Effective Trading Strategies Research Articles

Dynamic model of currency exchange based on investor behavior

In the modern financial environment, cryptocurrencies have gained significant popularity, becoming an important element of the global economy and financial markets. The dynamic development of blockchain technologies and decentralized financial instruments fosters increased interest from both private investors and institutional players. However, the high volatility of cryptocurrencies and the complexity of the mechanisms behind their price formation necessitate a detailed study of these processes. This paper models cryptocurrency exchange operations, analyzing price formation influenced by buying, selling, and introducing new crypto coins to the market. The system simulates investor behavior with individual parameters: initial balances, risk profiles, and profit-driven trading strategies over a specified period. The model takes into account the psychological aspects of investor behavior, their reaction to changing market conditions, and the impact of external factors such as news and regulatory changes. Special attention is paid to analyzing the impact of adding additional quantities of coins to the exchange at a reduced price during peak cryptocurrency price values. This creates conditions for activating trading operations, increasing liquidity, and affecting overall market dynamics, particularly volatility and price fluctuation trends. The study shows how such interventions can be used to stabilize the market or stimulate its further growth. The analysis of the obtained data allows for detailed observation of changes in the cryptocurrency’s value over time, identifying patterns and trends. Using statistical and analytical methods, the impact of different investor strategies on their financial results and the overall market situation was investigated. This enables assessing how investor decisions-timing, trade volume, and market reactions-impact profits and market dynamics. The research emphasizes the importance of a deep understanding of market mechanisms and trading psychology and can serve as a basis for developing effective trading strategies on cryptocurrency exchanges. The obtained results may be useful for traders, financial analysts, and developers of algorithmic trading systems, contributing to increased efficiency and stability of cryptocurrency markets. Moreover, the findings of the work can be applied to improve regulatory approaches and policies regarding cryptocurrencies.

Exploring multifractality in African stock markets: A multifractal detrended fluctuation analysis approach

This paper investigates the multifractal behavior of the six largest African stock markets, including the Johannesburg, Casablanca, Botswana, Nigerian, Egyptian, and Regional Stock Exchanges. Despite the growing significance of these markets in the global economy, there is limited understanding of their underlying dynamics, particularly regarding their multifractal properties. This lack of knowledge raises concerns about the informational efficiency of these markets, as traditional models may not adequately capture the complexities of price movements. To achieve the goals of the study, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method is applied to capture the multifractal dynamics, and shuffling and phase randomization techniques are performed to identify the sources of the multifractality of the six African stock markets. The empirical results, derived from the generalized Hurst exponents, Rényi exponents, and Singularity spectrum, show that all six stock markets display multifractal behavior, characterized by irregular and complex price movements that vary across different scales and timeframes. Additionally, the study finds that both long-term correlations and heavy-tailed distributions contribute to the observed multifractality. Long-term correlations lead to persistent price trends, challenging the Efficient Market Hypothesis (EMH), while heavy tails increase market unpredictability by raising the likelihood of extreme events like crashes or booms. The findings have significant practical implications for stakeholders in African stock markets, enabling investors and portfolio managers to enhance risk assessment and develop effective trading strategies while helping market regulators improve efficiency and stability through appropriate policies. Financial institutions can also refine risk management frameworks to better account for extreme events.

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.

