Trading Strategies Research Articles

AI-Driven Optimization of Financial Quantitative Trading Algorithms and Enhancement of Market Forecasting Capabilities

Abstract: The application of artificial intelligence (AI) into financial markets has revolutionised quantitative trading and market forecasting by increasing the efficiency of algorithmic trading, improving the accuracy of market predictions and facilitating real-time market decisions. This paper will provide an overview of the application of Al in the financial markets focusing on the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) in optimizing the trading algorithms, specifically the capability of Al to process very high data points and complex relationships that other quantitative models are unable to capture. We will discuss trading algorithms such as XGBoost, deep neural networks such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs), how they can outperform traditional quantitative trading models and real-time decision making in stock price prediction, pattern recognition and trading strategy optimisation. We will also look at Al-enhanced predictive models that utilise deep learning and layered models, such as Natural language processing (NLP) sentiment analysis to capture the public sentiment in the market to forecast employing diverse datasets such as historical prices, market volatility, macroeconomic factors and social media sentiment to improve the forecasting accuracy. By going through several experiments and case studies, this paper will shed light on the impact of entrusting quantitative trading and market forecasting decisions to AI for improved performance and reduced errors. There are many challenges ahead but AI plays a constructive role in improving the trading strategies and forecasting market outcomes accurately.

Applications of machine learning in quantitative trading

Abstract. This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are used to determine trends that inform and optimise trading decisions. Reinforcement learning is another machine learning technique in quantitative trading where adaptive strategies using a reward system are designed. These strategies then analyse market dynamics and optimise trades through interaction and feedback. The third technique, deep learning, takes neural networks and uses them to process large-scale, complex data. It's particularly useful in quantitative trading as it generates more accurate predictions; for instance, neural networks are capable of learning trading signals from price and volume data and even from news headlines. By enabling computers to analyse large quantities of training data, deep learning improves the predictive accuracy of future price trends. It also helps to generate trading signals that are more robust to market conditions.

Optimization of High-Frequency Trading Strategies Using Deep Reinforcement Learning

This study presents a new method for optimising high-risk trading (HFT) strategies using deep learning (DRL). We propose a multi-time DRL framework integrating advanced neural network architectures with sophisticated business data processing techniques. The framework employs a combination of convolutional neural networks for manual order analysis, short-term memory networks for time series processing, and a multi-head listening mechanism for body fusion. We formulate the HFT problem based on Markov Decision Processes and use the Proximal Policy Optimization algorithm for training. The model is evaluated using tick-by-tick data from the NASDAQ exchange, including ten liquid stocks in 6 months. The experimental results show the superiority of our method, achieving a Sharpe ratio of 3.42, outperforming the learning model and machine learning based on benchmarks up to 33%. The proposed strategy has demonstrated strong performance across a wide range of regulatory markets and has shown potential for strategic objectives. Sensitivity analysis confirms the model's stability across a range of hyperparameters. Our findings suggest that the DRL-based approach can improve HFT performance and provide better market adaptation and risk management. This research leads to the continuous evolution of algorithmic trading strategies and shows the potential of AI-driven approaches in financial markets.

Cooperative energy and reserve trading strategies for multiple integrated energy systems based on asymmetric nash bargaining theory

To tackle the issues of cooperative energy and reserve trading as well as fair cooperative benefit allocation among multiple integrated energy systems (IESs), this paper proposes a two-stage cooperative energy and reserve trading model for multiple integrated energy systems (MIESs). Specifically, at day-ahead stage, MIESs aim to maximize their overall profit through cooperative electricity and heat trading. At real-time stage, MIESs trade demand response (DR) reserve to minimize the overall wind power deviation compensation cost. To reduce the complexity in model solution, we transform the model into two sub-problems. In sub-problem 1, we determine the energy and DR reserve trading volumes. Here, distributionally robust optimization (DRO) is utilized to manage the severe uncertainties in wind power distribution. In sub-problem 2, based on the outcomes from sub-problem 1, we settle the energy and DR reserve trading prices. To ensure the fairness of benefit allocation, asymmetric Nash bargaining theory is applied to assess each IES's contributions in trading volumes and profit growth. Interval adaptive alternating direction method of multipliers (IA-ADMM) is used to preserve each IES's privacy. Finally, simulation results demonstrate that, compared with independent operation, cooperative trading among MIESs increases profits for all IESs, thereby motivating their participation in cooperative trading.

