648 research outputs found

    Advances in machine learning algorithms for financial risk management

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    In this thesis, three novel machine learning techniques are introduced to address distinct yet interrelated challenges involved in financial risk management tasks. These approaches collectively offer a comprehensive strategy, beginning with the precise classification of credit risks, advancing through the nuanced forecasting of financial asset volatility, and ending with the strategic optimisation of financial asset portfolios. Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression model is then applied to predict the probability of default using the heuristically balanced datasets. The results underscore the effectiveness of our proposed technique, with superior performance observed in comparison to other imbalanced preprocessing approaches. This advancement in credit risk classification lays a solid foundation for understanding individual financial behaviours, a crucial first step in the broader context of financial risk management. Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a Triple Discriminator Generative Adversarial Network with a continuous wavelet transform is proposed. The proposed model has the ability to decompose volatility time series into signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a Generative Adversarial Network consisting of triple Discriminator and Generator networks. The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised loss and reconstruction loss as part of its framework. Data from nine financial assets are employed to demonstrate the effectiveness of the proposed model. This approach not only enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis. Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio optimisation using historical Low, High, and Close prices of assets as input with weights of assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return on investment based on deep reinforcement learning. To provide more learning stability in an online training process, a Markov Differential Sharpe Ratio reward function has been proposed as the reinforcement learning objective function. Additionally, a Multi-Memory Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout a specified trading period. The use of the insights gained from volatility forecasting into this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving superior results based on risk-adjusted reward performance measures. In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the accuracy of credit risk classification, through the improvement and understanding of market volatility, to optimisation of investment strategies. These methodologies collectively show the potential of the use of machine learning to improve financial risk management

    A First Course in Causal Inference

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    I developed the lecture notes based on my ``Causal Inference'' course at the University of California Berkeley over the past seven years. Since half of the students were undergraduates, my lecture notes only require basic knowledge of probability theory, statistical inference, and linear and logistic regressions

    Revisiting the capitalization of public transport accessibility into residential land value: an empirical analysis drawing on Open Science

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    Background: The delivery and effective operation of public transport is fundamental for a for a transition to low-carbon emission transport systems’. However, many cities face budgetary challenges in providing and operating this type of infrastructure. Land value capture (LVC) instruments, aimed at recovering all or part of the land value uplifts triggered by actions other than the landowner, can alleviate some of this pressure. A key element of LVC lies in the increment in land value associated with a particular public action. Urban economic theory supports this idea and considers accessibility to be a core element for determining residential land value. Although the empirical literature assessing the relationship between land value increments and public transport infrastructure is vast, it often assumes homogeneous benefits and, therefore, overlooks relevant elements of accessibility. Advancements in the accessibility concept in the context of Open Science can ease the relaxation of such assumptions. Methods: This thesis draws on the case of Greater Mexico City between 2009 and 2019. It focuses on the effects of the main public transport network (MPTN) which is organised in seven temporal stages according to its expansion phases. The analysis incorporates location based accessibility measures to employment opportunities in order to assess the benefits of public transport infrastructure. It does so by making extensive use of the open-source software OpenTripPlanner for public transport route modelling (≈ 2.1 billion origin-destination routes). Potential capitalizations are assessed according to the hedonic framework. The property value data includes individual administrative mortgage records collected by the Federal Mortgage Society (≈ 800,000). The hedonic function is estimated using a variety of approaches, i.e. linear models, nonlinear models, multilevel models, and spatial multilevel models. These are estimated by the maximum likelihood and Bayesian methods. The study also examines possible spatial aggregation bias using alternative spatial aggregation schemes according to the modifiable areal unit problem (MAUP) literature. Results: The accessibility models across the various temporal stages evidence the spatial heterogeneity shaped by the MPTN in combination with land use and the individual perception of residents. This highlights the need to transition from measures that focus on the characteristics of transport infrastructure to comprehensive accessibility measures which reflect such heterogeneity. The estimated hedonic function suggests a robust, positive, and significant relationship between MPTN accessibility and residential land value in all the modelling frameworks in the presence of a variety of controls. The residential land value increases between 3.6% and 5.7% for one additional standard deviation in MPTN accessibility to employment in the final set of models. The total willingness to pay (TWTP) is considerable, ranging from 0.7 to 1.5 times the equivalent of the capital costs of the bus rapid transit Line-7 of the MetrobĂșs system. A sensitivity analysis shows that the hedonic model estimation is sensitive to the MAUP. In addition, the use of a post code zoning scheme produces the closest results compared to the smallest spatial analytical scheme (0.5 km hexagonal grid). Conclusion: The present thesis advances the discussion on the capitalization of public transport on residential land value by adopting recent contributions from the Open Science framework. Empirically, it fills a knowledge gap given the lack of literature around this topic in this area of study. In terms of policy, the findings support LVC as a mechanism of considerable potential. Regarding fee-based LVC instruments, there are fairness issues in relation to the distribution of charges or exactions to households that could be addressed using location based measures. Furthermore, the approach developed for this analysis serves as valuable guidance for identifying sites with large potential for the implementation of development based instruments, for instance land readjustments or the sale/lease of additional development rights

