Stock Index Spot-Futures Arbitrage Prediction Using Machine Learning Models.

With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot-futures arbitrage, relevant work is still rare. Furthermore, existing work is mostly retrospective, rather than anticipatory of arbitrage opportunities. To close the gap, this study uses machine learning approaches based on historical high-frequency data to forecast spot-futures arbitrage opportunities for the China Security Index (CSI) 300. Firstly, the possibility of spot-futures arbitrage opportunities is identified through econometric models. Then, Exchange-Traded-Fund (ETF)-based portfolios are built to fit the movements of CSI 300 with the least tracking errors. A strategy consisting of non-arbitrage intervals and unwinding timing indicators is derived and proven profitable in a back-test. In forecasting, four machine learning methods are adopted to predict the indicator we acquired, namely Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Long Short-Term Memory neural network (LSTM). The performance of each algorithm is compared from two perspectives. One is an error perspective based on the Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and goodness of fit (R2). Another is a return perspective based on the trade yield and the number of arbitrage opportunities captured. Finally, a performance heterogeneity analysis is conducted based on the separation of bull and bear markets. The results show that LSTM outperforms all other algorithms over the entire time period, with an RMSE of 0.00813, MAPE of 0.70 percent, R2 of 92.09 percent, and an arbitrage return of 58.18 percent. Meanwhile, in certain market conditions, namely both the bull market and bear market separately with a shorter period, LASSO can outperform.

Key Roles of Crypto-Exchanges in Generating Arbitrage Opportunities.

The evolving crypto-currency market is seen as dynamic, segmented, and inefficient, coupled with a lack of regulatory oversight, which together becomes conducive to observing the arbitrage. In this context, a crypto-network is designed using bid/ask data among 20 crypto-exchanges over a 2-year period. The graph theory technique is employed to describe the network and, more importantly, to determine the key roles of crypto-exchanges in generating arbitrage opportunities by estimating relevant network centrality measures. Based on the proposed arbitrage ratio, Gatecoin, Coinfloor, and Bitsane are estimated as the best exchanges to initiate arbitrage, while EXMO and DSX are the best places to close it. Furthermore, by means of canonical correlation analysis, we revealed that higher volatility and the decreasing price of dominating crypto-currencies and CRIX index signal bring about a more likely arbitrage appearance in the market. The findings of research include pre-tax and after-tax arbitrage opportunities.

Partial differential equation methods for stochastic dynamic optimization: an application to wind power generation with energy storage.

A mixed financial/physical partial differential equation (PDE) can optimize the joint earnings of a single wind power generator (WPG) and a generic energy storage device (ESD). Physically, the PDE includes constraints on the ESD's capacity, efficiency and maximum speeds of charge and discharge. There is a mean-reverting daily stochastic cycle for WPG power output. Physically, energy can only be produced or delivered at finite rates. All suppliers must commit hourly to a finite rate of delivery C, which is a continuous control variable that is changed hourly. Financially, we assume heavy 'system balancing' penalties in continuous time, for deviations of output rate from the commitment C Also, the electricity spot price follows a mean-reverting stochastic cycle with a strong evening peak, when system balancing penalties also peak. Hence the economic goal of the WPG plus ESD, at each decision point, is to maximize expected net present value (NPV) of all earnings (arbitrage) minus the NPV of all expected system balancing penalties, along all financially/physically feasible future paths through state space. Given the capital costs for the various combinations of the physical parameters, the design and operating rules for a WPG plus ESD in a finite market may be jointly optimizable.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'.

Managing dynamic epidemiological risks through trade.

There is growing concern that trade, by connecting geographically isolated regions, unintentionally facilitates the spread of invasive pathogens and pests - forms of biological pollution that pose significant risks to ecosystem and human health. We use a bioeconomic framework to examine whether trade always increases private risks, focusing specifically on pathogen risks from live animal trade. When the pathogens have already established and traders bear some private risk, we find two results that run counter to the conventional wisdom on trade. First, uncertainty about the disease status of individual animals held in inventory may increase the incentives to trade relative to the disease-free case. Second, trade may facilitate reduced long-run disease prevalence among buyers. These results arise because disease risks are endogenous due to dynamic feedback processes involving valuable inventories, and markets facilitate the management of private risks that producers face with or without trade.

