Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators

Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark

Social Machines: How Recent Technological Advances have Aided Financialisation

In recent years, financial markets have been fundamentally transformed by innovations in information technology, in particular with regard to the web, social networks, high-speed computer networks and mobile technologies. We borrow the concept of Social Machines from Web Science as a single concept that captures the essence of all these recent technological changes to argue that the emergence of these Social Machines has aided the transformation of financial markets and society. This study explores the formation of these Social Machines with three sample disruptive technologies – automated/high-frequency trading, social network analytics and smart mobile technology. Through critical reflective analysis of these three case studies, we assess the impact of information technology innovation on financialisation. We adopt three case studies – automated trading; market information extraction using social media technologies; and information diffusion and trader decision-making with mobile technology on financial and real sector changes – which demonstrate the increasing trend of transaction velocity, speculative trading, increased complex information network, accelerated inequality and leverage. Our findings demonstrate that technologically enabled financial Social Machines harness crowd wisdom, engage disparate individual traders to produce more accurate price estimations, and have enhanced decision-making capability. However, these same changes can also have a simultaneously detrimental effect on financial and real sectors, in some situations exacerbating underlying distortions, such as misinformation due to complex information networks, speculative trading behaviour, and higher volatility with transaction velocity. Overall, we conclude that these innovations have transformed the fundamental nature of key aspects of the finance industry and society as a whole

Statistical properties of volume and calendar effects in prediction markets

Prediction markets have proven to be an exceptional tool for harnessing the "wisdom of the crowd", consequently making accurate forecasts about future events. Motivated by the lack of quantitative means of validations for models of prediction markets, in this paper we analyze the statistical properties of volume as well as the seasonal regularities (i.e., calendar effects) shown by volume and price. To accomplish this, we use a set of 3385 prediction market time series provided by PredictIt. We find that volume, with the exception of its seasonal regularities, possesses different properties than what is observed in financial markets. Moreover, price does not seem to exhibit any calendar effect. These findings suggest a significant difference between prediction and financial markets, and offer evidence for the need of studying prediction markets in more detail.<br/

More heat than light:Investor attention and bitcoin price discovery

We investigate how increased attention affects bitcoin’s price discovery process. We first decompose bitcoin price into efficient and noise components and then show that the noise element of bitcoin pricing is driven by high levels of attention. This implies that high levels of attention are linked with an increase in uninformed trading activity in the market for bitcoin, while informed trading activity is driven by arbitrage rather than attention

The Brexit vote and currency markets

This paper studies the effect of the Brexit vote on the intraday correlation and volatility transmission among major currencies. We find that the vote causes an increase in the correlation among the safe-haven currencies of the Swiss franc and Japanese yen as well as gold, and also find a decrease in their correlation with the directly involved currencies of British sterling and the Euro. These changes are due to the appreciation of the former group and the depreciation of the latter group which represents a flight to quality of investors. We also observe a substantial decrease in volatility transmission between British sterling and the Euro following the Brexit vote due to lower levels of market integration. However the volatility transmission among the currencies has increased in general and their net spillover is positively correlated with their level of volatility and trading activities. Therefore we document the significant impact of the politically important Brexit vote on the high frequency correlation and volatility spillover in the foreign exchange market

Estimating the impact of the Internet of Things on productivity in Europe

Funding statement This work was supported by the EU/FIRE IoT Lab project – STREP ICT-610477.Peer reviewedPublisher PD

Stock-ADR Arbitrage: Microstructure Risk

This paper is the first to highlight that the stock-ADR arbitrage pair trading found by Alsayed and McGroarty (2012) is directly influenced by the market microstructure of ADRs. In Alsayed and McGroarty (2012) they are the first to demonstrate that arbitrage opportunities exist between stocks and their ADRs, through convergence pairs trading. Given that such arbitrage opportunities exist, we pose the question as to why such pair trades occur, rather than be eliminated by the law of one price? Using high frequency data over a 3 year sample period, with over 3.7 million 1-min observations, we investigate stock-ADR arbitrage pair trading. In this paper, we find pair trading returns exhibit substantial asymmetry in returns: pair trades involving ADR shorts (compared to stock shorts) have significantly less probability of loss, substantially higher returns but higher convergence risk. The asymmetric results are consistent with the market microstructure of ADR trading, specifically the sourcing of ADRs. Whilst long and short stocks can be easily sourced from the relevant markets, long and short ADR sourcing is less viable due to the market microstructure, but also, ADR’s microstructure directly impacts the stock’s price. We test our microstructure hypothesis further for robustness, with respect to specific investor types (such as retail traders), as well as during different market conditions (before, during and after the commencement of the global financial crisis), and find our results are consistent with our ADR microstructure hypothesis. This is also supported by CFD (contracts for difference) and ADR pairs trading results. Our results also confirm the results of Alsayed and McGroarty (2012) by conducting trades over a substantially longer and more varied trading period. Our results have implications for ADR markets, as well as market microstructures upon financial innovations such as exchange traded funds

Future directions in international financial integration research. A crowdsourced perspective

This paper is the result of a crowdsourced effort to surface perspectives on the present and future direction of international finance. The authors are researchers in financial economics who attended the INFINITI 2017 conference in the University of Valencia in June 2017 and who participated in the crowdsourcing via the Overleaf platform. This paper highlights the actual state of scientific knowledge in a multitude of fields in finance and proposes different directions for future research