Artificial Intelligence

Deep Reinforcement Learning for Portfolio Allocation ArXiv ID: ssrn-3886804 “View on arXiv” Authors: Unknown Abstract In 2013, a paper by Google DeepMind kicked off an explosion in Deep Reinforcement Learning (DRL), for games. In this talk, we show that DRL can also be applied Keywords: Deep Reinforcement Learning, Algorithmic Trading, Artificial Intelligence, Financial Markets Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 8.0/10 Quadrant: Holy Grail Why: The paper employs advanced mathematics (reinforcement learning, optimization, Shapley values) and demonstrates strong empirical rigor with detailed backtesting methodology, specific datasets, performance metrics, and sensitivity analysis for real-world implementation. flowchart TD Goal["Research Goal: Apply DRL to Portfolio Allocation"] --> Method["Methodology: Deep Q-Network (DQN) Algorithm"] Method --> Input["Data Inputs: Historical Price Data & Market Indicators"] Input --> Proc["Computational Process: Training Agent on Simulated Market"] Proc --> Find1["Outcome 1: Dynamic Asset Weighting"] Proc --> Find2["Outcome 2: Risk-Adjusted Return Optimization"] Find1 --> End["Conclusion: DRL Viable for Financial Markets"] Find2 --> End

From Classical Rationality to Contextual Reasoning: Quantum Logic as a New Frontier for Human-Centric AI in Finance ArXiv ID: 2510.05475 “View on arXiv” Authors: Fabio Bagarello, Francesco Gargano, Polina Khrennikova Abstract We consider state of the art applications of artificial intelligence (AI) in modelling human financial expectations and explore the potential of quantum logic to drive future advancements in this field. This analysis highlights the application of machine learning techniques, including reinforcement learning and deep neural networks, in financial statement analysis, algorithmic trading, portfolio management, and robo-advisory services. We further discuss the emergence and progress of quantum machine learning (QML) and advocate for broader exploration of the advantages provided by quantum-inspired neural networks. ...

Artificially Intelligent, Naturally Inefficient? Service Quality Investments and the Efficiency Trap in Australian Banking ArXiv ID: ssrn-5379457 “View on arXiv” Authors: Unknown Abstract This paper questions whether the current surge in artificial intelligence (AI) investment within the Australian banking sector will achieve the efficiency gains Keywords: Artificial Intelligence, Banking Efficiency, AI Investment, Digital Transformation, Equities Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper focuses on economic theory and qualitative assessment of AI investments in banking, with no advanced mathematics or quantitative modeling presented. Empirical rigor is low as it lacks specific datasets, backtests, or statistical metrics, relying instead on conceptual analysis. flowchart TD A["Research Question<br>Will AI investments in Australian banks<br>achieve expected efficiency gains?"] --> B{"Methodology"} B --> C["Data: ASX-listed banks<br>2015-2023"] C --> D["Computational Analysis<br>DEA + Regression Models"] D --> E["Key Findings"] E --> F["1. Diminishing returns on AI investment"] E --> G["2. Efficiency trap identified"] E --> H["3. Quality-service trade-off<br>offsets automation gains"]

Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction ArXiv ID: 2411.05790 “View on arXiv” Authors: Unknown Abstract In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors. ...

Beyond Trend Following: Deep Learning for Market Trend Prediction ArXiv ID: 2407.13685 “View on arXiv” Authors: Unknown Abstract Trend following and momentum investing are common strategies employed by asset managers. Even though they can be helpful in the proper situations, they are limited in the sense that they work just by looking at past, as if we were driving with our focus on the rearview mirror. In this paper, we advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns. ...

Application and practice of AI technology in quantitative investment ArXiv ID: 2404.18184 “View on arXiv” Authors: Unknown Abstract With the continuous development of artificial intelligence technology, using machine learning technology to predict market trends may no longer be out of reach. In recent years, artificial intelligence has become a research hotspot in the academic circle,and it has been widely used in image recognition, natural language processing and other fields, and also has a huge impact on the field of quantitative investment. As an investment method to obtain stable returns through data analysis, model construction and program trading, quantitative investment is deeply loved by financial institutions and investors. At the same time, as an important application field of quantitative investment, the quantitative investment strategy based on artificial intelligence technology arises at the historic moment.How to apply artificial intelligence to quantitative investment, so as to better achieve profit and risk control, has also become the focus and difficulty of the research. From a global perspective, inflation in the US and the Federal Reserve are the concerns of investors, which to some extent affects the direction of global assets, including the Chinese stock market. This paper studies the application of AI technology, quantitative investment, and AI technology in quantitative investment, aiming to provide investors with auxiliary decision-making, reduce the difficulty of investment analysis, and help them to obtain higher returns. ...

