Machine Learning methods in climate finance: a systematic review

Evitar la materialización del cambio climático es uno de los principales retos de nuestro tiempo. En esta tarea, el sector financiero desempeña un papel fundamental, motivando a economistas académicos a desarrollar un nuevo campo de investigación, las finanzas climáticas. A la vez, el uso de tecnologías de aprendizaje automático (ML, por sus siglas en inglés) se ha popularizado para analizar problemas relacionados con las finanzas climáticas, debido principalmente a la necesidad de gestionar un volumen elevado de datos relacionados con el clima, y para modelizar relaciones no lineales entre variables climáticas y económicas. De esta manera, proponemos una revisión de la literatura académica para explorar cómo esta tecnología está posibilitando el crecimiento de las finanzas climáticas. Para ello, primero realizamos una búsqueda sistemática de estudios en esta materia en tres bases de datos científicas. Luego, usando un modelo de identificación automática de temas (Latent Dirichlet Allocation), identificamos estadísticamente siete áreas del conocimiento donde el ML está desempeñando un papel relevante: catástrofes naturales, biodiversidad, riesgo agrícola, mercados de carbono, energía, inversión responsable y datos climáticos. Para finalizar, hacemos un análisis de las principales tendencias de publicación, así como una clasificación de los modelos estadísticos utilizados en función del área de estudio. La principal contribución de este artículo es la provisión de una estructura de temas o problemas solventados gracias al uso del ML en finanzas climáticas, lo cual esperamos que facilite a expertos en esta tecnología la comprensión de las principales fortalezas y limitaciones de dicha tecnología aplicada en este campo de investigación.Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts. To pursue this objective, first we perform a systematic search of three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular areas where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Second, we perform an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area

Building Energy Modeling with OpenStudio : A Practical Guide for Students and Professionals

The energy, environmental, and societal challenges of the twenty-first century are here; they are crystal clear; and they are daunting. Our responses to those challenges are less clear, but one component at least is obvious—we need a better building stock, one that uses less energy, provides greater comfort and security, and houses and supports the economic activity of a rapidly growing and urbanizing population. One of the most powerful tools in our collective belts is building energy modeling (BEM), physics-based software simulation of building energy use given a description of the physical building, its use patterns, and prevailing weather conditions. BEM is a sine qua non tool for designing and operating buildings to the levels of energy efficiency that our future and present require. According to the AIA 2030 Commitment report, buildings designed using BEM use 20% less energy than those designed without it. BEM is also instrumental in developing and updating the codes, standards, certificates, and financial incentive infrastructure that supports energy efficiency in all building projects, including those that don’t directly use BEM. The OpenStudio project has been a driving force in the evolution of BTO’s BEM program. OpenStudio was BTO’s first truly open-source software project, a strategic direction that has influenced BTO’s entire BEM portfolio. Open-source is not an altruistic emergent enterprise. Successful open-source projects are funded, centrally managed, and resemble proprietary software projects in many structural and operational ways. Source control. Code reviews. Regression testing. Bug reporting and fixing. Pre-feature documentation. Post-feature documentation. The full Monty

A deep generative model framework for creating high quality synthetic transaction sequences

Synthetic data are artificially generated data that closely model real-world measurements, and can be a valuable substitute for real data in domains where it is costly to obtain real data, or privacy concerns exist. Synthetic data has traditionally been generated using computational simulations, but deep generative models (DGMs) are increasingly used to generate high-quality synthetic data. In this thesis, we create a framework which employs DGMs for generating highquality synthetic transaction sequences. Transaction sequences, such as we may see in an online banking platform, or credit card statement, are important type of financial data for gaining insight into financial systems. However, research involving this type of data is typically limited to large financial institutions, as privacy concerns often prevent academic researchers from accessing this kind of data. Our work represents a step towards creating shareable synthetic transaction sequence datasets, containing data not connected to any actual humans. To achieve this goal, we begin by developing Banksformer, a DGM based on the transformer architecture, which is able to generate high-quality synthetic transaction sequences. Throughout the remainder of the thesis, we develop extensions to Banksformer that further improve the quality of data we generate. Additionally, we perform extensively examination of the quality synthetic data produced by our method, both with qualitative visualizations and quantitative metrics

