Stock2Vec: An Embedding to Improve Predictive Models for Companies (2201.11290v1)

Abstract: Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations, and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.

• The paper's main contribution is Stock2Vec, a specialized embedding that leverages stock price movements to enable cross-company inference and enhance prediction accuracy.
• It adapts the Word2Vec algorithm to a five-year span of S&P 500 data, reducing complex company features to a validated four-dimensional representation via PCA.
• Empirical validation shows Stock2Vec improves ESG rating predictions by 9.3% and positively influences feature importance in predictive models.

Analyzing STOCK2VEC: Enhancing Company Predictive Models through Stock-Based Embeddings

The paper "STOCK2VEC: An Embedding to Improve Predictive Models for Companies" contributes a novel approach to enhancing company predictive models by introducing a specialized embedding, termed Stock2Vec. This embedding leverages stock price movements to facilitate cross-company inference, addressing key limitations of traditional sector-based prediction methodologies.

Traditional predictive models predominantly focus on leveraging historical data from companies within the same industry sector to make predictions. However, such models often fail to encapsulate the multi-dimensional similarities that transcend industry boundaries, particularly for large corporations diversified across various sectors. The Stock2Vec approach sets out to resolve these issues by utilizing stock price information as a richer informational source, offering a dynamic representation of company characteristics, with the main advantage being its ability to integrate non-sector-specific cross-company information.

The Stock2Vec Embedding Approach

Stock2Vec utilizes the Word2Vec algorithm adapted to stock data, drawing from a five-year span of daily price changes in S&P 500 companies. Through a detailed data preprocessing pipeline, it transforms stock price data into a 'sentence' structure suitable for embedding. The process results in a four-dimensional representation, confirmed by predictive experiments as sufficient to encapsulate relevant company features. Principal Component Analysis (PCA) further substantiates this four-dimensional constraint, illustrating its efficiency in capturing the inherent variance within the stock data while maintaining computational feasibility.

Empirical Validation

The paper presents empirical evidence highlighting the efficacy of the Stock2Vec embedding in augmenting predictive models. Two primary prediction tasks—evaluating environmental impact and estimating company size—were employed to demonstrate gains in predictive accuracy. When incorporated into these tasks, Stock2Vec improved the R-squared values significantly: a notable 9.3% increase for ESG ratings and a modest 1.9% for employee count predictions. Feature importance analysis using Random Forest Importance solidified the contribution of Stock2Vec variables, asserting their substantial influence on prediction outcomes.

Methodological Distinctions

The research delineates itself from previous works by focusing on pure quantitative inputs rather than incorporating sentiment-laden textual data. This transition to a purely numeric basis for embedding is pivotal, as it addresses the unreliability of text-based information that may suffer from temporal decay and subjectivity. The Stock2Vec embedding, thus, provides a non-textual alternative that aligns more closely with the financial phenomena impacting companies in real-time.

Theoretical and Practical Implications

Theoretically, the introduction of Stock2Vec opens multiple avenues for future research. It challenges existing paradigms in predictive modeling by suggesting that granular stock-based embeddings can viably supplement or even supplant traditional descriptive data representations in certain scenarios. Practically, it offers a cost-efficient supplemental approach that industries could adopt to improve their predictive analyses without extensive informational overhauls.

Future Outlook

Potential future work could explore extending Stock2Vec embeddings to non-traditional market indices or global datasets. Likewise, integrating this embedding with more advanced machine learning architectures could uncover deeper patterns and nuances, potentially fostering new lines of inquiry into the interdependencies across varying sectors and geographies.

Conclusion

The introduction of Stock2Vec in the paper represents a significant step forward in the utilization of stock data for cross-company predictive modeling. By abstracting complex company representations into a coherent, numerically distilled format, this approach not only enhances model accuracy but also invites further exploration into innovative embedding strategies in financial data analysis. While the research presents promising outcomes, ongoing evaluation and adaptation will be essential to maximize Stock2Vec's practical applicability in diverse economic environments.