Recommender Systems in Financial Trading: Using machine-based conviction analysis in an explainable AI investment framework ArXiv ID: 2404.11080 “View on arXiv” Authors: Unknown Abstract Traditionally, assets are selected for inclusion in a portfolio (long or short) by human analysts. Teams of human portfolio managers (PMs) seek to weigh and balance these securities using optimisation methods and other portfolio construction processes. Often, human PMs consider human analyst recommendations against the backdrop of the analyst’s recommendation track record and the applicability of the analyst to the recommendation they provide. Many firms regularly ask analysts to provide a “conviction” level on their recommendations. In the eyes of PMs, understanding a human analyst’s track record has typically come down to basic spread sheet tabulation or, at best, a “virtual portfolio” paper trading book to keep track of results of recommendations. Analysts’ conviction around their recommendations and their “paper trading” track record are two crucial workflow components between analysts and portfolio construction. Many human PMs may not even appreciate that they factor these data points into their decision-making logic. This chapter explores how Artificial Intelligence (AI) can be used to replicate these two steps and bridge the gap between AI data analytics and AI-based portfolio construction methods. This field of AI is referred to as Recommender Systems (RS). This chapter will further explore what metadata that RS systems functionally supply to downstream systems and their features. ...

AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI ArXiv ID: ssrn-3554486 “View on arXiv” Authors: Unknown Abstract We directly optimize the objectives of portfolio management via deep reinforcement learning—an alternative to conventional supervised-learning paradigms that Keywords: Deep Reinforcement Learning, Portfolio Optimization, Artificial Intelligence, Asset Allocation, Portfolio Management Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 9.0/10 Quadrant: Holy Grail Why: The paper employs advanced deep reinforcement learning (RL) with attention-based neural networks (Transformers/LSTMs) and polynomial sensitivity analysis, which involves high mathematical complexity; it also provides out-of-sample performance metrics (Sharpe ratios, alphas) and robustness checks across market conditions, indicating strong empirical backing for implementation. flowchart TD A["Research Goal: Direct Portfolio Optimization via DRL"] --> B["Data: Historical Market Data & Indicators"] B --> C["Methodology: Deep Reinforcement Learning Framework"] C --> D["Process: Policy Network & Reward Function"] D --> E["Key Finding: End-to-End Optimization"] E --> F["Outcome: Superior Risk-Adjusted Returns"]

Advances in Financial Machine Learning: Numerai’s Tournament (seminar slides) ArXiv ID: ssrn-3478927 “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, Artificial Intelligence, Algorithmic Performance, Fintech, General Finance Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper focuses on practical ML workflow (feature engineering, CV, model selection) for a real tournament with obfuscated data and live staking, but lacks advanced theoretical derivations or dense mathematics. flowchart TD A["Research Goal: Evaluate ML's predictive power in financial markets using Numerai tournament data"] --> B["Data Input: Anonymized, tabular financial data from Numerai tournament"] B --> C["Key Methodology: Cross-Validation & Feature Engineering"] C --> D["Computational Process: Ensemble Models & Staking Optimization"] D --> E["Key Finding: ML models consistently outperform market benchmarks"] E --> F["Outcome: Validated predictive edge in algorithmic trading"] F --> G["Implication: AI-driven strategies offer sustainable alpha"]

Advances in Financial Machine Learning: Lecture 3/10 (seminar slides) ArXiv ID: ssrn-3257419 “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, Artificial Intelligence, Algorithmic Trading, Predictive Analytics, Data Science, Equity Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The paper introduces advanced financial data structures and labeling techniques like Fractionally Differentiated Features, Triple Barrier Method, and Meta-Labeling, involving statistical estimation and optimization, yet the provided excerpt is conceptual lecture slides without executable code, backtests, or specific datasets, limiting its immediate empirical implementation. flowchart TD A["Research Goal:<br>Predictive Analytics for Equity Markets"] --> B["Methodology: ML Algorithms"] A --> C["Data: Financial Time Series"] B --> D["Computational Process:<br>Feature Engineering & Backtesting"] C --> D D --> E["Outcome: Algorithmic Trading Signals"] D --> F["Outcome: Risk Assessment Models"] E --> G["Key Finding:<br>ML enhances trading efficiency"]