pwc-archive/papers-with-abstracts huggingface dataset:
Informed trading, limit order book and implementation shortfall: equilibrium and asymptotics
Authors: Umut Çetin, Henri Waelbroeck
Date: 2020-03-09 00:00:00

We propose a static equilibrium model for limit order book where profit-maximizing investors receive an information signal regarding the liquidation value of the asset and execute via a competitive dealer with random initial inventory, who trades against a competitive limit order book populated by liquidity suppliers. We show that an equilibrium exists for bounded signal distributions, obtain closed form solutions for Bernoulli-type signals and propose a straightforward iterative algorithm to compute the equilibrium order book for the general case. We obtain the exact analytic asymptotics for the market impact of large trades and show that the functional form depends on the tail distribution of the private signal of the insiders. In particular, the impact follows a power law if the signal has fat tails while the law is logarithmic in case of lighter tails. Moreover, the tail distribution of the trade volume in equilibrium obeys a power law in our model. We find that the liquidity suppliers charge a minimum bid-ask spread that is independent of the amount of `noise' trading but increasing in the degree of informational advantage of insiders in equilibrium. The model also predicts that the order book flattens as the amount of noise trading increases converging to a model with proportional transactions costs.. Competition among the insiders leads to aggressive trading causing the aggregate profit to vanish in the limiting case $N\to\infty$. The numerical results also show that the spread increases with the number of insiders keeping the other parameters fixed. Finally, an equilibrium may not exist if the liquidation value is unbounded. We conjecture that existence of equilibrium requires a sufficient amount of competition among insiders if the signal distribution exhibit fat tails.
Unveiling the relation between herding and liquidity with trader lead-lag networks
Authors: Carlo Campajola, Fabrizio Lillo, Daniele Tantari
Date: 2019-09-24 00:00:00
Tasks: Relation

We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade. The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative "opinion" to all agents about whether the traded asset price will go up or down. This opinion is reflected by their trading behavior, but whenever the trader is not active in a given time window, a missing value will arise. Using a recently developed inference algorithm, we are able to reconstruct a lead-lag network and to estimate the unobserved opinions, giving a clearer picture about the state of supply and demand in the market at all times. We apply our method to a dataset of clients of a major dealer in the Foreign Exchange market at the 5 minutes time scale. We identify leading players in the market and define a herding measure based on the observed and inferred opinions. We show the causal link between herding and liquidity in the inter-dealer market used by dealers to rebalance their inventories.
Hedging Non-Tradable Risks with Transaction Costs and Price Impact
Authors: Alvaro Cartea, Ryan Donnelly, Sebastian Jaimungal
Date: 2019-07-31 00:00:00
Tasks: Position

A risk-averse agent hedges her exposure to a non-tradable risk factor $U$ using a correlated traded asset $S$ and accounts for the impact of her trades on both factors. The effect of the agent's trades on $U$ is referred to as cross-impact. By solving the agent's stochastic control problem, we obtain a closed-form expression for the optimal strategy when the agent holds a linear position in $U$. When the exposure to the non-tradable risk factor $\psi(U_T)$ is non-linear, we provide an approximation to the optimal strategy in closed-form, and prove that the value function is correctly approximated by this strategy when cross-impact and risk-aversion are small. We further prove that when $\psi(U_T)$ is non-linear, the approximate optimal strategy can be written in terms of the optimal strategy for a linear exposure with the size of the position changing dynamically according to the exposure's "Delta" under a particular probability measure.
Crowded trades, market clustering, and price instability
Authors: Marc van Kralingen, Diego Garlaschelli, Karolina Scholtus, Iman van Lelyveld
Date: 2020-02-09 00:00:00
Tasks: Clustering, Diversity

Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the effect of crowded trades on stock price stability and show that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability.
The microscopic relationships between triangular arbitrage and cross-currency correlations in a simple agent based model of foreign exchange markets
Authors: Alberto Ciacci, Takumi Sueshige, Hideki Takayasu, Kim Christensen, Misako Takayasu
Date: 2020-02-07 00:00:00

