On Explainable Deep Learning for Macroeconomic Forecasting and Finance

Deep Learning (DL) has gained momentum in recent years due to its incredible generalisation performance achieved across many learning tasks. Nevertheless, practitioners and academics have sometime been reluctant to apply these models because perceived as black boxes. This is particularly problematic in Economics and Finance. The objective of this thesis is to develop interpretable DL models and explainable DL tools with a focus on macroeconomic and financial applications. In doing so we highlight connections between such models and the standard economic ones. The first part of this work introduces a new class of interpretable models called Deep Dynamic Factor Models. The study merges the DL literature on autoencoders with that of the Econometrics on Dynamic Factor Models. Empirical validations of the approach are carried out both on synthetic and on real-time macroeconomic data. Part two of the work analyses feature attribution methods and Shapley values among explainability tools that are used to additively decompose model predictions. One of their limitations is highlighted, given that it is necessary to define a baseline that represents the missingness of a feature. A solution to the problem is proposed and compared against the ones currently in use both on simulated data and in the financial context of credit card default. We show that the proposed baseline is the only one that accounts for the specific use of the model. The final part of the work discusses the use of DL techniques for dynamic asset allocation. Using US market data, a comparison in recursive out-of-sample among different machine learning, economic-financial and hybrid models, including the one introduced in the first part of the work, is performed. Finally, a nonlinear factor-based portfolio performance attribution via the use of Shapley values and the baseline proposed in part two of the work is presented

Deep Dynamic Factor Models

We propose a novel deep neural net framework - that we refer to as Deep Dynamic Factor Model (D2FM) -, to encode the information available, from hundreds of macroeconomic and financial time-series into a handful of unobserved latent states. While similar in spirit to traditional dynamic factor models (DFMs), differently from those, this new class of models allows for nonlinearities between factors and observables due to the deep neural net structure. However, by design, the latent states of the model can still be interpreted as in a standard factor model. In an empirical application to the forecast and nowcast of economic conditions in the US, we show the potential of this framework in dealing with high dimensional, mixed frequencies and asynchronously published time series data. In a fully real-time out-of-sample exercise with US data, the D2FM improves over the performances of a state-of-the-art DFM

A Baseline for Shapley Values in MLPs: from Missingness to Neutrality

Being able to explain a prediction as well as having a model that performs well are paramount in many machine learning applications. Deep neural networks have gained momentum recently on the basis of their accuracy, however these are often criticised to be black-boxes. Many authors have focused on proposing methods to explain their predictions. Among these explainability methods, feature attribution methods have been favoured for their strong theoretical foundation: the Shapley value. A limitation of Shapley value is the need to define a baseline (aka reference point) representing the missingness of a feature. In this paper, we present a method to choose a baseline based on a neutrality value: a parameter defined by decision makers at which their choices are determined by the returned value of the model being either below or above it. Based on this concept, we theoretically justify these neutral baselines and find a way to identify them for MLPs. Then, we experimentally demonstrate that for a binary classification task, using a synthetic dataset and a dataset coming from the financial domain, the proposed baselines outperform, in terms of local explanability power, standard ways of choosing them

The Large Observatory For X-ray Timing: LOFT

LOFT, the Large Observatory for X-ray Timing, is a new space mission concept devoted to observations of Galactic and extra-Galactic sources in the X-ray domain with the main goals of probing gravity theory in the very strong field environment of black holes and other compact objects, and investigating the state of matter at supra-nuclear densities in neutron stars. The instruments on-board LOFT, the Large area detector and the Wide Field Monitor combine for the first time an unprecedented large effective area (~10 m2 at 8 keV) sensitive to X-ray photons mainly in the 2-30 keV energy range and a spectral resolution approaching that of CCD-based telescopes (down to 200 eV at 6 keV). LOFT is currently competing for a launch of opportunity in 2022 together with the other M3 mission candidates of the ESA Cosmic Vision Progra

Effect of aliskiren on post-discharge outcomes among diabetic and non-diabetic patients hospitalized for heart failure: insights from the ASTRONAUT trial

Aims The objective of the Aliskiren Trial on Acute Heart Failure Outcomes (ASTRONAUT) was to determine whether aliskiren, a direct renin inhibitor, would improve post-discharge outcomes in patients with hospitalization for heart failure (HHF) with reduced ejection fraction. Pre-specified subgroup analyses suggested potential heterogeneity in post-discharge outcomes with aliskiren in patients with and without baseline diabetes mellitus (DM). Methods and results ASTRONAUT included 953 patients without DM (aliskiren 489; placebo 464) and 662 patients with DM (aliskiren 319; placebo 343) (as reported by study investigators). Study endpoints included the first occurrence of cardiovascular death or HHF within 6 and 12 months, all-cause death within 6 and 12 months, and change from baseline in N-terminal pro-B-type natriuretic peptide (NT-proBNP) at 1, 6, and 12 months. Data regarding risk of hyperkalaemia, renal impairment, and hypotension, and changes in additional serum biomarkers were collected. The effect of aliskiren on cardiovascular death or HHF within 6 months (primary endpoint) did not significantly differ by baseline DM status (P = 0.08 for interaction), but reached statistical significance at 12 months (non-DM: HR: 0.80, 95% CI: 0.64-0.99; DM: HR: 1.16, 95% CI: 0.91-1.47; P = 0.03 for interaction). Risk of 12-month all-cause death with aliskiren significantly differed by the presence of baseline DM (non-DM: HR: 0.69, 95% CI: 0.50-0.94; DM: HR: 1.64, 95% CI: 1.15-2.33; P < 0.01 for interaction). Among non-diabetics, aliskiren significantly reduced NT-proBNP through 6 months and plasma troponin I and aldosterone through 12 months, as compared to placebo. Among diabetic patients, aliskiren reduced plasma troponin I and aldosterone relative to placebo through 1 month only. There was a trend towards differing risk of post-baseline potassium ≥6 mmol/L with aliskiren by underlying DM status (non-DM: HR: 1.17, 95% CI: 0.71-1.93; DM: HR: 2.39, 95% CI: 1.30-4.42; P = 0.07 for interaction). Conclusion This pre-specified subgroup analysis from the ASTRONAUT trial generates the hypothesis that the addition of aliskiren to standard HHF therapy in non-diabetic patients is generally well-tolerated and improves post-discharge outcomes and biomarker profiles. In contrast, diabetic patients receiving aliskiren appear to have worse post-discharge outcomes. Future prospective investigations are needed to confirm potential benefits of renin inhibition in a large cohort of HHF patients without D

