Asset Management

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: Why do ML funds fail?"] --> D["Data: Financial ML papers & strategies"] D --> M["Methodology: Cross-sectional analysis of failures"] M --> C["Computational Process: Identify recurring pitfalls"] C --> F["Findings: 10 systemic reasons e.g., overfitting, data snooping"] F --> O["Outcome: Risk management framework for ML funds"]

Seven Sins of Fund Management ArXiv ID: ssrn-881760 “View on arXiv” Authors: Unknown Abstract How can behavioural finance inform the investment process? We have taken a hypothetical ’typical’ large fund management house and analysed their process. This c Keywords: Investment Process, Asset Management, Decision Making, Behavioral Bias, Asset Management Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual critique of fund management practices based on behavioral finance psychology, with no advanced mathematical formulas or statistical testing. Its empirical basis consists of anecdotes, citations of existing literature, and industry observations rather than original backtests, datasets, or implementation-heavy analysis. flowchart TD A["Research Goal: Identifying Behavioral Biases in Asset Management Decision Making"] --> B["Key Methodology"] B --> B1["Hypothesis Testing"] B --> B2["Process Analysis"] B --> B3["Case Study Review"] B1 & B2 & B3 --> C["Data & Inputs • Investment Process Documentation • Decision Records • Market Data • Manager Interviews"] C --> D["Computational Processes • Bias Detection Algorithms • Performance Attribution • Scenario Analysis • Risk Assessment"] D --> E["Key Findings & Outcomes • Seven Behavioral Sins Identified • Process Gaps Revealed • Mitigation Strategies Developed • Enhanced Decision Framework"]