We extend the framework of trading strategies of Gatheral [2010] from single stocks to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cros…
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48 results for “stock trading”
Decision trees improve intraday trading strategies for NIFTY50 stocks.
problem Creating optimal trading rules for individual stocks.
method Using decision trees to generate unique trading rules for each stock based on technical indicators.
result Decision tree strategies outperform simple buy-and-hold for many stocks.
Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into classes using the -shell decomposition m…
Intraday trading strategy based on time series and machine learning for Chinese stock marketq-fin.TR
Article proposes a profitable intraday trading strategy for Chinese stocks.
problem Intraday trading opportunities in Chinese stock market.
method Markowitz optimization and Multilayer Perceptron (MLP) for stock price prediction.
result Validation of Markowitz portfolio optimization and MLP for intraday stock price prediction.
Deep RL applied for Indian stock trading strategies.
problem Designing profitable trading strategies for Indian stock markets.
method Applied deep reinforcement learning to ten Indian stock datasets.
result Models' performance compared and evaluated.
Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the ef…
Study classifies stock price data into stationary and non-stationary periods for mechanical trading.
problem Classifying stock price fluctuations into stationary and non-stationary periods for trading.
method Stationarity analysis using KMO-Langevin theory and trend-based indicators for stationary periods, oscillator-based indicators for non-stationary periods.
result Back testing confirms the strategy is a safe trading strategy with small maximum drawdown.
StockAgent uses AI to simulate real-world stock trading, analyzing external factors and profitability.
problem Investors need to understand how external factors affect stock trading.
method Developed StockAgent, a multi-agent system driven by large language models.
result Identified how external factors impact trading behavior and profitability.
Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as ou…
Paper examines trade/no trade patterns in illiquid stocks, highlighting effects of varying zero returns probabilities.
problem Detecting long-run trade/no trade effects in illiquid stocks with varying zero returns probabilities.
method Proposes a framework considering constant and time-varying zero returns probabilities, analyzing trade/no trade categorical sequences.
result Long-run trade/no trade effects may be spuriously detected in presence of non-constant zero returns probabilities.
EXAMM evolves RNNs for stock return prediction and portfolio trading.
problem Predicting stock returns for optimal portfolio trading.
method Evolutionary Neural Architecture Search (EXAMM) for evolving RNNs.
result Evolving RNNs outperform traditional benchmarks in stock trading.
Manipulation is an important issue for both developed and emerging stock markets. For the study of manipulation, it is critical to analyze investor behavior in the stock market. In this paper, an analysis of the full transaction records of over a hundred stocks in a one-year period is conducted. For each stock, a tradi…
A new DRL system using LSTM improves stock trading performance.
problem Adapting DRL to financial data with low signal-to-noise ratios.
method Cascaded LSTM networks for feature extraction and reinforcement learning.
result Our model outperforms previous models in cumulative returns and Sharp ratio.
A new trading model combines GARCH and PPO for better stock trading profits.
problem Limited performance of reinforcement learning in stock market trading.
method Parallel-network continuous trading model using GARCH and PPO.
result The model achieves more profit compared to traditional reinforcement learning methods.
This paper uses cointegration to identify profitable pair-trading strategies for Indian stocks.
problem Finding profitable pair-trading opportunities in Indian stock market.
method Cointegration analysis to identify co-movement stocks, forming pairs, evaluating portfolios.
result Pairs from auto and realty sectors generally yielded the highest returns, while IT sector pairs had negative returns.
Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensitiesq-fin.TR
Study improves Cox model for predicting stock trading signs using Japanese market data.
problem Improving Cox model for predicting stock trading signs using Japanese market data.
method Added new covariates and used high-frequency trading data for 222 Nikkei 225 stocks.
result Cox-type model performs well in Japanese market and identifies key factors for accurate estimation.
Study on stock trading model with uncertain market status, proving free boundaries and optimal strategies.
problem Optimal trading strategies in a stock market with uncertain market status.
method Free boundary problem, variational inequality system, degenerate operator, C^∞-smoothness.
result All four switching free boundaries are no-overlapping, monotonic, and C^∞-smooth, and their relative localities are completely determined.
Paper proposes using CNN for stock trading with data normalization.
problem Improving stock trading accuracy in volatile markets.
method Developed CNN-based trading framework with novel data normalization.
result CNN-based framework outperforms other methods on 29 stocks.