Research on integrated energy multi-body trading strategy under electricity market based on double-layer game

Abstract Driven by the energy revolution, the energy development method is developed in the direction of diversification, greening, and intelligence. To improve the efficiency of the interaction between integrated energy systems and electricity markets, a multi-constituent trading strategy for integrated energy under the power market based on a two-layer game is proposed, and an improved BAS algorithm is used to solve the two-layer model to obtain the optimal trading strategy. The optimal trading strategy is obtained by solving the two-layer model with the improved BAS algorithm. Taking an integrated energy system in a certain region as an example, three trading strategies are set up for the comparative analysis of the optimization results, and the results show that the proposed trading strategy based on the two-layer game model can effectively improve the interaction efficiency and interaction effectiveness between the system and the market and energy entities.

Numerical Methods and Computation in Financial Mathematics: Solving PDEs, Monte Carlo Simulations, and Machine Learning Applications

Abstract. This paper delves into three fundamental numerical methods and computational techniques in financial mathematics: Finite Difference Methods (FDM), Monte Carlo Simulations (MCS), and Machine Learning (ML) applications. Finite Difference Methods are widely utilized for solving partial differential equations (PDEs) in option pricing, with various schemes offering different stability and convergence properties. Monte Carlo Simulations provide a powerful approach for pricing complex derivatives and risk management, addressing the challenges of high-dimensionality and computational complexity. Machine Learning has revolutionized predictive modeling in finance, enabling sophisticated analysis of large datasets to uncover hidden patterns and enhance trading strategies. Through a detailed examination of these methods, including specific examples and data, this paper highlights their theoretical foundations, practical implementations, and the advancements they bring to computational finance. By bridging theoretical approaches with practical applications, we aim to offer insights into the future directions and challenges in financial mathematics.

Increasing revenues of offshore wind farms with co-located energy storage systems

Abstract Co-located onshore and offshore energy storage systems exhibit the potential of diversifying and increasing revenue streams of offshore wind farms. However, these systems should be designed and sized considering the power generation profile, the route to market of these assets, i.e. power trading strategy, and dynamics of the various segments of the power market. In this study, the feasibility of deploying subsea hydro-pneumatic storage systems was investigated. It was seen that if a 50MW capacity system with a storage duration of 4 hours is deployed in an offshore wind farm of 1000MW capacity, the wind farm can potentially participate in the day-ahead and ancillary services markets more confidently from a risk perspective. The relatively higher volatility in these markets presents an opportunity to improve economics of these offshore wind projects as long as costs can be kept under control.

US Funds’ returns-based ESG extraction and implementation: a multifaceted quantile regression approach

ABSTRACT This study introduces a novel ESG intrinsic-based return factor and its application in asset pricing. This factor is extracted using a parallelized rolling window estimation and extreme value-weighted quantile portfolios. It carries a positive risk premium, indicating that investors are willing to assess its risk exposure. We further show that higher returns can be obtained in the top 30% quantiles using a long-only trading strategy. We apply a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to explain US fund returns and address the needs of investors seeking to optimize their investment strategies. This model surpasses traditional benchmark models by performing deep quantile estimation and considering the nonlinear relationships between fund returns and six firm-based characteristics. This approach empowers investors by explaining the core principles of impact investing and highlighting how our constructed ESG risk factor can generate competitive returns even in volatile markets when its risk is well assessed. Highlights A sustainable smart beta factor construction is ensured ESG Long-only trading strategy grants higher returns in the top 30% quantiles Using ML, we further assessed the need to implement ESG factors into asset pricing Positive ESG risk premium reveals the investors’ interest in impact investing US market remains promising for positive ESG investment signals

An innovative method for short-term forecasting of blockchain cryptocurrency price

Cryptocurrency market sentiment is relatively unstable, which makes cryptocurrency price an attribute of high volatility. Accurate forecasting methods help to clarify the volatility trend of the cryptocurrency price, thereby reducing the investment risk of participants in the cryptocurrency market. Therefore, this research proposed a new method for short-term forecasting of the cryptocurrency price based on a small sample. This study took three typical blockchain cryptocurrencies (Bitcoin, Ethereum, Litecoin) as experimental objects, chose data intervals with different volatility trends in the U.S. stock indices between 2022 and 2023 as sample data, and used grey correlation analysis to select core affecting variables. Furthermore, this study built a grey multivariate convolution model with prioritized accumulating novel information for conducting prediction experiments on blockchain cryptocurrency price. The research findings demonstrate that the proposed model achieves high prediction accuracy in all experiments, and the model accuracy is superior to the comparison models. This study proposes a scientific prediction approach for blockchain cryptocurrency price, which can guide financial investors in developing and analyzing quantitative financial trading strategies to a certain extent. Meanwhile, this study provides a specific reference for relevant government departments to strengthen cryptocurrency regulation, prevent financial risks, and maintain financial stability.

