Beating the Market with Machine Learning

Author: Denis Avetisyan

A new approach leverages earnings data and portfolio optimization to potentially deliver returns exceeding the S&P 500.

Figure 2: Dataset Division The partitioning of data into distinct subsets - training, validation, and testing - establishes a controlled environment for evaluating a system’s capacity to generalize beyond the initially observed data, with the training set used for parameter optimization, the validation set for hyperparameter tuning, and the testing set providing an unbiased assessment of performance on unseen data, ultimately revealing the system’s true resilience against entropy. — Figure 2: Dataset Division The partitioning of data into distinct subsets – training, validation, and testing – establishes a controlled environment for evaluating a system’s capacity to generalize beyond the initially observed data, with the training set used for parameter optimization, the validation set for hyperparameter tuning, and the testing set providing an unbiased assessment of performance on unseen data, ultimately revealing the system’s true resilience against entropy.

This review details a practical machine learning scheme for dynamic stock recommendation utilizing financial ratios and minimum-variance portfolio allocation.

Efficient stock selection remains a persistent challenge for investors, given the limitations of both traditional analysis and the sheer volume of available securities. This paper, ‘A Practical Machine Learning Approach for Dynamic Stock Recommendation’, addresses this by introducing a data-driven scheme that dynamically ranks S&P 500 stocks using machine learning models trained on key earnings factors. Empirical results demonstrate that a portfolio constructed from the top-ranked stocks-and allocated via a minimum-variance strategy-outperforms a simple long-only approach based on Sharpe ratio and cumulative returns. Could this approach offer a scalable solution for enhancing portfolio performance in dynamic market conditions?

The Erosion of Static Valuation

Conventional financial modeling, while foundational, frequently encounters limitations due to its inherent dependence on established assumptions and retrospective data. These models often project future performance based on past trends, a methodology that struggles to accommodate the unpredictable nature of contemporary markets and the emergence of novel disruptions. The simplification of complex systems into quantifiable variables, while necessary for calculation, can inadvertently obscure critical nuances-such as shifts in consumer behavior, technological advancements, or geopolitical events-that significantly impact an asset’s true value. Consequently, valuations derived from purely historical analysis may fail to accurately reflect current realities or anticipate future market movements, potentially leading to mispricing and flawed investment decisions. This reliance on the past creates a static picture in a world defined by dynamic change, highlighting the need for more adaptable and forward-looking valuation techniques.

Conventional valuation methods, frequently rooted in discounted cash flow analysis and comparable company multiples, increasingly falter when applied to the intricacies of contemporary finance. These static models presume a degree of stability that rarely exists, particularly with the proliferation of complex derivatives, structured products, and intangible assets. The rapid evolution of economic landscapes – characterized by disruptive technologies, geopolitical shifts, and unforeseen crises – further exacerbates these limitations. Consequently, valuations derived from these approaches can significantly diverge from actual market prices, failing to adequately capture risk or potential. The inherent inflexibility of static methods struggles to account for non-linear relationships, feedback loops, and the dynamic interplay of variables that define modern financial instruments and environments, ultimately hindering accurate assessment of true economic value.

Valuation models frequently lean on macroeconomic data – GDP growth, interest rates, and inflation – as primary drivers of company worth, yet this approach often obscures the unique characteristics that define a firm’s true value. A company’s intrinsic worth isn’t solely determined by the overall economic climate; factors such as proprietary technology, brand reputation, management expertise, and competitive positioning within a specific niche are critically important. Overreliance on broad indicators can therefore lead to mispricing, as it fails to account for a company’s ability to innovate, capture market share, or maintain profitability independent of, or even in spite of, prevailing economic conditions. Consequently, a thorough valuation necessitates a deep dive into company-specific fundamentals, examining qualitative aspects alongside quantitative data to arrive at a more accurate and nuanced assessment of its true worth.

Harnessing Complexity: The Rise of Machine Learning

Traditional financial models often rely on linear assumptions and pre-defined relationships, which can limit their accuracy when applied to complex, non-linear financial data. Machine learning algorithms, conversely, are capable of identifying and modeling intricate, multi-dimensional relationships without explicit programming of those relationships. These algorithms achieve this through iterative learning from data, allowing them to capture subtle patterns and interactions that may be missed by conventional statistical methods. This capability is particularly valuable in areas such as credit risk assessment, fraud detection, and algorithmic trading, where the underlying dynamics are often highly complex and constantly evolving. The ability to automatically discover these relationships improves model performance and provides insights that would be difficult or impossible to obtain through manual analysis.

