Profitable Trading Research Articles

Machine learning methods for financial forecasting and trading profitability: Evidence during the Russia–Ukraine war

PurposeThis study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine.Design/methodology/approachThis study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making.FindingsOn average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process.Practical implicationsInvestors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy.Originality/valueThis paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.

Optimizing Energy Arbitrage: Benchmark Models for LFP Battery Dynamic Activation Costs in Reactive Balancing Market

This study introduces a novel benchmark model for lithium iron phosphate (LFP) batteries in reactive energy imbalance markets, filling a notable gap by incorporating comprehensive operational parameters and market dynamics that are overlooked by conventional models. Addressing the absence of a holistic benchmark for energy-storage systems in electricity markets, this research focuses on the integration of LFP batteries, considering their unique characteristics and market responsiveness. Regression and regularization techniques, coupled with temporal cross-validation, were employed to ensure model robustness and accuracy in predicting energy trading outcomes. This methodological approach allows for a nuanced analysis of battery degradation, power capacity, energy content, and real-time market prices. The model, validated using Belgium’s system imbalance market data from the 2020–2023 period, incorporates both capital and operational expenditures to assess the economic and operational viability of LFP battery energy-storage systems (BESSs). The findings reveal that considering a broader range of operational parameters in energy arbitrage, beyond just the usual energy prices and round-trip efficiency, significantly influences the cost-effectiveness and performance benchmarking of energy storage solutions. This paper advocates for the strategic use of LFP batteries in energy markets, highlighting their potential to enhance grid stability and energy trading profitability. The proposed benchmark model serves as a critical tool for energy traders, providing a detailed framework for informed decision making in the evolving landscape of energy storage technologies.

On the Capital Market Consequences of Big Data: Evidence from Outer Space

Abstract We use the introduction of satellite coverage of major retailers to study the capital market implications of unequal access to big data. Satellite data enabled sophisticated investors with access to such data to formulate profitable trading strategies, especially by targeting the upcoming reports of retailers with bad news for the quarter. The introduction of satellite data led to more informed short-selling activity, less informed individual buying activity, and lower stock liquidity around the reports of retailers with satellite coverage. We conclude that unequal access to big data can increase information asymmetry among market participants without immediately enhancing price discovery.

ECHO-GL: Earnings Calls-Driven Heterogeneous Graph Learning for Stock Movement Prediction

Stock movement prediction serves an important role in quantitative trading. Despite advances in existing models that enhance stock movement prediction by incorporating stock relations, these prediction models face two limitations, i.e., constructing either insufficient or static stock relations, which fail to effectively capture the complex dynamic stock relations because such complex dynamic stock relations are influenced by various factors in the ever-changing financial market. To tackle the above limitations, we propose a novel stock movement prediction model ECHO-GL based on stock relations derived from earnings calls. ECHO-GL not only constructs comprehensive stock relations by exploiting the rich semantic information in the earnings calls but also captures the movement signals between related stocks based on multimodal and heterogeneous graph learning. Moreover, ECHO-GL customizes learnable stock stochastic processes based on the post earnings announcement drift (PEAD) phenomenon to generate the temporal stock price trajectory, which can be easily plugged into any investment strategy with different time horizons to meet investment demands. Extensive experiments on two financial datasets demonstrate the effectiveness of ECHO-GL on stock price movement prediction tasks together with high prediction accuracy and trading profitability.

ESG and aggregate disagreement

This paper investigates the role of aggregate disagreement in the relationship between environmental, social, and governance (ESG) scores and future stock returns in the United States (US), European Union (EU), and United Kingdom (UK). We find that firms with high ESG scores are likely to have higher exposure to aggregate disagreement than firms with low ESG scores because of the divergence of opinions about long-term earnings growth. Consistent with our conjecture, the results suggest that when aggregate disagreement is high, a profitable trading strategy is to long firms with low ESG scores and to short those with higher ESG scores. Our results have clear implications for the growing debate over ESG investment strategies.

Cryptocurrency anomalies and economic constraints

The asset pricing literature documents a growing list of predictable patterns in the cross-section of cryptocurrency returns. But can they be forged into viable trading profits? We answer this question by examining the interplay between economic restrictions and return predictability in cryptocurrency markets. We find that size and volume anomalies originate from micro-cap coins of negligible economic importance. Conversely, the momentum effect prevails in larger cryptocurrencies but incurs substantial trading costs and extracts alphas largely from short positions. Most abnormal returns occur primarily in bull markets and fade over time. Therefore, protocols for identifying tradable cryptocurrency anomalies should focus on long positions, account for transaction costs, consider hard-to-trade coins, and emphasize performance in recent years.

