Deep modeling complex couplings within financial markets

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The global financial crisis occurred in 2008 and its contagion to other regions, as well as the long-lasting impact on different markets, show that it is increasingly important to understand the complicated coupling relationships across financial markets. This is indeed very difficult as complex hidden coupling relationships exist between different financial markets in various countries, which are very hard to model. The couplings involve interactions between homogeneous markets from various countries (we call intra-market coupling), interactions between heterogeneous markets (inter-market coupling) and interactions between current and past market behaviors (temporal coupling). Very limited work has been done towards modeling such complex couplings, whereas some existing methods predict market movement by simply aggregating indicators from various markets but ignoring the inbuilt couplings. As a result, these methods are highly sensitive to observations, and may often fail when financial indicators change slightly. In this paper, a coupled deep belief network is designed to accommodate the above three types of couplings across financial markets. With a deep-architecture model to capture the high-level coupled features, the proposed approach can infer market trends. Experimental results on data of stock and currency markets from three countries show that our approach outperforms other baselines, from both technical and business perspectives

Cross-market behavior modeling

University of Technology Sydney. Faculty of Engineering and Information Technology.During the 2007 global financial crisis which was triggered by subprime borrowers in the US mortgage markets, strong market linkages were observed between different financial markets. The sharp fluctuations in the global stock market, commodity market and interest market illustrate some of the coupled behaviors that exist between various markets, namely the crisis effect is passed from one market to another through couplings. Here coupled behaviors refer to the activities (e.g. changes of market indexes) of financial markets which are associated with each other in terms of particular relationships. Therefore, a good understanding of coupled behaviors is of great importance in cross-market applications such as crisis detection and market trend forecasting. For instance, if the coupled behaviors are properly understood and modeled, investors can predict financial crisis and avoid the big loss, by detecting the changes of coupled relations between financial crisis period and non-crisis period. However, understanding and modeling coupled behaviors is quite challenging for following reasons: (1) The various coupled structures across financial markets (e.g. coupled relations between different types of markets, and coupled relations between the same type of market in different countries) bring challenges in terms of understanding and modeling them. (2) Various types of couplings. The typical forms are intra-coupling, inter-coupling and temporal-coupling. (3) The complex interactions between markets are driven by hidden features which cannot be observed directly from observation/data. (4) Different applications in cross-market analysis lead to the consideration of input factors/variables selection. All of these challenges the existing methods for cross-market analysis, which can be roughly categorized into two types: time series analysis represented by Logistic regression, Autoregressive Integrated Moving Average (ARIMA) and Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) models. Model-based methods explore machine models such as Artificial Neural Networks (ANN) and Hidden Markov Models (HMM). The main limitations lie in their deficiencies: (1) Existing approaches are usually focused on the simple correlations of the cross-market, rather than coupled behaviors between markets. (2) State-of-the-art research work is usually built directly from the observation/data. Hidden features behind the observation/data are often ignored or only weakly addressed. (3) Some approaches follow assumptions that are too strong to match real financial markets. Based on the above research limitations and challenges, this thesis reports state-of-the-art advances and our research innovations in understanding and modeling complex coupled behaviors for the purpose of cross-market analysis. Chapter 3 presents a new approach, called Coupled Market Behavior Analysis (CMBA) for financial crisis detection. This caters for nonlinear couplings between major indicators selected from different markets, and it detects different coupled market behaviors at crisis and non-crisis periods. Chapter 4 seeks to overcome the limitations of most current methods which conduct financial crisis forecasting directly through observation and overlook the hidden interactions between markets. In this chapter, Coupled Market State Analysis (CMSA) is presented to build forecasters based on coupled market states instead of observation. Chapter 5 reports a new approach for market trend forecasting by analyzing its hidden coupling relationships with different types of related financial markets. Chapter 6 proposes Hierarchical Cross-market Behavior Analysis (HCBA) to forecast a stock market’s movements, by exploring the complex coupling relationships between variables of markets from a country (Layer-1 coupling) and couplings between markets from various countries (Layer-2 coupling). In addition, Chapter 7 designs a Coupled Temporal Deep Belief Network (CTDBN) which accommodates three different types of couplings across financial markets: interactions between homogeneous markets from various countries (intra-market coupling), interactions between heterogeneous markets (inter-market coupling) and interactions between current and past market behaviors (temporal coupling). With a deep-architecture model to capture the high-level coupled features, the proposed approach can infer market trends. In terms of cross-market applications (i.e. financial crisis detection and market trend forecasting), our proposed approaches and frameworks for modeling coupled behaviors across financial markets outperform state-of-the-art methods from both technical and business perspectives. All of these outcomes provide insightful knowledge for investors who naturally seek to make profits and avoid losses. Accordingly, cross-market behavior modeling is a promising research topic with lots of potential for further exploration and development

Relating Theories via Renormalization

The renormalization method is specifically aimed at connecting theories describing physical processes at different length scales and thereby connecting different theories in the physical sciences. The renormalization method used today is the outgrowth of one hundred and fifty years of scientific study of thermal physics and phase transitions. Different phases of matter show qualitatively different behavior separated by abrupt phase transitions. These qualitative differences seem to be present in experimentally observed condensed-matter systems. However, the "extended singularity theorem" in statistical mechanics shows that sharp changes can only occur in infinitely large systems. Abrupt changes from one phase to another are signaled by fluctuations that show correlation over infinitely long distances, and are measured by correlation functions that show algebraic decay as well as various kinds of singularities and infinities in thermodynamic derivatives and in measured system parameters. Renormalization methods were first developed in field theory to get around difficulties caused by apparent divergences at both small and large scales. The renormalization (semi-)group theory of phase transitions was put together by Kenneth G. Wilson in 1971 based upon ideas of scaling and universality developed earlier in the context of phase transitions and of couplings dependent upon spatial scale coming from field theory. Correlations among regions with fluctuations in their order underlie renormalization ideas. Wilson's theory is the first approach to phase transitions to agree with the extended singularity theorem. Some of the history of the study of these correlations and singularities is recounted, along with the history of renormalization and related concepts of scaling and universality. Applications are summarized.Comment: This note is partially a summary of a talk given at the workshop "Part and Whole" in Leiden during the period March 22-26, 201

A self-adjusted Monte Carlo simulation as model of financial markets with central regulation

Properties of the self-adjusted Monte Carlo algorithm applied to 2d Ising ferromagnet are studied numerically. The endogenous feedback form expressed in terms of the instant running averages is suggested in order to generate a biased random walk of the temperature that converges to criticality without an external tuning. The robustness of a stationary regime with respect to partial accessibility of the information is demonstrated. Several statistical and scaling aspects have been identified which allow to establish an alternative spin lattice model of the financial market. It turns out that our model alike model suggested by S. Bornholdt, Int. J. Mod. Phys. C {\bf 12} (2001) 667, may be described by L\'evy-type stationary distribution of feedback variations with unique exponent $\alpha_1 \sim 3.3$. However, the differences reflected by Hurst exponents suggest that resemblances between the studied models seem to be nontrivial.Comment: 19 pages, 9 figures, 30 reference

Machine Learning for Smart and Energy-Efficient Buildings

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning