Representation learning in finance

Finance studies often employ heterogeneous datasets from different sources with different structures and frequencies. Some data are noisy, sparse, and unbalanced with missing values; some are unstructured, containing text or networks. Traditional techniques often struggle to combine and effectively extract information from these datasets. This work explores representation learning as a proven machine learning technique in learning informative embedding from complex, noisy, and dynamic financial data. This dissertation proposes novel factorization algorithms and network modeling techniques to learn the local and global representation of data in two specific financial applications: analysts’ earnings forecasts and asset pricing. Financial analysts’ earnings forecast is one of the most critical inputs for security valuation and investment decisions. However, it is challenging to fully utilize this type of data due to the missing values. This work proposes one matrix-based algorithm, “Coupled Matrix Factorization,” and one tensor-based algorithm, “Nonlinear Tensor Coupling and Completion Framework,” to impute missing values in analysts’ earnings forecasts and then use the imputed data to predict firms’ future earnings. Experimental analysis shows that missing value imputation and representation learning by coupled matrix/tensor factorization from the observed entries improve the accuracy of firm earnings prediction. The results confirm that representing financial time-series in their natural third-order tensor form improves the latent representation of the data. It learns high-quality embedding by overcoming information loss of flattening data in spatial or temporal dimensions. Traditional asset pricing models focus on linear relationships among asset pricing factors and often ignore nonlinear interaction among firms and factors. This dissertation formulates novel methods to identify nonlinear asset pricing factors and develops asset pricing models that capture global and local properties of data. First, this work proposes an artificial neural network “auto enco der” based model to capture the latent asset pricing factors from the global representation of an equity index. It also shows that autoencoder effectively identifies communal and non-communal assets in an index to facilitate portfolio optimization. Second, the global representation is augmented by propagating information from local communities, where the network determines the strength of this information propagation. Based on the Laplacian spectrum of the equity market network, a network factor “Z-score” is proposed to facilitate pertinent information propagation and capture dynamic changes in network structures. Finally, a “Dynamic Graph Learning Framework for Asset Pricing” is proposed to combine both global and local representations of data into one end-to-end asset pricing model. Using graph attention mechanism and information diffusion function, the proposed model learns new connections for implicit networks and refines connections of explicit networks. Experimental analysis shows that the proposed model incorporates information from negative and positive connections, captures the network evolution of the equity market over time, and outperforms other state-of-the-art asset pricing and predictive machine learning models in stock return prediction. In a broader context, this is a pioneering work in FinTech, particularly in understanding complex financial market structures and developing explainable artificial intelligence models for finance applications. This work effectively demonstrates the application of machine learning to model financial networks, capture nonlinear interactions on data, and provide investors with powerful data-driven techniques for informed decision-making

Two Papers of Financial Engineering Relating to the Risk of the 2007--2008 Financial Crisis

This dissertation studies two financial engineering and econometrics problems relating to two facets of the 2007-2008 financial crisis. In the first part, we construct the Spatial Capital Asset Pricing Model and the Spatial Arbitrage Pricing Theory to characterize the risk premiums of futures contracts on real estate assets. We also provide rigorous econometric analysis of the new models. Empirical study shows there exists significant spatial interaction among the S&P/Case-Shiller Home Price Index futures returns. In the second part, we perform empirical studies on the jump risk in the equity market. We propose a simple affine jump-diffusion model for equity returns, which seems to outperform existing ones (including models with Levy jumps) during the financial crisis and is at least as good during normal times, if model complexity is taken into account. In comparing the models, we made two empirical findings: (i) jump intensity seems to increase significantly during the financial crisis, while on average there appears to be little change of jump sizes; (ii) finite number of large jumps in returns for any finite time horizon seem to fit the data well both before and after the crisis

The Impact of Organizational Structure and Lending Technology on Banking Competition

Recent theoretical models argue that a bank's organizational structure reflects its lending technology.A hierarchically organized bank will employ mainly hard information, whereas a decentralized bank will rely more on soft information.We investigate theoretically and empirically how bank organization shapes banking competition.Our theoretical model illustrates how a bank's geographical reach and loan pricing strategy is determined not only by its own organizational structure but also by organizational choices made by its rivals. We take our model to the data by estimating the impact of the rival banks' organization on the geographical reach and loan pricing of a singular, large bank in Belgium.We employ detailed contract information from more than 15,000 bank loans granted to small firms, comprising the entire loan portfolio of this large bank, and information on the organizational structure of all rival banks located in the vicinity of the borrower.We find that the organizational structure of the close rival banks matters for both branch reach and loan pricing.The geographical footprint of the lending bank is smaller when the close rival banks are large, hierarchically organized, and technologically advanced. Such rival banks may rely more on hard information.Large rival banks in the vicinity also lower the degree of spatial pricing.We also find that the effects on spatial pricing are more pronounced for firms that generate less hard information, such as small firms.In short, size and hierarchy of rival banks in the vicinity influences both branch reach and loan pricing of the lender.banking sector;bank size;competition;mode of organization