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.

Optimal two-stage arbitraging and scheduling strategy for distributed energy resource aggregators.

Given the rapid development of the distributed energy resources (DER), involving DERs into the wholesale market under the market and renewable uncertainties to achieve economic benefits is necessary but challenging. In this work, an arbitraging strategy is proposed for DER aggregators that bridge DERs with the wholesale market through energy trading. Besides, a novel self-adaptive minimax regret (MMR)-based optimal offering model is proposed for the DER aggregator to handle the uncertainties in both renewable generations and market prices. Additionally, an exact and communication-free solution methodology is proposed to resolve the formulated optimization problem with high computational efficiency. In the numerical results, the proposed methods can achieve near-to-optimal profits. Moreover, the performance of the proposed approach remains satisfactory even if the environment becomes very volatile.

What (If Anything) is Wrong with High-Frequency Trading?

This essay examines three potential arguments against high-frequency trading and offers a qualified critique of the practice. In concrete terms, it examines a variant of high-frequency trading that is all about speed-low-latency trading-in light of moral issues surrounding arbitrage, information asymmetries, and systemic risk. The essay focuses on low-latency trading and the role of speed because it also aims to show that the commonly made assumption that speed in financial markets is morally neutral is wrong. For instance, speed is a necessary condition for low-latency trading's potential to cause harm in "flash crashes." On the other hand, it also plays a crucial role in a Lockean defense against low-latency trading being wasteful developed in this essay. Finally, this essay discusses the implications of these findings for related high-frequency trading techniques like futures arbitrage or latency arbitrage-as well as for an argument as to why quote stuffing is wrong. Overall, the qualifications offered in this essay act as a counterbalance to overblown claims about trading at high speeds being wrong.

A note on Insider information and its relation with the arbitrage condition and the utility maximization problem.

We prove that Theorem 4.16 in [1] is false by constructing a strategy that generates $ (FLVR)_{ \mathcal{H}(\mathbb{G})} $. However, we success to prove that the no arbitrage property still holds when the agent only plays with strategies belonging to the admissible set called buy-and-hold.

Dataset for Bitcoin arbitrage in different cryptocurrency exchanges.

Bitcoin market's efficiency and liquidity questions are being comprehensively analyzed in scientific literature. This dataset serves academics for deeper analysis of these topics as well as it gives relevant information for spotting and evaluating risks in the market. Moreover, practitioners can benefit from the dataset and use it to identify patterns in the market, discover potential earning capabilities, and create effective arbitrage trading strategies. This is the first publicly available dataset that provides unique arbitrage data about pairs of cryptocurrency exchanges. The raw dataset was received by the Bitlocus LT, UAB. Using dplyr, reshape2, plyr packages in R we transformed dataset to show the amount of arbitrage which could be earned in 13 different cryptocurrency exchanges from 2019-01-01 to 2020-04-01. We used this dataset to create matrices for each day from 2019-01-01 to 2020-04-01 in order to perform network analysis on Bitcoin arbitrage opportunities (Bruzge and Sapkauskiene [1]). However, this dataset is beneficial for other purposes such as the evaluation of market's seasonality and day of week effects. The dataset provides values in high-frequency intervals but it is possible to convert data to a suitable data format depending on the research question.

Statistical Arbitrage for Multiple Co-integrated Stocks.

