Selection mechanisms affect volatility in evolving markets

Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism as substrate, we analyze the role played by evolutionary selection mechanisms in determining macro-observable market statistics. In particular, we show that selection mechanisms incorporating a local fitness-proportionate component are associated with high correlation between a micro, risk-aversion parameter and a commonly-used macro-volatility statistic, while a purely quantile-based selection mechanism shows significantly less correlation.Comment: 9 pages, 7 figures, to appear in proceedings of GECCO 2019 as a full pape

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.Comment: 12 pages, 9 figure

The impact of mobile telephony on developing country micro-enterprises: a Nigerian case study

Informational challenges-absence, uncertainty, asymmetry-shape the working of markets and commerce in many developing countries. For developing country micro-enterprises, which form the bulk of all enterprises worldwide, these challenges shape the characteristics of their supply chains. They reduce the chances that business and trade will emerge. They keep supply chains localised and intermediated. They make trade within those supply chains slow, costly, and risky. Mobile telephony may provide an opportunity to address the informational challenges and, hence, to alter the characteristics of trade within micro-enterprise supply chains. However, mobile telephony has only recently penetrated. This paper, therefore, presents one of the first case studies of the impact of mobile telephony on the numerically-dominant form of enterprise, based around a case study of the cloth-weaving sector in Nigeria. It finds that there are ways in which costs and risks are being reduced and time is saved, often by substitution of journeys. But it also finds a continuing need for journeys and physical meetings due to issues of trust, design intensity, physical inspection and exchange, and interaction complexity. As a result, there are few signs of the de-localisation or disintermediation predicted by some commentators. An economising effect of mobile phones on supply chain processes may therefore co-exist with the entrenchment of supply chain structures and a growing 'competitive divide' between those with and without access to telephony

Order Flow and Exchange Rate Dynamics

Macroeconomic models of nominal exchange rates perform poorly. In sample, R2 statistics as high as 10 percent are rare. Out of sample, these models are typically out-forecast by a na‹ve random walk. This paper presents a model of a new kind. Instead of relying exclusively on macroeconomic determinants, the model includes a determinant from the field of microstructure-order flow. Order flow is the proximate determinant of price in all microstructure models. This is a radically different approach to exchange rate determination. It is also strikingly successful in accounting for realized rates. Our model of daily exchange-rate changes produces R2 statistics above 50 percent. Out of sample, our model produces significantly better short-horizon forecasts than a random walk. For the DM/$spot market as a whole, we find that$1 billion of net dollar purchases increases the DM price of a dollar by about 1 pfennig.

Does Pre-trade Transparency Affect Market Quality in the Tokyo Stock Exchange?

This paper presents an examination of the relation between pre-trade transparency and market quality in the Tokyo Stock Exchange (TSE). Mixed evidence related to this relation has been reported worldwide. We analyzed this relation using a discrete change of disclosure policy in the 2000s. A positive relation pertains between pre-trade transparency and market quality. This result implies that the change of disclosure policy on the TSE might be effective for market quality improvement to some extent.Pre-trade transparency; Market quality; Quote Disclosure