    The association of formal and informal institutions with total entrepreneurial activity: a neo-institutional theory approach

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    The thesis aims to identify the antecedents of total entrepreneurial activity (TEA) across OECD nations as this is key to national economic progress. Successive studies have associated TEA with informal institutions such as power distance (PD), individualism-collectivism (IND) and uncertainty avoidance (UA) but policymakers can do nothing about these in the short term. Two contributions are claimed. First, the thesis considers relatively new informal institutional dimensions (long-term orientation (LTO) and indulgence versus restraint (IVR)). However, these informal institutions (dimensions of national culture) cannot be modified by governments within a country but may influence the design of formal institutions (FIs). Thus, the second contribution arises from a consideration of two FIs (property rights (PRs) and access to finance (ATF)) as moderators of the main associations between informal institutions and TEA. These institutions may be modified by governments, so it is important to understand them, theoretically and empirically. PRs are analysed as they allow an organised market system to function, providing certainty for entrepreneurs engaging in TEA. Similarly, ATF may be needed for entrepreneurs to sustain or grow their ventures. A lack of ATF encountered by entrepreneurs is commonly viewed as the largest constraint to the creation and development of ventures. The results for this thesis are mixed, Model 1 looks at direct associations between national culture and TEA, and half of the hypotheses are supported. Model 2 (which studies the moderation by PRs and ATF of the relationships between informal institutions and TEA) has five out of the six hypotheses accepted for the PR-moderated hypotheses. This demonstrates that PRs have generally been found to have a positive moderating association with TEA, but ATF surprisingly generates an overall negative moderating association on informal institutions’ associations with TEA. OECD nations may therefore wish to encourage more effective PRs to further exploit the potential of TEA. In relation to ATF, FIs may need to tailor specific financial packages (or assistance) for specific industries which may have different gestation periods and tangible assets. Only one hypothesis appears to be accepted for ATF moderations. This may be explained by the quality of the institutional environment where a higher-quality institutional environment may have less impact as a moderator on TEA. In lower-quality institutional environments, FIs may have much more explanatory power. Overall, the models utilised in the thesis have resulted in a more nuanced study of the institutions that influence TEA

    LOBBYING – A FINANCIAL PERSPECTIVE

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    U.S. based bank holding companies (BHCs) exert influence at every step in the legislative process where financial regulatory reforms are enacted into law, such as the Dodd-Frank Act, to promulgation of regulations. In Chapter II, we maintain that BHCs, upon facing salient regulation, lobby regulators to have their opinions heard with the goal of favorable regulatory change and to increase non-traditional revenues. We undertook a novel collection of political and financial data from 2003 to 2018, matching 180 pairs of parsed proposed and final regulations. BHCs that participated in commenting on proposed rules are highly successful at having their views noted in the final regulation, and other forms of lobbying increased this success. We fill an instrumental gap in financial literature, as we confirm that BHCs may well lobby regulators to preserve gains in all important, yet risky revenues. In Chapter III, we ask how these non-traditional revenues and separately, systemic risk, impact BHC value and share price volatility. Surprisingly few scholars have explored the effect of revenues or systemic risk upon BHC value. An increase in the use of aggregate non-tradtional revenues or an increase of systemic risk, using Marginal Expected Shortfall, led to a decline in value of the BHC. It further led to a sharp increase share price volatility, illustrating a process of negative feedback loops. Lastly, in Chapter IV, it is demonstrated that the U.S. Congress struggles in lifting the statutory debt limit in a timely manner, while tied to appropriations legislation. We maintain that Google Trends Economic Policy Uncertainty (EPU) and Interest Group Competition/conflict take a toll on U.S. Treasury Bill Yield Spread during contentious debt ceiling crises. We did so by employing auto-regressive distributed lag model on a novel collection of financial and political time series data from 2010 to 2016, at daily intervals. Our EPU proxy and Interest iv Group Competition/Conflict led to a decrease in Treasury Yield Spreads and increased excess borrowing costs owed by the U.S. Treasury, due in part to the default premium. By examining all three chapters, we touch on the good, the bad, and the ugly of lobbying and political influence in finance

