Trade Execution Strategies Research Articles

Enhancing Agricultural Commodity Price Forecasting Using Generative Models: A Deep Learning Approach

Predicting stock market prices is a critical yet challenging task in finance. Technical analysis, a widely used methodology in investment theory, involves forecasting price movements by analyzing historical market data. Recently, deep learning has gained prominence for its exceptional ability to process complex data, making it a popular tool for financial applications such as stock prediction, portfolio optimization, financial information analysis, and trade execution strategies. In this study, we propose a novel deep learning architecture that integrates a Generative Adversarial Network (GAN) with a Convolutional Neural Network (CNN) as the discriminator and Gated Recurrent Units (GRU) as the generator. This framework generates distributions of daily stock prices through adversarial learning to predict stock closing prices. Using daily trading data from Ruchi Soya Industries Limited, an empirical analysis was performed across a broad time frame. Results show that the proposed GAN-based architecture significantly outperforms traditional deep learning models, including GRU, LSTM, and Bi-LSTM, in predicting stock closing prices. These findings demonstrate the potential of this novel approach for improving stock price forecasting accuracy.

Researches Advanced in Finance based on Generative Adversarial Networks

Topics about how to apply Generative Adversarial Networks (GANs) to finance industry and research to promote financial market prosperity and progress with deep learning technology never fail to arouse public attention. For instance, financial academic realm such as portfolio optimization, trade execution strategies and financial information progressing have made use of machine learning. After a large number of literature retrieval and attempts to classify from different aspects, we ultimately find out that GAN is one of the most famous and appropriate technologies widely adopted in academic research and commercial applications. Aiming at assisting reader with having insight into how GAN improved financial work’s efficiency, in this paper, we propose three research perspectives of GAN in financial work: Stock Market Prediction, Fault Detection, and Time Series. By organizing from an innovative and macro perspective, we present and classify the application methods of GAN and the improvement of the basic model of GAN in order to adapt to the corresponding fields under the corresponding research directions. In general, the following improved models based on the basic GAN model for the corresponding financial perspective are involved: GAN of three-layer dense network, GAN with the MLP and LSTM separately as the discriminator and the generator, GAN-FD stock projection model, BERT and GAN in Stock Prediction; SSGANs, DAEGAN in Fault Detection; SeqGAN, Quant GAN, RNN-GAN, PSA-GAN in Time Series.

MOBILE U-NET V3 AND BILSTM: PREDICTING STOCK MARKET PRICES BASED ON DEEP LEARNING APPROACHES

The development of reliable stock market models has enabled investors to make better-informed decisions. Investors may be able to locate companies that offer the highest dividend yields and lower their investment risks by using a trading strategy. The degree to which stock prices are significantly correlated, however, makes stock market analysis more complicated when using batch processing methods. The stock market prediction has entered a time of advanced technology with the rise of technological wonders like global digitalization. The significance of artificial intelligence models has greatly increased as a result of the significantly enhance in market capitalization. Because it builds a strong time-series framework based on Deep Learning (DL) for predicting future stock prices, the proposed study is novel. Deep learning has recently enjoyed considerable success in some domains due to its exceptional capacity for handling data. For instance, it is commonly used in financial disciplines such as trade execution strategies, portfolio optimization, and stock market forecasting. In this research, we propose a structure based on Mobile U-Net V3 and a hybrid of a (Mobile U-Net V3-BiLSTM) with BiLSTM to forecast the closing prices of Apple, Inc. and S&P 500 stock data. The Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Pearson's Correlation (R), and Normalization Root Mean Squared Error (NRMSE) metrics were utilized to calculate the outcomes of the DL stock prediction methods. The Mobile U-Net V3-BiLSTM model outperformed other techniques for forecasting stock market prices.

Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm (MMGAN-HPA) for stock market prices prediction

Deep learning has achieved greater success in optimizing solutions associated with Artificial Intelligence (AI). In the financial domain, it is widely used for stock market prediction, trade execution strategies and portfolio optimization. Stock market prediction is a very significant use case in this domain. Generative Adversarial Networks (GANs) with advanced AI models have gained significance of late. However, it is used in image-image-translation and other computer vision scenarios. GANs are not used much for stock market prediction due to its difficulty in setting the right set of hyperparameters. In this paper, overcome this problem with reinforcement learning and Bayesian optimization. A deep learning framework based on GAN, named Stock-GAN, is implemented with generator and discriminator. The former is realized with LSTM, a variant of Recurrent Neural Network (RNN), while the latter uses Convolutional Neural Network. An algorithm named Generative Adversarial Network based Hybrid Prediction Algorithm (GAN-HPA) is proposed. An empirical study revealed that Stock-GAN achieves promising performance in stock price prediction when compared with the state of the art model known as Multi-Model based Hybrid Prediction Algorithm (MM-HPA). Afterwards, MM-HPA and GAN-HPA combined to form yet another hybrid model known as MMGAN-HPA for improved performance over MM-HPA and GAN-HPA.

Mechanics of good trade execution in the framework of linear temporary market impact

We define the concept of good trade execution and we construct explicit adapted good trade execution strategies in the framework of linear temporary market impact. Good trade execution strategies are dynamic, in the sense that they react to the actual realisation of the traded asset price path over the trading period; this is paramount in volatile regimes, where price trajectories can considerably deviate from their expected value. Remarkably, however, the implementation of our strategies does not require the full specification of an SDE evolution for the traded asset price, making them robust across different models. Moreover, rather than minimising the expected trading cost, good trade execution strategies minimise trading costs in a pathwise sense, a point of view not yet considered in the literature. The mathematical apparatus for such a pathwise minimisation hinges on certain random Young differential equations that correspond to the Euler–Lagrange equations of the classical Calculus of Variations. These Young differential equations characterise our good trade execution strategies in terms of an initial value problem that allows for easy implementations.

Stock Market Prediction Based on Generative Adversarial Network

Deep learning has recently achieved great success in many areas due to its strong capacity in data process. For instance, it has been widely used in financial areas such as stock market prediction, portfolio optimization, financial information processing and trade execution strategies. Stock market prediction is one of the most popular and valuable area in finance. In this paper, we propose a novel architecture of Generative Adversarial Network (GAN) with the Multi-Layer Perceptron (MLP) as the discriminator and the Long Short-Term Memory (LSTM) as the generator for forecasting the closing price of stocks. The generator is built by LSTM to mine the data distributions of stocks from given data in stock market and generate data in the same distributions, whereas the discriminator designed by MLP aims to discriminate the real stock data and generated data. We choose the daily data on S&P 500 Index and several stocks in a wide range of trading days and try to predict the daily closing price. Experimental results show that our novel GAN can get a promising performance in the closing price prediction on the real data compared with other models in machine learning and deep learning.

Mechanics of Good Trade Execution in the Framework of Linear Temporary Market Impact

We define the concept of good trade execution and we construct explicit adapted good trade execution strategies in the framework of linear temporary market impact. Good trade execution strategies are dynamic, in the sense that they react to the actual realisation of the traded asset price path over the trading period; this is paramount in volatile regimes, where price trajectories can considerably deviate from their expected value. Remarkably however, the implementation of our strategies does not require the full specification of an SDE evolution for the traded asset price, making them robust across different models. Moreover, rather than minimising the expected trading cost, good trade execution strategies minimise trading costs in a pathwise sense, a point of view not yet considered in the literature. The mathematical apparatus for such a pathwise minimisation hinges on certain random Young differential equations that correspond to the Euler-Lagrange equations of the classical Calculus of Variations. These Young differential equations characterise our good trade execution strategies in terms of an initial value problem that allows for easy implementations.

Optimal trade execution in order books with stochastic liquidity

AbstractIn financial markets, liquidity changes randomly over time. We consider such random variations of the depth of the order book and evaluate their influence on optimal trade execution strategies. If the stochastic structure of liquidity changes satisfies certain conditions, then the unique optimal trading strategy exhibits a conventional structure with a single wait region and a single buy region, and profitable round‐trip strategies do not exist. In other cases, optimal strategies can feature multiple wait regions and optimal trade sizes that can be decreasing in the size of the position to be liquidated. Furthermore, round‐trip strategies can be profitable depending on bid–ask spread assumptions. We illustrate our findings with several examples including the Cox–Ingersoll–Ross model for the evolution of liquidity.

