Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination

Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, the analyst needs to proper fine-tune its strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact into such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategies calibration and aggregation. To this purpose, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategies calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategies calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha

Program Evaluation and Causal Inference with High-Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function-valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post-regularization and post-selection inference that are uniformly valid (honest) across a wide-range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets.Comment: 118 pages, 3 tables, 11 figures, includes supplementary appendix. This version corrects some typos in Example 2 of the published versio

The Kentucky Noisy Monte Carlo Algorithm for Wilson Dynamical Fermions

We develop an implementation for a recently proposed Noisy Monte Carlo approach to the simulation of lattice QCD with dynamical fermions by incorporating the full fermion determinant directly. Our algorithm uses a quenched gauge field update with a shifted gauge coupling to minimize fluctuations in the trace log of the Wilson Dirac matrix. The details of tuning the gauge coupling shift as well as results for the distribution of noisy estimators in our implementation are given. We present data for some basic observables from the noisy method, as well as acceptance rate information and discuss potential autocorrelation and sign violation effects. Both the results and the efficiency of the algorithm are compared against those of Hybrid Monte Carlo. PACS Numbers: 12.38.Gc, 11.15.Ha, 02.70.Uu Keywords: Noisy Monte Carlo, Lattice QCD, Determinant, Finite Density, QCDSPComment: 30 pages, 6 figure

Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases

In many applications, such as physiology and finance, large time series data bases are to be analyzed requiring the computation of linear, nonlinear and other measures. Such measures have been developed and implemented in commercial and freeware softwares rather selectively and independently. The Measures of Analysis of Time Series ({\tt MATS}) {\tt MATLAB} toolkit is designed to handle an arbitrary large set of scalar time series and compute a large variety of measures on them, allowing for the specification of varying measure parameters as well. The variety of options with added facilities for visualization of the results support different settings of time series analysis, such as the detection of dynamics changes in long data records, resampling (surrogate or bootstrap) tests for independence and linearity with various test statistics, and discrimination power of different measures and for different combinations of their parameters. The basic features of {\tt MATS} are presented and the implemented measures are briefly described. The usefulness of {\tt MATS} is illustrated on some empirical examples along with screenshots.Comment: 25 pages, 9 figures, two tables, the software can be downloaded at http://eeganalysis.web.auth.gr/indexen.ht

A Panel Threshold Model of Tourism Specialization and Economic Development

The significant impact of international tourism in stimulating economic growth is especially important from a policy perspective. For this reason, the relationship between international tourism and economic growth would seem to be an interesting empirical issue. In particular, if there is a causal link between international tourism demand and economic growth, then appropriate policy implications may be developed. The purpose of this paper is to investigate whether tourism specialization is important for economic development in East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, the Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa, over the period 1991-2008. The impact of the degree of tourism specialization, which is incorporated as a threshold variable, on economic growth is examined for a wide range of countries at different stages of economic development. The empirical results from threshold estimation identify two endogenous cut-off points, namely 14.97% and 17.50%. This indicates that the entire sample should be divided into three regimes. The results from panel threshold regression show that there exists a positive and significant relationship between economic growth and tourism in two regimes, the regime with the degree of tourism specialization lower than 14.97% (regime 1) and the regime with the degree of tourism specialization between 14.97% and 17.50% (regime 2). However, the magnitudes of the impact of tourism on economic growth in those two regimes are not the same, with the higher impact being found in regime 2. An insignificant relationship between economic growth and tourism is found in regime 3, in which the degree of tourism specialization is greater than 17.50%. The empirical results suggest that tourism growth does not always lead to economic growth.

"A Panel Threshold Model of Tourism Specialization and Economic Development"

The significant impact of international tourism in stimulating economic growth is especially important from a policy perspective. For this reason, the relationship between international tourism and economic growth would seem to be an interesting empirical issue. In particular, if there is a causal link between international tourism demand and economic growth, then appropriate policy implications may be developed. The purpose of this paper is to investigate whether tourism specialization is important for economic development in East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, the Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa, over the period 1991-2008. The impact of the degree of tourism specialization, which is incorporated as a threshold variable, on economic growth is examined for a wide range of countries at different stages of economic development. The empirical results from threshold estimation identify two endogenous cut-off points, namely 14.97% and 17.50%. This indicates that the entire sample should be divided into three regimes. The results from panel threshold regression show that there exists a positive and significant relationship between economic growth and tourism in two regimes, the regime with the degree of tourism specialization lower than 14.97% (regime 1) and the regime with the degree of tourism specialization between 14.97% and 17.50% (regime 2). However, the magnitudes of the impact of tourism on economic growth in those two regimes are not the same, with the higher impact being found in regime 2. An insignificant relationship between economic growth and tourism is found in regime 3, in which the degree of tourism specialization is greater than 17.50%. The empirical results suggest that tourism growth does not always lead to economic growth.

Economic regimes identification using machine learning technics

43 páginas.Trabajo de Máster en Economía, Finanzas y Computación. Director: Dr. José Manuel Bravo Caro. Economic conditions over long time periods can be distinguished by regimes. Regime identification has been object of numerous investigations in economics and financial modeling for years. Recently, new machine learning technics such as decision trees, support vector machines and neural networks, among others, followed by alternative datasets and cheap computational processing power became available, allowing for alternative ways to model complex economic relationships. In the present work, we develop a supervised machine learning classifier using Random Forest technic to identify economic regimes using the S&P 500 stock market index series.Las condiciones económicas durante largos períodos de tiempo pueden distinguirse por regímenes. La identificación del régimen ha sido objeto de numerosas investigaciones en economía y modelos financieros durante años. Recientemente, se pusieron a disposición nuevas técnicas de aprendizaje automático, como árboles de decisión, máquinas de suporte vectorial y redes neuronales, entre otras, seguidas de conjuntos de datos alternativos y una capacidad de procesamiento computacional barata, que permite formas alternativas de modelar relaciones económicas complejas. En el presente trabajo, desarrollamos un clasificador de aprendizaje automático supervisado utilizando la técnica de Random Forest para identificar regímenes económicos utilizando la serie del índices de mercado S&P 500

Measuring Economic Journals' Citation Efficiency: A Data Envelopment Analysis Approach

This paper by using Data Envelopment Analysis (DEA) and statistical inference evaluates the citation performance of 229 economic journals. The paper categorizes the journals into four main categories (A to D) based on their efficiency levels. The results are then compared to the 27 “core economic journals” as introduced by Dimond (1989). The results reveal that after more than twenty years Diamonds’ list of “core economic journals” is still valid. Finally, for the first time the paper uses data from four well-known databases (SSCI, Scopus, RePEc, Econlit) and two quality ranking reports (Kiel Institute internals ranking and ABS quality ranking report) in a DEA setting and in order to derive the ranking of 229 economic journals. The ten economic journals with the highest citation performance are Journal of Political Economy, Econometrica, Quarterly Journal of Economics, Journal of Financial Economics, Journal of Economic Literature, American Economic Review, Review of Economic Studies, Journal of Econometrics, Journal of Finance, Brookings Papers on Economic Activity.Ranking journals; Data Envelopment Analysis; Indexing techniques; Nonparametric analysis.