Spatial Asset Pricing: A First Step

People choose where to live and how much to invest in housing. Traditionally, the first decision has been the domain of spatial economics, while the second has been analyzed in finance. Spatial asset pricing is an attempt to combine equilibrium concepts from both disciplines. In the finance context, we show how spatial decisions can be framed as an expanded portfolio problem. Within spatial economics, we identify the consequences of hedging motives for location decisions. We characterize a number of observable deviations from standard predictions in finance (e.g. the definition of the relevant market portfolio for the pricing of risk includes homeownership rates) and in spatial economics (e.g. hedging considerations and the pricing of risk affect the geographic allocation of human capital).

Green noise or green value? Measuring the price effects of environmental certification in commercial buildings

This paper investigates the price effects of environmental certification on commercial real estate assets. It is argued that there are likely to be three main drivers of price differences between certified and non-certified buildings. First, certified buildings offer a bundle of benefits to occupiers relating to business productivity, image and occupancy costs. Second, due to these occupier benefits, certified buildings can result in higher rents and lower holding costs for investors. Third, certified buildings may require a lower risk premium. Drawing upon the CoStar database of US commercial real estate assets, hedonic regression analysis is used to measure the effect of certification on both rent and price. We first estimate the rental regression for a sample of 110 LEED and 433 Energy Star as well as several thousand benchmark buildings to compare the sample to. The results suggest that, compared to buildings in the same metropolitan region, certified buildings have a rental premium and that the more highly rated that buildings are in terms of their environmental impact, the greater the rental premium. Furthermore, based on a sample of transaction prices for 292 Energy Star and 30 LEED-certified buildings, we find price premia of 10% and 31% respectively compared to non-certified buildings in the same metropolitan are

Vertical integration and firm boundaries : the evidence

Since Ronald H. Coase's (1937) seminal paper, a rich set of theories has been developed that deal with firm boundaries in vertical or input–output structures. In the last twenty-five years, empirical evidence that can shed light on those theories also has been accumulating. We review the findings of empirical studies that have addressed two main interrelated questions: First, what types of transactions are best brought within the firm and, second, what are the consequences of vertical integration decisions for economic outcomes such as prices, quantities, investment, and profits. Throughout, we highlight areas of potential cross-fertilization and promising areas for future work

Labor and the market value of the firm

What role does labor play in a firm’s market value? We explore this question using a production-based asset pricing model with frictions in the adjustment of both capital and labor. We posit that hiring of labor is akin to investment in capital and that the two interact, with the interaction being a crucial determinant of the time series behavior of market value. We use aggregate U.S. corporate sector data to estimate firms' optimal hiring and investment decisions and the consequences for firms' value. The model generates a good fit of the data. We decompose the estimated market value, thereby quantifying the link between firms' value and gross hiring flows, employment, gross investment flows, and physical capital. We find that a conventional specification -- quadratic adjustment costs for capital and no hiring costs -- performs poorly. Hiring and investment flows, unlike employment and capital stocks, are volatile and both are essential to account for market value volatility. A key result is that firms' value embodies the value of hiring and investment over and above the capital stock

Spatial Linkages in International Financial Markets

Spatial dependency has been broadly studied in several research areas, such as environmental criminology, economic geography, environmental sciences, and urban economics. However, it has been essentially overlooked in other subfields of economics and in the field of finance as a whole. A key element at stake is the definition of contiguity. In the context of financial markets, defining a metric distance is not a simple matter. In this article, we explore the notion of spatial dependency in a panel of 126 Latin American firms from Brazil, Chile, and Mexico over the period 1997-2006. Firstly, we formulate a spatial version of the capital asset pricing model (S-CAPM), which accounts for alternative measures of distance between firms, such as market capitalization, the market-to-book, enterprise value-to-EBITDA, and the debt ratios. Secondly, we analyze the potential existence of spatial linkages in investment and dividend decisions. We conclude that there may be contemporaneous linkages in firms’ decisions of such ratios, which may be indicative of some strategic behavior.