Optimizing Investment Strategies with Lazy Factor and Probability Weighting: A Price Portfolio Forecasting and Mean-Variance Model with Transaction Costs Approach

ArXiv ID: 2306.07928 “View on arXiv”

Authors: Unknown

Abstract

Market traders often engage in the frequent transaction of volatile assets to optimize their total return. In this study, we introduce a novel investment strategy model, anchored on the ’lazy factor.’ Our approach bifurcates into a Price Portfolio Forecasting Model and a Mean-Variance Model with Transaction Costs, utilizing probability weights as the coefficients of laziness factors. The Price Portfolio Forecasting Model, leveraging the EXPMA Mean Method, plots the long-term price trend line and forecasts future price movements, incorporating the tangent slope and rate of change. For short-term investments, we apply the ARIMA Model to predict ensuing prices. The Mean-Variance Model with Transaction Costs employs the Monte Carlo Method to formulate the feasible region. To strike an optimal balance between risk and return, equal probability weights are incorporated as coefficients of the laziness factor. To assess the efficacy of this combined strategy, we executed extensive experiments on a specified dataset. Our findings underscore the model’s adaptability and generalizability, indicating its potential to transform investment strategies.

Keywords: Mean-Variance Model, Monte Carlo Method, ARIMA, Transaction Costs, Portfolio Forecasting, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper introduces a novel ’lazy factor’ concept combined with established quantitative models like EXPMA, ARIMA, and Markowitz Mean-Variance, utilizing Monte Carlo simulations, which signifies high mathematical density. However, the empirical section describes extensive experiments on a ‘specified dataset’ without providing specific performance metrics, backtesting results, or code/data availability, leading to lower empirical rigor.

  flowchart TD
    A["Research Goal:<br>Optimize investment strategies with lazy factor and probability weighting"] --> B["Data & Inputs:<br>Market dataset, Transaction costs, Volatile assets"]
    B --> C["Methodology Step 1:<br>Price Portfolio Forecasting Model<br>EXPMA Mean Method & ARIMA Model"]
    B --> D["Methodology Step 2:<br>Mean-Variance Model with Transaction Costs<br>Monte Carlo Method for feasible region"]
    C --> E["Computational Process:<br>Incorporate probability weights as<br>coefficients of laziness factors"]
    D --> E
    E --> F["Key Findings/Outcomes:<br>Adaptive and generalizable model<br>Optimal balance of risk and return<br>Transformative potential for investment strategies"]