MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization

Portfolio management is a fundamental problem in finance. It involves periodic reallocations of assets to maximize the expected returns within an appropriate level of risk exposure. Deep reinforcement learning (RL) has been considered a promising approach to solving this problem owing to its strong capability in sequential decision making. However, due to the non-stationary nature of financial markets, applying RL techniques to portfolio optimization remains a challenging problem. Extracting trading knowledge from various expert strategies could be helpful for agents to accommodate the changing markets. In this paper, we propose MetaTrader, a novel two-stage RL-based approach for portfolio management, which learns to integrate diverse trading policies to adapt to various market conditions. In the first stage, MetaTrader incorporates an imitation learning objective into the reinforcement learning framework. Through imitating different expert demonstrations, MetaTrader acquires a set of trading policies with great diversity. In the second stage, MetaTrader learns a meta-policy to recognize the market conditions and decide on the most proper learned policy to follow. We evaluate the proposed approach on three real-world index datasets and compare it to state-of-the-art baselines. The empirical results demonstrate that MetaTrader significantly outperforms those baselines in balancing profits and risks. Furthermore, thorough ablation studies validate the effectiveness of the components in the proposed approach

Evolution of Risks for Energy Companies from the Energy Efficiency Perspective: The Brazilian Case

This study aims to evaluate whether energy savings from energy efficiency programs in Brazil affected the risks taken by energy companies during the period 2000–2013, based on the following research question: Can we assume that energy conservation programs affect return risks to electrical energy companies? The results obtained through risk assessment models, exponentially weighted moving averages, and the capital asset pricing model indicated that during periods of crisis, both volatility and required returns were higher, but during less difficult periods, risks taken were significantly reduced.  Further, as research contribution, this research suggests the elimination the affirmative hypothesis that a possible increase in energy efficiency affects the risks taken by electrical energy companies. Keywords: Capital Asset Pricing Model; PROCEL, Energy Conservation Programs. JEL Classifications: E30; G38; K23; M48; Q

Equity Style Investing

Despite the well documented benefits of equity style investing in today’s financial markets, the academic view of the underlying cause for such benefits remains an ongoing debate. A number of theories have been proposed to explain why some asset classes earn better returns than others do under the same economic regimes. Rational finance links the outperformance of some stock groups to the equity characteristics that proxy for the common risk factors, behavioural finance, however, argues that mispricing resulting from irrational investor’s sentiment to fundamentals plays a key role. Meanwhile, a variety of business cycle variables have also suggested to contain information useful in explaining the expected stock returns. The observed style returns change all the time with predictable time-varying components, reflecting the structural and cyclical shocks to the macroeconomy. Motivated by the current ongoing controversy of anomaly versus risk compensation over interpreting equity style premiums, this thesis investigates how firm characteristics and business cycle conditions function separately to affect the style return dynamics based on the size and value-growth categorisations. It adds to the extant literature by explicitly examining the relative importance of the common risk factors versus firm-specific information as driving sources in the divergent equity style returns in the U.K. market. By identifying the dominant driving force that determines the relative style performance, it provides a further dimension to the current debate regarding the sources of style premiums and offers the choice of corresponding style investing strategies. The divergent style returns and its time-varying nature offer astute investors the opportunity to implement active style management to enhance portfolio returns. Motivated by the benefits of capitalising on such style return cyclicality and in particular the availability and popularity of Exchange Traded Funds based on market segments in leading financial markets as investment vehicle that offers low cost and high liquidity, this thesis examines a dynamic long-short tactical trading strategy by applying a binomial approach to focus on the rotation between pairs of equity styles. By answering key questions of whether equity style cycles exist in the U.K. market and whether the return dynamics of such style momentum strategy is distinct from the price and industry momentum effects, it contributes to the literature by providing valuable empirical evidence to compare with other studies in different economic and institutional environments. In response to the increasing popularity of using macro information to aid optimal style selection for the quant circles in the investment community, building on the methodology of Brandt and Santa-Clara (2006), this thesis approximates a solution of a mean-variance multi-style investor’s optimal style investing problem incorporating the business cycle predictability. This approach is parsimonious as the optimal style weights are parameterised directly on a set of pervasive business cycle predictors. By exploring how the distributions of the expected style returns and the location or the shape of the optimal style allocations are affected by given shocks to the business cycles, this thesis contributes to the extant literature by demonstrating the transmission mechanism of how business cycle volatility affects equity style return volatility and in turn a mean-variance investor’s optimal style allocation

