Arbitrage Trading Research Articles

An ETF-based measure of stock price fragility

Equity mutual fund flows are commonly employed to measure stock price fragility - a stock’s exposure to non-fundamental demand risk. However, this approach may be biased by confounding fundamental information, potentially underestimating risk exposure. We propose an alternative method that uses the primary market data of exchange-traded funds (ETFs). This approach overcomes many limitations of mutual fund data, incorporates the influence of a broader set of investor demand, and strongly predicts stock return volatility and return comovement. Our study highlights the significant role that the arbitrage trading activity of ETFs play in signaling non-fundamental demand shocks.

Impact of arbitrage trading between an ETF and its underlying assets on market liquidity of their markets using an agent-based simulation

Impact of arbitrage trading between an ETF and its underlying assets on market liquidity of their markets using an agent-based simulation

An LSTM-based optimization algorithm for enhancing quantitative arbitrage trading.

Arbitrage trading is a common quantitative trading strategy that leverages the long-term cointegration relationships between multiple related assets to conduct spread trading for profit. Specifically, when the cointegration relationship between two or more related series holds, it utilizes the stability and mean-reverting characteristics of their cointegration relationship for spread trading. However, in real quantitative trading, determining the cointegration relationship based on the Engle-Granger two-step method imposes stringent conditions for the cointegration to hold, which can easily be disrupted by price fluctuations or trend characteristics presented by the linear combination, leading to the failure of the arbitrage strategy and significant losses. To address this issue, this article proposes an optimized strategy based on long-short-term memory (LSTM), termed Dynamic-LSTM Arb (DLA), which can classify the trend movements of linear combinations between multiple assets. It assists the Engle-Granger two-step method in determining cointegration relationships when clear upward or downward non-stationary trend characteristics emerge, avoiding frequent strategy switches that lead to losses and the invalidation of arbitrage strategies due to obvious trend characteristics. Additionally, in mean-reversion arbitrage trading, to determine the optimal trading boundary, we have designed an optimized algorithm that dynamically updates the trading boundaries. Training results indicate that our proposed optimization model can successfully filter out unprofitable trades. Through trading tests on a backtesting platform, a theoretical return of 23% was achieved over a 10-day futures trading period at a 1-min level, significantly outperforming the benchmark strategy and the returns of the CSI 300 Index during the same period.

Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.

Price Delay and Market Efficiency of Cryptocurrencies: The Impact of Liquidity and Volatility during the COVID-19 Pandemic

The rise of cryptocurrencies as alternative financial investments, with potential safe-haven and hedging properties, highlights the need to examine their market efficiency. This study is the first to investigate the combined impact of liquidity and volatility features of cryptocurrencies on their price delays. Using a wide spectrum of cryptocurrencies, we investigate whether the COVID-19 outbreak has affected market efficiency by studying price delays to market information. We find that as liquidity increases and volatility decreases, cryptocurrencies demonstrate stronger market efficiency. Additionally, we show that price delay differences during the COVID-19 outbreak increase with higher levels of illiquidity, particularly for highly volatile quintiles. We suggest that perceived risks and high transaction costs in illiquid and highly volatile cryptocurrencies reduce active traders’ willingness to engage in arbitrage trading, leading to increased market inefficiencies. Our findings are relevant to investors, aiding in improving their decision-making processes and enhancing their investment efficiency. Our paper also presents significant implications for policymakers, emphasizing the need for reforms aimed at enhancing the speed at which information is incorporated into cryptocurrency returns. These reforms would help mitigate market distortions and increase the sustainability of cryptocurrency markets.