ECONOMIC ASSESSMENT OF THE ACTIVITY OF RETAIL INVESTORS ON THE STOCK MARKET

The article examines the activity of a retail investor on the stock market. Issues of economic assessment of their activity and problems of increasing its effectiveness are studied. The dynamics of the share of retail investors in the total volume of trading in the stock market is analyzed. The key factors of growth of retail investors' activity in the stock market are studied. It was determined that the share of retail investors in the volume of trades on the stock market has almost doubled. The factors that caused such a powerful dynamic were analyzed. Two factors such as available investment opportunities and empowerment through information are discussed in detail. An economic assessment of the effectiveness of the activities of retail investors was carried out. The reasons for the negative results of their work on the stock market have been revealed. The importance of developing specific investment goals before starting investment activities on the stock market and adapting investment strategies to them is determined. It is suggested to use paper trading simulators that reproduce the real trading environment. It has been found to allow investors to make virtual trades and track their performance in real time. It is recommended to engage more comprehensive analytical and research platforms that provide tools for charting and price research. This has been proven to allow investors to more effectively analyze market trends, identify potential economic opportunities, and develop effective trading strategies. Possibilities of minimizing transaction costs through the use of ETFs-shares of investment funds, which are often traded on the stock exchange, are considered. As a result of the conducted research, the possibilities of further scientific research on this topic in the direction of using the latest financial programs based on artificial intelligence and comprehensive assessment of the interaction of various sectors of the stock market with their help were considered.

A model based LSTM and graph convolutional network for stock trend prediction.

Stock market is a complex system characterized by collective activity, where interdependencies between stocks have a significant influence on stock price trends. It is widely believed that modeling these dependencies can improve the accuracy of stock trend prediction and enable investors to earn more stable profits. However, these dependencies are not directly observable and need to be analyzed from stock data. In this paper, we propose a model based on Long short-term memory (LSTM) and graph convolutional network to capture these dependencies for stock trend prediction. Specifically, an LSTM is employed to extract the stock features, with all hidden state outputs utilized to construct the graph nodes. Subsequently, Pearson correlation coefficient is used to organize the stock features into a graph structure. Finally, a graph convolutional network is applied to extract the relevant features for accurate stock trend prediction. Experiments based on China A50 stocks demonstrate that our proposed model outperforms baseline methods in terms of prediction performance and trading backtest returns. In trading backtest, we have identified a set of effective trading strategies as part of the trading plan. Based on China A50 stocks, our proposed model shows promising results in generating desirable returns during both upward and downward channels of the stock market. The proposed model has proven beneficial for investors to seeking optimal timing and pricing when dealing with shares.

Exploring Calendar Anomalies and Volatility Dynamics in Cryptocurrencies: A Comparative Analysis of Day-of-the-Week Effects before and during the COVID-19 Pandemic

This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market.

Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users’ electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally.

Optimal risk management considering environmental and climatic changes.

Climate change presents challenges to policy and economic stability, necessitating effective trading strategies to reduce environmental risks. This article addresses gaps in existing studies by using a Markov-switching model to consider climate risk. Backward stochastic differential equationsare used to optimize utility with three hedging strategies based on the concept of risk aversion. Numerical scenarios confirm the model's superiority in incorporating exogenous events, with our risk-averse strategy outperforming classical approaches. Our strategy outperforms classical strategies by taking a flexible risk trading when investors face risk-averse behavior due to climate risk events. The findings presented in this article have important implications for the development of more resilient investment portfolios and can contribute to climatepolicy.

A novel stock trading utilizing long short term memory prediction and evolutionary operating-weights strategy

In the realm of quantitative investing, predicting stock prices and developing effective trading strategies are paramount. The inherent randomness of the stock market, however, limits the precision of stock price predictions, thus undermining investment performance when these predictions are the sole basis for strategy. Nonetheless, forecasting future prices can offer invaluable insights for strategic models. When predictions are paired with well-designed strategies, they can augment investment returns. This paper proposes a novel approach to enhance investment returns by integrating Long Short Term Memory (LSTM) predictions with the Evolutionary Operating-weights (EOW) algorithmic strategy. The proposed method employs a multi-layer LSTM to forecast future stock prices, incorporating the predictions with real market data, subsequently deriving an operational strategy using the EOW algorithm. The results of this research indicate that this proposed methodology outperforms existing approaches.

