Frank McGroarty

Automated trading with performance weighted random forests and seasonality

Seasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades. This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy. While simple approaches to trading these regularities can prove profitable, such trading leads to potential large drawdowns (peak-to-trough decline of an investment measured as a percentage between the peak and the trough) in profit. In this paper, we introduce an automated trading system based on performance weighted ensembles of random forests that improves the profitability and stability of trading seasonality events. An analysis of various regression techniques is performed as well as an exploration of the merits of various techniques for expert weighting. The performance of the models is analysed using a large sample of stocks from the DAX. The results show that recency-weighted ensembles of random forests produce superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. It is also found that using seasonality effects produces superior results than not having them modelled explicitly.

High-Frequency Exchange-Rate Prediction With An Artificial Neural Network

This paper examines how market microstructure variables can be used to forecast foreign exchange (FX) rates at frequencies of one to several minutes. We use a unique FX dataset of global inter-dealer electronic transactions and applied the artificial neural network (ANN) as the predicting model. The immediately preceding bid and ask prices are significant factors in these predictions, which is in keeping with market microstructure theory. These microstructure factors have not been tested in an ANN model before. High-frequency trading strategies based on the ANN model are shown to be profitable even when transaction costs are included. Copyright

The Components of Electronic Inter-Dealer Spot FX Bid-Ask Spreads

This paper applies an established bid-ask spread decomposition model to the inter-dealer spot foreign exchange market. In addition, the paper presents and tests a modified decomposition model which is specifically adapted to the features of order-driven markets and which is found to produce more plausible results than the original model. Price clustering is introduced as a new explanatory factor within this framework and is shown to be vitally important in understanding the composition of bid-ask spreads in this market.

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.

Optimal Portfolio Selection in Nonlinear Arbitrage Spreads

This paper analytically solves the portfolio optimization problem of an investor faced with a risky arbitrage opportunity (e.g. relative mispricing in equity pairs). Unlike the extant literature, which typically models mispricings through the Ornstein--Uhlenbeck (OU) process, we introduce a nonlinear generalization of OU which jointly captures several important risk factors inherent in arbitrage trading. While these factors are absent from the standard OU, we show that considering them yields several new insights into the behavior of rational arbitrageurs: Firstly, arbitrageurs recognizing these risk factors exhibit a diminishing propensity to exploit large mispricings. Secondly, optimal investment behavior in light of these risk factors precipitates the gradual unwinding of losing trades far sooner than is entailed in existing approaches including OU. Finally, an empirical application to daily FTSE100 pairs data shows that incorporating these risks renders our models risk-management capabilities superior to both OU and a simple threshold strategy popular in the literature. These observations are useful in understanding the role of arbitrageurs in enforcing price efficiency.

Time is money: Costing the impact of duration misperception in market prices

We explore whether, and to what extent, traders in a real world financial market, where participants’ judgements are reportedly well calibrated, are subject to duration misperception. To achieve this, we examine duration misperception in the horserace betting market. We develop a two-stage algorithm to predict horses’ winning probabilities that account for a duration-related factor that is known to affect horses’ winning prospects. The algorithm adapts survival analysis and combines it with the conditional logit model. Using a dataset of 4736 horseraces and the lifetime career statistics of the 53,295 horses running in these races, we demonstrate that prices fail to discount fully information related to duration since a horses last win. We show that this failure is extremely costly, since a betting strategy based on the predictions arising from the model shows substantial profits (932.5 percent and 16.27 percent, with and without reinvestment of winnings, respectively). We discuss the important implications of duration neglect in the wider economy.

Performance-weighted ensembles of random forests for predicting price impact

For any large player in financial markets, the impact of their trading activity represents a substantial proportion of transaction costs. This paper proposes a novel machine learning algorithm for predicting the price impact of order book events. Specifically, we introduce a prediction system based on ensembles of random forests (RFs). The system is trained and tested on depth-of-book data from the BATS and Chi-X exchanges and performance is benchmarked using ensembles of other popular regression algorithms including: linear regression, neural networks and support vector regression. The results show that recency-weighted ensembles of RFs produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks. Feature importance ranking is used to explore the significance of various market features on the price impact, finding them to be highly variable through time. Finally, a novel procedure for extracting the directional effects of features is proposed and used to explore the features most dominant in the price formation process.

Does Technical Analysis Beat the Market? – Evidence from High Frequency Trading in Gold and Silver

Previous research has identified that investors place more emphasis on technical analysis than fundamental analysis, however the research has largely been confined to daily data and stock market indices. This paper studies whether intraday technical trading rules produce significant payoffs in the gold and silver market using three popular moving average rules. We find that using the standard parameters previously used in the literature, technical trading rules offer are not profitable. However after utilising a universe of parameters, we find a number of parameter combinations offer significant profits in the gold market, but there remains no significant payoff in the silver market. Our results show that parameters that use longer histories are more successful than the traditional parameters chosen in the literature. Intraday technical trading rules can be profitable in the gold market but offer no significant profit in the silver market.

Predicting equity market price impact with performance weighted ensembles of random forests

For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events. Specifically, we introduce a prediction system based on performance weighted ensembles of random forests. The systems performance is benchmarked using ensembles of other popular regression algorithms including: liner regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency-weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks.

Private Information, Excessive Volatility and Intraday Empirical Regularities in the Spot Foreign Exchange Market

Financial markets generally, and the spot foreign exchange market in particular, are reputed to be excessively volatile. Previous research has linked this excess volatility to private information. This article re-examines the theory and challenges that link. Empirical evidence suggests that random variation between buy and sell volumes is a more important driver than private informaion in the spot foreign exchange market. The paper also develops theoretical propositions for the relationships between key market variables on an intraday basis. High frequency data is used to examine the role of private information in explaining well documented intraday patterns that persist in the time series of a number of trade related variables, including return volatility.