A theory of growth and volatility at the aggregate and firm level

We present an endogenous growth model that explains the evolution of the first and second moments of productivity growth at the aggregate and firm level during the post-war period. Growth is driven by the development of both (i) idiosyncratic R&D innovations and (ii) general innovations that can be freely adopted by many firms. Firm-level volatility is affected primarily by the Schumpeterian dynamics associated with the development of R&D innovations. The variance of aggregate productivity growth is driven by the arrival rate of general innovations. Ceteris paribus, the share of resources spent on development of general innovations increases with the stability of the market share of the industry leader. As market shares become less persistent, the model predicts an endogenous shift in the allocation of resources from the development of general innovations to the development of R&D innovations. This results in an increase in R&D, an increase in firm-level volatility, and a decline in aggregate volatility. The effect on productivity growth is ambiguous.On the empirical side, this paper presents new cross-country evidence that R&D subsidies are not significantly associated with higher growth but are associated with lower aggregate volatility. It also documents an upward trend in the instability of market shares, a positive association between firm volatility and R&D spending, and a negative association across sectors between R&D and how correlated the sector is with the rest of the economy.     

A computationally fast estimator for random coefficients logit demand models using aggregate data

This article proposes a computationally fast estimator for random coefficients logit demand models using aggregate data that Berry, Levinsohn, and Pakes ( 1995 ; hereinafter, BLP) suggest. Our method, which we call approximate BLP (ABLP), is based on a linear approximation of market share functions. The computational advantages of ABLP include (i) the linear approximation enables us to adopt an analytic inversion of the market share equations instead of a numerical inversion that BLP propose, (ii) ABLP solves the market share equations only at the optimum, and (iii) it minimizes over a typically small dimensional parameter space. We show that the ABLP estimator is equivalent to the BLP estimator in large data sets. Our Monte Carlo experiments illustrate that ABLP is faster than other approaches, especially for large data sets.     

Measuring Value‐at‐Risk in Project Finance Transactions

Despite the remarkable importance of project finance in international financial markets, no quantitative models to measure and quantify the risk associated with a deal for the project's lenders have been developed yet. The topic has recently become crucial, since the New Basle Capital Accord gives banks a choice of whether to adopt simpler (but possibly higher) standard capital requirements or to develop internal rating models for project finance transactions. The paper proposes how Monte Carlo simulations may be used to derive a Value‐at‐Risk estimate for project finance deals and discusses the critical issues that must be considered when developing such a model.     

Neuro‐fuzzy time‐series analysis of large‐volume data

This paper describes a framework that utilizes an adaptive‐network‐based fuzzy inference system to perform user‐constrained pattern recognition on time‐series data. Using a customizable fuzzy logic grammar, the architecture allows an analyst to capture domain expertise in a context‐relevant manner. Fuzzy logic rules constructed by the analyst are used to perform feature extraction and influence the training of a neural network to perform pattern recognition. We demonstrate that the architecture is capable of performing noise‐tolerant searches across multiple features on large volumes of time‐series data. The experiments presented here are from the domain of stock analysis. We are able to create simple rule sets automatically to search a data warehouse of stocks to select stocks that exhibit desirable behaviours. Copyright © 2011 John Wiley & Sons, Ltd.     

Dynamic Olley‐Pakes productivity decomposition with entry and exit

We propose an extension of the Olley and Pakes ( 1996 ) productivity decomposition that accounts for the contributions of surviving, entering, and exiting firms to aggregate productivity changes. We argue that the other decompositions that break down aggregate productivity changes into similar components introduce some biases in the measurement of the contributions of entry and exit. We apply our proposed decomposition to Slovenian manufacturing data and contrast our results with those of other decompositions. We find that, over a five‐year period, the measurement bias associated with entry and exit is substantial, accounting for up to 10 percentage points of aggregate productivity growth.     

Information transfer and firm‐level strategy

In this article, I examine a previously unexplored avenue for information transfer: similarities in firms’ strategies. This examination is based on the idea that information transfer is likely to be positively related to similarity in business activities. Drawing on the concept of generic strategies found in the strategy literature, I develop a measure of strategic distance between firms. Using this measure, I provide evidence that information transfer is positively related to the similarity of firms’ strategies. In addition, I find that this information transfer is strongest when the announcing firm is large or has persistent earnings.     

‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables

Aggregate stock return volatility is both persistent and countercyclical. This paper tests whether it is possible to improve volatility forecasts at monthly and quarterly horizons by conditioning on additional macroeconomic variables. I find that several variables related to macroeconomic uncertainty, time-varying expected stock returns, and credit conditions Granger cause volatility. It is more difficult to find evidence that forecasts exploiting macroeconomic variables outperform a univariate benchmark out-of-sample. The most successful approaches involve simple combinations of individual forecasts. Predictive power associated with macroeconomic variables appears to concentrate around the onset of recessions.     

Endogenous productivity of demand‐induced R&D: evidence from pharmaceuticals

We examine trends in the productivity of the pharmaceutical sector over the past three decades. Motivated by Ricardo's insight that productivity and rents are endogenous to demand when inputs are scarce, we examine the industry's aggregate Research and Development (R&D) production function. Using exogenous demand shocks to instrument investments, we find that demand growth can explain a large portion of R&D growth. Returns to scale have been stable, whereas total factor productivity has declined significantly. Predicted rents based on our estimates and Ricardo's theory closely match the trends we observe.     

Measuring Inflation Expectations Uncertainty Using High‐Frequency Data

Inflation expectations play a key role in determining future economic outcomes. The associated uncertainty provides a direct gauge of how well‐anchored the inflation expectations are. We construct a model‐based measure of inflation expectations uncertainty by augmenting a standard unobserved components model of inflation with information from noisy and possibly biased measures of inflation expectations obtained from financial markets. This new model‐based measure of inflation expectations uncertainty is more accurately estimated and can provide valuable information for policymakers. Using U.S. data, we find significant changes in inflation expectations uncertainty during the Great Recession.     

The Use of Performance‐based Remuneration: High versus Low‐growth Firms

This study analyses the CEO remuneration structure and level for 100 Australian‐listed entities. Consistent with expectations, it finds that high‐growth firms pay their CEOs a greater proportion of performance‐based pay, when equity‐based rewards only are considered. High‐growth firms also place greater reliance on market and/or non‐financial performance standards for the award of performance‐based pay. The extent to which performance‐based remuneration is used as a component of CEO pay is positively associated with firm size and growth options. Other potential determinants of performance‐based pay, such as financial performance, are not significantly associated with the use of performance‐based remuneration.     

The Influence of Country‐ and Firm‐level Governance on Financial Reporting Quality: Revisiting the Evidence

This paper examines how firm‐level governance and country‐level governance interplay in shaping financial reporting quality. Using IFRS adoption as a source of variation in firms’ reporting discretion, and a large sample of European firms that mandatorily switch to the new set of standards, we find that in countries with low enforcement and weak oversight over financial reporting, only firms with strong board‐level corporate governance mechanisms experience an increase in financial reporting quality, consistent with firm‐ and country‐level governance mechanisms being substitutes. However, in countries with high enforcement and strict oversight over financial reporting, firms with either strong or weak board‐level governance mechanisms experience an increase in financial reporting quality, even if the increase is larger for the former group. Overall, our findings indicate that in the debate about the effects of governance on the quality of financial reporting, it is important to consider both country‐ and firm‐level corporate governance mechanisms.     

Measuring the Cost‐Effectiveness of an R&D Tax Credit for the UK

This paper investigates the economic impact of the government's proposed new UK R&D tax credit. We measure the benefit of the credit by the effect on value added in the short and long runs. This is simulated from existing econometric estimates of the tax‐price elasticity of research and development (R&D) and the effect of R&D on productivity. For the latter, we allow R&D to have an effect on technology transfer (catching up with the technological frontier) as well as innovation (pushing the frontier forward). We then compare the increase in value added to the likely exchequer costs of the programme under a number of scenarios. In the long run, the increase in GDP far outweighs the costs of the tax credit. The short‐run effect is far smaller, with value added only exceeding cost if R&D grows at or below the rate of inflation.     

Measuring consumerwelfareinthe CPU market: anapplication of the pure‐characteristics demand model

In this article, I estimate demand for the personal computer central processing unit and measure consumer welfare using the pure characteristics demand model. The model is based on a quasilinear utility function with multiplicative random variables and does not have the idiosyncratic logit error term, so that consumer welfare directly reflects consumers' valuation of product characteristics. Welfare calculations show that consumer surplus comprises approximately 90% of total social surplus and that large welfare gains have resulted from the introduction of new products.     

Product market competition and earnings management: A firm‐level analysis

In this paper, we employ a firm‐level measure of product market competition constructed from the textual analysis of firms’ 10‐K filings to examine the relationship between managers’ perceived competition pressure and earnings management. We find that accounting irregularities and accrual‐based earnings management are positively related to product market competition. This finding is consistent with the notion that competition pressure increases managerial incentives to manage earnings, due to their career concerns. We also find that real earnings management is negatively related to product market competition. This finding suggests that real earnings management involves actions that decrease firms’ competitiveness and thus is costly for firms confronted with high competition pressure.     