Foreign exchange rates movements exhibit significant cross-correlations even on very short time-scales. The effect of these statistical relationships become evident during extreme market events, such as flash crashes.In this scenario, an abrupt price swing occurring on a given market is immediately followed by anomalous movements in several related foreign exchange rates. Although a deep understanding of cross-currency correlations would be clearly beneficial for conceiving more stable and safer foreign exchange markets, the microscopic origins of these interdependencies have not been extensively investigated. We introduce an agent-based model which describes the emergence of cross-currency correlations from the interactions between market makers and an arbitrager. Our model qualitatively replicates the time-scale vs. cross-correlation diagrams observed in real trading data, suggesting that triangular arbitrage plays a primary role in the entanglement of the dynamics of different foreign exchange rates. Furthermore, the model shows how the features of the cross-correlation function between two foreign exchange rates, such as its sign and value, emerge from the interplay between triangular arbitrage and trend-following strategies.
Market making and incentives design in the presence of a dark pool: a deep reinforcement learning approach
Authors: Bastien Baldacci, Iuliia Manziuk, Thibaut Mastrolia, Mathieu Rosenbaum
Date: 2019-12-02 00:00:00
Tasks: Deep Reinforcement Learning

We consider the issue of a market maker acting at the same time in the lit and dark pools of an exchange. The exchange wishes to establish a suitable make-take fees policy to attract transactions on its venues. We first solve the stochastic control problem of the market maker without the intervention of the exchange. Then we derive the equations defining the optimal contract to be set between the market maker and the exchange. This contract depends on the trading flows generated by the market maker's activity on the two venues. In both cases, we show existence and uniqueness, in the viscosity sense, of the solutions of the Hamilton-Jacobi-Bellman equations associated to the market maker and exchange's problems. We finally design deep reinforcement learning algorithms enabling us to approximate efficiently the optimal controls of the market maker and the optimal incentives to be provided by the exchange.
Clusters of investors around Initial Public Offering
Authors: Margarita Baltakienė, Kęstutis Baltakys, Juho Kanniainen, Dino Pedreschi, Fabrizio Lillo
Date: 2019-05-31 00:00:00

The complex networks approach has been gaining popularity in analysing investor behaviour and stock markets, but within this approach, initial public offerings (IPO) have barely been explored. We fill this gap in the literature by analysing investor clusters in the first two years after the IPO filing in the Helsinki Stock Exchange by using a statistically validated network method to infer investor links based on the co-occurrences of investors' trade timing for 69 IPO stocks. Our findings show that a rather large part of statistically similar network structures form in different securities and persist in time for mature and IPO companies. We also find evidence of institutional herding.
Fragmentation of Distributed Exchanges
Authors: Marius Zoican, Sorin Zoican
Date: 2019-10-24 00:00:00

Distributed securities exchanges may become de facto fragmented if they span geographical regions with asymmetric computer infrastructure. First, we build an economic model of a decentralized exchange with two miner clusters, standing in for compact areas of economic activity (e.g., cities). "Local" miners in the area with relatively higher trading activity only join a decentralized exchange if they enjoy a large speed advantage over "long-distance" competitors. This is due to a transfer of economic value across miners, specifically from high- to low-activity clusters. Second, we estimate the speed advantage of "local" over "long-distance" miners in a series of Monte Carlo experiments over a two-cluster, unstructured peer-to-peer network simulated in C. We find that the speed advantage increases in the level of infrastructure asymmetry between clusters. Cross-region DEX blockchains are feasible as long as the asymmetry levels in trading activity and infrastructure availability across regions are positively correlated.
How Option Hedging Shapes Market Impact
Authors: Emilio Said
Date: 2019-10-11 00:00:00

We present a perturbation theory of the market impact based on an extension of the framework proposed by [Loeper, 2018] -- originally based on [Liu and Yong, 2005] -- in which we consider only local linear market impact. We study the execution process of hedging derivatives and show how these hedging metaorders can explain some stylized facts observed in the empirical market impact literature. As we are interested in the execution process of hedging we will establish that the arbitrage opportunities that exist in the discrete time setting vanish when the trading frequency goes to infinity letting us to derive a pricing equation. Furthermore our approach retrieves several results already established in the option pricing literature such that the spot dynamics modified by the market impact. We also study the relaxation of our hedging metaorders based on the fair pricing hypothesis and establish a relation between the immediate impact and the permanent impact which is in agreement with recent empirical studies on the subject.
Accelerated Share Repurchase and other buyback programs: what neural networks can bring
Authors: Olivier Guéant, Iuliia Manziuk, Jiang Pu
Date: 2019-07-23 00:00:00
Tasks: Management