    Firm Size as a Moderator of the Relationship Between Sustainability Practices and Organizational Performance in Banks

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    Bank managers are facing increasing pressure to adopt sustainable finance models that address stakeholders\u27 diverse interests. It is important to understand how ESG strategies relate to corporate financial performance (CFP) to facilitate the adoption by bank leaders. Grounded in the triple bottom line and stakeholder theories, the purpose of this ex-post facto study was to examine the relationship between sustainability practices and the CFP of banks within the contingency of firm size. Secondary data on 226 global banks were collected from the Sustainalytics and FitchConnect databases. The results of the moderated multiple regression analysis indicated the two full models comprising four predictor variables (ESG risk ratings and firm size) were significant in explaining the variations in CFP, R2 = .142, F(7, 218) = 5.155, p \u3c .05 and R2 = .140, F(7, 218) = 5.086, p \u3c .05. In the first model, the relationships between the banks\u27 ESG risk management and CFP were nonsignificant. The interaction effect of bank size and governance risk management was significant (p = .015, ÎČ = -3.664). In the second model, the linkage between social risk management and CFP was significant (p = .034, ÎČ = -.028). The (a) connections between environmental and governance risk management and CFP and (b) interaction impacts of bank size and ESG risk management were nonsignificant. The key recommendations are for bank leaders to clarify the financial and nonfinancial motivations for adopting sustainable strategies and apply appropriate benchmarks to evaluate the outcomes. The implications for positive social change include the potential for banks to foster financial inclusion, reduce social inequalities, positively influence other players\u27 sustainability behaviors, and catalyze the transition to low-carbon economies

    Essay on international economics

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    This thesis should appeal to several audiences. The literature reviews and empirical examinations will aid economists and academic researchers in navigating the literature and will be valuable for their work. Practitioners and forecasters at central banks and commercial companies are likewise interested in learning which predictors, models, and approaches accurately estimate currency rates. Policymakers, for whom the success of policy choices depends heavily on accurate projections, should also be interested in our review of the current state of the research. Lastly, the regular coverage of exchange rate predictions in the media suggests that this study might be applicable outside academic and policy circles. This thesis studies two aspects of international economics: international finance and international trade, and it is organised as follows: Part I provides an in-depth description of the background research that formed the basis for this thesis. Part II consists of three empirically-based original chapters that are independent of one another and each make a unique contribution to the international economics literature. In the Appendix, more technical theories, such as machine learning and decomposition analysis, are described in greater detail

    Adaptations of green growth and degrowth in an oil-dependent economy toward a better future

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    Throughout the history of Newfoundland and Labrador (NL), the province has been relying on natural resources as the main sources of economic production. Consequently, NL is prone to external shocks from demand and price fluctuations. For example, the collapse of fisheries during the 1990s and the fall in global oil prices during the 2008 financial crisis have had negative impacts on the NL socioeconomic system, increasing unemployment and out-migration rates. A lack of modeling studies in the literature related to NL natural resources dependency, unemployment, and migration is the motivation for this research. This research focuses on studying the impact of oil, as a major natural resource for NL, dependency on other industries within the economy, employment, and migration through implementing green growth and degrowth policies as an alternative to decoupling the natural resources dependency and shifting away from the region’s historical sources of economic growth. This research links econometric, input-output (IO), and agent-based modeling techniques as a novel combination of methodologies to study the impact of an oil-dependent economy using oil prices and production reduction rates (scenarios of green growth and degrowth) as exogenous variables. The data used in this empirical analysis is obtained from Statistics Canada. The results help create suggestions for policymakers to steer socio-economic policies toward developing their economy for a better future