Criticism of the short-term event analysis method in evaluating the effectiveness of mergers and acquisitions

The article presents the analysis of the contemporary theoretical and empirical research devoted to evaluation of the effectiveness of mergers and acquisitions by means of short-term event analysis method, managers’ motivation in making decisions on the acquisition of other companies from the perspective of the timing of the expected effect; it adduces and systematizes the limitations of the cumulative abnormal returns (or CARs) method. The authors carry out scientific synthesis of the limitations of the method of capabilities of accurate prediction of the final results of transactions. In particular, the following problems of the CARs method have been identified: determining the date of the event, determining the event window, inconsistencies in research results, outflow of funds from purchasing companies during mergers and acquisitions, arbitrage trading strategies, the lack of long-term predictive value of the method of accumulated excessive profitability, efficient market hypotheses, overcharging of the purchasing company shares. The authors have made a conclusion about possible inaccuracy of the method if used to evaluate the effectiveness of mergers and acquisitions.

Multi-Use Optimization of a Depot for Battery-Electric Heavy-Duty Trucks

Battery-electric trucks offer a high battery capacity and good predictability, making them attractive for the implementation of bidirectional charging strategies. Nevertheless, most of the previous charging strategy studies focus on electric passenger cars. These charging strategies are usually formulated as separate use cases like tariff-optimized charging, arbitrage trading, peak shaving, and self-consumption optimization. By combining different use cases, their economic potential can be increased. In this paper, we introduce a model to optimize charging processes in depots for electric vehicles considering the combination of different use cases. This model is applied to a depot for battery-electric trucks. The savings obtained through optimized bidirectional charging highlight the enormous potential of this technology for the future, especially in the heavy-duty sector.

Arbitrage and non-linear taxes

AbstractIncorporating a progressive income tax into an economic decision problem raises the question whether this tax does not create arbitrage opportunities. We investigate this problem in a riskless (multi-period) economy. With a convex tax function we identify a particular kind of arbitrage (called bounded arbitrage): In this case the gain achievable through arbitrage trade is limited and cannot reach infinity.We are able to give a complete characterisation based on prizes of the traded assets as to whether bounded as well as unbounded arbitrage opportunities will exist.

The Impact of Different Trading Strategies on the Same Portfolio

The frequent occurrence of political events, the spread of the epidemic and changes in policies of various countries have made the market more volatile. Due to the instability of the overall economic environment, investors are increasingly focusing on using strategies to improve returns, so this article selects several hedge fund trading strategies, analyzes the same trading portfolio, and compares the performance of different trading portfolios. Different effects of the same combination give spirits to investors when making transactions. The market neutral trading strategy is effective for the trading combination of F and NVDA. Based on different data, the long-short equity trade strategies are not completely effective, and the credit arbitrage trade strategy is not suitable for this trading combination. Market neutral trading strategies and long-short equity trade strategies basically meet the combination of F and NVDA, but due to the possibility of credit rating fluctuations, credit arbitrage trade strategy does not meet this combination. These different effects reflect some of the underlying factors in the market, so the analysis in this article will improve investors' understanding of investment strategies and change their previous trading thoughts. Taking these effects into consideration can help investors effectively avoid potential risks, make better choices, or obtain more returns.

Does investors’ site visits improve the capital market pricing efficiency?

This article empirically tested the impact of investors' site visits on capital market pricing efficiency. Leveraging the data from 2009 to 2022 of Shenzhen Stock Exchange's listed companies, we found that: (1) Investors' site visits could reduce stock price synchronicity, indicating improved pricing efficiency of the capital market. (2) The mechanisms tests showed that: investors could obtain private incremental information through site visits. (3) The conclusions remained intact after robust tests, including alternative key variables, samples and specifications, and various endogenous treatments. (4) Investors added arbitrage trading behavior after the site visit without significantly impounding industry-level information into the stock price. This paper enriched the knowledge of how site visits of different investment entities affect capital market pricing efficiency.

