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.
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
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…
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…
Abstract: A new approach to technical indicators without lag.
Technical trading rules have been widely used by practitioners in financial markets for a long time. The profitability remains controversial and few consider the stationarity of technical indicators used in trading rules. We convert MA, KDJ and Bollinger bands into stationary processes and investigate the profitability…
Machine learning models outperform traditional technical analysis in Bitcoin trading.
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…
This paper uses feature preprocessing and RRL to automate profitable financial trading.
Study finds traditional technical indicators underperform in high-frequency trading, suggesting risk management over prediction.
Online trading platforms manipulate profits and losses, causing 82% of retail traders to lose money.
GA-MSSR optimizes forex trading rules for higher returns and reduced risk.
TINs use neural networks to interpret technical indicators for trading.
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…
Decision trees improve intraday trading strategies for NIFTY50 stocks.
Study improves cryptocurrency price prediction using deep learning with trading and social media indicators.
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…
We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well a…
Research integrates sentiment analysis with reinforcement learning for better trading strategies.
Developed Forex trading heuristics with high profit potential.
A novel algorithm for actively trading stocks is presented. While traditional expert advice and "universal" algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirica…
Improved MACD trading strategies with other indicators for better performance.
NEAT algorithm optimizes stock trading with reduced risk.
Technical trading represents a class of investment strategies for Financial Markets based on the analysis of trends and recurrent patterns of price time series. According standard economical theories these strategies should not be used because they cannot be profitable. On the contrary it is well-known that technical t…
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.
VGRSI uses price visibility graphs to generate profitable trading signals.
Hybrid AI system combines technical, sentiment analysis for adaptive equity trading.
Wavelet denoised-ResNet with LightGBM predicts Forex rate of change.
Banks must manage their trading books, not just value them. Pricing includes valuation adjustments collectively known as XVA (at least credit, funding, capital and tax), so management must also include XVA. In trading book management we focus on pricing, hedging, and allocation of prices or hedging costs to desks on an…
A general framework is suggested to describe human decision making in a certain class of experiments performed in a trading laboratory. We are in particular interested in discerning between two different moods, or states of the investors, corresponding to investors using fundamental investment strategies, technical ana…
Research compares ML and Time Series methods for generating trading signals.
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…
CNN model predicts financial market movement with better performance.
Although technical trading rules have been widely used by practitioners in financial markets, their profitability still remains controversial. We here investigate the profitability of moving average (MA) and trading range break (TRB) rules by using the Shanghai Stock Exchange Composite Index (SHCI) from May 21, 1992 th…
New framework analyzes pre-stock jump trading behaviors using multivariate time series analysis.
We use machine learning for designing a medium frequency trading strategy for a portfolio of 5 year and 10 year US Treasury note futures. We formulate this as a classification problem where we predict the weekly direction of movement of the portfolio using features extracted from a deep belief network trained on techni…
This paper describes recent development and test implementation of a continuous time recurrent neural network that has been configured to predict rates of change in securities. It presents outcomes in the context of popular technical analysis indicators and highlights the potential impact of continuous predictive capab…
A new oscillator measures trending behavior of financial instruments.
FinGPT uses LLMs for real-time market sentiment analysis.
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…
New trading strategy uses deep neural networks for future stock price predictions.
Algorithm combines ESG ratings with pairs trading for sustainable investing.
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…
Deep RL strategies outperform traditional methods in cryptocurrency trading.
Whether you trade futures for yourself or a hedge fund, your strategy is counted. Long and short position limits make the number of unique strategies finite. Formulas of the numbers of strategies, transactions, do nothing actions are derived. A discrete distribution of actions, corresponding probability mass, cumulativ…
Paper combines LSTM and Random Forest for better stock market predictions.
A trading system predicts stock prices using DNNs for Abercrombie & Fitch Co. shares.
Study finds IBS useful for predicting ETF price movements.
Paper proposes a reinforcement learning method for trading using expert trajectories.