Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Management accounting and supply chains: actions, concerns, and networks

What does this thesis do? This thesis uses Actor-Network Theory (ANT) to examine how a UK retailer’s organization and strategy, and, in turn, its form of management accounting was shaped by its supply chain. The thesis does this by reporting on four related themes in the form of four inter-connected essays. The first essay undertakes a state-of-the-art review of the literature. It examines how accounting issues within supply chains permeate ‘matters of concern’. In accordance with this idea of ANT, the essay illustrates how issues emerged, controversies developed, and matters evolved through an actor-network of accounting researchers within the supply chain domain. This leads on to the second essay, which exemplifies the nature of the UK’s retailing industry within which the supply chain case organization emerged and developed. The purposes of the essay are twofold: to introduce the contextual ramifications of the case organization; and to illustrate the emergence of a new market logic, which led to the creation of a global supply chain and a new form of management accounting therein. The third essay reports on a qualitative case study. It analyses the dualistic relation between ostensive and performative aspects of supply chain strategy, reveals how accounting numbers act as an obligatory passage point within this dualism, and makes a contribution to the ANT debate around the issue of whether and how a dualism between ostensive and performative aspects exists. The final essay reports on another case analysis of institutionalizing a heterarchical form of management accounting: a distributed form of intelligence that penetrates through lateral accountable relations. The analysis reveals a new form of management accounting characterised by ambiguity; it emphasizes the possibilities of compromises and negotiations, and it thus contributes to knowledge by combining an aspect of ANT with heterarchical tendencies in the world of contemporary organizations. Finally, the thesis concludes that it is the supply chain that organises today’s neoliberal capitalism; and it is management accounting that unites both human and non-human actors within such supply chains, despite that form of management accounting being ambiguous. The thesis comprises the introduction, these four essays, and the conclusion.

Essays in corporate finance

China has been growing rapidly over the last decades. The private sector is the driving force of this growth. This thesis focuses on firm-level investment and cash holdings in China, and the chapters are structured around the following issues. 1. Why do private firms grow so fast when they are more financially constrained? In Chapter 3, we use a panel of over 600,000 firms of different ownership types from 1998 to 2007 to find the link between investment opportunities and financial constraints. The main finding indicates that private firms, which are more likely to be financially constrained, have high investment-investment opportunity sensitivity. Furthermore, this sensitivity is relatively lower for state-owned firms in China. This shows that constrained firms value investment opportunities more than unconstrained firms. To better measure investment opportunities, we attempt to improve the Q model by considering supply and demand sides simultaneously. When we capture q from the supply side and the demand side, we find that various types of firms respond differently towards different opportunity shocks. 2. In China, there are many firms whose cash flow is far greater than their fixed capital investment. Why is their investment still sensitive to cash flow? To explain this, in Chapter 4, we attempt to introduce a new channel to find how cash flow affects firm-level investment. We use a dynamic structural model and take uncertainty and ambiguity aversion into consideration. We find that uncertainty and ambiguity aversion will make investment less sensitive to investment opportunities. However, investment-cash flow sensitivity will increase when uncertainty is high. This suggests that investment cash flow sensitivities could still be high even when the firms are not financially constrained. 3. Why do firms in China hold so much cash? How can managers’ confidence affect corporate cash holdings? In Chapter 5, we analyse corporate cash holdings in China. Firms hold cash for precautionary reasons, to hedge frictions such as financing constraints and uncertainty. In addition, firms may act differently if they are confident or not. In order to determine how confidence shocks affect precautionary savings, we develop a dynamic model taking financing constraints, uncertainty, adjustment costs and confidence shocks into consideration. We find that without confidence shocks, firms will save money in bad times and invest in good times to maximise their value. However, if managers lose their confidence, they tend to save money in good times to use in bad times, to hedge risks and financing constraint problems. This can help explain why people find different results on the cash flow sensitivity of cash. Empirically, we use a panel of Chinese listed firms. The results show that firms in China save more money in good times, and the confidence shock channel can significantly affect firms’ cash holdings policy.

The role of context in image annotation and recommendation

With the rise of smart phones, lifelogging devices (e.g. Google Glass) and popularity of image sharing websites (e.g. Flickr), users are capturing and sharing every aspect of their life online producing a wealth of visual content. Of these uploaded images, the majority are poorly annotated or exist in complete semantic isolation making the process of building retrieval systems difficult as one must firstly understand the meaning of an image in order to retrieve it. To alleviate this problem, many image sharing websites offer manual annotation tools which allow the user to “tag” their photos, however, these techniques are laborious and as a result have been poorly adopted; Sigurbjörnsson and van Zwol (2008) showed that 64% of images uploaded to Flickr are annotated with < 4 tags. Due to this, an entire body of research has focused on the automatic annotation of images (Hanbury, 2008; Smeulders et al., 2000; Zhang et al., 2012a) where one attempts to bridge the semantic gap between an image’s appearance and meaning e.g. the objects present. Despite two decades of research the semantic gap still largely exists and as a result automatic annotation models often offer unsatisfactory performance for industrial implementation. Further, these techniques can only annotate what they see, thus ignoring the “bigger picture” surrounding an image (e.g. its location, the event, the people present etc). Much work has therefore focused on building photo tag recommendation (PTR) methods which aid the user in the annotation process by suggesting tags related to those already present. These works have mainly focused on computing relationships between tags based on historical images e.g. that NY and timessquare co-exist in many images and are therefore highly correlated. However, tags are inherently noisy, sparse and ill-defined often resulting in poor PTR accuracy e.g. does NY refer to New York or New Year? This thesis proposes the exploitation of an image’s context which, unlike textual evidences, is always present, in order to alleviate this ambiguity in the tag recommendation process. Specifically we exploit the “what, who, where, when and how” of the image capture process in order to complement textual evidences in various photo tag recommendation and retrieval scenarios. In part II, we combine text, content-based (e.g. # of faces present) and contextual (e.g. day-of-the-week taken) signals for tag recommendation purposes, achieving up to a 75% improvement to precision@5 in comparison to a text-only TF-IDF baseline. We then consider external knowledge sources (i.e. Wikipedia & Twitter) as an alternative to (slower moving) Flickr in order to build recommendation models on, showing that similar accuracy could be achieved on these faster moving, yet entirely textual, datasets. In part II, we also highlight the merits of diversifying tag recommendation lists before discussing at length various problems with existing automatic image annotation and photo tag recommendation evaluation collections. In part III, we propose three new image retrieval scenarios, namely “visual event summarisation”, “image popularity prediction” and “lifelog summarisation”. In the first scenario, we attempt to produce a rank of relevant and diverse images for various news events by (i) removing irrelevant images such memes and visual duplicates (ii) before semantically clustering images based on the tweets in which they were originally posted. Using this approach, we were able to achieve over 50% precision for images in the top 5 ranks. In the second retrieval scenario, we show that by combining contextual and content-based features from images, we are able to predict if it will become “popular” (or not) with 74% accuracy, using an SVM classifier. Finally, in chapter 9 we employ blur detection and perceptual-hash clustering in order to remove noisy images from lifelogs, before combining visual and geo-temporal signals in order to capture a user’s “key moments” within their day. We believe that the results of this thesis show an important step towards building effective image retrieval models when there lacks sufficient textual content (i.e. a cold start).