The Large Observatory for x-ray timing

The Large Observatory For x-ray Timing (LOFT) was studied within ESA M3 Cosmic Vision framework and participated in the final down-selection for a launch slot in 2022-2024. Thanks to the unprecedented combination of effective area and spectral resolution of its main instrument, LOFT will study the behaviour of matter under extreme conditions, such as the strong gravitational field in the innermost regions of accretion flows close to black holes and neutron stars, and the supra-nuclear densities in the interior of neutron stars. The science payload is based on a Large Area Detector (LAD, 10 m2 effective area, 2-30 keV, 240 eV spectral resolution, 1° collimated field of view) and a WideField Monitor (WFM, 2-50 keV, 4 steradian field of view, 1 arcmin source location accuracy, 300 eV spectral resolution). The WFM is equipped with an on-board system for bright events (e.g. GRB) localization. The trigger time and position of these events are broadcast to the ground within 30 s from discovery. In this paper we present the status of the mission at the end of its Phase A study

The LOFT mission concept: a status update

The Large Observatory For x-ray Timing (LOFT) is a mission concept which was proposed to ESA as M3 and M4 candidate in the framework of the Cosmic Vision 2015-2025 program. Thanks to the unprecedented combination of effective area and spectral resolution of its main instrument and the uniquely large field of view of its wide field monitor, LOFT will be able to study the behaviour of matter in extreme conditions such as the strong gravitational field in the innermost regions close to black holes and neutron stars and the supra-nuclear densities in the interiors of neutron stars. The science payload is based on a Large Area Detector (LAD, >8m2 effective area, 2-30 keV, 240 eV spectral resolution, 1 degree collimated field of view) and a Wide Field Monitor (WFM, 2-50 keV, 4 steradian field of view, 1 arcmin source location accuracy, 300 eV spectral resolution). The WFM is equipped with an on-board system for bright events (e.g., GRB) localization. The trigger time and position of these events are broadcast to the ground within 30 s from discovery. In this paper we present the current technical and programmatic status of the mission

XIPE: the x-ray imaging polarimetry explorer

XIPE, the X-ray Imaging Polarimetry Explorer, is a mission dedicated to X-ray Astronomy. At the time of writing XIPE is in a competitive phase A as fourth medium size mission of ESA (M4). It promises to reopen the polarimetry window in high energy Astrophysics after more than 4 decades thanks to a detector that efficiently exploits the photoelectric effect and to X-ray optics with large effective area. XIPE uniqueness is time-spectrally-spatially- resolved X-ray polarimetry as a breakthrough in high energy astrophysics and fundamental physics. Indeed the payload consists of three Gas Pixel Detectors at the focus of three X-ray optics with a total effective area larger than one XMM mirror but with a low weight. The payload is compatible with the fairing of the Vega launcher. XIPE is designed as an observatory for X-ray astronomers with 75 % of the time dedicated to a Guest Observer competitive program and it is organized as a consortium across Europe with main contributions from Italy, Germany, Spain, United Kingdom, Poland, Sweden

Thermoregulated natural leather using phase change materials: An example of bioinspiration.

Natural leather is a very attractive material for applications involving industries ranging from that of technical clothes to fashion. Microencapsulated Phase Change Materials (PCMs) can be coated onto natural leathers using a polymer binder to add the thermoregulating properties demanded by modern consumers. The use of microcapsules enhances the thermal response of the leather during heating or cooling and the degree of thermal sensitivity depends on the percentage of microcapsules added onto the leather. The thermoregulating properties of leathers have been evaluated by both differential scanning calorimetry (DSC) and infrared thermocamera (IRT); water vapour transmission and mechanical properties were analyzed and compared to those of uncoated leather. The results of mechanical characterization indicate that tensile strength and elongation at break are not significantly affected by this coating treatment

Infrared Thermography: Some Applications of Microencapsulated PCMs as Investigative Tool for Heat Transport Evaluation

Microcapsulated Phase Change Materials (PCMs) were additivated onto the surface of natural leathers and mixed in the formulation of composites tailored for secondary indoor applications. The thermal response to external temperature dumping has been evaluated using an experimental equipment composed of a Peltier-effect device linked to an electrical wave generator and an infrared thermocamera to record images