    Three essays on credit supply

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    This thesis consists of three independent essays on credit supply, each addressing different components, including the different impact of credit supply shocks financed through different supply channels, how different credit constraints impact debt structure and productivity, and how it affects their individual and collective exposure over time. Chapter 1: Its conceptual appeal has made the Conditional Value at Risk (CoVaR) one of the most influential systemic risk indicators. Despite its popularity, an outstanding methodological challenge may hamper the CoVaRs’ accuracy in measuring the time-series dimension of systemic risk. The dynamics of the CoVaR are entirely due to the behaviour of the state variables and therefore without their inclusion, the CoVaR would be constant over time. The key contribution of this chapter is to relax the assumption of time-invariant tail dependence between the financial system and each institution’s losses, by allowing the estimated parameters of the model to change over time, in addition to changing over quantiles and different financial institutions. We find that the dynamic component that we introduce does not affect the estimations for the risk of individual financial institutions, but it largely affects estimations of systemic risk which exhibits more procyclicality than the one implied by the standard CoVaR. As expected, larger financial institutions have a higher effect on systemic risk, although they are also shown to be individually more robust. When adding balance sheet data, it introduces additional volatility into our model relative to the standard one. In terms of forecasting, the results depend on the horizon used or the variables included. There is no clear outperformance between either model when we add the balance sheet data, or in the short term (less than 12 weeks). However, our model outperforms the standard one for medium (between 15 and 25 weeks) to long term horizons (between 30 and 40 weeks). Chapter 2: We seek to evaluate the impact of the different segments within the lending sector to the private non-financial sector can have on subsequent GDP growth. We isolate the bank lending channel as one of the main components, and group the remaining ones into a second segment which we classify as market based finance (MBF). We also include the 2 different segments of the borrowing sector, household debt and non-financial firm debt, to compare with the results obtained by the standard model. We debate the main source of these effects, and focus on either credit demand or credit supply shocks, in addition to other alternatives. We find that a rise in bank credit and/or household debt to GDP ratio lowers subsequent GDP growth. The predictive power is large in magnitude and robust across time and space. The bank credit booms and household debt booms are connected to lower interest rate spread environments, as well as periods with better financial conditions. And although the overall impact on subsequent GDP growth is negative, we found contrasting evidence when using the Financial Conditions Index (FCI) as an instrument. This would point to the potential different effects that bank credit and household debt could have on future economic growth (good booms vs bad booms), depending on the underlying cause of the boom. The results and the evidence that we found are more consistent with models where the fundamental source of the changes in household debt or bank credit lie in changes in the credit supply (credit supply shocks), rather than credit demand or other possibilities. This would likely be connected to incorrect expectations formation by lenders and investors (what many authors classify as “credit market sentiment” in the literature), which is an important element in explaining shifts in credit supply. Although credit demand shocks could play an important role in prolonging or amplifying the effects of the booms, it is unlikely that they are the source, as it would lead to results that conflict with empirical evidence. Finally, we find some differences in terms of statistical significance and magnitude in the different scenarios, where the bank credit shows more robustness to different specifications than the household debt. This would imply that there is a significance of the bank credit that goes well beyond the household debt. It would also mean that the main component that generates the boom bust cycle in GDP would be the bank credit, independent of its destination, rather than household debt, independent of its financing. Chapter 3: We construct a dataset at the firm-year level by merging the syndicated loan data, provided by Refinitiv LPC DealScan ("DealScan"), with the firm level data, provided by Center for Research in Security Prices (CRSP)/Compustat Merged Database ("CCM"). We conduct an analysis on firms subjected to different covenants, and find that firms with earnings-based constraints have lower levels of TFP (Total Factor Productivity), and short-term debt, when compared to firms with asset-based constraints. The data also shows that this is connected to an additional negative impact that short-term debt has on the productivity for the firms with earnings based constraints, which does not verify in the firms with asset-based constraints. Both these characteristics are robust to the use of 3 different TFP estimation methods, different subsamples, and additional controls, including age and size of the firm. Thus, we consider a quantitative dynamic stochastic partial equilibrium model, with three main types of firms, distinguished by their constraints, which explores the impact of short-term and long term borrowing on firm’s balance sheets, on the different variables. We construct replications for this theoretical model, and assess the how well it fits our actual data. Our findings show that constraints exert an impact on short-term borrowing, but not on the remaining variables. More specifically, firms that face an earnings-based constraint show lower levels of short-term borrowing, compared with firms that are either unconstrained, or asset-based constraint. The adjustment is made through lower dividend distribution, as can be seen by the lower values of the value function. They also point to the impact being larger for firms with lower productivity shocks, which is in accordance withour empirical findings. Even though that our data shows differences in some of this variables (for example, on long-term debt), these were not robust to some of the controls, including the size of the firm
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