Regression-based models are utilized in financial forecasting to estimate the relationship between dependent variables – key financial metrics such as stock prices, credit risk, or sales figures – and one or more independent variables. Linear Regression establishes a linear relationship, modeled as $y = \beta_0 + \beta_1x_1 + \dots + \beta_nx_n$, where ‘y’ represents the predicted metric and ‘x’ values are the predictors. Ridge Regression addresses multicollinearity by adding a penalty term to the linear regression equation, reducing model complexity and improving generalization. Generalized Boosting Regression, a more advanced technique, sequentially builds an ensemble of regression trees, iteratively correcting errors from previous trees to achieve higher predictive accuracy. These models differ in their complexity and ability to capture non-linear relationships, allowing for tailored solutions based on the specific financial metric being predicted and the characteristics of the available data.

The integration of diverse datasets, encompassing both internal financial records and external macroeconomic indicators, enhances model robustness and predictive capability. To mitigate overfitting – a common issue where models perform well on training data but poorly on unseen data – techniques like Stepwise Regression are employed. Stepwise Regression systematically adds or removes predictor variables based on statistical significance, optimizing the model’s complexity and generalization ability. This iterative process identifies the most relevant features, reducing noise and improving the model’s capacity to accurately predict future outcomes with a minimized risk of being overly sensitive to specific training data points. The resulting models demonstrate improved predictive power and reliability when applied to new, previously unseen datasets.

Portfolio Construction: Seeking Optimal Trade-offs

Mean-Variance Optimization (MVO) and Minimum-Variance Optimization (MVO) are portfolio construction techniques employing historical data to identify optimal asset allocations. MVO seeks to maximize expected return for a given level of risk, quantified by portfolio variance, while MVO directly minimizes portfolio variance without explicitly considering expected returns. Both methods rely on estimating asset expected returns, volatilities, and correlations, utilizing these parameters within quadratic programming frameworks to determine portfolio weights. The resulting allocations aim to construct portfolios that lie on the efficient frontier, representing the best possible risk-return trade-off, and are frequently used as benchmarks for evaluating other portfolio strategies.

The portfolio optimization strategies utilize the Sharpe Ratio – calculated as the excess return over the risk-free rate divided by portfolio volatility – as the primary metric for evaluating performance. This approach prioritizes maximizing returns relative to the level of risk undertaken. Testing demonstrated consistent outperformance compared to the S&P 500 index, evidenced by a demonstrably higher Sharpe Ratio achieved through our scheme. Specifically, the higher ratio indicates a superior risk-adjusted return, meaning that for each unit of risk assumed, the portfolio generated a greater return than the benchmark index. This outperformance was maintained throughout the evaluation period, confirming the effectiveness of the methodology in delivering enhanced returns for a given level of volatility, measured by standard deviation.

The implementation of a Rolling Window approach utilizes a fixed-length historical data window that shifts forward in time, recalculating portfolio allocations at each step to reflect current market conditions. This technique, applied to data derived from the S&P 500 Index, provides adaptability by dynamically adjusting to shifts in asset correlations and volatilities. Backtesting revealed consistent outperformance of the optimized portfolios both within the historical data used for optimization (in-sample) and across broader, out-of-sample trade periods. Specifically, the rolling window methodology mitigates the risk of static optimization becoming obsolete due to evolving market dynamics, thereby enhancing the robustness and long-term viability of the portfolio strategies.

Refining the Lens: Comprehensive Financial Data

A comprehensive evaluation of a company’s financial health necessitates a detailed analysis of key ratios, moving beyond simple metrics to reveal a nuanced performance picture. The Price-to-Earnings (P/E) Ratio, for example, indicates how much investors are willing to pay for each dollar of a company’s earnings, while the Price-to-Sales (P/S) Ratio offers insight into revenue generation relative to market capitalization. Critically, Return on Equity (ROE) measures a company’s profitability in relation to shareholder equity, demonstrating how effectively management utilizes invested capital. Examining these ratios – and others – in concert provides a holistic view, enabling a more informed assessment of a company’s current financial standing, its historical trends, and its potential for future growth, ultimately facilitating better investment decisions.

Rigorous financial analysis relies on dependable data, and the utilization of the Compustat Database coupled with the Global Industry Classification Standard (GICS) provides a robust foundation for valuation. Compustat delivers a comprehensive collection of financial statement data, while GICS offers a consistent and standardized framework for categorizing companies across various sectors and sub-industries. This standardization is critical, enabling meaningful comparisons of key performance indicators – such as profitability, efficiency, and leverage – between companies that might otherwise appear dissimilar. By leveraging this combination, analysts can move beyond simple observation and conduct in-depth comparative analyses, identifying relative strengths and weaknesses and ultimately refining assessments of intrinsic value with greater confidence. The system’s detailed categorization also supports portfolio construction and risk management strategies by facilitating the identification of diversified investment opportunities within and across industries.

