A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
Dematerialization is the reduction in the quantity of materials needed to produce something useful over time. Dematerialization fundamentally derives from ongoing increases in technical performance but it can be counteracted by demand rebound - increases in usage because of increased value (or decreased cost) that also…
Note removes degeneracy in Kähler geometry estimates.
A3T-GCN model forecasts FTSE100 stock prices using technical indicators and financial ratios.
Study improves cryptocurrency price prediction using deep learning with trading and social media indicators.
Technical trading rules have a long history of being used by practitioners in financial markets. Their profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousands traditional technical trading rules on the Shanghai Securities Co…
Transformer model predicts stock trends using technical data and sentiment analysis.
With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more convenient and effective. This paper aims to improve the accuracy of stock price forecasting. It improv…
RAGuard improves safety in LLMs for offshore wind maintenance.
TINs use neural networks to interpret technical indicators for trading.
Improved NTL detection using human-in-the-loop approach with explainability.
Decision trees improve intraday trading strategies for NIFTY50 stocks.
Technical analysis is used to discover investment opportunities. To test this hypothesis we propose an hybrid system using machine learning techniques together with genetic algorithms. Using technical analysis there are more ways to represent a currency exchange time series than the ones it is possible to test computat…
Study improves cryptocurrency price prediction using neural networks and technical indicators.
Technical report on f-divergences and f-GAN training properties.
This paper optimizes cryptocurrency portfolios by integrating sentiment analysis with technical indicators.
Improved MACD trading strategies with other indicators for better performance.
NEAT algorithm optimizes stock trading with reduced risk.
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
H-GAT improves stock selection by capturing complex higher-order stock relations and integrating both technical and fundamental analysis.
Abstract: A new approach to technical indicators without lag.
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
Revisits life insurance surplus models with new technical bases.
High-dimensional data acquired from biological experiments such as next generation sequencing are subject to a number of confounding effects. These effects include both technical effects, such as variation across batches from instrument noise or sample processing, or institution-specific differences in sample acquisiti…
We investigate the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for contr…
Survey on LSTM-based anomaly detection for technical systems.
Weak form of the Efficiency Market Hypothesis (EMH) excludes predictions of future market movements from historical data and makes the technical analysis (TA) out of law. However the technical analysis is widely used by traders and speculators who steadely refuse to consider the market as a "fair game" and survive with…
Develops accelerated methods for optimization using low-dimensional projected-gradient information.
In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Mult…
In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…
Study finds traditional technical indicators underperform in high-frequency trading, suggesting risk management over prediction.
A large class of trading strategies focus on opportunities offered by the yield curve. In particular, a set of yield curve trading strategies are based on the view that the yield curve mean-reverts. Based on these strategies' positive performance, a multiple pairs trading strategy on major currency pairs was implemente…
This paper tackles hidden technical debts in fair ML systems for Fintech.
We present a solution to an optimal stopping problem for a process with a wide-class of novel dynamics. The dynamics model the support/resistance line concept from financial technical analysis.
Have you ever felt miserable because of a sudden whipsaw in the price that triggered an unfortunate trade? In an attempt to remove this noise, technical analysts have used various types of moving averages (simple, exponential, adaptive one or using Nyquist criterion). These tools may have performed decently but we show…
Paper improves risk bound for MTL with graph-dependent data.
CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be used to develop more robust machine learning models and to provide standardized benchmarks of models' performance in the adversarial setting. …
Transformer model with mixed-frequency data improves stock volatility prediction.
Machine learning models outperform traditional technical analysis in Bitcoin trading.
Online boosting method improves weak to strong learner.
This paper provides the technical details of gradient flow construction and related problems, which are essential for our construction of Lagrangian torus fibrations for Calabi-Yau hypersurfaces.
DeepSupp detects financial support levels using attention mechanisms.
Lie groups applied to tech progress in economic growth.
Study finds similar companies in Dhaka Stock Exchange using technical data.
We propose a new indicator for technical analysis. The indicator emphasizes maximums and minimums in price series with inherent smoothing and has a potential to be useful in both mechanical trading rules and chart pattern analysis.
The study redefines algorithmic fairness as a sociotechnical concept.
In this survey, a short introduction in the recent discovery of log-normally distributed market-technical trend data will be given. The results of the statistical evaluation of typical market-technical trend variables will be presented. It will be shown that the log-normal assumption fits better to empirical trend data…
Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts. Despite its ubiquity in financial media, the reliability of its signals remains a contentious and highly subjective form of 'domain knowledge'. We investiga…