Leveraging data mining and cybersecurity techniques to enhance algorithmic trading performance and forensic investigations in financial markets

In the evolving landscape of financial markets, the convergence of data mining, cybersecurity, and algorithmic trading plays a pivotal role in enhancing trading performance and forensic investigations. This study investigates how data mining techniques are leveraged to extract meaningful patterns and trends from vast financial datasets, improving the accuracy and profitability of algorithmic trading strategies. By identifying historical trends, price movements, and trade margins, data mining enables traders to optimize decision-making processes and manage risks more effectively. Cybersecurity emerges as a critical factor in safeguarding both trading algorithms and sensitive financial data from cyber threats. The integration of advanced cybersecurity measures ensures the integrity, confidentiality, and availability of trading systems, reducing vulnerabilities that could be exploited by malicious actors. Additionally, forensic investigation techniques are employed to detect fraudulent trading activities, such as insider trading and market manipulation, thereby protecting market participants and maintaining regulatory compliance. This research highlights the importance of combining secure data mining practices with robust cybersecurity measures to enhance the overall performance of algorithmic trading systems. Furthermore, it explores how forensic methodologies can help detect anomalies and ensure the transparency and fairness of financial markets. Through an integrated approach, this study emphasizes the potential of data mining and cybersecurity in transforming trading operations while mitigating risks associated with cyber threats and financial fraud.

A two-stage bi-level electricity-carbon coordinated optimization model for China's coal-fired power system considering variable renewable energy bidding

With China's grid-connected large-scale variable renewable energy, coal-fired power system is progressively changing from being the main supplier of electric energy to providing flexibility services. The coal-fired power system must increase operating efficiency during this transition process by considering the technical characteristics of various coal-fired power units. Additionally, the trading strategies of coal-fired power units in the electricity-carbon market are impacted differently depending on the extent of VRE penetration. This paper innovatively considers these two factors and proposes a two-stage bi-level clearing model in electricity-carbon joint market to coordinate the synergistic development of variable renewable energy and coal-fired power. The upper level of the model aims to maximize the total profits of the coal-fired power system and variable renewable energy units, while the lower level of the model minimizes the total purchasing costs. Coal-fired power units and variable renewable energy units compete to sell electric energy, with the latter buying peaking regulation from the former. The research results indicate that the proposed model can significantly reduce 66.79 % carbon emissions, increase 33.96 % non-fossil variable renewable energy integration and optimize the generation structure of coal-fired power system.

Research on International Trade Development Path of Small and Medium-sized Enterprises in the Context of E-commerce Trade

With the rapid development of Internet technology and the advancement of globalization, e-commerce has become an important part of international trade activities. In this context, how to expand the international market and enhance the competitiveness of small and medium-sized enterprises with the help of e-commerce trading platform has become a hot issue in current research. This paper first summarizes the development of e-commerce trade and its impact on the international trade of small and medium-sized enterprises (SMEs), and then analyzes the current situation and main problems of the international trade of small and medium-sized enterprises. On this basis, it puts forward the development path and strategy of SMEs' international trade under the background of e-commerce trade, including establishing e-commerce trade platform, strengthening supply chain management, utilizing cross-border e-commerce, strengthening brand building, and innovating service mode. This paper aims to provide theoretical support and practical guidance for SMEs to carry out international trade under the background of e-commerce trade.

From Algorithms to Market Dynamics: A Literature Review on High-Frequency Trading

Abstract: High-frequency trading (HFT) uses advanced technologies and sophisticated algorithms to make transactions at unprecedented speeds, which could significantly impact modern financial markets. This literature review examines the technological foundations, primary trading strategies, market impacts, regulatory environment, risk management practices, and future research directions in HFT. The findings reveal that HFT could increase market liquidity and price discovery but also increase volatility during periods of high market stress. Regulatory frameworks such as the SECs Market Access Rule and MiFID II aim to monitor and control HFT activities, at the same time effective risk management practices are crucial for maintaining market stability. Future research should focus on emerging technologies such as AI, ML, quantum computing, and blockchain, along with a better understanding of market structure and global regulatory coordination. This review provides valuable insights for market participants, regulators and researchers, contributing to a balanced perspective on HFTs role in contemporary financial markets.