On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

Epidemiological studies of bovine digital dermatitis in pasture-based dairy system in New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Veterinary Sciences at Massey University, Palmerston North, New Zealand

Listed in 2020 Dean's List of Exceptional ThesesAppendices 1, 2, 3, 6 & 7 were removed for copyright reasons, but the published articles may be accessed via the following links: Appendix 1. Farm level risk factors for bovine digital dermatitis in Taranaki, New Zealand: An analysis using a Bayesian hurdle model https://doi.org/10.1016/j.tvjl.2018.02.012 Appendix 2. Effects of climate and farm management practices on bovine digital dermatitis in spring-calving pasture-based dairy farms in Taranaki, New Zealand https://doi.org/10.1016/j.tvjl.2019.03.004 Appendix 3. Estimating the herd and cow level prevalence of bovine digital dermatitis on New Zealand dairy farms: A Bayesian superpopulation approach https://doi.org/10.1016/j.prevetmed.2019.02.014 Appendix 6. Inter-observer agreement between two observers for bovine digital dermatitis identification in New Zealand using digital photographs https://doi.org/10.1080/00480169.2019.1582369 Appendix 7. Detecting bovine digital dermatitis in the milking parlour: To wash or not to wash, a Bayesian superpopulation approach https://doi.org/10.1016/j.tvjl.2019.02.011Bovine digital dermatitis (BDD) is an infectious disease of the feet of cattle. Worldwide, it is one of the most commonly observed foot diseases on many dairy farms, and is the most important infectious cause of lameness in cattle in confined dairy system. Although BDD is generally less common in pasture-based dairy system it can still cause significant production losses and welfare issues, in such systems. This thesis contains seven original research works covering the epidemiological aspects of BDD in pasture-based cattle in New Zealand. Firstly, cross-sectional and longitudinal data obtained from Taranaki were analysed to identify the factors (including climate) associated with the disease. This was followed by a large scale cross-sectional study covering four regions in New Zealand looking at the prevalence of and risk factors for BDD. A longitudinal study was then undertaken on three farms in order to collect disease data (including BDD lesion type) over a lactation. Using this dataset, a deterministic compartment model was built to study the transmission dynamics of BDD within a dairy herd in New Zealand. Along with these large studies, two small validation studies were also carried out. The first study evaluated the agreement between two trained BDD observers in determining BDD presence/ absence in digital photographs, while the second one evaluated the reliability of clinical examination of BDD lesions in the milking parlour without prior washing of the animals’ feet. This work suggests that BDD has spread widely across New Zealand, although it has yet to reach the West Coast. In the four regions where BDD was identified, true between herd prevalences varied by region (from ~ 40% to > 65%). Furthermore, although BDD was found in many herds, true cow level prevalence was low in all affected regions, being generally less than 4% in affected herds. Several biosecurity related management practices were repeatedly identified as factors associated with increased BDD prevalence at both the herd and cow level. These included mixing heifers with animals from other properties; purchasing heifers for replacement and using outside staff to treat lame cows. In addition to the identified management practices, climate (rainfall and soil temperature) was also found to have had a significant association with the prevalence of BDD. These studies used examination in the milking parlour as the method of identifying BDD lesions. This method while the best method of lesion detection for large scale studies is not perfect. It generally requires that feet are washed prior to examination, as lesions masked by dirt are difficult to identify. Our study quantified the effect, under New Zealand conditions, of feet washing prior to examination finding sensitivities of 0.34 (95% credible interval [CrI]: 0.088-0.69) and 0.63 (95%CrI: 0.46- 0.78) for pre- and post-washing, respectively. There was a 93.95% probability that the sensitivity of examination post-washing was greater than that prewashing. Limited information on the reliability of examination in the milking parlour prompted comparison of two trained observers using digital photographs. Agreement between the two observers was good; we could be 75% sure that the two observers had almost perfect agreement and 95% sure the two observers had at least substantial agreement. It is crucial that since examination in the milking parlour is not a perfect reference test for detecting BDD lesions that when estimating prevalence, the sensitivity and specificity of this method is factored into the analysis. This is often achieved using an approach based on the binomial distribution. However, as the dairy herd is a finite population and the sampling of animals for BDD lesion is effectively sampling without replacement, the correct distribution to use is the hypergeometric one. This is computationally complex so the Bayesian superpopulation approach was developed to allow continued use of the binomial distribution. The superpopulation approach was used to estimate prevalence in this thesis, one of the first uses of this approach in the veterinary field. The appearance of BDD in New Zealand is different from that elsewhere. Most lesion have been observed are small grey, rubbery lesions which may or may not have thickened, darker edges. Less commonly larger, more proliferative lesions can also be found. Red active lesions are extremely rare. Post-treatment lesions are not a feature of the disease in New Zealand as lesions are treated only very rarely. Thus modelling approach used a BDD score system which focuses on early stage of BDD. This found that in infected dairy herds, although BDD prevalence will tend to increase year-on-year it is likely to remain relatively low (<18%) even after 10 years of within-herd transmission. It is likely that the low transmission rate during the late lactation (model assumption) results in more cases resolving than developing during this period and therefore results in the low prevalence of infectious cattle at the start of each subsequent lactation. Cattle with larger, more proliferative lesions had a stronger influence on the establishment and maintenance of DD than cattle with small lesions highlighting the importance of targeting these animals for intervention

