Decision support from financial disclosures with deep neural networks and transfer learning (1710.03954v1)

Abstract: Company disclosures greatly aid in the process of financial decision-making; therefore, they are consulted by financial investors and automated traders before exercising ownership in stocks. While humans are usually able to correctly interpret the content, the same is rarely true of computerized decision support systems, which struggle with the complexity and ambiguity of natural language. A possible remedy is represented by deep learning, which overcomes several shortcomings of traditional methods of text mining. For instance, recurrent neural networks, such as long short-term memories, employ hierarchical structures, together with a large number of hidden layers, to automatically extract features from ordered sequences of words and capture highly non-linear relationships such as context-dependent meanings. However, deep learning has only recently started to receive traction, possibly because its performance is largely untested. Hence, this paper studies the use of deep neural networks for financial decision support. We additionally experiment with transfer learning, in which we pre-train the network on a different corpus with a length of 139.1 million words. Our results reveal a higher directional accuracy as compared to traditional machine learning when predicting stock price movements in response to financial disclosures. Our work thereby helps to highlight the business value of deep learning and provides recommendations to practitioners and executives.

• The paper shows that LSTM networks enhanced with transfer learning achieve a 7.1-point gain in balanced accuracy for predicting nominal returns.
• It compares traditional machine learning methods with deep learning, revealing LSTMs' superior ability to capture context and long-range dependencies in financial texts.
• The study emphasizes the practical impact of integrating deep learning into decision support systems, paving the way for advanced applications in finance and beyond.

Deep Neural Networks for Financial Decision Support: An In-depth Analysis

The paper "Decision support from financial disclosures with deep neural networks and transfer learning" explores the application of advanced neural network models to enhance decision support systems in financial markets. The paper specifically investigates the use of deep learning, particularly Long Short-Term Memory networks (LSTMs), and transfer learning to predict stock price movements following financial disclosures.

Key Methodological Insights

The authors employ a dataset of 13,135 regulated German ad hoc announcements to evaluate the predictive performance of various models. The dataset is preprocessed to convert text into numerical vectors using techniques like term frequency-inverse document frequency (tf-idf). The paper then contrasts traditional machine learning methods, such as random forests and support vector machines (SVMs), with deep learning architectures, including Recurrent Neural Networks (RNNs) and LSTMs.

Deep learning demonstrates superior performance owing to its ability to capture context-dependent meanings and long-range dependencies in text. The LSTM networks, which are particularly adept at handling sequential data, outperform classical methods when it comes to predicting the direction and magnitude of stock price changes.

Transfer Learning and Its Impact

A significant aspect of this research is the incorporation of transfer learning—a process where a model is pre-trained on a large, related corpus before fine-tuning on the target dataset. In this paper, the authors utilize a separate dataset of 8-K filings for pre-training. The use of transfer learning with LSTM facilitates further improvements in predictive performance, as it helps the model start from a more informed state, thus requiring fewer data to converge efficiently on the target task.

Numerical Results

The numerical results reveal that LSTMs, especially when augmented with pre-trained word embeddings and transfer learning, significantly outperform their traditional counterparts. For instance, in the classification task predicting the direction of nominal returns, LSTMs with transfer learning showed an approximately 7.1-point gain in balanced accuracy over naive baselines. In the regression tasks for nominal and abnormal returns, they demonstrated substantive reductions in both Mean Squared Error (MSE) and Mean Absolute Error (MAE).

Implications and Future Directions

This paper provides compelling evidence for the utility of deeper and more complex learning models in financial decision-making scenarios. The ability of LSTMs to model complex, non-linear relationships and context within textual data showcases their potential in applications beyond finance, such as natural language processing and time-series prediction in various domains.

Looking forward, the practical implications of this research suggest that organizations can substantially enhance their decision support systems by integrating deep learning frameworks. In particular, as the financial domain is increasingly data-driven, LSTMs could become integral to systems processing large volumes of unstructured data.

Theoretical advancements hinted at in this research include a deeper exploration of transfer learning techniques specific to financial texts, akin to developments in computer vision with pre-trained models. Moreover, it would be prudent for future studies to address the explainability of deep models in finance, thus enabling practitioners to trust and effectively leverage these powerful tools.

In conclusion, this paper skillfully demonstrates the efficacy of deep learning models, particularly LSTMs enhanced with transfer learning, for extracting business value from financial disclosures. This marks a significant stride in the domain of artificial intelligence-enhanced decision support systems.