Convolutional neural networks applied to high-frequency market microstructure forecasting

Highly sophisticated artificial neural networks have achieved unprecedented performance across a variety of complex real-world problems over the past years, driven by the ability to detect significant patterns autonomously. Modern electronic stock markets produce large volumes of data, which are very suitable for use with these algorithms. This research explores new scientific ground by designing and evaluating a convolutional neural network in predicting future financial outcomes. A visually inspired transformation process translates high-frequency market microstructure data from the London Stock Exchange into four market-event based input channels, which are used to train six deep networks. Primary results indicate that con-volutional networks behave reasonably well on this task and extract interesting microstructure patterns, which are in line with previous theoretical findings. Furthermore, it demonstrates a new approach using modern deep-learning techniques for exploiting and analysing market microstructure behaviour

A systemic risk assessment of OTC derivatives reforms and skin-in-the-game for CCPs

The G20 OTC (over-the-counter) derivatives reforms impose large collateral/liquidity demands on clearing members of Central Counterparty (CCP) clearing platforms in the form of initial margins, variation margins and contributions to the default fund. In Heath et al. (2016), it was shown how this introduces a trade-off between liquidity risk and solvency risk with the system manifesting considerable systemic risk from these two sources of risk while CCP penetration is at current levels. The authors extend this analysis to include the European Market Infrastructure Regulation (EMIR) skin-in-the-game requirements for CCPs, which aim to ameliorate the contributions to the default fund by clearing members and also to prevent moral hazard problems associated with the too-interconnected-to-fail (TITF) status of CCPs as more and more derivatives are centrally cleared. The authors provide a systemic risk assessment of these features of the OTC derivatives reforms using network analysis based on 2015-end data on the derivatives positions for 40 globally systemically important banks (G-SIBs)

Non-Performing Loans: Regulatory and Accounting Treatments of Assets

Asset quality is an essential part of sound banking. However, asset quality is difficult for banking regulators and investors to assess in the absence of a common, cross-border scheme to classify assets. Currently no standard is applied universally to classify loans, the most sizable asset on many banks’ balance sheets. As a corollary, no common definition of non-performing loans (NPLs) exists. This paper documents divergences in the definition of NPLs across countries, accounting regimes, firms and data sources. The paper’s originality is in attending to the legal, accounting, statistical, economic and strategic aspects of loan loss provisioning (LLP) and NPLs, topics that are multidisciplinary by nature but have not been dealt with in the literature in an integrated fashion before. Since the 2007 Great Financial Crisis (GFC), accounting bodies and prudential regulators are increasingly focused on early recognition of credit losses and enhanced disclosure. A common approach to NPL recognition might complement these initiatives

A power-law distribution for tenure lengths of sports managers

We show that the tenure lengths for managers of sport teams follow a power law distribution with an exponent between 2 and 3. We develop a simple theoretical model which replicates this result. The model demonstrates that the empirical phenomenon can be understood as the macroscopic outcome of pairwise interactions among managers in a league, threshold effects in managerial performance evaluation, competitive market forces, and luck at the microscopic level

Non-performing loans at the dawn of IFRS 9: regulatory and accounting treatment of asset quality

Asset quality is a key indicator of sound banking. However, it is difficult for banking regulators and investors to assess it in the absence of a common, cross-border scheme to classify assets. Currently no standard is applied universally to categorise loans, the most sizeable asset on banks’ balance sheets. As a corollary, definitions of nonperforming loans (NPLs), despite recent steps towards greater harmonisation, continue to vary between jurisdictions. This paper offers a comprehensive analysis of NPLs and considers variations in the treatment of NPLs across countries, accounting regimes, and firms. The paper relies on a multi-disciplinary perspective and addresses legal, accounting, economic and strategic aspects of loan loss provisioning (LLP) and NPLs. A harmonised approach to NPL recognition is particularly desirable, in view of the fact that IFRS 9, the new accounting standard on loan loss provisioning, will be mandatory from January 2018. IFRS 9 changes the relationship between NPLs and provisions, by relying on greater judgement to determine provisions. The potential for divergence makes the need for comparable indicators against which to assess asset quality all the greater

Credit Default Swaps Drawup Networks: Too Tied To Be Stable?

