Dynamic modeling of mean-reverting spreads for statistical arbitrage

Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is a noisy realization of some hidden states. Real-time estimation of the unobserved spread process can reveal temporary market inefficiencies which can then be exploited to generate excess returns. Building on previous work, we embrace the state-space framework for modeling spread processes and extend this methodology along three different directions. First, we introduce time-dependency in the model parameters, which allows for quick adaptation to changes in the data generating process. Second, we provide an on-line estimation algorithm that can be constantly run in real-time. Being computationally fast, the algorithm is particularly suitable for building aggressive trading strategies based on high-frequency data and may be used as a monitoring device for mean-reversion. Finally, our framework naturally provides informative uncertainty measures of all the estimated parameters. Experimental results based on Monte Carlo simulations and historical equity data are discussed, including a co-integration relationship involving two exchange-traded funds.Comment: 34 pages, 6 figures. Submitte

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

TRANSPARENCY AND BIDDING COMPETITION IN INTERNATIONAL WHEAT TRADE

One of the major trade policy problems identified by U.S. interests, including grower groups, traders, and policymakers, is that of pricing transparency. This has been a gnawing issue generally related to the pricing practices of competitor exporting countries with state trading enterprises (STEs). The transparency problem generally refers to the inability to observe rivals' terms of trade (including price, quality, credit, etc.) and is normally associated with commercial exporters competing against STE rivals. The perception being the less transparent competitors (STEs) would have a strategic advantage. A game theory model of bidding competition was developed to simulate the effects of information asymmetry amongst rivals. A Bayes-Nash equilibrium was used to derive equilibrium solutions. Several stylized examples were used to illustrate aspects of competition and to analyze effects on bidding strategies. Results indicate that: 1) anything that reduces uncertainties among rivals would reduce equilibrium bids and prices; 2) bidding situations in which there is less transparency have the effect of increasing bids and prices to buyers, and payoffs to sellers; and 3) increases in the number of rivals have the effect of reducing bids and mitigating the informational advantages of STEs. In all cases, less transparent sellers have an advantage in bidding competition relative to more transparent sellers. That advantage in our stylized case was in the area of 1-2$/mt. However, that advantage is mitigated with an increase in the number of transparent rivals and in the case where more transparent players have acted as agents for an STE and have more information about costs of an STE. Further, cessation of exports under U.S. EEP programs should have decreased the transparency of U.S. firms, increasing their competitiveness in the international grain trade.Price Transparency, Strategic Bidding, Game Theory, Bayesian-Nash, State Trading Enterprises, Export Enhancement Program, Wheat, International Relations/Trade,

Hybridizing data stream mining and technical indicators in automated trading systems

Automated trading systems for financial markets can use data mining techniques for future price movement prediction. However, classifier accuracy is only one important component in such a system: the other is a decision procedure utilizing the prediction in order to be long, short or out of the market. In this paper, we investigate the use of technical indicators as a means of deciding when to trade in the direction of a classifier’s prediction. We compare this “hybrid” technical/data stream mining-based system with a naive system that always trades in the direction of predicted price movement. We are able to show via evaluations across five financial market datasets that our novel hybrid technique frequently outperforms the naive system. To strengthen our conclusions, we also include in our evaluation several “simple” trading strategies without any data mining component that provide a much stronger baseline for comparison than traditional buy-and-hold or sell-and-hold strategies