РОЛЬ ТЕХНИЧЕСКОГО И ФУНДАМЕНТАЛЬНОГО АНАЛИЗА В РАЗРАБОТКЕ ТОРГОВЫХ СТРАТЕГИЙ НА РЫНКЕ КРИПТОВАЛЮТ

Using an empirical and comparative approach, this study examines the economic factors influencing price dynamics in the cryptocurrency market. Using content analysis methodology, two main methods of forecasting market prices necessary to create an effective trading strategy are studied. These methods are technical analysis, based on the study of price dynamics itself and on identifying certain patterns of price movements, and fundamental analysis, the meaning of which is to analyze the internal value of an asset and to study the factors of movement of cryptocurrency prices. Fundamental analysis also discusses the impact of news and technological changes on the value of a cryptocurrency. From a technical analysis perspective, some of the basic graphical tools needed for forecasting are discussed. This work is intended to highlight the need to use both types of analysis to develop a profitable trading strategy.

Trading Volume Activity Surrounding Earnings Releases: Evidence from Indonesia

The study investigates trading volume activities post-earnings announcement and their impact on abnormal returns. The objectives are to examine the changes in trading volume activities in response to earnings release and to measure the impact of the changes in trading volume activities on the cumulative average abnormal returns post-earning announcement. The study analyzes the trading volume response around the earnings announcement by comparing the ratio of trading volume activity (TVA) post-earnings announcements with the TVA during the estimation window. It reports an apparent increase in trading volume activity on day +2, indicating a slightly delayed reaction to the event's information. However, the findings indicate a significant relationship between cumulative abnormal returns and trading volume during the post-earning announcement abnormal return in a short period, following the semi-strong form of market efficiency. The study focuses on year-end announcements and does not evaluate interim announcements. The market response to interim data may vary and requires validation through an additional investigation. This implies that market traders should implement an effective trading strategy before the entire market reacts to the earnings announcement. Furthermore, the research findings underscore opportunities for policymakers to improve market infrastructure, hence augmenting the market's efficacy in conveying information pertaining to earnings releases.

Прогноз мировых цен нефти на основе монетарной политики Соединённых Штатов Америки

This study explores the critical realm of forecasting oil prices within the global energy resources market. The research is particularly timely as it provides new insights into the future growth prospects of Brent crude oil prices, which are increasingly influenced by the indicators of the monetary policy set forth by the USA Federal Reserve. The primary objective of this scientific endeavor is to predict Brent crude oil prices in response to shifts in the indicators of the US Federal Reserve's monetary policy. To fulfill this objective, the study systematically tackled several key tasks. These tasks encompassed a thorough examination of the theoretical foundations underpinning oil price forecasting, the meticulous compilation of statistical data related to the target indicators, and the application of specialized economic methodologies. Mul[1]tiple linear regression equations were formulated to establish the relationship be[1]tween Brent crude oil prices and the indicators of the US Federal Reserve's monetary policy. Furthermore, the study ventured into the prediction of these Federal Reserve indicators and arrived at substantive conclusions. The research methodology employed a range of general and specialized scientific techniques, encompassing analysis, synthesis, and statistical analysis. Notably, the scientific innovation of this study lies in the construction of multiple linear regres[1]sion equations to forecast Brent crude oil prices, hinging on key indicators of the USA Federal Reserve's monetary policy, including the effective federal funds rate, the M2 monetary aggregate, the US dollar index, the core personal consumption expenditure price index, and the US unemployment rate. Additionally, this study cal[1]culated an increasing trend equation for the M2 monetary aggregate in the United States. The practical implications of this research are profound, offering valuable insights for investors looking to craft effective trading strategies within the global energy resources market.