Railway Research

This book focuses on selected research problems of contemporary railways. The first chapter is devoted to the prediction of railways development in the nearest future. The second chapter discusses safety and security problems in general, precisely from the system point of view. In the third chapter, both the general approach and a particular case study of a critical incident with regard to railway safety are presented. In the fourth chapter, the question of railway infrastructure studies is presented, which is devoted to track superstructure. In the fifth chapter, the modern system for the technical condition monitoring of railway tracks is discussed. The compact on-board sensing device is presented. The last chapter focuses on modeling railway vehicle dynamics using numerical simulation, where the dynamical models are exploited

Survey of quantitative investment strategies in the Russian stock market : Special interest in tactical asset allocation

Russia’s financial markets have been an uncharted area when it comes to exploring the performance of investment strategies based on modern portfolio theory. In this thesis, we focus on the country’s stock market and study whether profitable investments can be made while at the same time taking uncertainties, risks, and dependencies into account. We also pay particular interest in tactical asset allocation. The benefit of this approach is that we can utilize time series forecasting methods to produce trading signals in addition to optimization methods. We use two datasets in our empirical applications. The first one consists of nine sectoral indices covering the period from 2008 to 2017, and the other includes altogether 42 stocks listed on the Moscow Exchange covering the years 2011 – 2017. The strategies considered have been divided into five sections. In the first part, we study classical and robust mean-risk portfolios and the modeling of transaction costs. We find that the expected return should be maximized per unit expected shortfall while simultaneously requiring that each asset contributes equally to the portfolio’s tail risk. Secondly, we show that using robust covariance estimators can improve the risk-adjusted returns of minimum variance portfolios. Thirdly, we note that robust optimization techniques are best suited for conservative investors due to the low volatility allocations they produce. In the second part, we employ statistical factor models to estimate higher-order comoments and demonstrate the benefit of the proposed method in constructing risk-optimal and expected utility-maximizing portfolios. In the third part, we utilize the Almgren–Chriss framework and sort the expected returns according to the assumed momentum anomaly. We discover that this method produces stable allocations performing exceptionally well in the market upturn. In the fourth part, we show that forecasts produced by VECM and GARCH models can be used profitably in optimizations based on the Black–Litterman, copula opinion pooling, and entropy pooling models. In the final part, we develop a wealth protection strategy capable of timing market changes thanks to the return predictions based on an ARIMA model. Therefore, it can be stated that it has been possible to make safe and profitable investments in the Russian stock market even when reasonable transaction costs have been taken into account. We also argue that market inefficiencies could have been exploited by structuring statistical arbitrage and other tactical asset allocation-related strategies.Venäjän rahoitusmarkkinat ovat olleet kartoittamatonta aluetta tutkittaessa moderniin portfolioteoriaan pohjautuvien