423 research outputs found
Advances in machine learning algorithms for financial risk management
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
Accelerated American option pricing with deep neural networks
Given the competitiveness of a market-making environment, the ability to speedily quote option prices consistent with an ever-changing market environment is essential. Thus, the smallest acceleration or improvement over traditional pricing methods is crucial to avoid arbitrage. We propose a method for accelerating the pricing of American options to near-instantaneous using a feed-forward neural network. This neural network is trained over the chosen (e.g., Heston) stochastic volatility specification. Such an approach facilitates parameter interpretability, as generally required by the regulators, and establishes our method in the area of eXplainable Artificial Intelligence (XAI) for finance. We show that the proposed deep explainable pricer induces a speed-accuracy trade-off compared to the typical Monte Carlo or Partial Differential Equation-based pricing methods. Moreover, the proposed approach allows for pricing derivatives with path-dependent and more complex payoffs and is, given the sufficient accuracy of computation and its tractable nature, applicable in a market-making environment
A strategic turnaround model for distressed properties
The importance of commercial real estate is clearly shown by the role it plays, worldwide, in the sustainability of economic activities, with a substantial global impact when measured in monetary terms. This study responds to an important gap in the built environment and turnaround literature relating to the likelihood of a successful distressed commercial property financial recovery. The present research effort addressed the absence of empirical evidence by identifying a number of important factors that influence the likelihood of a successful distressed, commercial property financial recovery. Once the important factors that increase the likelihood of recovery have been determined, the results can be used as a basis for turnaround strategies concerning property investors who invest in distressed opportunities. A theoretical turnaround model concerning properties in distress, would be of interest to ‘opportunistic investing’ yield-hungry investors targeting real estate transactions involving ‘turnaround’ potential. Against this background, the main research problem investigated in the present research effort was as follows: Determine the important factors that would increase the likelihood of a successful distressed commercial property financial recovery. A proposed theoretical model was constructed and empirically tested through a questionnaire distributed physically and electronically to a sample of real estate practitioners from across the globe, and who had all been involved, directly or indirectly, with reviving distressed properties. An explanation was provided to respondents of how the questionnaire was developed and how it would be administered. The demographic information pertaining to the 391 respondents was analysed and summarised. The statistical analysis performed to ensure the validity and reliability of the results, was explained to respondents, together with a detailed description of the covariance structural equation modelling method used to verify the proposed theoretical conceptual model. vi The independent variables of the present research effort comprised; Obsolescence Identification, Capital Improvements Feasibility, Tenant Mix, Triple Net Leases, Concessions, Property Management, Contracts, Business Analysis, Debt Renegotiation, Cost-Cutting, Market Analysis, Strategic Planning and Demography, while the dependent variable was The Perceived Likelihood of a Distressed Commercial Property Financial Recovery. After analysis of the findings, a revised model was then proposed and assessed. Both validity and reliability were assessed and resulted in the following factors that potentially influence the dependent variables; Strategy, Concessions, Tenant Mix, Debt Restructuring, Demography, Analyse Alternatives, Capital Improvements Feasibility, Property Management and Net Leases while, after analysis, the dependent variable was replaced by two dependent variables; The Likelihood of a Distressed Property Turnaround and The Likelihood of a Distressed Property Financial Recovery. The results showed that Strategy (comprising of items from Strategic Planning, Business Analysis, Obsolescence Identification and Property Management) and Concessions (comprising of items from Concessions and Triple Net Leases) had a positive influence on both the dependent variables. Property Management (comprising of items from Business Analysis, Property Management, Capital Improvements Feasibility and Tenant Mix) had a positive influence on Financial Turnaround variable while Capital Improvements Feasibility (comprising of items from Capital Improvements Feasibility, Obsolescence Identification and Property Management) had a negative influence on both. Demography (comprising of items only from Demography) had a negative influence on the Financial Recovery variable. The balance of the relationships were depicted as non-significant. The present research effort presents important actions that can be used to influence the turnaround and recovery of distressed real estate. The literature had indicated reasons to recover distressed properties as having wide-ranging economic consequences for the broader communities and the countries in which they reside. The turnaround of distressed properties will not only present financial rewards for opportunistic investors but will have positive effects on the greater community and economy and, thus, social and economic stability. Vii With the emergence of the COVID-19 pandemic crisis, issues with climate change and sustainability, global demographic shifts, changing user requirements, shifts in technology, the threat of obsolescence, urbanisation, globalisation, geo-political tensions, shifting global order, new trends and different generational expectations, it is becoming more apparent that the threat of distressed, abandoned and derelict properties is here to stay, and which will present future opportunities for turnaround, distressed property owners, as well as future worries for urban authorities and municipalities dealing with urban decay. The study concluded with an examination of the perceived limitations of the study as well as presenting a comprehensive range of suggestions for further research.Thesis (PhD) -- Faculty of Engineering, Built Environment and Information Technology, School of the built Environment, 202
Online learning in financial time series
We wish to understand if additional learning forms can be combined with sequential optimisation to provide superior benefit over batch learning in various tasks operating in financial time series.
In chapter 4, Online learning with radial basis function networks, we provide multi-horizon forecasts on the returns of financial time series. Our sequentially optimised radial basis function network (RBFNet) outperforms a random-walk baseline and several powerful supervised learners. Our RBFNets naturally measure the similarity between test samples and prototypes that capture the characteristics of the feature space.
