Analytic solutions for optimal statistical arbitrage trading

In this paper we derive analytic formulae for statistical arbitrage trading where the security price follows an Ornstein-Uhlenbeck process. By framing the problem in terms of the first-passage time of the process, we derive expressions for the mean and variance of the trade length and the return. We examine the problem of choosing an optimal strategy under two different objective functions: the expected return, and the Sharpe ratio. An exact analytic solution is obtained for the case of maximising the expected return.     

Optimal trading from minimizing the period of bankruptcy risk

Assuming that financial markets behave similar to random walk processes we derive a trading strategy with variable investment which is based on the equivalence of the period of bankruptcy risk and the risk to profit ratio. We define a state dependent predictability measure which can be attributed to the deterministic and stochastic components of the price dynamics. The influence of predictability variations and especially of short term inefficiency structures on the optimal amount of investment is analyzed in the given context and a method for adaptation of a trading system to the proposed objective function is presented. Finally we show the performance of our trading strategy on the DAX and S&P 500 as examples for real world data using different types of prediction models in comparison. Received 15 September 2000 and Received in final form 2 October 2000     

Gambling on the Budapest stock exchange

The statistical properties of the total yield are analyzed for an assembly of gamblers in an erratic period on the Budapest stock exchange. Random trading results in a log-normal limit distribution of a surprisingly large width, while the simplest profit realizing strategy narrows down the peak around a positive average value. The effect of transaction costs, the statistics of extremes, and patterns of successful trading are also investigated. In spite of the very simple approach, we present strong indications that large trading activity (e.g. day trading) is a rather risky way of capital investment. A comparison with the yield distribution of 32 public investment funds in the given period does not reflect the presence of a sophisticated investment strategy in the background. Received 5 May 2000     

Application of the Cluster Expansion to a Mathematical Model of the Long Memory Phenomenon in a Financial Market

Empirical studies of the high frequency data in stock markets show that the time series of trade signs or signed volumes has a long memory property. In this paper, we present a discrete time stochastic process for polymer model which describes trader’s trading strategy, and show that a scale limit of the process converges to superposition of fractional Brownian motions with Hurst exponents $H_{m} >\frac{1}{2}$ and Brownian motion, provided that the index γ of the time scale about the trader’s investment strategy coincides with the index δ of the interaction range in the discrete time process. The main tool for the investigation is the method of cluster expansion developed in the mathematical study of statistical mechanics.     

Subjective modelling of supply and demand—the minimum of Fisher information solution

Two of the present authors have put forward a projective geometry based model of rational trading that implies a model for subjective demand/supply profiles if one considers closing of a position as a random process. We would like to present the analysis of a subjectivity in such trading models. In our model, the trader gets the maximal profit intensity when the probability of transaction is ∼0.5853. We also present a comparison with the model based on the Maximum of Entropy Principle. To the best of our knowledge, this is one of the first analyses that show a concrete situation in which trader profit optimal value is in the class of price-negotiating algorithms (strategies) resulting in non-monotonic demand (supply) curves of the Rest of the World (a collective opponent). Our model suggests that there might be a new class of rational trader strategies that (almost) neglects the supply-demand profile of the market. This class emerges when one tries to minimize the information that strategies reveal.     

Preferred numbers and the distributions of trade sizes and trading volumes in the Chinese stock market

The distributions of trade sizes and trading volumes are investigated based on the limit order book data of 22 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. We observe that the size distribution of trades for individualstocks exhibits jumps, which is caused by the number preference of traders when placing orders. We analyze the applicability of the “q-Gamma” function for fitting the distribution by the Cramér-von Mises criterion. The empirical PDFs of tradingvolumes at different timescales Δt ranging from 1 min to 240 min can be well modeled. The applicability of the q-Gamma functions for multiple trades is restricted to the transaction numbers Δn≤ 8. We find that all the PDFs have power-law tails for large volumes. Using careful estimation of the average tail exponents α of the distributions of trade sizes and trading volumes, we get α> 2, well outside the Lévy regime.     

From market games to real-world markets

This paper uses the development of multi-agent market models to present a unified approach to the joint questions of how financial market movements may be simulated, predicted, and hedged against. We first present the results of agent-based market simulations in which traders equipped with simple buy/sell strategies and limited information compete in speculatory trading. We examine the effect of different market clearing mechanisms and show that implementation of a simple Walrasian auction leads to unstable market dynamics. We then show that a more realistic out-of-equilibrium clearing process leads to dynamics that closely resemble real financial movements, with fat-tailed price increments, clustered volatility and high volume autocorrelation. We then show that replacing the `synthetic' price history used by these simulations with data taken from real financial time-series leads to the remarkable result that the agents can collectively learn to identify moments in the market where profit is attainable. Hence on real financial data, the system as a whole can perform better than random. We then employ the formalism of Bouchaud in conjunction with agent based models to show that in general risk cannot be eliminated from trading with these models. We also show that, in the presence of transaction costs, the risk of option writing is greatly increased. This risk, and the costs, can however be reduced through the use of a delta-hedging strategy with modified, time-dependent volatility structure. Received 30 August 2000     

