Machine Learning

Explaining AI in Finance: Past, Present, Prospects ArXiv ID: 2306.02773 “View on arXiv” Authors: Unknown Abstract This paper explores the journey of AI in finance, with a particular focus on the crucial role and potential of Explainable AI (XAI). We trace AI’s evolution from early statistical methods to sophisticated machine learning, highlighting XAI’s role in popular financial applications. The paper underscores the superior interpretability of methods like Shapley values compared to traditional linear regression in complex financial scenarios. It emphasizes the necessity of further XAI research, given forthcoming EU regulations. The paper demonstrates, through simulations, that XAI enhances trust in AI systems, fostering more responsible decision-making within finance. ...

Predicting Stock Market Time-Series Data using CNN-LSTM Neural Network Model ArXiv ID: 2305.14378 “View on arXiv” Authors: Unknown Abstract Stock market is often important as it represents the ownership claims on businesses. Without sufficient stocks, a company cannot perform well in finance. Predicting a stock market performance of a company is nearly hard because every time the prices of a company stock keeps changing and not constant. So, its complex to determine the stock data. But if the previous performance of a company in stock market is known, then we can track the data and provide predictions to stockholders in order to wisely take decisions on handling the stocks to a company. To handle this, many machine learning models have been invented but they didn’t succeed due to many reasons like absence of advanced libraries, inaccuracy of model when made to train with real time data and much more. So, to track the patterns and the features of data, a CNN-LSTM Neural Network can be made. Recently, CNN is now used in Natural Language Processing (NLP) based applications, so by identifying the features from stock data and converting them into tensors, we can obtain the features and then send it to LSTM neural network to find the patterns and thereby predicting the stock market for given period of time. The accuracy of the CNN-LSTM NN model is found to be high even when allowed to train on real-time stock market data. This paper describes about the features of the custom CNN-LSTM model, experiments we made with the model (like training with stock market datasets, performance comparison with other models) and the end product we obtained at final stage. ...

Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms ArXiv ID: 2309.00618 “View on arXiv” Authors: Unknown Abstract Creating accurate predictions in the stock market has always been a significant challenge in finance. With the rise of machine learning as the next level in the forecasting area, this research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE in the short term from March 2020 to May 2022. We deploy, develop, and tune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models. We report the models that produce the highest accuracies from our evaluation metrics: RMSE, MAPE, MTT, and MPE. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite running longer (up to 10 seconds). Results from this study may improve by further tuning the individual parameters or introducing more exogenous variables. ...

ChatGPT: Unlocking the Future of NLP inFinance ArXiv ID: ssrn-4323643 “View on arXiv” Authors: Unknown Abstract This paper reviews the current state of ChatGPT technology in finance and its potential to improve existing NLP-based financial applications. We discuss the eth Keywords: ChatGPT, Natural Language Processing (NLP), Financial Technology (FinTech), Machine Learning, Ethics in AI, General Finance Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: This paper is a literature review discussing the capabilities and applications of ChatGPT in finance, featuring no mathematical derivations, formulas, or empirical backtesting. It focuses on conceptual discussion, ethical considerations, and future research directions, resulting in low scores for both math complexity and empirical rigor. flowchart TD A["Research Goal:<br/>Evaluate ChatGPT in Finance NLP"] --> B["Key Inputs:<br/>Financial Texts, NLP Benchmarks"] B --> C["Methodology:<br/>Review, Compare, Analyze Ethics"] C --> D{"Computational Process"} D --> E["Application:<br/>Sentiment/Forecasting Models"] D --> F["Constraint:<br/>Hallucinations/Data Privacy"] E & F --> G["Outcomes:<br/>Enhanced NLP Capabilities"] G --> H["Outcomes:<br/>Ethical & Bias Considerations"]

From Data to Trade: A Machine Learning Approach to Quantitative Trading ArXiv ID: ssrn-4315362 “View on arXiv” Authors: Unknown Abstract “Machine learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that lev Keywords: Machine Learning, Quantitative Trading, Algorithmic Trading, Time Series Forecasting, Financial Markets, Multi-Asset Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a broad, introductory survey of ML concepts in quantitative trading with minimal advanced mathematics or original derivations, and lacks any code, backtests, or specific empirical results. flowchart TD A["Research Goal"] --> B["Data Collection"] A --> C["ML Model Selection"] B --> D["Feature Engineering"] C --> D D --> E["Model Training"] E --> F["Backtesting"] F --> G["Key Findings"]