sijoitusstrategioiden käyttäytymistä. Tässä tutkielmassa keskitymme maan osakemarkkinoihin ja tarkastelemme, voidaanko taloudellisesti kannattavia sijoituksia tehdä otettaessa samalla huomioon epävarmuudet, riskit ja riippuvuudet. Kiinnitämme erityistä huomiota myös taktiseen varojen kohdentamiseen. Tämän lähestymistavan etuna on, että optimointimenetelmien lisäksi voimme hyödyntää aikasarjaennustamisen menetelmiä kaupankäyntisignaalien tuottamiseksi. Empiirisissä sovelluksissa käytämme kahta data-aineistoa. Ensimmäinen koostuu yhdeksästä teollisuusindeksistä kattaen ajanjakson 2008–2017, ja toinen sisältää 42 Moskovan pörssiin listattua osaketta kattaen vuodet 2011–2017. Tarkasteltavat strategiat on puolestaan jaoteltu viiteen osioon. Ensimmäisessä osassa tarkastelemme klassisia ja robusteja riski-tuotto -portfolioita sekä kaupankäyntikustannusten mallintamista. Havaitsemme, että odotettua tuottoa on syytä maksimoida suhteessa odotettuun vajeeseen edellyttäen samalla, että jokainen osake lisää sijoitussalkun häntäriskiä yhtä suurella osuudella. Toiseksi osoitamme, että minimivarianssiportfolioiden riskikorjattuja tuottoja voidaan parantaa robusteilla kovarianssiestimaattoreilla. Kolmanneksi toteamme robustien optimointitekniikoiden soveltuvan parhaiten konservatiivisille sijoittajille niiden tuottamien matalan volatiliteetin allokaatioiden ansiosta. Toisessa osassa hyödynnämme tilastollisia faktorimalleja korkeampien yhteismomenttien estimoinnissa ja havainnollistamme ehdotetun metodin hyödyllisyyttä riskioptimaalisten sekä odotettua hyötyä maksimoivien salkkujen rakentamisessa. Kolmannessa osassa käytämme Almgren–Chrissin viitekehystä ja asetamme odotetut tuotot suuruusjärjestykseen oletetun momentum-anomalian mukaisesti. Havaitsemme, että menetelmä tuottaa vakaita allokaatioita menestyen erityisen hyvin noususuhdanteessa. Neljännessä osassa osoitamme, että VECM- että GARCH-mallien tuottamia ennusteita voidaan hyödyntää kannattavasti niin Black–Littermanin malliin kuin kopulanäkemysten ja entropian poolaukseenkin perustuvissa optimoinneissa. Viimeisessä osassa laadimme varallisuuden suojausstrategian, joka kykenee ajoittamaan markkinoiden muutoksia ARIMA-malliin perustuvien tuottoennusteiden ansiosta. Voidaan siis todeta, että Venäjän osakemarkkinoilla on ollut mahdollista tehdä turvallisia ja tuottavia sijoituksia myös silloin kun kohtuulliset kaupankäyntikustannukset on huomioitu. Toiseksi väitämme, että markkinoiden tehottomuutta on voitu hyödyntää suunnittelemalla tilastolliseen arbitraasiin ja muihin taktiseen varojen allokointiin pohjautuvia strategioita

Textual Information and IPO Underpricing: A Machine Learning Approach

This study examines the predictive power of textual information from S-1 filings in explaining IPO underpricing. Our empirical approach differs from previous research, as we utilize several machine learning algorithms to predict whether an IPO will be underpriced, or not. We analyze a large sample of 2,481 U.S. IPOs from 1997 to 2016, and we find that textual information can effectively complement traditional financial variables in terms of prediction accuracy. In fact, models that use both textual data and financial variables as inputs have superior performance compared to models using a single type of input. We attribute our findings to the fact that textual information can reduce the ex-ante valuation uncertainty of IPO firms, thus leading to more accurate estimates

Six papers on computational methods for the analysis of structured and unstructured data in the economic domain

This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events.This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events