Profitability of exponential and simple moving average crossover strategy and the profitability of trend trading

The study focused on trading based on the signals of indicators, specifically examining a strategy using moving average crossovers. One of the main purposes of the paper was to analyse the efficiency differences between the simple and exponential moving average crossover strategies. Additionally, the study explored the impact of trend-following trading on the performance of indicators. The research tested the performance of indicators using the example of companies with the highest market capitalization. The main conclusion of the article is that there was no superior indicator in cases of the examined stocks. Furthermore, during periods of predominantly rising trends, the buy-and-hold strategy outperformed the active trading strategy based on indicators.

An intelligent stock trading decision system based on ensemble classifier through multimodal perturbation

Candlesticks are widely used as an effective technical analysis tool in financial markets. Traditionally, different combinations of candlesticks have formed specific bullish/bearish patterns providing investors with increased opportunities for profitable trades. However, most patterns derived from subjective expertise without quantitative analysis. In this article, combining bullish/bearish patterns with ensemble learning, we present an intelligent system for making stock trading decisions. The Ensemble Classifier through Multimodal Perturbation (ECMP) is designed to generate a diverse set of precise base classifiers to further determine the candlestick patterns. It achieves this by: first, introducing perturbations to the sample space through bootstrap sampling; second, employing an attribute reduction algorithm based on neighborhood rough set theory to select relevant features; third, perturbing the feature space through random subspace selection. Ultimately, the trading decisions are guided by the classification outcomes of this procedure. To evaluate the proposed model, we apply it to empirical investigations within the context of the Chinese stock market. The results obtained from our experiments clearly demonstrate the effectiveness of the approach.

Customer identity concealing and insider selling profitability: Evidence from China

This study investigates whether insiders exploit the information advantage arising from customer identity concealment to profit from selling their shares. We examine this issue in the context of China, given the voluntary nature of customer information disclosure there. We find that insider selling profitability is significantly greater when firms conceal customer identities than when they disclose them, especially when customer identities are more informative. We also find evidence of significant trading profitability for both insiders and their relatives, as well as both core and general executives. Furthermore, we find that the results are weaker for firms with more effective internal monitoring and those with more attention from sophisticated market participants but are stronger for firms with more information disclosure manipulation. These findings are consistent with the hypothesis that insiders opportunistically trade on customer-related private information rather than unintentionally sell shares. In additional tests, we find that insiders deliberately conceal customer identities in response to their personal trading incentives, indicating that insiders may increase their information advantage through strategic customer information disclosure. Overall, our research sheds light on the black box of information sources of insider trading from the perspective of supply chains and the insider trading incentives behind firms' concealment of customer identities.

On the design of searching algorithm for parameter plateau in quantitative trading strategies using particle swarm optimization

Quantitative trading, relying on diverse parameter combinations, is becoming increasingly the norm for trading strategies in financial investments. The performance of these strategies is intricately linked to these parameters. However, the performance on the training set after backtesting does not ensure success on a test set and may lead to overfitting. This study emphasizes enhancing stability and robustness in trading-strategy parameters by introducing a ’parameter plateau.’ Traditional brute-force methods for exploring high-dimensional parameter spaces can be intricate and time-consuming. To address this challenge, we present an efficient alternative that identifies stable and robust parameters by configuring parameter plateaus to mitigate overfitting risks. A step-by-step search algorithm is proposed to determine the optimal parameters, leveraging the power of particle-swarm optimization. In continuous, multi-dimensional solution spaces, particle-swarm optimization is invaluable for the swift and effective discovery of the desired solutions. Experiments underscore the substantial influence of the parameter plateau concept on parameter selection, highlighting the pivotal role of particle-swarm optimization in efficiently navigating complex solution spaces and thereby enabling the discovery of stable and profitable trading strategies.