Increasing the lifetime profitability of battery energy storage systems through aging aware operation

Lithium-ion cells are subject to degradation due to a multitude of cell-internal aging effects, which can significantly influence the economics of battery energy storage systems (BESS). Since the rate of degradation depends on external stress factors such as the state-of-charge, charge/discharge-rate, and depth of cycle, it can be directly influenced through the operation strategy. In this contribution, we propose a model predictive control (MPC) framework for designing aging aware operation strategies. By simulating the entire BESS lifetime on a digital twin, different aging aware optimization models can be benchmarked and the optimal value for aging cost can be determined. In a case study, the application of generating profit through arbitrage trading on the EPEX SPOT intraday electricity market is investigated. For that, a linearized model for the calendar and cyclic capacity loss of a lithium iron phosphate cell is presented. The results show that using the MPC framework to determine the optimal aging cost can significantly increase the lifetime profitability of a BESS, compared to the prevalent approach of selecting aging cost based on the cost of the battery system. Furthermore, the lifetime profit from energy arbitrage can be increased by an additional 24.9% when using the linearized calendar degradation model and by 29.3% when using both the linearized calendar and cyclic degradation model, compared to an energy throughput based aging cost model. By examining price data from 2019 to 2022, the case study demonstrates that the recent increases in prices and price fluctuations on wholesale electricity markets have led to a substantial increase of the achievable lifetime profit.

The dynamic effects of monsoon rainfall shocks on agricultural yield, wages, and food prices in India*

AbstractThis paper shows that, first, the effects of monsoon rainfall shocks on agricultural yield in India are highly asymmetric: yield falls strongly after droughts, whereas excessive rainfall has only little effects. Second, our key novel finding is that the short‐lived yield loss after a widespread drought elicits a persistent decline (increase) in wages (food prices), which lasts for up to five years. Third, affiliation to the same National Sample Survey region (and thus to the same state) seems to be a key determinant of internal migration, whereas distance appears more relevant for food arbitrage trading.

Explainable AI via learning to optimize

Indecipherable black boxes are common in machine learning (ML), but applications increasingly require explainable artificial intelligence (XAI). The core of XAI is to establish transparent and interpretable data-driven algorithms. This work provides concrete tools for XAI in situations where prior knowledge must be encoded and untrustworthy inferences flagged. We use the “learn to optimize” (L2O) methodology wherein each inference solves a data-driven optimization problem. Our L2O models are straightforward to implement, directly encode prior knowledge, and yield theoretical guarantees (e.g. satisfaction of constraints). We also propose use of interpretable certificates to verify whether model inferences are trustworthy. Numerical examples are provided in the applications of dictionary-based signal recovery, CT imaging, and arbitrage trading of cryptoassets. Code and additional documentation can be found at https://xai-l2o.research.typal.academy.

Law of one price and return on Arbitrage Trading: Bitcoin vs. Ethereum

Law of one price and return on Arbitrage Trading: Bitcoin vs. Ethereum

Reinforcement learning with dynamic convex risk measures

AbstractWe develop an approach for solving time‐consistent risk‐sensitive stochastic optimization problems using model‐free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time‐consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules that aid in obtaining optimal policies. We further develop an actor–critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to three optimization problems: statistical arbitrage trading strategies, financial hedging, and obstacle avoidance robot control.

A hybrid forecasting model based on deep learning feature extraction and statistical arbitrage methods for stock trading strategies

AbstractThe time series data of financial markets are nonlinear, owing to rapid data accumulation. Thus, research on stock price prediction has always been a challenge. This study proposes a quantitative trading strategy that combines basic quantitative trading rules and deep learning methods to help investors realize arbitrage. We combine basic quantitative trading arbitrage with deep learning frameworks to fully extract market characteristics and develop trading strategies for investors. The hybrid forecasting model is a new signal‐trading system that uses a genetic algorithm to obtain optimal parameters for the technical indicator timing method of the moving average price. The deep learning structure of the CNN‐Bi‐LSTM, with the attention mechanism and modified loss function, optimizes the trading signal to achieve local optimization. Its core concept is to determine the trading signal through the local trend of price fluctuations and then correct the trading signal through the prediction results. A‐shares in the Chinese market trading data are used as the statistical arbitrage analysis process to output actual trading signals and verify the effectiveness of the system. The results demonstrate that an arbitrage strategy based only on moving average trading rules is ineffective. With the optimization of the deep learning CNN‐Bi‐LSTM framework, the arbitrage ability improves significantly. The optimized strategy can increase the final profit by 1.6042 to the greatest extent. The annualized revenue increased by 35.16%, and the winning rate increased by 15.22%. In addition, we consider the transaction costs during the simulated transaction process. An optimized trading strategy can effectively seize arbitrage opportunities; hence, its profitability and stability are significantly improved.