VolTS uses stats & ML to forecast stock market trends based on volatility.
problem Capturing profitable trading opportunities from market dynamics.
method Combines statistical analysis with machine learning; k-means++ clustering, Granger causality test.
result Effective at identifying profitable trading opportunities through volatility clusters and Granger causality.
Improved stock trading model using feature selection and ensemble learning.
problem Challenges in making profit in the US stock market.
method Feature selection from 148 to 30, dynamic selection of top 25 features, ensemble learning with four classifiers.
result Best model generated 54.35% profit over 18 months.
New framework analyzes pre-stock jump trading behaviors using multivariate time series analysis.
problem Understanding micro-trading behaviors before stock price jumps.
method Multivariate time series analysis considering temporal information.
result Identifies highly informative attributes for predicting price jumps.
In this paper, we propose stock trading based on the average tax basis. Recall that when selling stocks, capital gain should be taxed while capital loss can earn certain tax rebate. We learn the optimal trading strategies with and without considering taxes by reinforcement learning. The result shows that tax ignorance …
Dynamics of the Number of Trades of Financial Securitiescond-mat.stat-mech
We perform a parallel analysis of the spectral density of (i) the logarithm of price and (ii) the daily number of trades of a set of stocks traded in the New York Stock Exchange. The stocks are selected to be representative of a wide range of stock capitalization. The observed spectral densities show a different power-…
Paper proposes TDQN, a DRL strategy for optimal stock trading.
problem Optimal trading position determination in stock markets.
method Deep reinforcement learning (DRL) with Trading Deep Q-Network (TDQN) algorithm.
result TDQN strategy significantly improves Sharpe ratio performance.
Previous studies of the stock price response to trades focused on the dynamics of single stocks, i.e. they addressed the self-response. We empirically investigate the price response of one stock to the trades of other stocks in a correlated market, i.e. the cross-responses. How large is the impact of one stock on other…
Co-trading networks reveal dynamic market structures and improve covariance estimation.
problem Modeling high-dimensional stock covariances in US equity markets.
method Co-trading-based pairwise similarity measure for constructing dynamic networks, spectral clustering, robust covariance estimator.
result Co-trading networks capture time-evolving stock dependencies and improve portfolio performance.
Study uses xLSTM in DRL for better stock trading performance.
problem Limited performance of LSTM in dynamic stock trading environments.
method Combines xLSTM in actor and critic components with PPO optimization.
result xLSTM-based model outperforms LSTM in trading metrics.
This paper extends SLS controllers to two stocks, proving the RPE property with cross-coupling.
problem Extending SLS controllers to two stocks without exploiting correlations.
method Developed a novel architecture for cross-coupling two SLS controllers, derived a closed-form expected value, and proved the RPE property.
result Guaranteed RPE property with cross-coupling for a large class of stock dynamics.
Long Memory in Stock Tradingcond-mat.stat-mech
Using a relationship between the moments of the probability distribution of times between the two consecutive trades (intertrade time distribution) and the moments of the distribution of a daily number of trades we show, that the underlying point process is essentially non-markovian. A detailed analysis of all trades i…
Meta-learning predicts stock trading volumes by learning from each stock's unique patterns.
problem Predicting trading volumes for different stocks using a universal model.
method Dual-process meta-learning framework that learns common patterns with a meta-learner and specific patterns with stock-dependent parameters.
result Improves performance of various baseline models in volume predictions.
Deep RL ensemble strategy outperforms individual algorithms in stock trading.
problem Designing profitable stock trading strategies in a complex market.
method Ensemble of three deep reinforcement learning algorithms (PPO, A2C, DDPG) for stock trading.
result Deep ensemble strategy outperforms individual algorithms and traditional min-variance portfolio.
This study shows how trade policy uncertainty affects stock-T bill correlations.
problem The impact of trade policy uncertainty on stock-T bill relationships.
method Extended Dynamic Conditional Correlation (DCC) framework incorporating exogenous variables.
result Trade policy uncertainty significantly alters stock-T bill correlations, especially under specific political conditions.