In this article, we analyse optimal statistical arbitrage strategies from stochastic control and optimisation problems for multiple co-integrated stocks with eigenportfolios being factors. Optimal portfolio weights are found by solving a Hamilton-Jacobi-Bellman (HJB) partial differential equation, which we solve for both an unconstrained portfolio and a portfolio constrained to be market neutral. Our analyses demonstrate sufficient conditions on the model parameters to ensure long-term stability of the HJB solutions and stable growth rates for the optimal portfolios. To gauge how these optimal portfolios behave in practice, we perform backtests on historical stock prices of the S&P 500 constituents from year 2000 through year 2021. These backtests suggest three key conclusions: that the proposed co-integrated model with eigenportfolios being factors can generate a large number of co-integrated stocks over a long time horizon, that the optimal portfolios are sensitive to parameter estimation, and that the statistical arbitrage strategies are more profitable in periods when overall market volatilities are high. Supplementary Information: The online version contains supplementary material available at 10.1007/s00245-022-09838-3.

Potential revenue and breakeven of energy storage systems in PJM energy markets.

The operation in energy arbitrage markets is an attractive possibility to energy storage systems developers and owners to justify an investment in this sector. The size and the point of connection to the grid can have significant impact on the net revenue in transmission and distribution systems. The decision to install an energy storage system cannot be based only on the cost of the equipment but also in its potential revenue, operation costs, and depreciation through its life cycle. This paper illustrates the potential revenue of a generic energy storage system with 70% round trip efficiency and 1-14 h energy/power ratio, considering a price-taking dispatch. The breakeven overnight installed cost is also calculated to provide the cost below which energy arbitrage would have been profitable for a flow battery. The analysis of the potential revenue was performed for 13 locations within the PJM Real-time market. We considered hourly data of day-ahead and real-time locational marginal prices over 7 years (2008-2014). Breakeven installed cost per MW ranged from $30 (1 MW, 14 MWh, 2009) to $340 (1 MW, 1 MWh, 2008).

A financial market with singular drift and no arbitrage.

We study a financial market where the risky asset is modelled by a geometric Itô-Lévy process, with a singular drift term. This can for example model a situation where the asset price is partially controlled by a company which intervenes when the price is reaching a certain lower barrier. See e.g. Jarrow and Protter (J Bank Finan 29:2803-2820, 2005) for an explanation and discussion of this model in the Brownian motion case. As already pointed out by Karatzas and Shreve (Methods of Mathematical Finance, Springer, Berlin, 1998) (in the continuous setting), this allows for arbitrages in the market. However, the situation in the case of jumps is not clear. Moreover, it is not clear what happens if there is a delay in the system. In this paper we consider a jump diffusion market model with a singular drift term modelled as the local time of a given process, and with a delay θ>0 in the information flow available for the trader. We allow the stock price dynamics to depend on both a continuous process (Brownian motion) and a jump process (Poisson random measure). We believe that jumps and delays are essential in order to get more realistic financial market models. Using white noise calculus we compute explicitly the optimal consumption rate and portfolio in this case and we show that the maximal value is finite as long as θ>0. This implies that there is no arbitrage in the market in that case. However, when θ goes to 0, the value goes to infinity. This is in agreement with the above result that is an arbitrage when there is no delay. Our model is also relevant for high frequency trading issues. This is because high frequency trading often leads to intensive trading taking place on close to infinitesimal lengths of time, which in the limit corresponds to trading on time sets of measure 0. This may in turn lead to a singular drift in the pricing dynamics. See e.g. Lachapelle et al. (Math Finan Econom 10(3):223-262, 2016) and the references therein.

Navigating the factor zoo around the world: an institutional investor perspective.

The literature on cross-sectional stock return predictability has documented over 450 factors. We take the perspective of an institutional investor and navigate this zoo of factors by focusing on the evidence relevant to the practicalities of factor-based investment strategies. Establishing a sound theoretical rationale is key to identifying "true" factors, and we emphasize the need to recognize data-mining concerns that may cast doubt on the relevance of many factors. From a practical investment perspective, much of the factor evidence documented by academics may be more apparent than real. The performance of many factors is dependent on the inclusion of small- and micro-cap stocks in academic studies, although such stocks would likely be excluded from the real investment universe due to illiquidity and transaction costs. Nevertheless, a parsimonious set of factors emerges in equities and other asset classes, including currencies, fixed income, and commodities. These factors can serve as meaningful ingredients to factor-based portfolio construction.