A Study on Technical Analaysis of Indian FMCG Sector

The Indian fast-moving consumer goods (FMCG) sector plays a crucial role in the country's economy, experiencing strong growth and catering to the needs of its vast population. This research aims to conduct a close examination of the Indian FMCG sector using technical analysis techniques in order to identify patterns, trends, and potential investment opportunities. Using a quantitative approach, this study employs various technical indicators and chart patterns to assess the price and volume movements of FMCG sector stocks listed on Indian stock exchanges. Historical price data and trading volumes are collected and analyzed to identify recurring patterns and trends that can assist investors in making well-informed decisions. The study covers a time period between 26/04/2021 to 02/06/2021, allowing for a comprehensive evaluation of the FMCG sector's performance and behaviour. Few technical analysis tools such as moving averages convergence and divergence (MACD), relative strength index (RSI), on balance volume (OBV), and trendlines are utilized. By applying these techniques, the study aims to identify potential signals for buying and selling, levels of support and resistance, and price targets within the Indian FMCG sector. Additionally, the research explores the impact of market trends, economic factors, and industry-specific events on the sector's performance. The findings of this study contribute to the existing knowledge in technical analysis and provide valuable insights for investors and traders interested in the Indian FMCG sector. The results enhance understanding of the sector's price movements, volatility, and potential investment opportunities, enabling market participants to develop effective trading strategies and risk management approaches. It is important to note that this study solely focuses on the technical analysis of the Indian FMCG sector and does not consider fundamental analysis or qualitative factors that may influence investment decisions. Therefore, readers are encouraged to combine the findings of this study with additional research and analysis before making investment choices.

Asynchronous Deep Double Dueling Q-learning for trading-signal execution in limit order book markets.

We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders. Based on the ABIDES limit order book simulator, we build a reinforcement learning OpenAI gym environment and utilize it to simulate a realistic trading environment for NASDAQ equities based on historic order book messages. To train a trading agent that learns to maximize its trading return in this environment, we use Deep Dueling Double Q-learning with the APEX (asynchronous prioritized experience replay) architecture. The agent observes the current limit order book state, its recent history, and a short-term directional forecast. To investigate the performance of RL for adaptive trading independently from a concrete forecasting algorithm, we study the performance of our approach utilizing synthetic alpha signals obtained by perturbing forward-looking returns with varying levels of noise. Here, we find that the RL agent learns an effective trading strategy for inventory management and order placing that outperforms a heuristic benchmark trading strategy having access to the same signal.

Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI

For highly time-constrained, very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations between authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull–bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors, while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull–bear words using a futures corpus detection method that extracts valuable information from financial news, allowing investors to quickly understand market trends. Generative AI models are trained to provide real-time bull–bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with random forest and SVMs achieving an impressive 80% accuracy rate. MLP and deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.

A decentralized peer-to-peer energy trading strategy considering flexible resource involvement and renewable energy uncertainty

With the widespread application of distributed power generation, traditional energy consumers are becoming active prosumers. Giving full play to the role of the electricity market in resource allocation, peer-to-peer (P2P) energy trading between multiple prosumers and consumers helps increase the revenue of electricity sales and reduce the cost of electricity consumption. The participation of flexible resources in P2P energy trading achieves local power complementarity, improves benefits, and reduces the operating pressure of lines. To ensure the benefits of prosumers and stimulate their enthusiasm, it is necessary to propose an effective trading strategy to fully allow flexible resources to participate in P2P energy trading. Therefore, this paper establishes a P2P energy market based on the continuous double auction (CDA) mechanism. Based on the model predictive control (MPC) theory, we propose a minimum cost rolling optimization model to optimize the bidding quantity of flexible resources. Moreover, the Automatic Learning (AL) pricing strategy with privacy and learning is proposed. The AL bidding price strategy enables interactive learning among prosumers. In addition, the influence of bidding price on bidding quantity during the trading process is also considered. Furthermore, this paper considers the impact of renewable energy source (RES) uncertainty on the bidding decisions of prosumers. Finally, the effectiveness of the proposed strategy is verified by the simulation results of cases. The simulation results demonstrate that the proposed strategy can improve the benefits for prosumers, enhance the local balance ability, achieve price learning, and increase the transaction rate.