Growth optimal investment and pricing of derivatives

We introduce a criterion how to price derivatives in incomplete markets, based on the theory of growth optimal strategy in repeated multiplicative games. We present reasons why these growth-optimal strategies should be particularly relevant to the problem of pricing derivatives. Under the assumptions of no trading costs, and no restrictions on lending, we find an appropriate equivalent martingale measure that prices the underlying and the derivative security. We compare our result with other alternative pricing procedures in the literature, and discuss the limits of validity of the lognormal approximation. We also generalize the pricing method to a market with correlated stocks. The expected estimation error of the optimal investment fraction is derived in a closed form, and its validity is checked with a small-scale empirical test.     

Complex stock trading network among investors

We provide an empirical investigation aimed at uncovering the statistical properties of intricate stock trading networks based on the order flow data of a highly liquid stock (Shenzhen Development Bank) listed on Shenzhen Stock Exchange during the whole year of 2003. By reconstructing the limit order book, we can extract detailed information of each executed order for each trading day and demonstrate that the trade size distributions for different trading days exhibit power-law tails and that most of the estimated power-law exponents are well within the Lévy stable regime. Based on the records of order matching among investors, we can construct a stock trading network for each trading day, in which the investors are mapped into nodes and each transaction is translated as a direct edge from the seller to the buyer with the trade size as its weight. We find that all the trading networks comprise a giant component and have power-law degree distributions and disassortative architectures. In particular, the degrees are correlated with order sizes by a power-law function. By regarding the size of executed order as its fitness, the fitness model can reproduce the empirical power-law degree distribution.     

Rates of profit as correlated sums of random variables

Profit realization is the dominant feature of market-based economic systems, determining their dynamics to a large extent. Rather than attaining an equilibrium, profit rates vary widely across firms, and the variation persists over time. Differing definitions of profit result in differing empirical distributions. To study the statistical properties of profit rates, I used data from a publicly available database for the US Economy for 2009–2010 (Risk Management Association). For each of three profit rate measures, the sample space consists of 771 points. Each point represents aggregate data from a small number of US manufacturing firms of similar size and type (NAICS code of principal product). When comparing the empirical distributions of profit rates, significant ‘heavy tails’ were observed, corresponding principally to a number of firms with larger profit rates than would be expected from simple models. An apparently novel correlated sum of random variables statistical model was used to model the data. In the case of operating and net profit rates, a number of firms show negative profits (losses), ruling out simple gamma or lognormal distributions as complete models for these data.     

Trading model with pair pattern strategies

A simple trading model based on pair pattern strategy space with holding periods is proposed. Power-law behavior is observed for the return variance σ², the price impact H and the predictability K for both models, with linear and square root impact functions. The sum of the traders’ wealth displays a positive value for the model with a square root price impact function, and a qualitative explanation is given based on the observation of the conditional excess demand 〈A|u〉. The cumulative wealth distribution also obeys a power-law behavior with an exponent close to that of real markets. An evolutionary trading model is further proposed. The elimination mechanism effectively changes the behavior of traders, and a power-law behavior is observed in the measure of zero return distribution P(r=0). The trading model with other types of traders, e.g., traders with the MG’s strategies and producers, are also carefully studied.     

Models of Self-Financing Hedging Strategies in Illiquid Markets: Symmetry Reductions and Exact Solutions

We study the general model of self-financing trading strategies in illiquid markets introduced by Schönbucher and Wilmott (SIAM J Appl Math 61(1):232?C272, 2000). A hedging strategy in the framework of this model satisfies a nonlinear partial differential equation (PDE) which contains some function g(??). This function is deeply connected to a marginal utility function. We describe the Lie symmetry algebra of this PDE and provide a complete set of reductions of the PDE to ordinary differential equations (ODEs). In addition, we show the way how to describe all types of functions g(??) for which the PDE admits an extended Lie group. Two of these special type functions correspond to the models introduced before by different authors, whereas one is new. We clarify the connection between these three special models and the general model for trading strategies in the illiquid markets. We also apply the Lie group analysis to the new special case of the PDE describing the self-financing strategies. For the general model, as well as for the new special model, we provide the optimal systems of subalgebras and study the complete set of reductions of the PDEs to ODEs. We provide explicit solutions to the new special model in all reduced cases. Moreover, in one of the cases the solutions describe power derivative products.     

Multi-scale correlations in different futures markets

In the present work we investigate the multiscale nature of the correlations for high frequency data (1 min) in different futures markets over a period of two years, starting on the 1st of January 2003 and ending on the 31st of December 2004. In particular, by using the concept of local Hurst exponent, we point out how the behaviour of this parameter, usually considered as a benchmark for persistency/antipersistency recognition in time series, is largely time-scale dependent in the market context. These findings are a direct consequence of the intrinsic complexity of a system where trading strategies are scale-adaptive. Moreover, our analysis points out different regimes in the dynamical behaviour of the market indices under consideration.     