When firms want to buy back their own shares, they have a choice between several alternatives. If they often carry out open market repurchase, they also increasingly rely on banks through complex buyback contracts involving option components, e.g. accelerated share repurchase contracts, VWAP-minus profit-sharing contracts, etc. The entanglement between the execution problem and the option hedging problem makes the management of these contracts a difficult task that should not boil down to simple Greek-based risk hedging, contrary to what happens with classical books of options. In this paper, we propose a machine learning method to optimally manage several types of buyback contract. In particular, we recover strategies similar to those obtained in the literature with partial differential equation and recombinant tree methods and show that our new method, which does not suffer from the curse of dimensionality, enables to address types of contract that could not be addressed with grid or tree methods.
Do speed bumps curb low-latency trading? Evidence from a laboratory market
Authors: Mariana Khapko, Marius Zoican
Date: 2019-10-07 00:00:00

Exchanges implement intentional trade delays to limit the harmful impact of low-latency trading. Do such "speed bumps" curb investment in fast trading technology? Data is scarce since trading technologies are proprietary. We build an experimental trading platform where participants face speed bumps and can invest in fast trading technology. We find that asymmetric speed bumps, on average, reduce investment in speed by only 20%. Increasing the magnitude of the speed bump by one standard deviation further reduces low-latency investment by 8.33%. Finally, introducing a symmetric speed bump leads to the same investment level as no speed bump at all.
Latency and Liquidity Risk
Authors: Álvaro Cartea, Sebastian Jaimungal, Leandro Sánchez-Betancourt
Date: 2019-08-08 00:00:00

Latency (i.e., time delay) in electronic markets affects the efficacy of liquidity taking strategies. During the time liquidity takers process information and send marketable limit orders (MLOs) to the exchange, the limit order book (LOB) might undergo updates, so there is no guarantee that MLOs are filled. We develop a latency-optimal trading strategy that improves the marksmanship of liquidity takers. The interaction between the LOB and MLOs is modelled as a marked point process. Each MLO specifies a price limit so the order can receive worse prices and quantities than those the liquidity taker targets if the updates in the LOB are against the interest of the trader. In our model, the liquidity taker balances the tradeoff between missing trades and the costs of walking the book. We employ techniques of variational analysis to obtain the optimal price limit of each MLO the agent sends. The price limit of a MLO is characterized as the solution to a new class of forward-backward stochastic differential equations (FBSDEs) driven by random measures. We prove the existence and uniqueness of the solution to the FBSDE and numerically solve it to illustrate the performance of the latency-optimal strategies.
Path-specific Underwater Acoustic Channel Tracking and its Application in Passive Time Reversal Mirror
Authors: Xiuqing Li, Wei Li, Xinlin Yi, Qihang Huang, Yuhang Wang, Chenzhe Ye
Date: 2021-03-01 00:00:00

We consider the underwater acoustic channel which is time-variant and doubly-spread in this work. Since conventional channel estimation and decision feedback equalizer (DFE) can not work well for this type of channel, a path-specific underwater acoustic channel tracking is proposed. It is based on the framework of Kalman filter. We provide a simplified sound propagation model as the state transition model. A multipath tracker is proposed which is tolerant of the model-mismatch. Then we can obtain the time-variant path number and path-specific parameters such as delay and Doppler scaling factor. We also consider the application of the proposed path-specific underwater acoustic channel tracking. We propose two types of passive time reversal mirror (PTRM) with our path-specific parameters for time-variant and doubly-spread underwater acoustic channel. With the path-specific parameters obtained by the proposed channel tracking, the proposed PTRM can not only match the time dispersion as conventional PTRM, but also the doubly-spread channel, since the path-specific delay and Doppler scaler factor can help to match the channel in both time and frequency domain. For extensive doubly-spread channel, we can further apply the path-specific compensation to the PTRM. Both simulations and experimental results by data from 2016 Qiandao Lake experiment show the efficiency of proposed path-specific channel tracking and proposed PTRMs with path-specific parameters.
Minimizing Information Leakage of Abrupt Changes in Stochastic Systems
Authors: Alessio Russo, Alexandre Proutiere
Date: 2021-03-02 00:00:00

This work investigates the problem of analyzing privacy of abrupt changes for general Markov processes. These processes may be affected by changes, or exogenous signals, that need to remain private. Privacy refers to the disclosure of information of these changes through observations of the underlying Markov chain. In contrast to previous work on privacy, we study the problem for an online sequence of data. We use theoretical tools from optimal detection theory to motivate a definition of online privacy based on the average amount of information per observation of the stochastic system in consideration. Two cases are considered: the full-information case, where the eavesdropper measures all but the signals that indicate a change, and the limited-information case, where the eavesdropper only measures the state of the Markov process. For both cases, we provide ways to derive privacy upper-bounds and compute policies that attain a higher privacy level. It turns out that the problem of computing privacy-aware policies is concave, and we conclude with some examples and numerical simulations for both cases.
The wealth of nations and the health of populations: A quasi-experimental design of the impact of sovereign debt crises on child mortality
Authors: Adel Daoud
Date: 2020-12-29 00:00:00
Tasks: Causal Inference, Experimental Design

