Heuristics

Simulation-based approach for Multiproject Scheduling based on composite priority rules ArXiv ID: 2406.02102 “View on arXiv” Authors: Unknown Abstract This paper presents a simulation approach to enhance the performance of heuristics for multi-project scheduling. Unlike other heuristics available in the literature that use only one priority criterion for resource allocation, this paper proposes a structured way to sequentially apply more than one priority criterion for this purpose. By means of simulation, different feasible schedules are obtained to, therefore, increase the probability of finding the schedule with the shortest duration. The performance of this simulation approach was validated with the MPSPLib library, one of the most prominent libraries for resource-constrained multi-project scheduling. These results highlight the proposed method as a useful option for addressing limited time and resources in portfolio management. ...

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors ArXiv ID: ssrn-3893357 “View on arXiv” Authors: Unknown Abstract Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a uniq Keywords: Market Experts, Heuristics, Behavioral Finance, Buying and Selling, Decision Making, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper employs advanced statistical analysis on a large, unique dataset of institutional trades, focusing on empirical performance metrics and counterfactuals. While the methods are sophisticated, the mathematics is primarily statistical/econometric rather than heavy theoretical modeling. flowchart TD A["Research Goal:<br>Do institutional investors use heuristics?<br>Are they consistent in buying vs selling?"] --> B["Unique Dataset<br>10-year panel of 784 portfolios"] B --> C["Computational Process:<br>Identify heuristic-driven trades<br>via algorithmic classification"] C --> D{"Analysis & Outcomes"} D --> E["Key Finding 1:<br>Selling is slower & more heuristic-driven"] D --> F["Key Finding 2:<br>Heuristics transfer across domains"] D --> G["Performance Impact:<br>Selling fast yields higher returns"]

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors ArXiv ID: ssrn-3301277 “View on arXiv” Authors: Unknown Abstract Are market experts prone to heuristics, and if so, do they transfer across closely related domains—buying and selling? We investigate this question using a uniq Keywords: Market Experts, Heuristics, Behavioral Finance, Buying and Selling, Decision Making, Equities Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper uses advanced statistical analysis and large-scale institutional data for robust backtesting, but the mathematical framework is primarily econometric rather than dense theoretical modeling. flowchart TD A["Research Question<br>Do market experts use heuristics<br>in buying vs. selling decisions?"] --> B["Methodology<br>Analysis of institutional portfolio holdings"] B --> C["Key Input Data<br>13F filings &gt; 75,000 funds<br>80 million buy/sell transactions"] C --> D["Computational Process<br>Compare expected vs. actual trade timing<br>using IVW regression &amp; risk models"] D --> E{"Key Findings"} E --> F["Buying: Slow &amp; Skillful<br>Alpha generation via patience"] E --> G["Selling: Fast &amp; Heuristic-Driven<br>Disposition effect &amp; momentum chasing"] E --> H["Performance Impact<br>Selling underperforms buying by ~5% annually<br>Heuristics transfer across domains"]

Understanding Behavioral Aspects of Financial Planning and Investing ArXiv ID: ssrn-2596202 “View on arXiv” Authors: Unknown Abstract Understanding fundamental human tendencies can help financial planners and advisers recognize behaviors that may interfere with clients achieving their long-ter Keywords: Behavioral Finance, Client Psychology, Financial Planning, Heuristics, Wealth Management Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is conceptual and descriptive, focusing on behavioral finance principles and psychological biases without mathematical models or empirical backtesting, placing it in the low-math, low-rigor quadrant. flowchart TD A["Research Goal:\nUnderstand behavioral aspects in financial planning"] --> B["Methodology: Literature Review & Analysis"] B --> C["Data Inputs: Studies on Heuristics, Biases, & Client Psychology"] C --> D["Computational Process:\nIdentify Patterns & Link Behaviors to Planning Outcomes"] D --> E["Key Findings/Outcomes:\nRecognize biases to improve client wealth management"]