A nuanced valuation extends beyond traditional metrics, incorporating the Book-to-Market Ratio – a comparison of a company’s market capitalization to its book value – and, crucially, forward-looking Future Earnings Estimates. This approach allows analysts to move beyond historical performance and assess a company’s potential, acknowledging that market perception and anticipated growth significantly influence intrinsic value. Rigorous data preparation was central to this process; the removal of 0.84% of identified outlier records and a reduction of missing data to under 7% across all sectors ensured the reliability and comparability of the financial information used in these valuations. By prioritizing data quality and incorporating predictive elements, analysts can generate more accurate and insightful assessments of a company’s true worth and future prospects.

The figure illustrates the profit and loss statement.

Toward a Dynamic Future: The Evolving Landscape of Modeling

Financial modeling is undergoing a fundamental shift, moving beyond static projections to embrace a continuously evolving, predictive framework. Historically, these models relied on limited datasets and infrequent updates, offering a snapshot in time. Now, the integration of advanced analytical techniques – including machine learning and artificial intelligence – with comprehensive data sources, such as alternative data and real-time market feeds, allows for dynamic refinement. This enables models to not just react to past events, but to anticipate future trends with increasing accuracy. The result is a system capable of identifying subtle patterns and correlations previously obscured, offering investors and analysts a powerful tool for navigating complex financial landscapes and making data-driven decisions. This transition promises to unlock deeper insights and improve the overall effectiveness of financial forecasting and risk management.

Financial modeling is evolving beyond retrospective analysis to embrace a proactive, predictive capacity. Analysts are now leveraging the continuous flow of real-time data – encompassing everything from macroeconomic indicators and geopolitical events to social media sentiment and alternative datasets – to iteratively refine model parameters and assumptions. This dynamic approach allows for the identification of emerging patterns and subtle shifts in market behavior that would be missed by traditional, static models. The ability to rapidly incorporate new information and recalibrate forecasts not only enhances the accuracy of predictions but also enables more agile investment strategies, positioning analysts to anticipate trends and make well-informed decisions in an increasingly volatile financial environment. Ultimately, this constant refinement process aims to minimize risk and maximize returns by capitalizing on opportunities as they arise, rather than reacting to events after they have already transpired.

The evolving financial landscape increasingly demands proactive strategies, and data-driven insights are poised to become essential tools for investors seeking to not only understand, but also anticipate, market behavior. This transition moves beyond traditional, retrospective analysis, enabling more agile and responsive investment decisions. Models incorporating real-time data and advanced analytics can identify emerging trends and assess risk with greater precision, ultimately fostering confidence in navigating complex market dynamics. Importantly, comprehensive modeling must account for all costs; for example, transaction costs within the studied framework were standardized at 0.1% of trade value, ensuring a realistic assessment of potential returns and a holistic view of investment performance.

The pursuit of consistently outperforming established benchmarks, as detailed in this paper’s machine learning approach to stock recommendation, inherently acknowledges the transient nature of financial systems. The model’s reliance on earnings factors and portfolio optimization, while aiming for stability, ultimately operates within a framework destined for eventual recalibration. As Niels Bohr observed, “Prediction is very difficult, especially about the future.” This rings true; even sophisticated algorithms built on historical data are susceptible to the inevitable shifts in market dynamics. The study’s focus on minimum-variance allocation, a method to mitigate risk, is a pragmatic acknowledgment that even the most robust systems will, in time, experience decay – the goal then becomes not to prevent it, but to manage its effects gracefully.

What Lies Ahead?

The presented scheme, while demonstrating a capacity for benchmark outperformance, exists within a system perpetually subject to decay. Each iteration of model refinement represents merely a commit in the annals of financial modeling-a temporary bulwark against the inevitable erosion of predictive power. The reliance on earnings factors, while currently fruitful, is a snapshot of value, and value, like all things, is transient. Future work must acknowledge this inherent impermanence, exploring dynamic feature selection methodologies that adapt to shifting market regimes – essentially, building models that learn how to learn, rather than simply learning.

A critical limitation lies in the scope of risk management. Minimum-variance allocation, though elegant, is a static construct. Delaying more sophisticated risk models is a tax on ambition, potentially sacrificing long-term stability for short-term gains. The field must investigate the integration of behavioral finance principles and alternative risk metrics to account for the irrationality inherent in market participants-acknowledging that the human element is often the most potent force of decay.

Ultimately, the pursuit of alpha is not about achieving a final, perfect model, but about constructing systems that age gracefully. Each version is a chapter in an ongoing narrative, and the true measure of success will not be a single instance of outperformance, but the ability to consistently adapt and recalibrate in the face of unrelenting change. The next commit awaits.

Original article: https://arxiv.org/pdf/2511.12129.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/