Assets Forecasting with Feature Engineering and Transformation Methods for LightGBM ArXiv ID: 2501.07580 “View on arXiv” Authors: Unknown Abstract Fluctuations in the stock market rapidly shape the economic world and consumer markets, impacting millions of individuals. Hence, accurately forecasting it is essential for mitigating risks, including those associated with inactivity. Although research shows that hybrid models of Deep Learning (DL) and Machine Learning (ML) yield promising results, their computational requirements often exceed the capabilities of average personal computers, rendering them inaccessible to many. In order to address this challenge in this paper we optimize LightGBM (an efficient implementation of gradient-boosted decision trees (GBDT)) for maximum performance, while maintaining low computational requirements. We introduce novel feature engineering techniques including indicator-price slope ratios and differences of close and open prices divided by the corresponding 14-period Exponential Moving Average (EMA), designed to capture market dynamics and enhance predictive accuracy. Additionally, we test seven different feature and target variable transformation methods, including returns, logarithmic returns, EMA ratios and their standardized counterparts as well as EMA difference ratios, so as to identify the most effective ones weighing in both efficiency and accuracy. The results demonstrate Log Returns, Returns and EMA Difference Ratio constitute the best target variable transformation methods, with EMA ratios having a lower percentage of correct directional forecasts, and standardized versions of target variable transformations requiring significantly more training time. Moreover, the introduced features demonstrate high feature importance in predictive performance across all target variable transformation methods. This study highlights an accessible, computationally efficient approach to stock market forecasting using LightGBM, making advanced forecasting techniques more widely attainable. ...

Hidformer: Transformer-Style Neural Network in Stock Price Forecasting ArXiv ID: 2412.19932 “View on arXiv” Authors: Unknown Abstract This paper investigates the application of Transformer-based neural networks to stock price forecasting, with a special focus on the intersection of machine learning techniques and financial market analysis. The evolution of Transformer models, from their inception to their adaptation for time series analysis in financial contexts, is reviewed and discussed. Central to our study is the exploration of the Hidformer model, which is currently recognized for its promising performance in time series prediction. The primary aim of this paper is to determine whether Hidformer will also prove itself in the task of stock price prediction. This slightly modified model serves as the framework for our experiments, integrating the principles of technical analysis with advanced machine learning concepts to enhance stock price prediction accuracy. We conduct an evaluation of the Hidformer model’s performance, using a set of criteria to determine its efficacy. Our findings offer additional insights into the practical application of Transformer architectures in financial time series forecasting, highlighting their potential to improve algorithmic trading strategies, including human decision making. ...

Robust and Sparse Portfolio Selection: Quantitative Insights and Efficient Algorithms ArXiv ID: 2412.19462 “View on arXiv” Authors: Unknown Abstract We extend the classical mean-variance (MV) framework and propose a robust and sparse portfolio selection model incorporating an ellipsoidal uncertainty set to reduce the impact of estimation errors and fixed transaction costs to penalize over-diversification. In the literature, the MV model under fixed transaction costs is referred to as the sparse or cardinality-constrained MV optimization, which is a mixed integer problem and is challenging to solve when the number of assets is large. We develop an efficient semismooth Newton-based proximal difference-of-convex algorithm to solve the proposed model and prove its convergence to at least a local minimizer with a locally linear convergence rate. We explore properties of the robust and sparse portfolio both analytically and numerically. In particular, we show that the MV optimization is indeed a robust procedure as long as an investor makes the proper choice on the risk-aversion coefficient. We contribute to the literature by proving that there is a one-to-one correspondence between the risk-aversion coefficient and the level of robustness. Moreover, we characterize how the number of traded assets changes with respect to the interaction between the level of uncertainty on model parameters and the magnitude of transaction cost. ...

Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading ArXiv ID: 2412.19372 “View on arXiv” Authors: Unknown Abstract High-frequency trading (HFT) has transformed modern financial markets, making reliable short-term price forecasting models essential. In this study, we present a novel approach to mid-price forecasting using Level 1 limit order book (LOB) data from NASDAQ, focusing on 100 U.S. stocks from the S&P 500 index during the period from September to November 2022. Expanding on our previous work with Radial Basis Function Neural Networks (RBFNN), which leveraged automated feature importance techniques based on mean decrease impurity (MDI) and gradient descent (GD), we introduce the Adaptive Learning Policy Engine (ALPE) - a reinforcement learning (RL)-based agent designed for batch-free, immediate mid-price forecasting. ALPE incorporates adaptive epsilon decay to dynamically balance exploration and exploitation, outperforming a diverse range of highly effective machine learning (ML) and deep learning (DL) models in forecasting performance. ...

Sentiment trading with large language models ArXiv ID: 2412.19245 “View on arXiv” Authors: Unknown Abstract We investigate the efficacy of large language models (LLMs) in sentiment analysis of U.S. financial news and their potential in predicting stock market returns. We analyze a dataset comprising 965,375 news articles that span from January 1, 2010, to June 30, 2023; we focus on the performance of various LLMs, including BERT, OPT, FINBERT, and the traditional Loughran-McDonald dictionary model, which has been a dominant methodology in the finance literature. The study documents a significant association between LLM scores and subsequent daily stock returns. Specifically, OPT, which is a GPT-3 based LLM, shows the highest accuracy in sentiment prediction with an accuracy of 74.4%, slightly ahead of BERT (72.5%) and FINBERT (72.2%). In contrast, the Loughran-McDonald dictionary model demonstrates considerably lower effectiveness with only 50.1% accuracy. Regression analyses highlight a robust positive impact of OPT model scores on next-day stock returns, with coefficients of 0.274 and 0.254 in different model specifications. BERT and FINBERT also exhibit predictive relevance, though to a lesser extent. Notably, we do not observe a significant relationship between the Loughran-McDonald dictionary model scores and stock returns, challenging the efficacy of this traditional method in the current financial context. In portfolio performance, the long-short OPT strategy excels with a Sharpe ratio of 3.05, compared to 2.11 for BERT and 2.07 for FINBERT long-short strategies. Strategies based on the Loughran-McDonald dictionary yield the lowest Sharpe ratio of 1.23. Our findings emphasize the superior performance of advanced LLMs, especially OPT, in financial market prediction and portfolio management, marking a significant shift in the landscape of financial analysis tools with implications to financial regulation and policy analysis. ...