We analyse time series of CDS spreads for a set of major US and European institutions on a pe- riod overlapping the recent financial crisis. We extend the existing methodology of {\epsilon}-drawdowns to the one of joint {\epsilon}-drawups, in order to estimate the conditional probabilities of abrupt co-movements among spreads. We correct for randomness and for finite size effects and we find significant prob- ability of joint drawups for certain pairs of CDS. We also find significant probability of trend rein- forcement, i.e. drawups in a given CDS followed by drawups in the same CDS. Finally, we take the matrix of probability of joint drawups as an estimate of the network of financial dependencies among institutions. We then carry out a network analysis that provides insights into the role of systemically important financial institutions.Comment: 15 pages, 5 figures, Supplementary informatio

Complex type 4 structure changing dynamics of digital agents: Nash equilibria of a game with arms race in innovations

The new digital economy has renewed interest in how digital agents can innovate. This follows the legacy of John von Neumann dynamical systems theory on complex biological systems as computation. The Gödel-Turing-Post (GTP) logic is shown to be necessary to generate innovation based structure changing Type 4 dynamics of the Wolfram-Chomsky schema. Two syntactic procedures of GTP logic permit digital agents to exit from listable sets of digital technologies to produce novelty and surprises. The first is meta-analyses or offline simulations. The second is a fixed point with a two place encoding of negation or opposition, referred to as the Gödel sentence. It is postulated that in phenomena ranging from the genome to human proteanism, the Gödel sentence is a ubiquitous syntactic construction without which escape from hostile agents qua the Liar is impossible and digital agents become entrained within fixed repertoires. The only recursive best response function of a 2-person adversarial game that can implement strategic innovation in lock-step formation of an arms race is the productive function of the Emil Post [58] set theoretic proof of the Gödel incompleteness result. This overturns the view of game theorists that surprise and innovation cannot be a Nash equilibrium of a game

Does Central Clearing Reduce Counterparty Risk in Realistic Financial Networks?

Novating a single asset class to a central counterparty (CCP) in an over-the-counter derivatives trading network impacts both the mean and variance of total net exposures between counterparties. When a small number of dealers trade in a relatively large number of asset classes, central clearing increases the mean and variance of net exposures, which may lead to increased counterparty risk and higher margin needs. There are intermediate cases where there is a tradeoff: The introduction of a CCP leads to an increase in expected net exposures but this increase is accompanied by a reduction in variance. We extend the work of Duffie and Zhu (2011) by considering general classes of network structures and focus on scale-free and core-periphery structures, which have been shown to be accurate models of real-world financial networks. We find that a CCP is unlikely to be beneficial when the link structure of the network relies on just a few key nodes. In particular, in large scale-free networks a CCP will always worsen expected netting efficiency. In such cases, CCPs can improve netting efficiency only if agents have some degree of risk aversion that allows them to trade off the reduced variance against the higher expected netted exposures. This may explain why, in the absence of regulation, traders in a derivatives network may not develop a CCP themselves

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

We analyse systemic risk in the core global banking system using a new network-based spectral eigen-pair method, which treats network failure as a dynamical system stability problem. This is compared with market price-based Systemic Risk Indexes, viz. Marginal Expected Shortfall, Delta Conditional Value-at-Risk, and Conditional Capital Shortfall Measure of Systemic Risk in a cross-border setting. Unlike paradoxical market price based risk measures, which underestimate risk during periods of asset price booms, the eigen-pair method based on bilateral balance sheet data gives early-warning of instability in terms of the tipping point that is analogous to the R number in epidemic models. For this regulatory capital thresholds are used. Furthermore, network centrality measures identify systemically important and vulnerable banking systems. Market price-based SRIs are contemporaneous with the crisis and they are found to covary with risk measures like VaR and betas