Deep learning for volatility forecasting in asset management

Predicting volatility is a critical activity for taking risk- adjusted decisions in asset trading and allocation. In order to provide effective decision-making support, in this paper we investigate the profitability of a deep Long Short-Term Memory (LSTM) Neural Network for forecasting daily stock market volatility using a panel of 28 assets representative of the Dow Jones Industrial Average index combined with the market factor proxied by the SPY and, separately, a panel of 92 assets belonging to the NASDAQ 100 index. The Dow Jones plus SPY data are from January 2002 to August 2008, while the NASDAQ 100 is from December 2012 to November 2017. If, on the one hand, we expect that this evolutionary behavior can be effectively captured adaptively through the use of Artificial Intelligence (AI) flexible methods, on the other, in this setting, standard parametric approaches could fail to provide optimal predictions. We compared the volatility forecasts generated by the LSTM approach to those obtained through use of widely recognized benchmarks models in this field, in particular, univariate parametric models such as the Realized Generalized Autoregressive Conditionally Heteroskedastic (R-GARCH) and the Glosten–Jagannathan–Runkle Multiplicative Error Models (GJR-MEM). The results demonstrate the superiority of the LSTM over the widely popular R-GARCH and GJR-MEM univariate parametric methods, when forecasting in condition of high volatility, while still producing comparable predictions for more tranquil periods.publishedVersionPeer reviewe

Correntropy-Based Evolving Fuzzy Neural System

In this paper, a correntropy-based evolving fuzzy neural system (correntropy-EFNS) is proposed for approximation of nonlinear systems. Different from the commonly used meansquare error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build EFNS based on the correntropy concept to achieve a more stable evolution of the rule base and update of the rule parameters instead of the commonly used meansquare error criterion. The correntropy-EFNS (CEFNS) begins with an empty rule base and all rules are evolved online based on the correntropy criterion. The consequent part parameters are tuned based on the maximum correntropy criterion where the correntropy is used as the cost function so as to improve the non-Gaussian noise rejection ability. The steady-state convergence performance of the CEFNS is studied through the calculation of the steady-state excess mean square error (EMSE) in two cases: i) Gaussian noise; and ii) non-Gaussian noise. Finally, the CEFNS is validated through a benchmark system identification problem, a Mackey-Glass time series prediction problem as well as five other real-world benchmark regression problems under both noise-free and noisy conditions. Compared with other evolving fuzzy neural systems, the simulation results show that the proposed CEFNS produces better approximation accuracy using the least number of rules and training time and also owns superior non-Gaussian noise handling capability

Particle Swarm Optimized Autonomous Learning Fuzzy System

The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this paper introduces a particles warm-based approach for EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the “one pass” learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without a full retraining. Experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Mid-Price Movement Prediction in Limit Order Books Using Feature Engineering and Machine Learning

The increasing complexity of financial trading in recent years revealed the need for methods that can capture its underlying dynamics. An efficient way to organize this chaotic system is by contracting limit order book ordering mechanisms that operate under price and time filters. Limit order book can be analyzed using linear and nonlinear models. The thesis develops novelmethods for the identification of limit order book characteristics which provide traders and market makers an information edge in their trading. A good proxy for traders and market makers is the prediction of mid-price movement, which is the main target of this thesis. The contributions of this thesis are categorized chronologically into three parts. The first part refers to the introduction in the literature of the first publicly available limit order book dataset for high-frequency trading for the task of mid-price movement prediction. This dataset comes together with the development of an experimental protocol that utilizes methods inspired by ridge regression and a single layer feed-forward neural network as classifiers. These classifiers use state-of-the-art limit order book features as inputs for the target task. The next contribution of this thesis is the use and development of a wide range of technical and quantitative indicators for the task of mid-price movement prediction via an extensive feature selection process. This feature selection process identifies which features improve predictability performance. The results suggest that the newly introduced quantitative feature based on an adaptive logistic regression model for online learning was selected first according to several criteria. These criteria operate according to entropy, linear discriminant analysis, and least mean square error. The third contribution is the introduction of econometric features as inputs to deep learning models for the task of mid-price movement prediction. An extensive comparison against other state-of-the-art hand-crafted features and fully automated feature extraction processes is provided. Furthermore, a new experimental protocol is developed for the task of mid-price prediction, to overcome the problem of time irregularities, which characterizes high-frequency data. Results suggest that advanced hand-crafted features such as econometric indicators can predict movements of proxies, such as mid-price

Socio-Economic Assessment of Fusion Energy Research, Development, Demonstration and Deployment Programme

