An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

An Exploration of Simple Optimized Technical Trading Strategies

Honors (Bachelor's)StatisticsEconomicsUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/91813/1/chben.pd

Stock Market Prediction Using Evolutionary Support Vector Machines: An Application To The ASE20 Index

The main motivation for this paper is to introduce a novel hybrid method for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use an alternative computational methodology named evolutionary support vector machine (ESVM) stock predictor for modeling and trading the ASE20 Greek stock index extending the universe of the examined inputs to include autoregressive inputs and moving averages of the ASE20 index and other four financial indices. The proposed hybrid method consists of a combination of genetic algorithms with support vector machines modified to uncover effective short-term trading models and overcome the limitations of existing methods. For comparison purposes, the trading performance of the ESVM stock predictor is benchmarked with four traditional strategies (a naïve strategy, a buy and hold strategy, a moving average convergence/divergence and an autoregressive moving average model), and a multilayer perceptron neural network model. As it turns out, the proposed methodology produces a higher trading performance, even during the financial crisis period, in terms of annualized return and information ratio, while providing information about the relationship between the ASE20 index and DAX30, NIKKEI225, FTSE100 and S&P500 indices

A new approach for the quantification of qualitative measures of economic expectations

In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents’ expectations. The research focuses on experts’ expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents’ expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents’ judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance. We obtain the best results for Belgium, Norway, Austria, Lithuania, Japan and the United Kingdom.Peer ReviewedPostprint (author's final draft

A new approach for the quantification of qualitative measures of economic expectations

In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents' expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents' judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance

Robust optimization of algorithmic trading systems

GAs (Genetic Algorithms) and GP (Genetic Programming) are investigated for finding robust Technical Trading Strategies (TTSs). TTSs evolved with standard GA/GP techniques tend to suffer from over-fitting as the solutions evolved are very fragile to small disturbances in the data. The main objective of this thesis is to explore optimization techniques for GA/GP which produce robust TTSs that have a similar performance during both optimization and evaluation, and are also able to operate in all market conditions and withstand severe market shocks. In this thesis, two novel techniques that increase the robustness of TTSs and reduce over-fitting are described and compared to standard GA/GP optimization techniques and the traditional investment strategy Buy & Hold. The first technique employed is a robust multi-market optimization methodology using a GA. Robustness is incorporated via the environmental variables of the problem, i.e. variablity in the dataset is introduced by conducting the search for the optimum parameters over several market indices, in the hope of exposing the GA to differing market conditions. This technique shows an increase in the robustness of the solutions produced, with results also showing an improvement in terms of performance when compared to those offered by conducting the optimization over a single market. The second technique is a random sampling method we use to discover robust TTSs using GP. Variability is introduced in the dataset by randomly sampling segments and evaluating each individual on different random samples. This technique has shown promising results, substantially beating Buy & Hold. Overall, this thesis concludes that Evolutionary Computation techniques such as GA and GP combined with robust optimization methods are very suitable for developing trading systems, and that the systems developed using these techniques can be used to provide significant economic profits in all market conditions

Nature inspired computational intelligence for financial contagion modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

Evolutionary Algorithms Based on Effective Search Space Reduction for Financial Optimization Problems

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 문병로.This thesis presents evolutionary algorithms incorporated with effective search space reduction for financial optimization problems. Typical evolutionary algorithms try to find optimal solutions in the original, or unrestricted search space. However, they can be unsuccessful if the optimal solutions are too complex to be discovered from scratch. This can be relieved by restricting the forms of meaningful solutions or providing the initial population with some promising solutions. To this end, we propose three evolution approaches including modular, grammatical, and seeded evolutions for financial optimization problems. We also adopt local optimizations for fine-tuning the solutions, resulting in hybrid evolutionary algorithms. First, the thesis proposes a modular evolution. In the modular evolution, the possible forms of solutions are statically restricted to certain combinations of module solutions, which reflect more domain knowledge. To preserve the module solutions, we devise modular genetic operators which work on modular search space. The modular genetic operators and statically defined modules help genetic programming focus on highly promising search space. Second, the thesis introduces a grammatical evolution. We restrict the possible forms of solutions in genetic programming by a context-free grammar. In the grammatical evolution, genetic programming works on more extended search space than modular one. Grammatically typed genetic operators are introduced for the grammatical evolution. Compared with the modular evolution, grammatical evolution requires less domain knowledge. Finally, the thesis presents a seeded evolution. Our seeded evolution provides the initial population with partially optimized solutions. The set of genes for the partial optimization is selected in terms of encoding complexity. The partially optimized solutions help genetic algorithm find more promising solutions efficiently. Since they are not too excessively optimized, genetic algorithm is still able to search better solutions. Extensive empirical results are provided using three real-world financial optimization problems: attractive technical pattern discovery, extended attractive technical pattern discovery, and large-scale stock selection. They show that our search space reductions are fairly effective for the problems. By combining the search space reductions with systematic evolutionary algorithm frameworks, we show that evolutionary algorithms can be exploited for realistic profitable trading.1. Introduction 1 1.1 Search Methods 3 1.2 Search Space Reduction 4 1.3 Main Contributions 5 1.4 Organization 7 2. Preliminaries 8 2.1 Evolutionary Algorithms 8 2.1.1 Genetic Algorithm 10 2.1.2 Genetic Programing 11 2.2 Evolutionary Algorithms in Finance 12 2.3 Search Space Reduction 12 2.3.1 Modular Evolution 12 2.3.2 Grammatical Evolution 13 2.3.3 Seeded Evolution 14 2.3.4 Summary 14 2.4 Terminology 15 2.4.1 Technical Pattern and Technical Trading Rule 15 2.4.2 Forecasting Model and Trading Model 16 2.4.3 Portfolio and Rebalancing 17 2.4.4 Data Snooping Bias 17 2.5 Financial Optimization Problems 19 2.5.1 Attractive Technical Pattern Discovery and Its Extension 19 2.5.2 Stock Selection 20 2.6 Issues 21 2.6.1 General Assumptions 21 2.6.2 Performance Measure 22 3. Modular Evolution 23 3.1 Modular Genetic Programming 24 3.2 Hybrid Genetic Programming 28 3.3 Attractive Technical Pattern Discovery 29 3.3.1 Introduction 29 3.3.2 Problem Formulation 31 3.3.3 Modular Search Space 33 3.3.4 Experimental Results 35 3.3.5 Summary 41 4. Grammatical Evolution 44 4.1 Grammatical Type System 45 4.2 Hybrid Genetic Programming 47 4.3 Extended Attractive Technical Pattern Discovery 51 4.3.1 Introduction 51 4.3.2 Problem Formulation 54 4.3.3 Experimental Results 56 4.3.4 Summary 73 5. Seeded Evolution 76 5.1 Heuristic Seeding 77 5.2 Hybrid Genetic Algorithm 78 5.3 Large-Scale Stock Selection 81 5.3.1 Introduction 81 5.3.2 Problem Formulation 83 5.3.3 Ranking with Partitions 85 5.3.4 Experimental Results 87 5.3.5 Summary 96 6. Conclusions 104Docto

A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

[EN] In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating im- portant innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pat- tern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are de- veloped and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.The fourth author of this work was partially supported by MINECO, Project MTM2016-75963-P.Arévalo, R.; García, J.; Guijarro, F.; Peris Manguillot, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications. 81:177-192. https://doi.org/10.1016/j.eswa.2017.03.0281771928