The wealth of nations and the health of populations are intimately strongly associated, yet the extent to which economic prosperity (GDP per capita) causes improved health remains disputed. The purpose of this article is to analyze the impact of sovereign debt crises (SDC) on child mortality, using a sample of 57 low- and middle-income countries surveyed by the Demographic and Health Survey between the years 1990 and 2015. These surveys supply 229 household data and containing about 3 million childbirth history records. This focus on SDC instead of GDP provides a quasi-experimental moment in which the influence of unobserved confounding is less than a moment analyzing the normal fluctuations of GDP. This study measures child mortality at six thresholds: neonatal, under-one (infant), under-two, under-three, under-four, and under-five mortality. Using a machine-learning (ML) model for causal inference, this study finds that while an SDC causes an adverse yet statistically insignificant effect on neonatal mortality, all other child mortality group samples are adversely affected between a probability of 0.12 to 0.14 (all statistically significant at the 95-percent threshold). Through this ML, this study also finds that the most important treatment heterogeneity moderator, in the entire adjustment set, is whether a child is born in a low-income country.
Growth, development, and structural change at the firm-level: The example of the PR China
Authors: Torsten Heinrich, Jangho Yang, Shuanping Dai
Date: 2020-12-28 00:00:00
Tasks: Open-Ended Question Answering

Understanding the microeconomic details of technological catch-up processes offers great potential for informing both innovation economics and development policy. We study the economic transition of the PR China from an agrarian country to a high-tech economy as one example for such a case. It is clear from past literature that rapidly rising productivity levels played a crucial role. However, the distribution of labor productivity in Chinese firms has not been comprehensively investigated and it remains an open question if this can be used to guide economic development. We analyze labor productivity and the dynamic change of labor productivity in firm-level data for the years 1998-2013 from the Chinese Industrial Enterprise Database. We demonstrate that both variables are conveniently modeled as L\'evy alpha-stable distributions, provide parameter estimates and analyze dynamic changes to this distribution. We find that the productivity gains were not due to super-star firms, but due to a systematic shift of the entire distribution with otherwise mostly unchanged characteristics. We also found an emerging right-skew in the distribution of labor productivity change. While there are significant differences between the 31 provinces and autonomous regions of the P.R. China, we also show that there are systematic relations between micro-level and province-level variables. We conclude with some implications of these findings for development policy.
Recovering Human 3D Model from Monocular 2d Images for Detecting Postures Deformilities
Authors: Mr. Mrinal Walia, Mr. Raghavendra, Mr. Raihan Iqbal, Mr. Ranjith T. R, Mr. Deepak G, Mr. Harish Kumar N
Date: 2021-01-01 00:00:00
Tasks: 3D Pose Estimation, Pose Estimation

Human 3D pose estimation is one of the most 
talked-about or the hottest in recent years and 
even in the future. Most of the present algorithm 
can only get us the 3D skeletons from the pose 
which we have detected. There can be cases 
wherein because of lack of knowledge and data 
which we have obtained from the human shape, 
angles and textures, the accuracy of many 
various algorithms is not enough for high 
development level artificial intelligence and 
image processing tasks. In our paper, we 
purposed an algorithm and develop a project 
which will recover a 3D model of the human 
skeleton from the images which are captured by 
the camera to detect postural deformities and 
sitting postures. The algorithm proposed is very 
sturdy, dynamic and can help us for 
implementation of high development level image 
processing and computer vision tasks which also 
including shapes and textures. It also briefs us 
about many important techniques and 
important information from the implementation 
of the algorithm.
Symbolic Music Generation with Diffusion Models
Authors: Gautam Mittal, Jesse Engel, Curtis Hawthorne, Ian Simon
Date: 2021-03-30 00:00:00
Tasks: Music Generation

Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in continuous domains such as images and audio. However, due to their Langevin-inspired sampling mechanisms, their application to discrete and sequential data has been limited. In this work, we present a technique for training diffusion models on sequential data by parameterizing the discrete domain in the continuous latent space of a pre-trained variational autoencoder. Our method is non-autoregressive and learns to generate sequences of latent embeddings through the reverse process and offers parallel generation with a constant number of iterative refinement steps. We apply this technique to modeling symbolic music and show strong unconditional generation and post-hoc conditional infilling results compared to autoregressive language models operating over the same continuous embeddings.