SME response to major exogenous shocks: The bright and dark sides of business model pivoting.

Within this short commentary, we explore the notion of pivoting; following major exogenous shocks, firms often contemplate business model pivoting where they change product or service offerings to capitalise on emerging opportunities. We assess the potential bright and dark sides of pivoting for new and existing firms in regard to quality of opportunities, fit with current capabilities and potential costs. The extant literature suggests that two forms of opportunities exist, arbitrage and innovation. We discern that post-shock, new firms may be better positioned to pursue arbitrage opportunities, whereas existing firms should target innovation. Existing firms may have more complications when pursuing arbitrage due to resource embeddedness and stakeholder obligations, and have a greater ability to innovate with an established resource base. Conversely, new firms can capitalise on arbitrage due to lack of embeddedness, as arbitrage requires a significant investment in opportunity selection. In addition, we offer suggestions for future research in regard to the current pandemic and more broadly, exogenous shocks.

Neural networks and arbitrage in the VIX: A deep learning approach for the VIX.

The Chicago Board Options Exchange Volatility Index (VIX) is considered by many market participants as a common measure of market risk and investors' sentiment, representing the market's expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options on the S&P 500 index. While smaller deviations between implied and realized volatility are a well-known stylized fact of financial markets, large, time-varying differences are also frequently observed throughout the day. Furthermore, substantial deviations between the VIX and its futures might lead to arbitrage opportunities on the VIX market. Arbitrage is hard to exploit as the potential strategy to exploit it requires buying several hundred, mostly illiquid, out-of-the-money (put and call) options on the S&P 500 index. This paper discusses a novel approach to predicting the VIX on an intraday scale by using just a subset of the most liquid options. To the best of the authors' knowledge, this the first paper, that describes a new methodology on how to predict the VIX (to potentially exploit arbitrage opportunities using VIX futures) using most recently developed machine learning models to intraday data of S&P 500 options and the VIX. The presented results are supposed to shed more light on the underlying dynamics in the options markets, help other investors to better understand the market and support regulators to investigate market inefficiencies.

Insider information and its relation with the arbitrage condition and the utility maximization problem.

Within the well-known framework of financial portfolio optimization, we analyze the existing relationships between the condition of arbitrage and the utility maximization in presence of insider information. We assume that, since the initial time, the information flow is altered by adding the knowledge of an additional random variable including future information. In this context we study the utility maximization problem under the logarithmic and the Constant Relative Risk Aversion (CRRA) utilities, with and without the restriction of no temporary-bankruptcy. In particular, we show that the value of the insider information may be bounded while the arbitrage condition holds and we prove that the insider information does not always imply arbitrage for the insider by providing an explicit example.

Is medicines parallel trade 'regulatory arbitrage'?

Parallel trade (PT) is a phenomenon that takes place at the distribution level, when a patented product is diverted from the official distribution chain to another one where it competes as a parallel distributor. Although some research regards PT in Europe as a 'common' form of arbitrage, there are reasons to believe that it is a type of 'regulatory arbitrage' that does not necessarily produce equivalent welfare effects. We draw upon a unique dataset that contains source country records of parallel imported medicine sales to the Netherlands for one therapeutic group (statins), that accounts for 5 % of the market at the time of study and it faced no generic competition. We estimate precise differences in prices and statutory distribution margins for each source country/product and, examine whether they drive parallel import flows using a gravity specification and an instrumental variable strategy. Our findings reveal that parallel imports are driven by cross-country differences in statutory distribution margins in addition to price differences, consistently with the hypothesis of PT being a type of 'regulatory arbitrage'.