A Deep Reinforcement Learning Framework for Dynamic Portfolio Optimization: Evidence from China’s Stock Market ArXiv ID: 2412.18563 “View on arXiv” Authors: Unknown Abstract Artificial intelligence is transforming financial investment decision-making frameworks, with deep reinforcement learning demonstrating substantial potential in robo-advisory applications. This paper addresses the limitations of traditional portfolio optimization methods in dynamic asset weight adjustment through the development of a deep reinforcement learning-based dynamic optimization model grounded in practical trading processes. The research advances two key innovations: first, the introduction of a novel Sharpe ratio reward function engineered for Actor-Critic deep reinforcement learning algorithms, which ensures stable convergence during training while consistently achieving positive average Sharpe ratios; second, the development of an innovative comprehensive approach to portfolio optimization utilizing deep reinforcement learning, which significantly enhances model optimization capability through the integration of random sampling strategies during training with image-based deep neural network architectures for multi-dimensional financial time series data processing, average Sharpe ratio reward functions, and deep reinforcement learning algorithms. The empirical analysis validates the model using randomly selected constituent stocks from the CSI 300 Index, benchmarking against established financial econometric optimization models. Backtesting results demonstrate the model’s efficacy in optimizing portfolio allocation and mitigating investment risk, yielding superior comprehensive performance metrics. ...

A mathematical framework for modelling CLMM dynamics in continuous time ArXiv ID: 2412.18580 “View on arXiv” Authors: Unknown Abstract This paper develops a rigorous mathematical framework for analyzing Concentrated Liquidity Market Makers (CLMMs) in Decentralized Finance (DeFi) within a continuous-time setting. We model the evolution of liquidity profiles as measure-valued processes and characterize their dynamics under continuous trading. Our analysis encompasses two critical aspects of CLMMs: the mechanics of concentrated liquidity provision and the strategic behavior of arbitrageurs. We examine three distinct arbitrage models – myopic, finite-horizon, and infinite-horizon with discounted and ergodic controls – and derive closed-form solutions for optimal arbitrage strategies under each scenario. Importantly, we demonstrate that the presence of trading fees fundamentally constrains the admissible price processes, as the inclusion of fees precludes the existence of diffusion terms in the price process to avoid infinite fee generation. This finding has significant implications for CLMM design and market efficiency. ...

Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms ArXiv ID: 2412.18202 “View on arXiv” Authors: Unknown Abstract This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key information. The filtered and dimensionality-reduced price data is fed into a GANs network, and its output serve as input of a fully connected network. Through cross-validation, a model is trained to capture features that precede large price fluctuations. The model predicts the likelihood and direction of significant price changes in real-time price sequences, placing trades at moments of high prediction accuracy. Empirical results demonstrate that using autoencoders and convolution to filter and denoise financial data, combined with GANs, achieves a certain level of predictive performance, validating the capabilities of machine learning algorithms to discover underlying patterns in financial sequences. Keywords - CNN;GANs; Cryptocurrency; Prediction. ...

Generalized Mean Absolute Directional Loss as a Solution to Overfitting and High Transaction Costs in Machine Learning Models Used in High-Frequency Algorithmic Investment Strategies ArXiv ID: 2412.18405 “View on arXiv” Authors: Unknown Abstract Regardless of the selected asset class and the level of model complexity (Transformer versus LSTM versus Perceptron/RNN), the GMADL loss function produces superior results than standard MSE-type loss functions and has better numerical properties in the context of optimization than MADL. Better results mean the possibility of achieving a higher risk-weighted return based on buy and sell signals built on forecasts generated by a given theoretical model estimated using the GMADL versus MSE or MADL function. In practice, GMADL solves the problem of selecting the most preferable feature in both classification and regression problems, improving the performance of each estimation. What is important is that, through additional parameterization, GMADL also solves the problem of optimizing investment systems on high-frequency data in such a way that they focus on strategy variants that contain fewer transactions so that transaction costs do not reduce the effectiveness of a given strategy to zero. Moreover, the implementation leverages state-of-the-art machine learning tools, including frameworks for hyperparameter tuning, architecture testing, and walk-forward optimization, ensuring robust and scalable solutions for real-world algorithmic trading. ...

Indices of quadratic programs over reproducing kernel Hilbert spaces for fun and profit ArXiv ID: 2412.18201 “View on arXiv” Authors: Unknown Abstract We give an abstract perspective on quadratic programming with an eye toward long portfolio theory geared toward explaining sparsity via maximum principles. Specifically, in optimal allocation problems, we see that support of an optimal distribution lies in a variety intersect a kind of distinguished boundary of a compact subspace to be allocated over. We demonstrate some of its intelligence by using it to solve mazes and interpret such behavior as the underlying space trying to understand some hypothetical platonic index for which the capital asset pricing model holds. ...