A Study on Optimizing Risk and Return through Options Trading Strategies

Options offer elective systems for financial specialists to benefit from trading underlying securities. There are assortments of methodologies including diverse blends of options, underlying assets, and other derivatives. Fundamental Strategies includes purchasing calls, purchasing puts, selling covered calls and purchasing protective puts. There are points of interest to trading choices instead of underlying assets, for example, downside protection and leveraged returns, however there are additionally burdens like the necessity for upfront premium payment. Here in the report an endeavor is to make to build up the methodologies dispensing with those disadvantages through direct of nitty gritty investigation on Nifty Index. The premium expenses being pursued to be the major influencing factor of options market, an attempt is made to optimize the risk and return by developing the effective trading strategies for the option traders irrespective of market conditions with special reference to NIFTY Index. As a part of study suggestions are drawn for the investors to improve their trading strategies without undertaking great deal of risk. This paper analyses the use of different option trading strategies for optimizing risk and return in both bullish and bearish markets. A study on optimizing risk and return through developing effective index options trading strategies is an attempt to apply the theoretical knowledge’s application and exposure to practical world of derivative market.

RETRACTED: Machine learning-based probabilistic profitable model in algorithmic trading

Machine learning models are nowadays becoming ubiquitous in algorithmic trading and investment management. These models are mostly used in the pre-trade analysis phase to determine the buy or sell decisions using various machine learning techniques. We aim to implement a machine learning-driven approach using various technical indicators to predict stock market prices and then accordingly make a decision about buying or selling. First, an effective trading strategy is discussed that selects the potentially profitable stocks, and then the technical indicators such as simple moving average (SMA), exponential moving average (EMA), relative strength index (RSI), and moving average convergence divergence (MACD) are calculated for those potentially profitable stocks. Then supervised machine learning algorithms such as multiple linear regression, support vector machine regression, and decision tree regression are applied, where the close price of the stock is predicted using technical indicators for the next day, and based on that buy or sell signals are generated. The model is then tested on 12 different SNP500 stocks, one for every month in 2018, with the mean squared error (MSE) varying between 30.33 and 48.16 and the root MSE varying between 5.51 and 6.93, where the error is calculated on the difference in the number of days when the stock price actually increases and the predicted number of days for various models.

Optimal Trading Strategy Based on BP Neural Network and Dynamic Programming Model

In order to avoid the risks brought by blind investment, it is necessary to forecast the trading price of gold and bitcoin on that trading day before investment. To aim at this prediction problem, this paper puts forward the price prediction model of the BP neural network. The experimental results show that the model can reasonably predict the trading price of gold and bitcoin on the next trading day, and the relative error between them is less than 10%.However, predicting the trading price of gold and bitcoin can roughly determine the future direction, and it cannot provide scientific and reasonable trading strategies for traders, so it is necessary to design a trading strategy planning model. Considering that every trading activity except holding financial products will charge additional trade costs, the trade costs of gold and bitcoin are 1% and 2% of the trade amount, respectively. By comparing various classic trading models, we propose to use a dynamic programming model to provide traders with effective trading strategies, which takes the trading changes of gold and bitcoin in the next three trading days as decision variables. Meanwhile, it takes the highest value of the Sharp ratio in the next trading day as the objective function. It takes the proportion of each part as equal to or greater than 0 as the constraint condition to establish a trading strategy planning model to obtain the maximum profit.

Energy trading strategy for storage-based renewable power plants

Despite the continuous growth and the widespread support of renewable energy sources, solar and wind power plants pose new challenges for Transmission System Operators and Distribution System Operators. Their uncontrollability limits their applicability; therefore, to encourage their further growth, fundamental modifications are needed. The research presented in this paper focuses on the predictive control of storage-based renewable power plants, and suggests a new model for profit optimization. Profit optimization is based on electricity price prediction and effective trading strategies that match the projected electricity prices. For the electricity price prediction, a recurrent Long Short-Term Memory neural network was developed and fine-tuned. For the optimization of the electricity trading, two trading strategies, namely an adaptive gradient-descent method and a differential evolution method were developed. Both optimization techniques were tested on mathematical models of most commercially available hybrid inverter systems and one year of historical data of electricity prices. As a result, a novel model predictive control workflow and sizing guide is proposed, which may significantly increase the profit generated by the system.