La revisión de los artículos científicos como procedimiento esencial para lograr calidad en la comunicación científica / Scientific articles revision as essential procedure to achieve quality in the scientific communication

La revisión de artículos científicos es importante en la comunicación científica. Es desarrollada por profesionales con experticia en la temática que se aborda. El propósito del estudio fue actualizar aspectos relacionados con la aplicación de guías o herramientas de la comunicación científica al desarrollo del rol de árbitro o revisor de las revistas biomédicas. Se realizó una revisión bibliográfica en Infomed (Scielo, MEDLINE, LILACS, Hinari, Pubmed, Pubmed Central y Biblioteca Virtual de Salud). Se emplearon las palabras claves: revisor, árbitro, arbitraje, revistas científicas, publicaciones científicas, guías, herramientas, comunicación científica; los operadores boleanos and y or. El 70 por ciento de los artículos seleccionados correspondió a los últimos cinco años y de estos el 75 por ciento a los últimos 3 años. Se utilizó como gestor bibliográfico la herramienta EndNote. Variadas guías y herramientas se han desarrollado para elevar la calidad de la comunicación científica en salud, como la declaración CONSORT, STROBE, TREND y otras. La iniciativa EQUATOR brinda acceso a muchos recursos importantes para la revisión de artículos científicos, propiciando un espacio para el intercambio, crecimiento y desarrollo a nivel mundial en este sentido. Los revisores, deben estudiar y difundir las guías que se desarrollan actualmente para elevar la calidad de cada uno de los diseños de investigación. El rol de los editores y revisores es fundamental para la preservación ética del escrito médico, de su actuación profesional depende la calidad de la revista científica a la que prestan servicio.

Revising the scientific articles is important for the scientific communication, generally developed by professionals with expertise in the treated theme. The purpose of this study was actualizing aspects related with the application of scientific communication guidelines or tools for the development of the role of arbiter or reviser of works for the biomedical journals. We carried out a bibliographic review in Infomed (Scielo, MEDLINE, LILACS, Hinari, Pubmed, Pubmed Central y Biblioteca Virtual de Salud). We used the key words: reviser, arbiter, arbitrage, scientific journal, scientific publications, guidelines, tools, scientific communication, the Boolean operators and y or. 70 percent of the selected articles corresponded to the last five years, and 75 percent of them to the last 3 years. As bibliographic manager we used EndNote tool. Several guidelines and tools have been developed to raise the quality of the scientific communication in health, like CONSORT, STROBE, TREND declaration and others. The initiative EQUATOR gives access to many important resources for revising scientific articles, creating a space for the interchange, growth and development at the world level in this sense. Revisers should study and spread the guidelines currently developed to increase the quality of each of the research designs. The editors’ and revisers’ role is important for ethical preserving medical written work; the quality of the scientific journal where they work depends on their professional performance.

A evolução recente do espaço financeiro no Brasil e alguns reflexos na cena política / Recent trends in the financial field in Brazil and some impacts on the political scenario

During the last decade Brazil has witnessed the expansion and differentiation of its financial field, with a major impact on society and the composition of its elites. I analyze this process based on data concerning the new players and the instruments they disseminate in companies and other organizations in Brazilian society. These include both financial instruments per se and organizational tools based on the same logic. I seek to demonstrate that the quest for legitimacy for new players and instruments has led to a new cultural judgment on what constitutes societys "general interest", which drastically constrains action by the different governments and partially explains the paradoxes faced by the Lula Administration in its first year.

Dans la dernière décennie, le Brésil a connu un essor et des changements dans son espace financier qui ont provoqué un grand impact dans la société et la composition de ses élites. On examine ici ce processus à partir de données concernant les nouveaux acteurs et les instruments qu'ils diffusent dans les entreprises et autres organisations de la société brésilienne. Leurs instruments sont non seulement d'ordre directement financier mais aussi d'ordre organisationnel inspirés par la même logique. On cherche à montrer que la quête de légitimité des nouveaux agents et instruments finit par créer un nouvel arbitraire culturel à propos de ce qui est "l'intérêt général" de la société; ce facteur, qui gêne fortement l'action des différents gouvernements, saurait expliquer, en partie, les paradoxes auxquels le gouvernement Lula doit faire face dans sa première année d'exercice.