Measuring fund style,performance and activity: a new style‐profiling approach

We construct new measures of fund style, performance and activity from linear combinations of off‐the‐shelf stock‐market indices. A fund's benchmark portfolio is a linear combination of two or more reference portfolios that in a least‐squares sense most closely approximates the fund's portfolio. The resulting linear combination scalar is itself a measure of fund style and the distance between a fund and its benchmark is a measure of fund activity. Our approach has a number of advantages over existing characteristic‐matching methods. We illustrate our approach using a data set of US institutional funds.     

Multi‐period Asset Lifetimes and Accounting‐based Equity Valuation: Take Care with Constant‐growth Terminal Value Models!

The merits of accruals in forecasting cash flows or mitigating the volatility of financials shortly after the valuation date are indisputable. However, the usefulness of accounting in equity valuation is very limited if we step beyond a certain forecasting horizon. In this paper, this limitation is emphasized by shedding new light on the accounting‐based value driver model (VDM), a widely used constant‐growth terminal value tool that uses accounting variables as input. The paper shows that, if the lifetime of a firm's assets is, on average, longer than one period, the VDM works accurately only in an idealized academic environment with an even historical corporate investment activity, a single depreciation method for all assets, and no historical inflation volatility. Artificially adjusting real‐world figures to this steady state is possible in principle, but bloats the valuation model and requires exactly the same information that is used in our cash flow‐driven benchmark model (where no adjustment phase is necessary). Beyond these theoretical shortcomings, the VDM is also prone to being misused in valuation practice due to its reliance on book (rather than economic) rates of return, and to its shortcomings in dealing adequately with the assets with an ex ante indefinite lifetime.     

Sub‐Optimality of Income Statement‐Based Methods for Measuring Operational Risk under Basel II: Empirical Evidence from Spanish Banks

The New Basel Capital Accord (Basel II) was created with the intention of establishing a framework in which financial entities can manage their risks in a more detailed and efficient way. Within this general reform movement, Operational Risk emerges as a fundamental variable. OR can be managed by three alternative methods: the Basic Indicator Approach, Standard Approach and Advanced Measurement Approach. The choice of which method to adopt has become of supreme interest for senior banking managers. This study analyzes the exactitude of the underlying implicit hypotheses that support each method, distinguishing between income statement based methods and the management accounting based method. In the present study the non‐optimum character of the two Income Statement‐based methods is empirically confirmed, in the light of the data provided by Spanish financial entities.     

Consistent valuation of project finance and LBOs using the flows‐to‐equity method

The flows‐to‐equity method is used to value transactions where debt amortizes according to a fixed schedule, requiring a formula that links the changing leverage with a time‐varying equity discount rate. We show that extant formulas yield incorrect valuations because they are inconsistent with the basic assumptions of this method. The error from using the wrong formula can be large, especially at currently low interest rates. We derive a formula that captures the effects of a fixed debt plan, potentially expensive debt, and costs of financial distress. We resolve an important issue about what to use as the cost of debt.     

Impact of Segment‐level Natural Resource Operational Risk Reporting on Earnings Predictions

Non‐financial reports alert investors to operational risks associated with issues such as insufficient access to natural resource inputs and related costly interruptions to production, while segment‐level reports alert investors to operational risk distribution across a firm. An important issue, to date unexplored, is how segment‐level non‐financial reporting has an impact on earnings predictions. We report the results of an experiment used to examine how mining company segment‐level water reports affect investors' earnings predictions, where water reports indicate whether the firm and its segments will have access to sufficient water to meet production needs. We find that investors do not change their earnings predictions when firm and segment‐level reports indicate low water risk but they do revise down their earnings predictions when firm and segment‐level water reports indicate high water risk. This is consistent with investors responding to the additional information provided in segment‐level reports confirming that water risk is high across the firm. Regardless of whether firm‐level water reports indicate high or low water risk, when segment‐level reports indicate that one segment is low water risk and another is high water risk, investors revise down their earnings predictions. This is consistent with investors recognizing that natural resource operational risk concentration in one segment can affect earnings more than evenly‐distributed risk. Overall, our findings suggest that belief‐adjustment theory explains how investors react to prospective operational risk information contained in segment‐level water reports according to the similarity of the segment‐level risks, and that this information is factored into earnings predictions.