In chapter 5, Reinforcement learning for systematic FX trading, we perform feature representation transfer from an RBFNet to a direct, recurrent reinforcement learning (DRL) agent. Earlier academic work saw mixed results. We use better features, second-order optimisation methods and adapt our model parameters sequentially. As a result, our DRL agents cope better with statistical changes to the data distribution, achieving higher risk-adjusted returns than a funding and a momentum baseline.
In chapter 6, The recurrent reinforcement learning crypto agent, we construct a digital assets trading agent that performs feature space representation transfer from an echo state network to a DRL agent. The agent learns to trade the XBTUSD perpetual swap contract on BitMEX. Our meta-model can process data as a stream and learn sequentially; this helps it cope with the nonstationary environment.
In chapter 7, Sequential asset ranking in nonstationary time series, we create an online learning long/short portfolio selection algorithm that can detect the best and worst performing portfolio constituents that change over time; in particular, we successfully handle the higher transaction costs associated with using daily-sampled data, and achieve higher total and risk-adjusted returns than the long-only holding of the S&P 500 index with hindsight
Impact of oil prices on stock market performance
The study investigates the impact of oil prices on stock market performance in ten countries, including Canada. The volatility in oil prices and the accompanying swings in stock market performance raised the question of what, if any, is the relationship between these variables. The research seeks to address six strands of the phenomenon. The study evaluates the impact of oil prices on stock performance at the stock market’s aggregate and sector market levels. It establishes the effects of macroeconomic variables on stock market performance. Furthermore, it evaluates the role of the business cycle in the oil price shocks and stock market interface. Lastly, it examines the influence of oil prices on stock market performance in net oil-importing and oil-exporting countries. The empirical investigation uses monthly data from January 2003 to December 2020 and quarterly data from 1990Q1 to 2020Q4. Primary and secondary data were analysed using statistical tools and econometric modelling. The investigation employs the impulse response function, EGARCH and Markov switching models. The thesis concludes that the relationship between oil prices and stock market performance is time-varying, asymmetrical, heterogeneous and complex as several sector or country-specific factors drive the relationship. Specifically, the findings suggest that the response of the stock market sectors to oil price shocks differs substantially, depending on their degree of oil dependence and multiple transmission mechanisms. The findings further indicate that stock returns-generating processes in a net oil-exporting country like Canada exhibited a high degree of persistence in conditional variance, and the modelling of asymmetry was positive. Positive shocks from macroeconomic variables impact the country’s stock market more than negative shocks of the same magnitude. Two structural breaks are identified in the Canadian economy between 1990 and 2020. The data was further divided into two subsamples to reflect the two possible states for an economy, the bear and bull periods. Empirical analysis revealed that GDP, exchange rate, inflation rate, interest rate, and oil prices are significant drivers of the country’s stock market performance in economic contraction. During the expansion era, all the variables considered in the study, excluding GDP, significantly drive stock market performance. Hence, oil prices and stock market relationships tend to improve more during the economic expansion period than during the contraction era. Further analysis affirmed that the impact of oil price shocks is only significant in the top two net oil-importing countries. These findings convey information that guides policymakers in formulating macroeconomic policies, investors and portfolio managers in risk diversification relating to decision-making and investment strategies. The study investigates the impact of oil prices on stock market performance in ten countries, including Canada. The volatility in oil prices and the accompanying swings in stock market performance raised the question of what, if any, is the relationship between these variables. The research seeks to address six strands of the phenomenon. The study evaluates the impact of oil prices on stock performance at the stock market’s aggregate and sector market levels. It establishes the effects of macroeconomic variables on stock market performance. Furthermore, it evaluates the role of the business cycle in the oil price shocks and stock market interface. Lastly, it examines the influence of oil prices on stock market performance in net oil-importing and oil-exporting countries. The empirical investigation uses monthly data from January 2003 to December 2020 and quarterly data from 1990Q1 to 2020Q4. Primary and secondary data were analysed using statistical tools and econometric modelling. The investigation employs the impulse response function, EGARCH and Markov switching models. The thesis concludes that the relationship between oil prices and stock market performance is time-varying, asymmetrical, heterogeneous and complex as several sector or country-specific factors drive the relationship. Specifically, the findings suggest that the response of the stock market sectors to oil price shocks differs substantially, depending on their degree of oil dependence and multiple transmission mechanisms. The findings further indicate that stock returns-generating processes in a net oil-exporting country like Canada exhibited a high degree of persistence in conditional variance, and the modelling of asymmetry was positive. Positive shocks from macroeconomic variables impact the country’s stock market more than negative shocks of the same magnitude. Two structural breaks are identified in the Canadian economy between 1990 and 2020. The data was further divided into two subsamples to reflect the two possible states for an economy, the bear and bull periods. Empirical analysis revealed that GDP, exchange rate, inflation rate, interest rate, and oil prices are significant drivers of the country’s stock market performance in economic contraction. During the expansion era, all the variables considered in the study, excluding GDP, significantly drive stock market performance. Hence, oil prices and stock market relationships tend to improve more during the economic expansion period than during the contraction era. Further analysis affirmed that the impact of oil price shocks is only significant in the top two net oil-importing countries. These findings convey information that guides policymakers in formulating macroeconomic policies, investors and portfolio managers in risk diversification relating to decision-making and investment strategies
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