Structural and Dynamic Changes in International Trade in Agricultural Products in Ukraine

The article is devoted to studying the structural and dynamic changes in international trade in agricultural products in Ukraine. The authors reveal the theoretical essence of international trade in agricultural products, considering export and import conditions in light of modern challenges. The assessment of the foreign trade regime is provided and the trends and impact of implementing the Agreement on Association between Ukraine and the EU on the development of Ukraine's foreign trade are established. It was found that, on one hand, the export orientation of raw materials to the EU crop production market has increased, while on the other hand, there remains a high import dependence on prepared food products, with a more diversified import structure. The peculiarities of key directions of Ukraine's agro-industrial market under martial law and its place in the global food security system are described. The article also examines the structure of Ukraine's trading partners, which have been revised since the beginning of the war. The peculiarities of cooperation between Ukraine and the EU in international trade in agricultural products are analysed. Ways of diversifying exports and increasing the profitability of foreign trade in agricultural products from Ukraine in the EU market are suggested.

The out-of-sample performance of carry trades

We carry out a large-scale investigation of the reliability of the profitability of carry trade strategies, using foreign exchange data for 48 countries over 36 years, employing reality check, superior predictive ability test, and stepwise tests to correct for data-snooping bias (the factor of luck in model selection). Carry trade strategies chosen as profitable in one period are generally not profitable in an ensuing out-of-sample sample period, especially after correcting for data-snooping and even after allowing for learning and stop-loss strategies. Any evidence of consistency in carry trade profitability that is found is concentrated in a relatively brief historical period, 1998-2005. We further investigate particular currency pairs that may drive the out-of-sample profitability during this period, and find their performance to be unstable in general. Our findings thus highlight the instability and the importance of chance or luck in generating profits from carry trades.

The pre-FOMC announcement drift: short-lived or long-lasting? Evidence from financial and volatility markets

ABSTRACT We examine the effects of Federal Open Market Committee (FOMC) announcements accompanied by press conferences (PC) on both financial and volatility markets. We find a pre-FOMC announcement drift, signifying positive excess returns on equities prior to the FOMC announcement. However, the influence of the FOMC-PC announcements is short-lived, becoming insignificant shortly after the disclosure. This contrasts with findings from Lucca and Moench (2015), who report that the FOMC announcement’s impact is persistent (or long-lasting) in their studied timeframe. Furthermore, we identify a significantly negative return of the VIX both before and after the FOMC announcement. This contrasts with the transient nature of stock returns, suggesting an evolving relationship between stock returns and VIX movements. To exploit the FOMC announcement drift, we construct a profitable trading strategy but note that such coexistence of the profitable trading strategy with the short lived impact by FOMC announcement raises a concern whether FOMC justifies a large price movement around FOMC announcement.

Battery degradation: Impact on economic dispatch

AbstractBatteries are crucial to manage the rising share of intermittent energy sources and variability in demand. Most technoeconomic models in the literature oversimplify battery degradation representation. Accounting properly for battery degradation allows for better cost tradeoffs and optimal battery usage, especially in dynamic settings. We propose a highly accurate and scalable formulation for battery degradation that considers the combined impact of cycle depth (CD) and state of charge on calendar and cycle aging. This includes a novel way to track charge‐discharge cycles. We test the consequences of battery degradation in a stylized price arbitrage model on battery operation and solution times. When ignoring battery degradation, ex post calculations reveal hidden degradation costs that exceed revenues and hence turn seemingly profitable trades into losing trades. Considering battery degradation leads to smaller CDs and lower average states of charge. Overall, we show that a much‐improved representation of battery degradation is possible at modest computational cost, allowing better decisions and higher profits.

Using Artificial Intelligence for Stock Price Prediction and Profitable Trading

Investing in the stock market is generally considered a safe and reliable way to protect and grow your savings over the long term. However, stock investments are risky and investing in the wrong companies can lead to financial loss. This research paper aims to see how the emerging field of Artificial Intelligence (AI) can be used to predict a stock’s future movement and capitalize on it by anticipating the movement and automatically buying or selling the stock, in an attempt to safely generate profit while minimizing risk. In this paper, a Multi-Layer Perceptron regressor model was trained to predict a stock’s future price using the stock’s previous prices, two industry competitors’ previous stock prices, and previous daily crude oil prices. The model was evaluated based on its average accuracy to the actual price in percent, and it was found that the model had an average 1.7% error on AAPL (Apple Inc) stock. An automated trading bot was then created and used the model’s daily output to perform a simulated backtest, where it was given a simulated $100,000 portfolio. Its performance was measured in multiple different trading scenarios. It was found that the model can reliably outperform stocks in fairly flat or downward market conditions and still profit on upward-moving stocks, but can still end up failing to profit if the underlying stock it was given drops in price significantly over the testing period.

