We consider a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. We describe a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades off expected return, risk, transaction cost and h…
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48 results for “periodic trading”
Model explains periodic trading in financial markets through game theory.
problem Understanding periodic trading activities in financial markets.
method Mean-field liquidation game with major-minor players.
result Existence and uniqueness of Nash equilibrium established.
We consider the optimal trade execution strategies for a large portfolio of single stocks proposed by Almgren (2003). This framework accounts for a nonlinear impact of trades on average market prices. The results of Almgren (2003) are based on the assumption that no shares of assets per unit of time are trade at the be…
In this paper, we present a multi-period trading model by assuming that traders face not only asymmetric information but also heterogenous prior beliefs, under the requirement that the insider publicly disclose his stock trades after the fact. We show that there is an equilibrium in which the irrational insider camoufl…
We study the profitability of optimal mean reversion trading strategies in the US equity market. Different from regular pair trading practice, we apply maximum likelihood method to construct the optimal static pairs trading portfolio that best fits the Ornstein-Uhlenbeck process, and rigorously estimate the parameters.…
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.
Model for multi-period carbon market pricing with allowances.
problem Carbon market pricing with multiple trading periods and compliance times.
method Singular forward-backward stochastic differential equations (SDEs).
result Value function convergence to infinite period model under certain conditions.
In this study, we applied a stochastic spread pairs trading strategy on the Indian commodity market. The complete set of commodities were taken whose spot price was available for the period of January 1st 2010 to December 31st 2018 including energy, metals and the agricultural commodity sector. Spot data was taken from…
Quarter-hour market bursts predict algorithmic trading and returns in crypto futures.
problem Predicting returns in cryptocurrency futures markets using quarter-hour market bursts.
method Analysis of trade data and Autocorrelation Map to identify and quantify algorithmic trading activity.
result Quarter-hour market bursts are associated with algorithmic trading and can predict returns.
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.
An ensemble method enhances cryptocurrency trading strategies using deep reinforcement learning.
problem Improving generalization performance in stochastic cryptocurrency trading environments.
method Model selection and mixture distribution policy to ensemble deep reinforcement learning models.
result Improved out-of-sample performance compared to benchmarks.
AMSAs adaptively manage crypto-currency trading by selecting multiple strategies based on market conditions.
problem Maximizing gains in volatile crypto-currency markets with high uncertainty.
method AMSAs use multiple sub-agents with different strategies, dynamically selecting them based on market conditions.
result AMSAs can achieve high positive alpha in long-term crypto-currency trading.
Study predicts global trade impacts using deep learning during the COVID-19 period.
problem Forecasting global trade impacts during the COVID-19 pandemic.
method Developed a sustainable prediction process using Long-Short Term Memory (LSTM) deep learning model.
result Accurately predicted daily imports and exports for the next 180 days during the pandemic.
We develop a single-period model for a large economic agent who trades with market makers at their utility indifference prices. A key role is played by a pair of conjugate saddle functions associated with the description of Pareto optimal allocations in terms of the utility function of a representative market maker.
A groundwater market modelq-fin.GN
A model for groundwater trading among stakeholders.
problem Groundwater trading among stakeholders in a basin.
method Optimization of production by agents considering water rights, consumption, and trading.
result Characterization of Nash equilibrium in a 1-period setting and initial insights into multi-period game.
Semistatic trading strategies can be taken to limits in discrete time.
problem Limits of semistatic trading strategies in discrete time.
method Analysis in full generality for a two-period model, and under a probabilistic condition for multi-period, multi-stock models.
result Pointwise limits of semistatic trading strategies are again semistatic strategies.
In this paper, we propose an equilibrium pricing model in a dynamic multi-period stochastic framework with uncertain income streams. In an incomplete market, there exist two traded risky assets (e.g. stock/commodity and weather derivative) and a non-traded underlying (e.g. temperature). The risk preferences are of expo…
Study reveals trade dynamics in dry bulk shipping networks, highlighting their randomness and periodic changes.
problem Understanding the randomness and periodic changes in dry bulk shipping networks.
method Analysis of micro-level trade flow data from 2015 to 2023, focusing on grain, coal, and iron ore networks.
result Dry bulk shipping networks exhibit small-world phenomena and periodic life cycles, influenced by importing ports and global events.
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.
Proposes a deep RL approach for high-frequency market making using tick data and periodic signals.
problem Challenges in high-frequency market making due to tick-level data complexity and high trading volume.
method Integrates tick-level data with periodic signals using deep reinforcement learning.
result The proposed framework outperforms existing methods in profitability and risk management.
Improved neural network depth-width trade-offs via dynamical systems.
problem Expressivity of neural networks in terms of depth and width.
method Connection with dynamical systems, focusing on periodic points and Lipschitz constants.
result Sharper width lower bounds for neural networks, yielding exponential depth-width separations.
Trade finance history traced from medieval origins to modern markets.
problem Evolution and standardization of trade finance products.
method Historical analysis of market structures and regulatory changes.
result Global trade finance market evolved from local to centralized, then decentralized.
Game theory model for optimal trading with end-of-day constraints.
problem Optimal trading strategy in a game between slow and fast traders.
method Coupled stochastic control problems, Fredholm integral equation solution.
result Explicit solution to the game with profitable strategies for both players.