Providing safe, clean and affordable energy supply is essential for meeting the basic needs of human society and for supporting economic growth. From the historical perspective, the constantly growing energy use was one of the main factors, which drove the industrialised countries to the current level of prosperity. Meanwhile, in recent decades, the issue of global energy security became a topic of increasing concern in the international policy agenda. On the one hand, the world is facing the problem of exhaustion of most convenient and cheep fuel reserves. The situation is becoming worse, because of the constantly growing demand in developing countries, and the oligopolistic behaviour of major energy exporting countries. On the other hand, the society is becoming more and more sensitive to the environmental pollution problems, caused by the excessive consumption of fossil fuels. In the face of energy security challenge, national governments ought to implement adequate strategies aimed at liberalisation of energy markets, diversification of energy supply mix, enhancement of energy efficiency, encouragement of investments in energy infrastructures, and promotion of innovation in energy sector. In a longer term perspective, the latter point becomes increasingly important, because the world is relying currently on the consumption of non-renewable fossil fuels, and the development of new safe, clean and resource unconstraint energy technologies is vitally needed. In line with this strategy, the major world economies pursue the joint R&D programme on thermonuclear Fusion technology, which represents numerous advantages due to its inherent safety, avoidance of CO2 emissions, relatively small environmental impact, abundance and world-wide uniform distribution of fuel resources. Considering the importance of the projected environmental and economic benefits of Fusion, the questions are raised whether the current level of financial support is sufficient, and what could be the optimal strategy to proceed with the demonstration of Fusion technology, given the time span and potential risks of Fusion RDDD programme. To put these questions into the context, one has to consider the current trends in energy R&D funding, which has seen a drastic decline ( ~50%) over the last three decades. The liberalisation of energy sector poses additional problem due to the fact that free markets partially failure to provide public goods, such as basic science and R&D, because of the so-called spillover effects meaning that the firms are not able to appropriate the integral results of their R&D investments. Regarding the thermonuclear Fusion technology, the decision makers responsible for national energy policies and allocation of public R&D funds may face the following specific questions: What is the expected net socio-economic payoff (social rate of return) of Fusion R&D programme, including both internal and external costs and benefits? What are the reasonable economic arguments that could justify the increase in public funding of the ongoing and future Fusion R&D activities and would stimulate greater involvement of the private sector? What additional value can be obtained through undertaking a more ambitious Fusion R&D programme (accelerated development path), which requires bigger number of experimental facilities, increased funding, and more intense overall efforts of international scientific and industrial community? In order to provide sound arguments for policymakers seeking to optimise public R&D funding, a robust socio-economic evaluation of the whole Fusion research, development, demonstration and deployment (RDDD) programme is needed. At the present stage, prospective analyses of Fusion technology have been emphasised mainly on the investigation of technological issues, estimation of the direct costs of Fusion power and analysis of its potential role in future energy systems. Meanwhile, methodological tools and practical studies aiming at a more comprehensive socio-economic assessment of global long-term energy R&D programmes, such as Fusion, are still incomplete. The primary difficulty concerns the evaluation of positive externalities that may reveal through different types of spillover effects, including but not limited to knowledge, network and market spillovers. While the presence of these effects has been identified in the economic theory and confirmed by empirical studies, their quantitative analysis in the specific case of large scale energy R&D programmes represents some methodological lacuna and deserves further investigation. Another problem relates to the methodology of cost-benefit analysis, which oftentimes ignores the hidden value of R&D projects arising due to the possible flexibility in managerial decisions. In fact, throughout the course of any R&D project, its prospective cash-flows can be significantly improved by pro-active management of different implementation stages, e.g. expanding the production, if market conditions are favourable, or abandoning, if R&D process appears to be unproductive. As a result, the strategic value of any R&D project normally exceeds its net present value (NPV) calculated with the traditional discounted cash flow (DCF) method. Although this strategic approach to capital budgeting, known as Real Options, has been propagated recently in several publications dealing with appraisal of lumpy irreversible investments, its practical application in the context of Fusion RDDD programme has not been mastered yet to the required extent. A particular challenge consists in the need for adequate treatment of different types of uncertainty in the model structure, parameters and input data. Accordingly, the main objective of this thesis consists in complementing the existing studies with an in-depth analysis of the positive externalities (spillover benefits) of Fusion RDDD programme and calculation of its strategic real options value subject to different managerial strategies throughout demonstration and deployment stages. Net social present value of Fusion RDDD programme and potential impact of Fusion R&D activities on the economic performance of the involved private companies are estimated using an integrated modelling framework, which includes the following components: (1) assessment of technological potential for deployment of Fusion power plants based on the simulation of multi-regional long term electricity supply scenarios with PLANELEC model; (2) economic evaluation of Fusion RDDD programme and analysis of different implementation strategies using Real Options model; (3) estimation of the economic value of spillover benefits from participation in Fusion R&D projects at the microeconomic level with the help of financial evaluation model; (4) strategic evaluation of Fusion RDDD programme, taking into account both spillover benefits and real options value, and policy recommendations