Detecting anomalies in currency market of BRICS countries: insights into emerging economies

PurposeThe primary motive of the research paper is to investigate the efficiency of currency market of emerging economies (BRICS countries), by analyzing the presence and consistency of calendar anomalies – specifically the day-of-the-week (DOW) effect, the January effect and the Turn-of-the-month (TOM) effect. Daily data of around 23 years and 4 months were examined for the purpose of study. Segmented data of 8-year intervals were also analyzed to observe the changes in the pattern over a period. The findings are intended to offer insights into market behaviors and potential trading strategies for astute traders.Design/methodology/approachThe study employed a quantitative research design utilizing ordinary least squares regression with dummy variables and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) (1,1) model. To study the calendar anomalies, the daily closing price of five currency pairs has been taken from Bloomberg’s software. The Kruskal–Wallis test was conducted to validate and check the robustness of analysis.FindingsThere are mixed results regarding calendar anomalies in the BRICS currency markets. Analysis of aggregate data revealed that DOW effect is present in all the currencies except USDZAR, with Monday showing positive return. The segmented analysis showed the presence of DOW effect in USDCNY and USDINR, while other currencies lacked consistent patterns. The January effect was evident for Indian Rupees and Chinese Yuan but not for other currencies. TOM effect was present only in Chinese Yuan. Overall, the Indian Rupee and Chinese Yuan displayed inefficiencies, indicating potential for trading strategies, while other currencies appeared efficient and lacked consistent patterns.Originality/valueThe literature on currency pricing anomalies is sparse, particularly in the context of developing and underdeveloped economies. While some research exists on anomalies within the currencies of developed nations, there is a significant gap in studies focusing on emerging economies. This paper seeks to address this deficiency by investigating the presence of calendar anomalies in the currencies of BRICS countries, providing new insights into the behaviors of these emerging economies.

Assessing the Impact of Technology on the Efficiency and Effectiveness of Investment Banking Services

This paper evaluates the effect of innovation on the effectiveness and adequacy of venture managing an account administrations. It reflects on the way digital innovations such as artificial intelligence, blockchain, and data management systems have transformed such missions as securities underwriting, mergers and acquisitions, and client advisory services. By combining these technologies, investment banks optimize their processes, improve precision, and bring in enhanced customer experiences. Deliberating through critical review of literature and specific cases from the industry, this paper explores how artificial intelligence changes the decision-making processes, automates trading strategies, and enhances risk management, while blockchain changes back-office operations by making transactions faster and more secure. Advanced data management tools equally provide improved insight into the enhanced client services and decisions in investments for the banks. Although this brings cost-cutting and operational efficiency, these technologies pose challenges in cybersecurity and regulatory compliance due to the expansion of digital platforms, which provide increased opportunities for cyber-attacks and a need for strict adherence to ever-changing regulations. The paper provides a panoramic view of the current and future role of technology in evolving investment banking.

Maximizing profitability in integrated energy markets: A novel framework for multi-faceted energy trading

The emergence of Integrated Energy Systems (IESs) has shifted energy management from traditional electricity paradigms to a more diverse approach involving electricity, thermal energy, and natural gas. This study introduces a framework for an Integrated Energy Network (IEN) that enables simultaneous trading across these interconnected markets. The objective is to maximize the profitability of integrated energy companies by optimizing energy trading strategies. The proposed business strategy incorporates integrated load response, addressing constraints within electric distribution systems, heating networks, and natural gas pipelines. A three-stage process is used: information sharing in the unified energy market, energy conversion in hubs, and flexible load planning in the retail market. Flexible loads are categorized as displaceable, reducible, or transformable. A Conditional Value-at-Risk (CVaR) mean-variance model addresses market price volatility. Simulations on a test system validate the model, demonstrating its effectiveness in improving profitability and operational efficiency for integrated energy companies.