El Mercado de Divisas (FOREX) como un modelo elástico de red

El Mercado de divisas, también conocido como FOREX, es un mercado financiero en el que bancos internacionales, compañías comerciales, inversores privados, entre otros agentes financieros pueden negociar sobre divisas. Este mercado mundial y descentralizado es considerado uno de los mayores mercados financieros en el mundo en términos de volumen de negociación. Así mismo, la predicción a tiempo real de los distintos activos financieros en el mercado FOREX aporta información de utilidad a los distintos participantes del mercado a la hora de tomar decisiones de negocio. La aportación de esta tesis a la literatura científica es la de elaborar un modelo predictivo del mercado FOREX, basado en principios físico-químicos, en concreto, un modelo elástico de red, bajo el contexto de un entorno de eficiencia de mercado en sentido débil. Este algoritmo, denominado elastic network model for the FOREX market (ENMX) presenta las siguientes características. En primer lugar, el algoritmo ENMX es capaz de reproducir la naturaleza inestable del mercado FOREX, permitiendo la predicción de precios de cotización alejados de su precio de equilibrio. Además, este modelo permite la simulación de mercado de hasta 21 pares de divisas relacionados entre sí, por lo tanto, es capaz de representar la evolución del mercado FOREX en su conjunto. En segundo lugar, la interacción entre los distintos inversores y cada precio de cotización particular, la cual podría producir pequeñas desviaciones en dicho precio de cotización, es representada por un movimiento aleatorio que sigue una distribución estadística que mejor se ajusta a los datos del histórico de cotizaciones analizado. De entre las distribuciones estadísticas analizadas, la distribución Pseudo-Voigt es la que mejor modeliza las variaciones de los precios de cotización del mercado FOREX. Tras el estudio de la especificación del modelo ENMX, se compararon las predicciones del modelo ENMX frente a modelos econométricos, tales como el VAR y el paseo aleatorio. Respecto a las métricas de comparación propuestas, se utilizaron el profit factor (PF), un indicador ampliamente utilizado en el mundo financiero como medida de rentabilidad y, por otro lado, la raíz del error cuadrático medio (RECM) como una de las métricas más utilizadas en el mundo de la econometría en términos de precisión de la predicción. Los resultados arrojaron luz de que el modelo ENMX fue capaz de predecir mejor en términos de rentabilidad y precisión frente a los modelos econométricos propuestos. Puesto que uno de los objetivos de esta tesis doctoral es el de realizar predicciones en tiempo real del mercado FOREX, la versión secuencial del algoritmo ENMX no es capaz de satisfacer estas necesidades. Por ello, surgió de manera natural la paralelización del modelo ENMX, al que denominamos parallel elastic network model for the FOREX market (PENMX). En esta tesis doctoral se proponen diferentes estrategias de paralelización heterogéneas basadas en computadores de memoria compartida, usando OpenMP, y computadores de memoria distribuida usando MPI. Siguiendo la línea anterior, se comparó la predicción del algoritmo PENMX frente al modelo VAR y paseo aleatorio usando las métricas anteriormente mencionadas. En este caso, se distinguieron dos situaciones de mercado frecuentemente detectadas en el mercado FOREX. Una de ellas, es una situación de mercado en la que se detecta una alta volatilidad presente en las diferentes divisas analizadas y la otra una situación de mercado tendencial, en la que se refleja una tendencia alcista o bajista en un período de tiempo determinado. Respecto a la situación de mercado de alta volatilidad, el modelo PENMX superó en términos de predicción al modelo VAR y al paseo aleatorio en un horizonte más a largo plazo (horizonte temporal superior a la hora). Sin embargo, en la situación de mercado tendencial, los modelos VAR y el paseo aleatorio mejoraron al algoritmo PENMX. Esto es debido a que el modelo PENMX no tiene en cuenta en la predicción los precios de cotización pasados mientras que estos modelos se basan en el comportamiento de los precios históricos para elaborar la predicción. Por último, dado que la presente tesis doctoral está enmarcada dentro del programa de doctorados industriales de la UCAM y como tal, esta tesis se enmarca dentro de las líneas de investigación e innovación de la empresa Artificial Intelligence Talentum, S.L., para el desarrollo de estrategias de trading automático mediante técnicas de inteligencia artificial y computación de altas prestaciones. Además de la investigación industrial realizada, en la presente tesis doctoral se ha realizado un estudio de modelo de negocio, que utilizando el modelo teórico propuesto y el código software operativo generado, pusiera a disposición de inversores privados e institucionales propuestas de inversión del mercado FOREX (señales de trading automático). El último capítulo de esta tesis doctoral tiene por motivación el realizar un estudio detallado de la aplicación de un modelo con capacidad de generación de predicciones de precios de cotización sobre varios instrumentos financieros de manera conjunta, relacionada y simultánea. En concreto, siete divisas con especial relevancia en el mercado estudiado (FOREX). De igual manera se pone de manifiesto la capacidad de generación de señales de trading automáticas en un corto período de tiempo, una hora, considerada en intervalos de cinco minutos. Se realiza un estudio del marco conceptual aplicable a esta aplicación industrial, tanto a nivel de tecnología aplicada como del mercado financiero específica en esta área. Se completa el mismo, con la investigación del mercado de los asistentes virtuales del mercado FOREX donde se ubica la aplicación industrial derivada de la tesis.Ingeniería, Industria y Construcció

Advanced neural networks : finance, forecast, and other applications

[no abstract

Investigating the structural diversity within a committee of classifiers and their generalization performance

This study investigates the measures of diversity within ensembles of classifiers. The use of neural networks is carried out in measuring ensemble diversity by the use of statistical and ecological methods and to some extent information theory. A new way of looking at ensemble diversity is proposed. This ensemble diversity is called ensemble structural diversity, for this study is concerned with the diversity within the structure of the individual classifiers forming an ensemble and not via the outcomes of the individual classifiers. Ensemble structural diversity was also induced within the ensemble by varying the structural parameters (learning parameters) of the artificial machines (classifiers). The importance or the use of these measures was judged by comparing the measure of structural diversity and the ensemble generalization performance. This was done so that comparisons can be made on the robustness of the idea of structural diversity and its relationship with ensemble generalization performance. It was found that diversity could be induced by having ensembles with different structural and implicit (e.g learning) parameters and that this diversity does influence the predictive ability of the ensemble. This was concurrent with literature even though within literature ensemble diversity was viewed from the output as opposed to the structure of the individual classifiers. As the structural diversity increased so did the generalization performance. However there was a point where structural diversity decreased the generalization performance of the ensemble, where from that point onwards as the structural diversity increased the generalization performance decreased. This makes sense because too much of diversity within the ensemble might mean no consensus is reached at all. The disadvantages of comparing structural diversity and the generalization performance (accuracy) of the ensemble are that: an ensemble can be structurally diverse even though all the classifiers within the ensemble approximate the same function which means in this case structural diversity is meaningless in terms of improving the accuracy of the ensemble. The use of ensemble structural diversity measures in developing efficient ensembles still remains to be explored. This study, however, has also shown that diversity can be measured from the structural parameters and moreover reducing the abstractness of diversity by being able to quantify structural diversity making it possible to map a relationship between structural diversity and accuracy. It was observed that structural diversity does improve the accuracy of the ensemble, however, within a limited region of structural diversity

Human Factors in Automated and Robotic Space Systems: Proceedings of a symposium. Part 1

Human factors research likely to produce results applicable to the development of a NASA space station is discussed. The particular sessions covered in Part 1 include: (1) system productivity -- people and machines; (2) expert systems and their use; (3) language and displays for human-computer communication; and (4) computer aided monitoring and decision making. Papers from each subject area are reproduced and the discussions from each area are summarized

Town report Milford, New Hampshire 2008.

This is an annual report containing vital statistics for a town/city in the state of New Hampshire

A Comparative Study of Middle School Deaf Students’ Perceptions towards Vocational Internships According to Their Gender, Grade Level and Family Income at The Special Education School of Qujing, China

The purpose of this study was to identify the demographic factors of the deaf students, to determine the deaf students’ perceptions towards vocational internships, and to compare the deaf students’ perceptions towards vocational internships at the Special Education School of Qujing according to gender, grade level and family income in 2015. A total of 147 deaf students (106 male and 41 female), from grade level 7 to vocational high school completed the survey. Statistical measures employed included frequency and percentage, mean and standard deviation, one-way ANOVA and independent samples t-test. The result of this study has indicated that gender difference was not a significant issue to impact students’ perceptions, yet the researcher discovered that students from different grade levels and different extents of family income had significant perception differences.Specifically, students from a higher grade level had higher perceptions than those from lower grade levels. In terms of family income, students from families whose monthly income was lower or included 1000 RMB had lower perceptions than other students. Recommendations for directors, teachers, the school, the students and future researchers are provided