Multivariate Transient Price Impact and Matrix-Valued Positive Definite Functions

We consider a model for linear transient price impact for multiple assets that takes cross-asset impact into account. Our main goal is to single out properties that need to be imposed on the decay kernel so that the model admits well-behaved optimal trade execution strategies. We first show that the existence of such strategies is guaranteed by assuming that the decay kernel corresponds to a matrix-valued positive definite function. An example illustrates, however, that positive definiteness alone does not guarantee that optimal strategies are well-behaved. Building on previous results from the one-dimensional case, we investigate a class of nonincreasing, non-negative, and convex decay kernels with values in a space of symmetric matrices. We show that these decay kernels are always positive definite and characterize when they are even strictly positive definite, a result that may be of independent interest. Optimal strategies for kernels from this class are particularly well-behaved if one requires that the decay kernel is also commuting. We show how such decay kernels can be constructed by means of matrix functions and provide a number of examples. In particular, we completely solve the case of matrix exponential decay.

Optimal Portfolio Execution using Market and Limit Orders

We investigate trade execution strategies that maximize expected exponential utility. In contrast to existing literature our strategy dynamically executes both market and limit orders. We derive a Hamilton-Jacobi-Bellman equation for the optimal combination of these order types and solve it numerically. We show that limit orders have a significant impact on the optimal strategy, and discuss the utility gains with respect to pure market order strategies. Our findings indicate an inverse relation between the speed at which market orders are submitted and the likelihood of limit orders being filled. For limit orders, we find that the optimal price moves away from the market price when any of the following hold: i) trade execution is not urgent, ii) the portfolio is relatively small, iii) the agent is not very risk averse. Finally, the optimal size of limit orders is largest for moderate portfolio sizes and long time horizons.

Dynamic trade execution: a grammatical evolution approach

Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument. Investors wishing to execute large orders face a tradeoff between market impact and opportunity cost. Trade execution strategies are designed to balance out these costs, thereby minimising total trading cost. Despite the importance of optimising the trade execution process, this is difficult to do in practice due to the dynamic nature of markets and due to our imperfect understanding of them. In this paper, we adopt a novel approach, combining an evolutionary methodology whereby we evolve high-quality trade execution strategies, with an agent-based artificial stock market, wherein the evolved strategies are tested. The evolved strategies are found to outperform a series of benchmark strategies and several avenues are suggested for future work.

Optimal Trading Execution with Nonlinear Market Impact: An Alternative Solution Method

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 beginning of the period. We propose a general solution method that accomodates the case of a positive stock of assets in the initial period. Our findings are twofold. First of all, we show that the problem admits a solution with no trading in the opening period only if additional parametric restrictions are imposed. Second, with positive asset holdings in the initial period, the optimal execution time depends on trading activity at the beginning of the planning period.

OPTIMAL TRADE EXECUTION IN ILLIQUID MARKETS

We study optimal trade execution strategies in financial markets with discrete order flow. The agent has a finite liquidation horizon and must minimize price impact given a random number of incoming trade counterparties. Assuming that the order flow N is given by a Poisson process, we give a full analysis of the properties and computation of the optimal dynamic execution strategy. Extensions, whereby N is a Markov-modulated compound Poisson process are also considered. We derive and compare the properties of the various cases and illustrate our results with computational examples.

A guided tour of new results on “Trade execution in illiquid markets”

We give an overview of the dissertation “Trade execution in illiquid markets: Optimal stochastic control and multi-agent equilibria” (Schoneborn, PhD thesis, TU Berlin, 2008). The dissertation focuses on two questions in the field of optimal trade execution strategies: First, how should traders best sell an illiquid asset position if they want to maximize the expected utility of liquidation proceeds? And second, in a situation where one market participant needs to liquidate a position, what is the effect of other market participants obtaining advance information of this impending liquidation?

Optimal Trade Execution and Absence of Price Manipulations in Limit Order Book Models

We analyze the existence of price manipulation and optimal trade execution strategies in a model for an electronic limit order book with nonlinear price impact and exponential resilience. Our main results show that, under general conditions on the shape function of the limit order book, placing deterministic trade sizes at trading dates that are homogeneously spaced is optimal within a large class of adaptive strategies with arbitrary trading dates. This extends results from our earlier work with A. Fruth. Perhaps even more importantly, our analysis yields as a corollary that our model does not admit price manipulation strategies. This latter result contrasts the recent findings of Gatheral [Quant. Finance, to appear], where, in a related but different model, exponential resilience was found to give rise to price manipulation strategies when price impact is nonlinear.