The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

A fresh engineering approach for the forecast of financial index volatility and hedging strategies

This thesis attempts a new light on a problem of importance in Financial Engineering. Volatility is a commonly accepted measure of risk in the investment field. The daily volatility is the determining factor in evaluating option prices and in conducting different hedging strategies. The volatility estimation and forecast are still far from successfully complete for industry acceptance, judged by their generally lower than 50% forecasting accuracy. By judiciously coordinating the current engineering theory and analytical techniques such as wavelet transform, evolutionary algorithms in a Time Series Data Mining framework, and the Markov chain based discrete stochastic optimization methods, this work formulates a systematic strategy to characterize and forecast crucial as well as critical financial time series. Typical forecast features have been extracted from different index volatility data sets which exhibit abrupt drops, jumps and other embedded nonlinear characteristics so that accuracy of forecasting can be markedly improved in comparison with those of the currently prevalent methods adopted in the industry. The key aspect of the presented approach is "transformation and sequential deployment": i) transform the data from being non-observable to observable i.e., from variance into integrated volatility; ii) conduct the wavelet transform to determine the optimal forecasting horizon; iii) transform the wavelet coefficients into 4-lag recursive data sets or viewed differently as a Markov chain; iv) apply certain genetic algorithms to extract a group of rules that characterize different patterns embedded or hidden in the data and attempt to forecast the directions/ranges of the one-step ahead events; and v)apply genetic programming to forecast the values of the one-step ahead events. By following such a step by step approach, complicated problems of time series forecasting become less complex and readily resolvable for industry application. To implement such an approach, the one year, two year and five year S&PlOO historical data are used as training sets to derive a group of 100 rules that best describe their respective signal characteristics. These rules are then used to forecast the subsequent out-of-sample time series data. This set of tests produces an average of over 75% of correct forecasting rate that surpasses any other publicly available forecast results on any type of financial indices. Genetic programming was then applied on the out of sample data set to forecast the actual value of the one step-ahead event. The forecasting accuracy reaches an average of 70%, which is a marked improvement over other current forecasts. To validate the proposed approach, indices of S&P500 as well as S&P 100 data are tested with the discrete stochastic optimization method, which is based on Markov chain theory and involves genetic algorithms. Results are further validated by the bootstrapping operation. All these trials showed a good reliability of the proposed methodology in this research work. Finally, the thus established methodology has been shown to have broad applications in option pricing, hedging, risk management, VaR determination, etc

Essays on modeling and analysis of dynamic sociotechnical systems

A sociotechnical system is a collection of humans and algorithms that interact under the partial supervision of a decentralized controller. These systems often display in- tricate dynamics and can be characterized by their unique emergent behavior. In this work, we describe, analyze, and model aspects of three distinct classes of sociotech- nical systems: financial markets, social media platforms, and elections. Though our work is diverse in subject matter content, it is unified though the study of evolution- and adaptation-driven change in social systems and the development of methods used to infer this change. We first analyze evolutionary financial market microstructure dynamics in the context of an agent-based model (ABM). The ABM’s matching engine implements a frequent batch auction, a recently-developed type of price-discovery mechanism. We subject simple agents to evolutionary pressure using a variety of selection mech- anisms, demonstrating that quantile-based selection mechanisms are associated with lower market-wide volatility. We then evolve deep neural networks in the ABM and demonstrate that elite individuals are profitable in backtesting on real foreign ex- change data, even though their fitness had never been evaluated on any real financial data during evolution. We then turn to the extraction of multi-timescale functional signals from large panels of timeseries generated by sociotechnical systems. We introduce the discrete shocklet transform (DST) and associated similarity search algorithm, the shocklet transform and ranking (STAR) algorithm, to accomplish this task. We empirically demonstrate the STAR algorithm’s invariance to quantitative functional parameteri- zation and provide use case examples. The STAR algorithm compares favorably with Twitter’s anomaly detection algorithm on a feature extraction task. We close by using STAR to automatically construct a narrative timeline of societally-significant events using a panel of Twitter word usage timeseries. Finally, we model strategic interactions between the foreign intelligence service (Red team) of a country that is attempting to interfere with an election occurring in another country, and the domestic intelligence service of the country in which the election is taking place (Blue team). We derive subgame-perfect Nash equilibrium strategies for both Red and Blue and demonstrate the emergence of arms race inter- ference dynamics when either player has “all-or-nothing” attitudes about the result of the interference episode. We then confront our model with data from the 2016 U.S. presidential election contest, in which Russian military intelligence interfered. We demonstrate that our model captures the qualitative dynamics of this interference for most of the time under stud

Machine Learning Methods to Exploit the Predictive Power of Open, High, Low, Close (OHLC) Data

Novel machine learning techniques are developed for the prediction of financial markets, with a combination of supervised, unsupervised and Bayesian optimisation machine learning methods shown able to give a predictive power rarely previously observed. A new data mining technique named Deep Candlestick Mining (DCM) is proposed that is able to discover highly predictive dataset specific candlestick patterns (arrangements of open, high, low, close (OHLC) aggregated price data structures) which significantly outperform traditional candlestick patterns. The power that OHLC features can provide is further investigated, using LSTM RNNs and XGBoost trees, in the prediction of a mid-price directional change, defined here as the mid-point between either the open and close or high and low of an OHLC bar. This target variable has been overlooked in the literature, which is surprising given the relative ease of predicting it, significantly in excess of noisier financial quantities. However, the true value of this quantity is only known upon the period's ending – i.e. it is an after-the-fact observation. To make use of and enhance the remarkable predictability of the mid-price directional change, multi-period predictions are investigated by training many LSTM RNNs (XGBoost trees being used to identify powerful OHLC input feature combinations), over different time horizons, to construct a Bayesian optimised trend prediction ensemble. This fusion of long-, medium- and short-term information results in a model capable of predicting market trend direction to greater than 70% better than random. A trading strategy is constructed to demonstrate how this predictive power can be used by exploiting an artefact of the LSTM RNN training process which allows the trading system to size and place trades in accordance with the ensemble's predictive certainty

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression

Volatility is a key parameter when measuring the size of the errors made in modelling returns and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving- Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching GARCH (MS-GARCH) models have been widely applied. In statistical learning theory, Support Vector Regression (SVR) plays an important role in predicting nonlinear and nonstationary time series data. We propose a new class model comprised of a combination of a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function (aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV) to predict financial data i.e. EUR-USD exchange rates. The proposed novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates. Our simulation results show that our model significantly outperforms simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models and Markov chain Monte Carlo (MCMC) regression.Open Acces