Deep Learning

Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies ArXiv ID: 2307.05522 “View on arXiv” Authors: Unknown Abstract We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies. DINs extract time series (TS) and cross sectional (CS) features directly from daily price returns. This removes the need for handcrafted features, and allows the model to learn from TS and CS information simultaneously. DINs benefit from a fully data-driven approach to feature extraction, whilst avoiding overfitting. Extending prior work on Deep Momentum Networks, DIN models directly output position sizes that optimise Sharpe ratio, but for the entire portfolio instead of individual assets. We propose a novel loss term to balance turnover regularisation against increased systemic risk from high correlation to the overall market. Using futures data, we show that DIN models outperform traditional TS and CS benchmarks, are robust to a range of transaction costs and perform consistently across random seeds. To balance the general nature of DIN models, we provide examples of how attention and Variable Selection Networks can aid the interpretability of investment decisions. These model-specific methods are particularly useful when the dimensionality of the input is high and variable importance fluctuates dynamically over time. Finally, we compare the performance of DIN models on other asset classes, and show how the space of potential features can be customised. ...

Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers ArXiv ID: 2306.05479 “View on arXiv” Authors: Unknown Abstract One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learning method to estimate the filltimes of limit orders posted in different levels of the LOB. We develop a novel model for survival analysis that maps time-varying features of the LOB to the distribution of filltimes of limit orders. Our method is based on a convolutional-Transformer encoder and a monotonic neural network decoder. We use proper scoring rules to compare our method with other approaches in survival analysis, and perform an interpretability analysis to understand the informativeness of features used to compute fill probabilities. Our method significantly outperforms those typically used in survival analysis literature. Finally, we carry out a statistical analysis of the fill probability of orders placed in the order book (e.g., within the bid-ask spread) for assets with different queue dynamics and trading activity. ...

Financial sentiment analysis using FinBERT with application in predicting stock movement ArXiv ID: 2306.02136 “View on arXiv” Authors: Unknown Abstract In this study, we integrate sentiment analysis within a financial framework by leveraging FinBERT, a fine-tuned BERT model specialized for financial text, to construct an advanced deep learning model based on Long Short-Term Memory (LSTM) networks. Our objective is to forecast financial market trends with greater accuracy. To evaluate our model’s predictive capabilities, we apply it to a comprehensive dataset of stock market news and perform a comparative analysis against standard BERT, standalone LSTM, and the traditional ARIMA models. Our findings indicate that incorporating sentiment analysis significantly enhances the model’s ability to anticipate market fluctuations. Furthermore, we propose a suite of optimization techniques aimed at refining the model’s performance, paving the way for more robust and reliable market prediction tools in the field of AI-driven finance. ...

Joint Latent Topic Discovery and Expectation Modeling for Financial Markets ArXiv ID: 2307.08649 “View on arXiv” Authors: Unknown Abstract In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations between companies and their stocks. However, current relational stock methods are limited by their reliance on predefined stock relationships and the exclusive consideration of immediate effects. To address these limitations, we present a groundbreaking framework for financial market analysis. This approach, to our knowledge, is the first to jointly model investor expectations and automatically mine latent stock relationships. Comprehensive experiments conducted on China’s CSI 300, one of the world’s largest markets, demonstrate that our model consistently achieves an annual return exceeding 10%. This performance surpasses existing benchmarks, setting a new state-of-the-art standard in stock return prediction and multiyear trading simulations (i.e., backtesting). ...

Thailand Asset Value Estimation Using Aerial or Satellite Imagery ArXiv ID: 2307.08650 “View on arXiv” Authors: Unknown Abstract Real estate is a critical sector in Thailand’s economy, which has led to a growing demand for a more accurate land price prediction approach. Traditional methods of land price prediction, such as the weighted quality score (WQS), are limited due to their reliance on subjective criteria and their lack of consideration for spatial variables. In this study, we utilize aerial or satellite imageries from Google Map API to enhance land price prediction models from the dataset provided by Kasikorn Business Technology Group (KBTG). We propose a similarity-based asset valuation model that uses a Siamese-inspired Neural Network with pretrained EfficientNet architecture to assess the similarity between pairs of lands. By ensembling deep learning and tree-based models, we achieve an area under the ROC curve (AUC) of approximately 0.81, outperforming the baseline model that used only tabular data. The appraisal prices of nearby lands with similarity scores higher than a predefined threshold were used for weighted averaging to predict the reasonable price of the land in question. At 20% mean absolute percentage error (MAPE), we improve the recall from 59.26% to 69.55%, indicating a more accurate and reliable approach to predicting land prices. Our model, which is empowered by a more comprehensive view of land use and environmental factors from aerial or satellite imageries, provides a more precise, data-driven, and adaptive approach for land valuation in Thailand. ...