Profitability of Pair Trading: A Case of Islamic Indices in Pakistan

The pair trading strategy under portfolio construction is one of the profitable strategies. This is first ever study to observe the pair trading strategy using Islamic Index KSE firms. Distance approach is applied in this study by taking firms under KMI-30 Islamic index by using daily data from year January 1, 2012 to year December 31, 2019. These firms are divided into three subcategories, high-cap, mid-cap and low-cap. After formation of pairs, trading algorithm under various parameterizations is used to observe the profitability. The study concluded with positive and significant returns of top 3, 5, 7 and 10 pairs for each category. In addition, this study also witnessed with positive returns after risk adjustment of market factor for the top 3, 5, 7, and 10 pairs of Islamic index’s firms. These results are accordance with theories of mean revision and market neutrality. The study is contributing to the literature regarding the profitability of pair trading in Islamic Indices firms in Pakistan. Policy Implication for Islamic fund managers and investors is recommended.

Adding double insurance to your investments: Evidence from the exchange rate market

The forecasting and trading of exchange rates is a prevalent and intricate undertaking within the realm of economics. While prior research has mostly concentrated on enhancing the accuracy of predictions through various methodologies, this study places greater emphasis on providing practical suggestions for effective trading. Hence, we propose a double insurance approach for exchange rate trading. Firstly, this study gathers time series data and textual information from various perspectives and markets. This enables us to generate point value forecasts by leveraging natural language processing and deep learning techniques. Simultaneously, this study employs an innovative interval forecasting method to track the upper and lower limits of daily exchange rate fluctuations. Finally, this study provides a novel trading method aimed at enhancing the possible financial gains associated with exchange rate fluctuations. The proposed methodology is validated through the utilization of exchange rates between the USD and four prominent currencies, namely CNY, JPY, EUR, and GBP. The findings indicate that the inclusion of economic variables and multi-platform sentiment indices enhances the precision of exchange rate prediction. Furthermore, the deep learning model (neural network model) exhibits superior performance compared to benchmark models (i.e. time series models and support vector regression). Furthermore, this study demonstrates that the implementation of the trading strategy with interval constraints significantly improves the profitability of exchange rate trading.

Reporting bias and feedback effect

AbstractStock prices often provide firm managers with new information that can be used in real decisions. Studies generally focus on the ex ante disclosure policy and show that the presence of market feedback crowds out firms' disclosure. We instead examine a manager's ex post biasing incentives and find that market feedback amplifies overreporting bias, but not necessarily underreporting bias, due to three interacting effects. First, the manager biases the report more with feedback since decreased information quality crowds in the speculator's private information acquisition and improves investment efficiency, regardless of the reporting scenario (the information rationing effect). Second, the manager biases more in the overreporting scenario and less in the underreporting scenario, because reporting more favorable information crowds in private information acquisition, as the speculator expects a higher subsequent investment and therefore higher trading profits (the investment scale effect). Third, market feedback influences reporting bias not only through the speculator's information acquisition but also directly through the market maker's pricing function. Specifically, the market maker decreases the price discount, as he expects that the manager may learn correct information from the price and may invest more efficiently. Expecting a lower price penalty in the presence of feedback, the manager biases more in the overreporting scenario and less in the underreporting scenario (the investment correction effect). Overall, our results suggest that granting firms reporting discretion could improve investment efficiency and firm value when managers can learn through price.

Asymmetric Efficiency: Contrasting Sustainable Energy Indices with Dirty Cryptocurrencies

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">This paper examines the efficiency, in its weak form, of the clean energy stock indices, Clean Coal Technologies, Clean Energy Fuels, and Wilderhill, as well as the cryptocurrencies classified as "dirty", due to their excessive energy consumption, such as Bitcoin (BTC), Ethereum (ETH), Ethereum Classic (ETH Classic), and Litecoin (LTC), from January 2020 to May 30, 2023. In order to meet the research objectives, the aim is to answer the following research question, namely whether: i) the events of 2020 and 2022 accentuated the persistence in the clean energy and dirty energy indices? The results show that clean energy indices such as digital currencies classified as "dirty" show autocorrelation in their returns; the prices are not independent and identically distributed (i.i.d). In conclusion, arbitrage strategies can be used to obtain abnormal returns, but caution is needed as prices can rise above their real market value and reduce trading profitability. This study contributes to the knowledge base on sustainable finance by teaching investors how to use forecasting strategies on the future values of their investments.</span></p>

Multi-level deep Q-networks for Bitcoin trading strategies

The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.