinanceStudents ArXiv ID: ssrn-2318687 “View on arXiv” Authors: Unknown Abstract While MS-Excel is a default software for finance students, command line econometrics softwares make financial analysis easier, especially for repetitive tasks. Keywords: Financial Econometrics, Data Analysis, Statistical Software, Quantitative Finance, Quantitative Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper focuses on implementing standard financial and econometric methods using Stata commands and data access, making it highly practical and data-driven rather than theoretical. flowchart TD A["Research Goal: Stata for Finance Students"] --> B["Methodology: Survey & Comparative Analysis"] B --> C{"Inputs"} C --> D["Excel Usage Data<br/>Quantitative Finance Tasks"] C --> E["Stata Command-line Features<br/>Repetitive Task Efficiency"] D & E --> F["Computational Process:<br/>Statistical Software Comparison"] F --> G["Key Findings/Outcomes"] G --> H["Stata superior for<br/>financial econometrics"] G --> I["Command-line tools<br/>enhance analysis speed"] G --> J["Recommendation:<br/>Integrate Stata in curriculum"]

Risk-Neutral Probabilities Explained ArXiv ID: ssrn-1395390 “View on arXiv” Authors: Unknown Abstract All too often, the concept of risk-neutral probabilities in mathematical finance is poorly explained, and misleading statements are made. The aim of this paper Keywords: risk-neutral probabilities, martingales, stochastic calculus, derivatives pricing, Quantitative Finance Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 2.0/10 Quadrant: Lab Rats Why: The paper focuses on theoretical foundations, including continuous-time stochastic processes like geometric Brownian motion and martingales, but lacks any empirical backtesting, data, or implementation details. flowchart TD A["Research Goal: Explain Risk-Neutral Probabilities clearly"] --> B["Methodology: Critical Review of Stochastic Calculus"] B --> C["Input: Misleading Statements in Texts"] C --> D["Computational Process: Martingale Measure Derivation"] B --> E["Input: Derivatives Pricing Models"] E --> D D --> F["Key Finding: Q-Measure vs. P-Measure"] D --> G["Key Finding: No-Arbitrage Pricing Framework"]