Deep Learning for Solving and Estimating Dynamic Macro-Finance Models ArXiv ID: 2305.09783 “View on arXiv” Authors: Unknown Abstract We develop a methodology that utilizes deep learning to simultaneously solve and estimate canonical continuous-time general equilibrium models in financial economics. We illustrate our method in two examples: (1) industrial dynamics of firms and (2) macroeconomic models with financial frictions. Through these applications, we illustrate the advantages of our method: generality, simultaneous solution and estimation, leveraging the state-of-art machine-learning techniques, and handling large state space. The method is versatile and can be applied to a vast variety of problems. ...

Deep Learning and Financial Stability ArXiv ID: ssrn-3723132 “View on arXiv” Authors: Unknown Abstract The financial sector is entering a new era of rapidly advancing data analytics as deep learning models are adopted into its technology stack. A subset of Artifi Keywords: Deep Learning, Data Analytics, Fintech, Natural Language Processing (NLP), Financial Modeling, Multi-Asset Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual policy analysis that identifies theoretical transmission pathways (e.g., data aggregation, model design) for systemic risk without presenting mathematical models, statistical metrics, or backtesting results. It focuses on qualitative governance frameworks rather than quantitative implementation. flowchart TD A["Research Goal: Deep Learning in Financial Stability"] --> B["Data Inputs & Methodology"] B --> C["Computational Processes"] C --> D["Key Findings & Outcomes"] B --> B1["Multi-Asset Data"] B --> B2["NLP on Financial Text"] B --> B3["Alternative Data Sources"] C --> C1["Deep Learning Models"] C --> C2["Financial Stability Metrics"] C --> C3["Risk Assessment Algorithms"] D --> D1["Enhanced Risk Prediction"] D --> D2["Systemic Stability Insights"] D --> D3["Fintech Innovation Pathways"] style A fill:#e1f5fe style D fill:#e8f5e8

Banking 4.0: ‘The Influence of Artificial Intelligence on the Banking Industry & How AI Is Changing the Face of Modern Day Banks’ ArXiv ID: ssrn-3661469 “View on arXiv” Authors: Unknown Abstract Artificial intelligence (AI), from time to time called machine intelligence is simulation of human intelligence in machines. It is the intellect exhibited by ma Keywords: Artificial Intelligence (AI), Neural Networks, Natural Language Processing (NLP), Deep Learning, Equities Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual literature review discussing AI applications in banking with no mathematical formulas or statistical models, and its empirical backing is limited to citing other studies without original data analysis or backtesting. flowchart TD A["Research Question:<br>How is AI changing modern banks?"] --> B["Methodology:<br>Review of Neural Networks, NLP, Deep Learning"] B --> C["Inputs:<br>Banking data & AI Equities"] C --> D["Computational Process:<br>AI Simulation of Human Intelligence"] D --> E["Key Findings:<br>Banking 4.0 Transformation"]

Advances in Financial Machine Learning (Chapter 1) ArXiv ID: ssrn-3104847 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: machine learning, deep learning, algorithmic trading, predictive modeling, Financial Technology Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The excerpt focuses on practical implementation and real-world data challenges in finance with an empirical approach, but does not present dense mathematical derivations or advanced formulas. flowchart TD A["Research Goal:<br>Application of ML in Finance"] --> B["Key Methodology:<br>Algorithmic Trading &<br>Predictive Modeling"] B --> C["Computational Process:<br>Deep Learning &<br>ML Algorithms"] C --> D["Data Input:<br>Financial Market Data"] D --> C C --> E["Key Findings:<br>ML replacing expert human tasks<br>in FinTech & Finance"]