Do Financial Community’s Tweet Sentiments Affect the Stock Market? Evidence from an Emerging Economy

In the era of social media and internet, Twitter (presently ‘X’) has been recognized as the prominent factor for financial markets by multiple corporates and experts. The motivation of present research hinges on burgeoning popularity of Twitter in the financial community for sharing information and impact of their hidden sentiments on financial markets. Research on tweet-based sentiments and their influence on financial markets in India is still in the infancy stage. To cover this gap, the present study empirically investigates the predictive impact of financial tweet sentiments on the Indian stock Index’s returns over a short period. The paper scraped 22,816 tweets and Nifty 50 index’s intraday hour end values for January 2023. Using Natural Processing Technique, the overall sentiments of the financial community are found as highly positive for each trading hour. Next, it presents a low positive correlation between positive sentiments, sentiment polarity, and hourly returns. Lagged negative sentiments reflect the better ability to impact current period returns than positive sentiments and polarity. Additionally, sentiment polarity and returns reflect bi-directional causation for the future period impact. Investors, stock market traders, and other practitioners can employ the proven concept of linkage between tweet sentiments and returns for constructing effective investment and trading strategies. JEL Classifications: G41, D53, G17, G02, G19

ESG and Administrative Costs in Equity Trading: Evidence from China

Recent findings suggest that firms with higher Environmental, social, and governance (ESG) scores may experience lower stock returns, contrary to the common belief that better ESG performance enhances market reputation and stock returns. This study aims to investigate the relationship between ESG performance, management costs, and stock returns by introducing an "ESG-cost framework." The framework proposes that the costs incurred in implementing ESG practices can reduce revenue, offsetting the positive effects of strong ESG performance. Using an empirical analysis of firms based on both their ESG scores and management costs, the study finds that firms with low management costs gain the most from high ESG scores, while those with high costs may see diminished stock returns despite strong ESG performance. Additionally, the study proposes trading strategies that integrate ESG scores and cost considerations, demonstrating that these strategies yield better returns than traditional market indices. These findings offer a new perspective on ESG decision-making and provide valuable insights for constructing effective trading strategies.

Quantum-Inspired AI for Optimized High-Frequency Trading

This article explores the transformative role of quantum-inspired AI in optimizing financial practices, particularly within high-frequency trading (HFT) in the financial sector. As HFT operates in an environment of rapid transactions and significant market volatility, the need for advanced optimization techniques becomes paramount. Quantum-inspired algorithms leverage principles from quantum mechanics, such as superposition and tunneling, to enhance various aspects of trading strategies. These algorithms enable rapid optimization of asset allocation, real-time trade execution, and proactive fraud detection, effectively addressing the challenges posed by traditional financial models. By facilitating simultaneous evaluation of multiple strategies and enabling real-time analysis of complex trading patterns, quantum-inspired AI significantly improves decision-making speed and accuracy. The financial implications of this advancement are profound, leading to higher profitability, improved market integrity, and enhanced trust among market participants. Ultimately, integrating quantum-inspired AI in finance represents a crucial step towards harnessing cutting-edge technology to reshape trading dynamics, paving the way for innovative strategies that can adapt to the evolving landscape of financial markets. This study underscores the potential of quantum-inspired AI to redefine operational efficiency in finance, ensuring competitiveness in an increasingly complex trading environment.

Examining trade strategies in Europe and Ukraine: Navigating challenges and building economic strength

Today, the global community operates within an integrated global economic system. Against the backdrop of globalised integration of the socio-economic sphere, international trade is positioned as a significant factor in the intensification of economic development. In this context, trade strategies can create new opportunities or risks for the national economy. This article aims to conduct a comparative analysis of the trade strategies of the European Union (EU) and Ukraine in the context of potential advantages and related challenges for economic resilience. The study finds that international trade helps to mitigate the limitations of national resource potential and the narrowness of the domestic market, intensifies the potential for innovative industrialisation, and accelerates economic growth. The specifics of forming the modern concept of international trade development in the context of the European community and Ukraine are identified. The main principles of modern trade strategies are considered, their strengths and weaknesses are analysed, and directions for improvement are highlighted. The impact of trade strategies on the level of economic resilience is examined. It is substantiated that a high level of competition in the global market requires the adaptability of producers’ trade strategies and the formation of appropriate structures to coordinate the functioning of the external economic relations infrastructure complex. The main success factors of a trading strategy when entering new markets are highlighted, including preliminary research of local legislation requirements and market dynamics, the development of adaptive marketing strategies, and effective management of customs processes. Active participation in the international trade environment has proven to open additional opportunities for realising economic potential. At the same time, it is necessary to specify the development strategy to ensure long-term competitive advantages.