We examine the relationship between trading volumes, number of transactions, and volatility using daily stock data of the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, we use trading volumes and the number of transactions as proxy for the rate of information arrivals affecting stock volatilit…
On non-markovian nature of stock tradingcond-mat.other
Using a relationship between the moments of the probability distribution of times between the two consecutive trades (intertrade time distribution) and the moments of the distribution of a daily number of trades we show, that the underlying point process generating times of the trades is an essentially non-markovian lo…
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Financeq-fin.TR
FinRL simplifies deep RL for stock trading, making it accessible to beginners.
problem Lack of accessible tools for beginners in deep RL for stock trading.
method Developed a DRL library with reproducible tutorials and backtesting.
result FinRL streamlines development and comparison of trading strategies.
Paper finds optimal selling rule for pairs trading with stock constraints.
problem Identifying the best time to sell in pairs trading of stocks.
method Optimal pairs-trading selling rule with constraints on trading.
result Closed-form solution for optimal policy determined by a threshold curve.
We investigate the temporal correlations and multifractal nature of trading volume of 22 liquid stocks traded on the Shenzhen Stock Exchange in 2003. We find that the trading volume exhibit size-dependent non-universal long memory and multifractal nature. No crossover in the power-law dependence of the detrended fluctu…
CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategyq-fin.PM
Paper proposes a novel trading strategy combining clustering and reinforcement learning for multi-period portfolio management.
problem Developing an effective trading strategy for multi-period portfolio management.
method The paper integrates clustering techniques with reinforcement learning to categorize and manage stocks across multiple trading periods.
result The proposed strategy outperforms conventional techniques in various metrics, achieving an average return of 151% over 360 trading periods.
Previous studies of the stock price response to individual trades focused on single stocks. We empirically investigate the price response of one stock to the trades of other stocks. How large is the impact of one stock on others and vice versa? -- This impact of trades on the price change across stocks appears to be tr…
Research compares ML and Time Series methods for generating trading signals.
problem Efficiency of on-line learning Algorithms in generating trading signals.
method Used technical indicators and ensemble of Random Forests, also Kalman Filter.
result Kalman Filter outperformed Random Forests in on-line learning predictions of stock prices.
In an analysis of the US, the UK, and the German stock market we find a change in the behavior based on the stock's beta values. Before 2006 risky trades were concentrated on stocks in the IT and technology sector. Afterwards risky trading takes place for stocks from the financial sector. We show that an agent-based mo…
AI algorithms outperform traditional trading methods in stock markets.
problem Traditional trading methods struggle with risk management and edge over classical approaches.
method Used Deep Reinforcement Learning (DRL) algorithms (DDQN and PPO) to compare with Buy and Hold benchmark.
result DRL algorithms provide a substantial edge over classical approaches in terms of risk-adjusted returns.
Trade Co-occurrence, Trade Flow Decomposition, and Conditional Order Imbalance in Equity Marketsq-fin.TR
The paper classifies trades into types based on proximity and measures their impact on stock prices.
problem Understanding the impact of high-frequency trades on stock prices and their predictability.
method Classifies trades into five types based on proximity, measures conditional order imbalance (COI), and develops trading strategies.
result Strong positive correlations between contemporaneous returns and COIs, and positive associations with future returns for isolated trades.
We studied non-dynamical stochastic resonance for the number of trades in the stock market. The trade arrival rate presents a deterministic pattern that can be modeled by a cosine function perturbed by noise. Due to the nonlinear relationship between the rate and the observed number of trades, the noise can either enha…
Statistical Properties and Pre-hit Dynamics of Price Limit Hits in the Chinese Stock Marketsq-fin.ST
Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders' short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of pri…
Improved DRQN-ARBR model for better stock trading performance.
problem Irrational investor behavior impacts stock market efficiency.
method DRQN-ARBR model with LSTM layer and ARBR sentiment indicators.
result Significantly improved stock trading performance.
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
problem The profitability of technical trading rules in stock markets is controversial.
method Proves the upper bound of cumulative return and investigates the profitability of technical trading rules using bootstrap methodology.
result Technical trading rules are not better than random trading and less profitable than the market.
Paper optimizes stock option forecasting using ML models and improved trading strategies.
problem Improving accuracy of stock option predictions and trading decisions.
method Application of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Quasi-Reversibility Method (QRM).
result Optimized stock option investment results through improved trading strategies and model combination.