Investment Strategy

Evaluating Investment Performance: The p-index and Empirical Efficient Frontier ArXiv ID: 2510.11074 “View on arXiv” Authors: Jing Li, Bowei Guo, Xinqi Xie, Kuo-Ping Chang Abstract The empirical results have shown that firstly, with one-week holding period and reinvesting, for SSE Composite Index stocks, the highest p-ratio investment strategy produces the largest annualized rate of return; and for NYSE Composite Index stocks, all the three strategies with both one-week and one-month periods generate negative returns. Secondly, with non-reinvesting, for SSE Composite Index stocks, the highest p-ratio strategy with one-week holding period yields the largest annualized rate of return; and for NYSE Composite stocks, the one-week EEF strategy produces a medium annualized return. Thirdly, under the one-week EEF investment strategy, for NYSE Composite Index stocks, the right frontier yields a higher annualized return, but for SSE Composite Index stocks, the left frontier (stocks on the empirical efficient frontier) yields a higher annualized return than the right frontier. Fourthly, for NYSE Composite Index stocks, there is a positive linear relationship between monthly return and the p-index, but no such relationship is evident for SSE Composite Index stocks. Fifthly, for NYSE Composite Index stocks, the traditional five-factor model performs poorly, and adding the p-index as a sixth factor provides incremental information. ...

Finding good bets in the lottery, and why you shouldn’t take them ArXiv ID: 2507.01993 “View on arXiv” Authors: Aaron Abrams, Skip Garibaldi Abstract We give a criterion under which the expected return on a ticket for certain large lotteries is positive. In this circumstance, we use elementary portfolio analysis to show that an optimal investment strategy includes a very small allocation for such tickets. Keywords: lottery ticket, portfolio analysis, expected return, investment strategy, risk allocation, lottery tickets ...

Neural Functionally Generated Portfolios ArXiv ID: 2506.19715 “View on arXiv” Authors: Michael Monoyios, Olivia Pricilia Abstract We introduce a novel neural-network-based approach to learning the generating function $G(\cdot)$ of a functionally generated portfolio (FGP) from synthetic or real market data. In the neural network setting, the generating function is represented as $G_θ(\cdot)$, where $θ$ is an iterable neural network parameter vector, and $G_θ(\cdot)$ is trained to maximise investment return relative to the market portfolio. We compare the performance of the Neural FGP approach against classical FGP benchmarks. FGPs provide a robust alternative to classical portfolio optimisation by bypassing the need to estimate drifts or covariances. The neural FGP framework extends this by introducing flexibility in the design of the generating function, enabling it to learn from market dynamics while preserving self-financing and pathwise decomposition properties. ...

Dynamic Investment-Driven Insurance Pricing and Optimal Regulation ArXiv ID: 2410.18432 “View on arXiv” Authors: Unknown Abstract This paper analyzes the equilibrium of insurance market in a dynamic setting, focusing on the interaction between insurers’ underwriting and investment strategies. Three possible equilibrium outcomes are identified: a positive insurance market, a zero insurance market, and market failure. Our findings reveal why insurers may rationally accept underwriting losses by setting a negative safety loading while relying on investment profits, particularly when there is a negative correlation between insurance gains and financial returns. Additionally, we explore the impact of regulatory frictions, showing that while imposing a cost on investment can enhance social welfare under certain conditions, it may not always be necessary. ...

Functional Clustering of Discount Functions for Behavioral Investor Profiling ArXiv ID: 2410.16307 “View on arXiv” Authors: Unknown Abstract Classical finance models are based on the premise that investors act rationally and utilize all available information when making portfolio decisions. However, these models often fail to capture the anomalies observed in intertemporal choices and decision-making under uncertainty, particularly when accounting for individual differences in preferences and consumption patterns. Such limitations hinder traditional finance theory’s ability to address key questions like: How do personal preferences shape investment choices? What drives investor behaviour? And how do individuals select their portfolios? One prominent contribution is Pompian’s model of four Behavioral Investor Types (BITs), which links behavioural finance studies with Keirsey’s temperament theory, highlighting the role of personality in financial decision-making. Yet, traditional parametric models struggle to capture how these distinct temperaments influence intertemporal decisions, such as how individuals evaluate trade-offs between present and future outcomes. To address this gap, the present study employs Functional Data Analysis (FDA) to specifically investigate temporal discounting behaviours revealing nuanced patterns in how different temperaments perceive and manage uncertainty over time. Our findings show heterogeneity within each temperament, suggesting that investor profiles are far more diverse than previously thought. This refined classification provides deeper insights into the role of temperament in shaping intertemporal financial decisions, offering practical implications for financial advisors to better tailor strategies to individual risk preferences and decision-making styles. ...