The nexus among maxing out effect and idiosyncratic volatility premium puzzle in China’s A Share Market under the background of eco-innovation and sustainability

The paper investigates whether eco-innovation, sustainability, and asset pricing effect of maximum daily return (maxing out effect), truly and steadily exists in A share market of China and the nexus among maxing out effect and idiosyncratic volatility in China’s A Share Market. Regression analyses were conducted using the data of listed companies in China’s A share market from July 1993 to May 2021. The empirical results show that the maxing out effect, eco-innovation and sustainability steadily exists in a sample country. In practice, this research help to improve asset pricing efficiency, provide high-quality materials for investor education, guide arbitrage trading activities, and improve resource allocation efficiency in the stock market.

Research on a Prediction Model and Influencing Factors of Cross-Regional Price Differences of Rebar Spot Based on Long Short-Term Memory Network

In this paper, taking rebar steel as an example, we study the causes and influencing factors of spot price differences in rebar steel in different regions, and put forward a prediction model of rebar steel regional price differences based on the spot price of rebar from 2013 to 2022, supply and demand, cost, macroeconomics, industrial economic indicators, and policy data. Through correlation analysis, we consider all influencing factors step by step, select indicators with high correlation to add to the model, and select the optimal combination of influencing factors by comparing the results of five groups of experiments. Using the long short-term memory network, we predict the weekly spot price differences of rebar in different regions. Based on the historical-price time series, the optimal time window setting is given as the final price difference prediction model. The experimental results show that the prediction model of rebar spot price differences can support a 72.3% effective trading rate by combining the influencing factors with the LSTM model. This study has a guiding role for spot trading and can help spot enterprises, determine arbitrage trading strategies based on the prediction results, obtain sustainable returns under low risk, and realize the maximization of cross-regional arbitrage.

Statistical arbitrage trading across electricity markets using advantage actor–critic methods

In this paper, risk-constrained arbitrage trading strategies that exploit price differences arising across short-term electricity markets, namely day-ahead (DAM), continuous intraday (CID) and balancing (BAL) markets, are developed and evaluated. To open initial DAM positions, a rule-based trading policy using DAM and CID price forecasts is proposed. DAM prices are predicted using both technical indicator features and data augmentation methods, such as autoencoders and generative adversarial networks. Meanwhile, CID prices are predicted using novel features that are engineered from the limit order book. Using the forecasts, the direction of price movements is correctly predicted the majority of the time. To manage open DAM positions while optimising the risk-reward ratio, deep reinforcement learning agents trained using the advantage actor–critic algorithm (A2C) are employed. Evaluated across Dutch short-term markets, A2C yields profits surpassing those obtained using A3C and other benchmarks. We expect our study to benefit electricity traders and researchers who seek to develop state-of-art intelligent trading strategies.

Anomaly Discovery and Arbitrage Trading

AbstractWe analyze a model in which an anomaly is unknown to arbitrageurs until its discovery, and test the model implications on both asset prices and arbitrageurs’ trading activities. Using data on 99 anomalies documented in the existing literature, we find that the discovery of an anomaly reduces the correlation between the returns of its decile-1 and decile-10 portfolios. This discovery effect is stronger if the aggregate wealth of hedge funds is more volatile. Finally, hedge funds increase (reverse) their positions in exploiting anomalies when their aggregate wealth increases (decreases), further suggesting that these discovery effects operate through arbitrage trading.