Tunneling through a time-dependent barrier — a numerical study

We present a numerical investigation of quantum mechanical tunneling process in a double well potential with fluctuating barrier. The tunneling probability and rate are calculated for two cases in which (i) the height of the barrier is undergoing harmonic oscillation with frequency θ and (ii) the height of the barrier is undergoing random fluctuation with frequency θ. It is observed that in both cases, the quantum mechanical tunneling probability and rate exhibit a maximum as a function of the fluctuation frequency. The optimal frequency i.e. the frequency at which rate exhibits a maximum shows a strong isotopic mass effect.     

Role models for complex networks

We present a framework for automatically decomposing (“block-modeling”) the functional classes of agents within a complex network. These classes are represented by the nodes of an image graph (“block model”) depicting the main patterns of connectivity and thus functional roles in the network. Using a first principles approach, we derive a measure for the fit of a network to any given image graph allowing objective hypothesis testing. From the properties of an optimal fit, we derive how to find the best fitting image graph directly from the network and present a criterion to avoid overfitting. The method can handle both two-mode and one-mode data, directed and undirected as well as weighted networks and allows for different types of links to be dealt with simultaneously. It is non-parametric and computationally efficient. The concepts of structural equivalence and modularity are found as special cases of our approach. We apply our method to the world trade network and analyze the roles individual countries play in the global economy.     

Distributions of positive correlations in sectoral value added growth in the global economic network*

International trade has grown considerably during the process of globalization. Complex supply chains for the production of goods have resulted in an increasingly connected International Trade Network (ITN). Traditionally, direct trade relations between industries have been regarded as mediators of supply and demand spillovers. With increasing network connectivity the question arises if higher-order relations become more important in explaining a national sector’s susceptibility to supply and demand changes of its trading partner. In this study we address this question by investigating empirically to what extent the topological properties of the ITN provide information about positive correlations in the production of two industry sectors. We observe that although direct trade relations between industries serve as important indicators for correlations in the industries’ value added growth, opportunities of substitution for required production inputs as well as second-order trade relations cannot be neglected. Our results contribute to a better understanding of the relation between trade and economic productivity and can serve as a basis for the improvement of crisis spreading models that evaluate contagion threats in the case of a node’s failure in the ITN.     

Detrended fluctuation analysis of intertrade durations

The intraday pattern, long memory, and multifractal nature of the intertrade durations, which are defined as the waiting times between two consecutive transactions, are investigated based upon the limit order book data and order flows of 23 liquid Chinese stocks listed on the Shenzhen Stock Exchange in 2003. An inverse U-shaped intraday pattern in the intertrade durations with an abrupt drop in the first minute of the afternoon trading is observed. Based on a detrended fluctuation analysis, we find a crossover of power-law scaling behaviors for small box sizes (trade numbers in boxes) and large box sizes and strong evidence in favor of long memory in both regimes. In addition, the multifractal nature of intertrade durations in both regimes is confirmed by a multifractal detrended fluctuation analysis for individual stocks with a few exceptions in the small-duration regime. The intraday pattern has little influence on the long memory and multifractality.     

Trading strategies in the overnight money market: Correlations and clustering on the e-MID trading platform

We analyze the correlations in patterns of trading for members of the Italian interbank trading platform e-MID. The trading strategy of a particular member institution is defined as the sequence of (intra-) daily net trading volumes within a certain semester. Based on this definition, we show that there are significant and persistent bilateral correlations between institutions’ trading strategies. In most semesters we find two clusters, with positively (negatively) correlated trading strategies within (between) clusters. We show that the two clusters mostly contain continuous net buyers and net sellers of money, respectively, and that cluster memberships of individual banks are highly persistent. Additionally, we highlight some problems related to our definition of trading strategies. Our findings add further evidence on the fact that preferential lending relationships on the micro-level lead to community structure on the macro-level.     

Optimized laser cutting on light guide plates using grey relational analysis

This paper present the optimum conditions for direct CO₂ laser cutting of 6-mm-thick polymethylmethacrylate (PMMA) for backlit module applications. The influence of the major processing parameters on the optical transmittance ratio and surface roughness of cut samples material have been discussed. In order to assess the effects of several operational parameters on multiple-performance characteristics, we applied the grey relational analysis method. In this paper, we studied the effects of several laser direct cut parameters, such as assisted gas-flow rate, pulse repetition frequency, cutting speed, and focus position to achieve optimum characteristics for two product characteristics, optical transmittance ratio and work-piece surface roughness. The study involved nine experiments based on an orthogonal array, and results indicate the optimal process parameters as 20 NL/min for assisted-gas flow rate, 5 kHz for pulse repetition frequency, 2 mm/s for cutting speed, and 0 mm for laser focusing position. Additionally, by analyzing the grey relational grade, we found that the assisted-gas flow rate has more influence than any other single parameter.