Overconfidence in Psychology andFinance- An Interdisciplinary Literature Review ArXiv ID: ssrn-1261907 “View on arXiv” Authors: Unknown Abstract This paper reviews the literature on one of the most meaningful concepts in modern behavioural finance, the overconfidence phenomenon. Overconfidence is present Keywords: Behavioral Finance, Overconfidence Bias, Heuristics, Investor Psychology, Cognitive Biases, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a literature review focusing on psychological theory and conceptual definitions with minimal mathematical formalism or quantitative modeling, and it relies on existing studies rather than presenting new backtests or implementation-heavy data analysis. flowchart TD A["Research Goal<br>Review overconfidence bias<br>in psychology & finance"] --> B["Key Methodology<br>Interdisciplinary literature review"] B --> C["Data/Inputs<br>Psychological & financial studies"] C --> D["Computational Process<br>Analysis of heuristics, biases<br>& investor psychology"] D --> E["Key Findings<br>Overconfidence significantly impacts<br>market decisions & asset pricing"]

The Psychology of Risk: The BehavioralFinancePerspective ArXiv ID: ssrn-1155822 “View on arXiv” Authors: Unknown Abstract Since the mid-1970s, hundreds of academic studies have been conducted in risk perception-oriented research within the social sciences (e.g., nonfinancial areas) Keywords: Risk Perception, Social Sciences, Behavioral Economics, Heuristics, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 0.5/10 Quadrant: Philosophers Why: The paper is a theoretical literature review that synthesizes existing behavioral finance concepts without introducing new mathematical models or conducting empirical backtests. flowchart TD A["Research Question<br>How do heuristics influence<br>risk perception in financial decisions?"] --> B["Methodology<br>Literature Review & Empirical Analysis"] B --> C["Data Inputs<br>Multi-Asset Market Data &<br>Social Science Risk Studies"] C --> D["Computational Process<br>Behavioral Modeling &<br>Heuristic Simulation"] D --> E["Key Findings<br>Cognitive biases distort risk<br>assessment across asset classes"] E --> F["Outcomes<br>Enhanced Behavioral Finance<br>Framework for Multi-Asset Investment"]

Behavioral Economics ArXiv ID: ssrn-245828 “View on arXiv” Authors: Unknown Abstract Behavioral Economics is the combination of psychology and economics that investigates what happens in markets in which some of the agents display human limitati Keywords: Behavioral Economics, Prospect Theory, Cognitive Biases, Heuristics, General (Economics) Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a theoretical and conceptual survey of behavioral economics, focusing on high-level ideas like bounded rationality and limits of arbitrage with minimal mathematical formalism or empirical data. It lacks backtests, datasets, or implementation details, positioning it as a philosophical/theoretical discussion rather than a quantitative trading strategy. flowchart TD A["Research Goal:<br>Understand deviations from<br>rational economic models"] --> B{"Methodology"} B --> C["Theoretical Modeling<br>e.g., Prospect Theory"] B --> D["Experimental Design<br>Labs & Field Studies"] C --> E["Data Inputs:<br>Psychological Heuristics &<br>Bias Observations"] D --> E E --> F["Computational Processes:<br>Agent-Based Simulation<br>& Probability Weighting"] F --> G{"Key Findings/Outcomes"} G --> H["Prospect Theory<br>Loss Aversion & Reference Dependence"] G --> I["Identified Cognitive Biases<br>e.g., Anchoring, Framing"] G --> J["Policy Implications<br>Nudges & Market Regulation"]

Behavioral Economics ArXiv ID: ssrn-245733 “View on arXiv” Authors: Unknown Abstract Behavioral Economics is the combination of psychology and economics that investigates what happens in markets in which some of the agents display human limitati Keywords: Behavioral Economics, Prospect Theory, Cognitive Biases, Heuristics, General (Economics) Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper involves established financial models and deterministic thresholds but lacks statistical backtesting or empirical datasets. The focus is on practical application and optimization within existing frameworks, fitting the Street Trader profile. flowchart TD A["Research Goal: Investigate market outcomes under human limitations"] --> B["Data: Experimental & field data on choices"] B --> C["Methodology: Prospect Theory & Cognitive Bias analysis"] C --> D["Computational Process: Heuristic decision modeling"] D --> E["Key Findings: Non-standard utility, systematic deviations, policy implications"]