Trading with Time Series Causal Discovery: An Empirical Study ArXiv ID: 2408.15846 “View on arXiv” Authors: Unknown Abstract This study investigates the application of causal discovery algorithms in equity markets, with a focus on their potential to build investment strategies. An investment strategy was developed based on the causal structures identified by these algorithms. The performance of the strategy is evaluated based on the profitability and effectiveness in stock markets. The results indicate that causal discovery algorithms can successfully uncover actionable causal relationships in large markets, leading to profitable investment outcomes. However, the research also identifies a critical challenge: the computational complexity and scalability of these algorithms when dealing with large datasets. This challenge presents practical limitations for their application in real-world market analysis. ...

Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning ArXiv ID: 2311.04946 “View on arXiv” Authors: Unknown Abstract In this study, we have developed a dynamic asset allocation investment strategy using reinforcement learning techniques. To begin with, we have addressed the crucial issue of incorporating non-stationarity of financial time series data into reinforcement learning algorithms, which is a significant implementation in the application of reinforcement learning in investment strategies. Our findings highlight the significance of introducing certain variables such as regime change in the environment setting to enhance the prediction accuracy. Furthermore, the application of reinforcement learning in investment strategies provides a remarkable advantage of setting the optimization problem flexibly. This enables the integration of practical constraints faced by investors into the algorithm, resulting in efficient optimization. Our study has categorized the investment strategy formulation conditions into three main categories, including performance measurement indicators, portfolio management rules, and other constraints. We have evaluated the impact of incorporating these conditions into the environment and rewards in a reinforcement learning framework and examined how they influence investment behavior. ...

Behavioral Portfolio Management ArXiv ID: ssrn-2210032 “View on arXiv” Authors: Unknown Abstract Behavioral Portfolio Management (BPM) is presented as a superior way to make investment decisions. Underlying BPM is the dynamic market interplay between Emotio Keywords: Behavioral Finance, Portfolio Management, Market Dynamics, Investment Strategy, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is primarily a conceptual framework discussing behavioral finance principles and critiques of MPT, lacking advanced mathematical derivations or statistical models, and presents only conceptual evidence rather than backtest-ready data or implementation details. flowchart TD A["Research Goal: Develop Behavioral Portfolio Management\nBPM as superior investment methodology"] --> B["Methodology: Quantifying Market Dynamics\nSimulating multi-asset interplay"] B --> C["Data: Historical Multi-Asset Returns\nBehavioral indicator datasets"] C --> D["Computational Process: Dynamic Optimization\nvs Traditional Models"] D --> E["Key Outcomes: BPM Outperformance\nRisk-adjusted returns & behavioral alpha"]

Keynes the Stock Market Investor: A Quantitative Analysis ArXiv ID: ssrn-2023011 “View on arXiv” Authors: Unknown Abstract The consensus view of the influential economist John Maynard Keynes is that he was a stellar investor. We provide an extensive quantitative appraisal of his per Keywords: Portfolio Performance, Quantitative Appraisal, Investment Strategy, Historical Analysis, Equities Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper relies on historical archival data reconstruction and extensive backtesting of Keynes’ trades over 25 years, indicating high empirical rigor, but its mathematical modeling is primarily statistical tests and factor analysis rather than advanced theoretical derivations. flowchart TD A["Research Goal<br>Appraise Keynes's Stock Market Performance"] --> B{"Methodology<br>Quantitative Analysis"} B --> C["Data Inputs<br>Historical Portfolio Records"] C --> D["Computational Process<br>Performance Metrics & Risk Analysis"] D --> E["Key Findings<br>Consensus of Stellar Investor Verified"]