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 10/10 (seminar slides) ArXiv ID: ssrn-3447398 “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, algorithms, computational methods, AI, predictive modeling, Equities Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 7.0/10 Quadrant: Holy Grail Why: The paper advances sophisticated mathematical concepts like gradient boosting and probabilistic graphical models, requiring advanced linear algebra and optimization theory. It also includes data-driven empirical validation, with specific attention to performance metrics, cross-validation, and real-world datasets, indicating backtest readiness. flowchart TD G["Research Goal: Predict Equities Returns"] --> D D["Input: Financial Data"] --> M subgraph M ["Key Methodology"] M1["Feature Engineering"] --> M2["Cross-Validation"] --> M3["Model Selection"] end M --> C["Computational Process: ML Algorithms"] C --> F["Outcomes: Predictive Models"] F --> K["Findings: Improved Accuracy & Risk Management"]

Trends and Applications of Machine Learning in QuantitativeFinance ArXiv ID: ssrn-3397005 “View on arXiv” Authors: Unknown Abstract Recent advances in machine learning are finding commercial applications across many industries, not least the finance industry. This paper focuses on applicatio Keywords: machine learning, algorithmic trading, predictive analytics, quantitative finance, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a broad literature review of ML applications in finance, focusing on conceptual categorization rather than novel mathematical derivations or empirical backtesting. It outlines common algorithms and use cases but lacks implementation details, statistical metrics, or specific experimental results. flowchart TD G["Research Goal: Evaluate ML in Quant Finance"] --> D["Data Sources"] D --> M["Key Methodology"] D --> C["Computational Processes"] M --> F["Key Findings/Outcomes"] C --> F subgraph D ["Data/Inputs"] D1["Multi-Asset Market Data"] D2["Historical Price & Volatility"] end subgraph M ["Methodology Steps"] M1["Algorithmic Trading Strategies"] M2["Predictive Analytics"] end subgraph C ["Computational Processes"] C1["Deep Learning Models"] C2["Reinforcement Learning"] end subgraph F ["Outcomes"] F1["Enhanced Portfolio Optimization"] F2["Improved Risk Management"] F3["Commercial Applications in Finance"] end

Machine Learning for Stock Selection ArXiv ID: ssrn-3330946 “View on arXiv” Authors: Unknown Abstract Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning technique Keywords: Machine learning, Quantitative finance, Predictive accuracy, Quantitative Strategies Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper provides a conceptual overview of machine learning techniques in finance with minimal advanced mathematical derivations, focusing more on the debate and methodology rather than deep theoretical proofs. Empirical rigor is limited as it discusses general challenges like overfitting and proposes forecast combinations without presenting detailed backtest results, code, or specific implementation datasets. flowchart TD A["Research Goal: Evaluate ML for Stock Selection"] --> B["Data: Historical Prices, Fundamentals, Sentiment"] B --> C["Methodology: Train ML Models e.g., Gradient Boosting, Neural Networks"] C --> D{"Computational Process: Backtest on Out-of-Sample Data"} D --> E["Key Finding: ML Models Achieve High Predictive Accuracy"] D --> F["Key Finding: Significant Risk of Overfitting"] E & F --> G["Outcome: Mixed Results; Strategy Viability Depends on Rigorous Validation"] style A fill:#e1f5fe style G fill:#fff3e0

A Backtesting Protocol in the Era of Machine Learning ArXiv ID: ssrn-3275654 “View on arXiv” Authors: Unknown Abstract Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, th Keywords: Machine Learning, Investment Management, Quantitative Finance, Asset Pricing, Algorithmic Trading Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper focuses on a research protocol for backtesting and data mining, with moderate empirical rigor involving practical concerns like overfitting and data scarcity, but lacks advanced mathematical derivations, centering instead on statistical concepts and real-world data challenges. flowchart TD A["Research Goal: Develop robust backtesting protocol for ML in finance"] --> B["Data: Cross-sectional stock data & fundamental features"] B --> C["Methodology: ML pipelines with walk-forward validation"] C --> D["Computation: Model training, hyperparameter tuning, & signal generation"] D --> E["Risk Controls: Transaction costs, liquidity constraints, & overfitting tests"] E --> F["Key Outcomes: Generalizable signals & realistic performance metrics"] F --> G["Implication: ML requires rigorous validation to avoid false discoveries"]