Study optimal portfolio management with periodic evaluations in stochastic models, considering convex constraints.
problem Optimal portfolio management under ratio-type periodic evaluations in stochastic factor models with convex trading constraints.
method Transformed infinite horizon optimal control problem into an auxiliary terminal wealth optimization problem. Introduced an auxiliary unconstrained optimization problem in a modified market model. Used martingale duality approach to establish dual minimizer and optimal unconstrained wealth process.
result Derived and verified the optimal constrained portfolio process for the original problem over an infinite horizon.
On the Effect of Alpha Decay and Transaction Costs on the Multi-period Optimal Trading Strategymath.OC
Optimizes trading strategy considering alpha decay and transaction costs.
problem Maximizing reward in a multi-period portfolio with transaction costs and alpha decay.
method Formulated as an infinite horizon Markov Decision Process, solved using a modified value iteration algorithm with convergence proof and asymptotic analysis.
result Characterized optimal trading policy that maximizes average expected reward.
A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Dataq-fin.ST
Deep learning predicts cryptocurrency price movements from trade data.
problem Predicting short-term price changes in cryptocurrencies.
method Long Short-term Memory Network (LSTM) trained on trade-by-trade data.
result Optimal LSTM model achieves over 60% accuracy on out-of-sample test periods.
We study trade-based manipulation of stock prices from the perspective of complex trading networks constructed by using detailed information of trades. A stock trading network consists of nodes and directed links, where every trader is a node and a link is formed from one trader to the other if the former sells shares …
Scaling analysis of multivariate intermittent time seriescond-mat.stat-mech
The scaling properties of the time series of asset prices and trading volumes of stock markets are analysed. It is shown that similarly to the asset prices, the trading volume data obey multi-scaling length-distribution of low-variability periods. In the case of asset prices, such scaling behaviour can be used for risk…
This study develops a multi-factor framework where not only market risk is considered but also potential changes in the investment opportunity set. Although previous studies find no clear evidence about a positive and significant relation between return and risk, favourable evidence can be obtained if a non-linear rela…
Mounting empirical evidence suggests that the observed extreme prices within a trading period can provide valuable information about the volatility of the process within that period. In this paper we define a class of stochastic volatility models that uses opening and closing prices along with the minimum and maximum p…
How Covid mobility restrictions modified the population of investors in Italian stock marketsq-fin.TR
Covid lockdown increased interest in Italian stock market, leading to new investors.
problem Impact of Covid lockdown on Italian stock market investors.
method Analysis of trading activity and investor demographics before and during lockdown.
result New investors during lockdown were more skilled traders than pre-lockdown investors.
AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networksq-fin.PM
Dual model combines HMM and neural networks for energy trading during volatile periods.
problem Optimizing energy trading performance during market volatility.
method Integrates Hidden Markov Models and neural networks with Black-Litterman portfolio optimization.
result Achieved 83% return with Sharpe ratio 0.77 during COVID period.
In this paper, we investigate the cooling-off effect (opposite to the magnet effect) from two aspects. Firstly, from the viewpoint of dynamics, we study the existence of the cooling-off effect by following the dynamical evolution of some financial variables over a period of time before the stock price hits its limit. S…
New connection between DNNs and Sharkovsky's Theorem for depth-width trade-offs.
problem Understanding why some functions are hard to represent by shallow ReLU networks.
method Connection to Sharkovsky's Theorem and analysis of dynamical systems.
result Lower bounds for width needed to represent periodic functions as a function of depth.
We analyze empirical data for 4,000 real-life trading portfolios (U.S. equities) with holding periods of about 0.7-19 trading days. We find a simple scaling C ~ 1/T, where C is cents-per-share, and T is the portfolio turnover. Thus, the portfolio return R has no statistically significant dependence on the turnover T. W…
Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Marketq-fin.TR
This study evaluates a dynamic pairs trading strategy in cryptocurrencies using cointegration tests.
problem Improving profitability and risk management in cryptocurrency trading.
method Engle-Granger, KSS, Johansen tests; optimal look-back window; mean-reversion speed calibration; microstructure limitations consideration.
result The strategy outperforms naive buy-and-hold in Bitmex exchange with low maximum drawdown.
The FCA improved insider trading regulation after 2012, reducing abnormal returns.
problem Regulation of insider trading before and after the UK Financial Services Act 2012.
method Event study methodology using abnormal returns analysis.
result Abnormal returns were reduced after the FCA took over from the FSA.
In this paper, we present a multi-period trading model in the style of Kyle (1985)'s inside trading model, by assuming that there are at least two insiders in the market with long-lived private information, under the requirement that each insider publicly discloses his stock trades after the fact. Based on this model, …
Novel OTT method for cryptocurrency trading offers high annualized profit.
problem Quantifying and exploiting trading opportunities in cryptocurrency markets.
method Bi-objective convex optimization for balancing profit and risk.
result Annualized profit of 15.49% in cryptocurrency market from 2020 to 2022.
Proposes overnight volatility model for better market dynamics.
problem Lack of high-frequency data during close-to-open period.
method Itô diffusion model with weighted least squares estimation.
result Developed and validated overnight volatility model.
We present a detailed study of the performance of a trading rule that uses moving average of past returns to predict future returns on stock indexes. Our main goal is to link performance and the stochastic process of the traded asset. Our study reports short, medium and long term effects by looking at the Sharpe ratio …
Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profile. Automating this process with machine learning remains a challenging problem. Here, we design a deep reinforcement learning (RL) architec…
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…
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…
In this note we study the bilateral merchandise trade flows between 186 countries over the 1948-2005 period using data from the International Monetary Fund. We use Pajek to identify network structure and behavior across thresholds and over time. In particular, we focus on the evolution of trade "islands" in the a world…
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…
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…
The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account …