# Research articles for the 2020-02-18

A Management Focused Tool for Developing Pro-Forma Financial Statements
Jalbert, Terrance
SSRN
Developing pro-forma financial statements and associated financial analysis is an important undertaking for new and existing business alike. This paper reports user experiences with a spreadsheet-based method for developing pro-forma financial statements as developed in Jalbert (2017). The paper also presents improvements and enhancements to the template. The forecasting approach reported on here requires users to simply enter their firm-specific figures. As such it is particularly useful for individuals without extensive training in finance. The tool does not require programming or the use of plug figures and does not result in circular references which are all common to other tools. The template provides a powerful tool for entrepreneurs and for teaching management, accounting and finance courses. The tool is suitable for both novice and advanced users.

Algorithmic market making for options
Bastien Baldacci,Philippe Bergault,Olivier Guéant
arXiv

In this article, we tackle the problem of a market maker in charge of a book of options on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic optimal control problem of an option market maker is in fact tractable. More precisely, when volatility is modeled using a classical stochastic volatility model -- e.g. the Heston model -- the problem faced by an option market maker is characterized by a low-dimensional functional equation that can be solved numerically using a Euler scheme along with interpolation techniques, even for large portfolios. In order to illustrate our findings, numerical examples are provided.

Asset Pricing around Earnings Announcement Days
Chan, Kam Fong,Marsh, Terry
SSRN
The equity betaâ€"return relation behaves distinctively on days when market influencers with large market capitalization make scheduled earnings announcements. On such days, a unit increase in beta is associated with 23.12 basis point increase in average equity excess returns. This strongly positive equity beta-return relation extends to various test portfolios and individual stocks. Furthermore, there is virtually no overlap between the â€œleadâ€ earnings days and the days when key macroeconomic news is announced. The aggregate market premium and Sharpe ratio are some 12 times higher on â€œleadâ€ earnings days, but there is no conclusive evidence suggesting that systematic risk is elevated around such days. On other days, the equity-beta relation is flat. We interpret the finding as a new puzzle in the asset pricing literature.

Asymmetric Trading Responses to Credit Rating Announcements from Issuer- versus Investor-Paid Rating Agencies
Nguyen, Pham Minh Quan,Do, Hung Xuan,Molchanov, Alexander,Nguyen, Lily ,Nguyen, Nhut (Nick) Hoang
SSRN
Credit rating industry business model has traditionally been based on an â€˜issuer-paysâ€™ principle. Issuer-paid credit rating agencies (CRAs) have recently faced criticism regarding untimely releases of negative ratings adjustments, which is attributed to conflict of interest of their business model. A recent model based on â€˜investor-paysâ€™ principle is arguably free of such conflict. We examine how institutional investors respond to changes in credit ratings issued by these two types of CRAs. We find that investors react asymmetrically: they abnormally sell equity stakes around rating downgrades by investor-paid CRAs, while abnormally buying around rating upgrades by issuer-paid CRAs. Further, a dynamic trading strategy based on such trading behavior generates significant abnormal returns. Our study suggests that, through their trades, institutional investors capitalize on value-relevant information provided by both types of credit rating agencies.

Blockchain Structure and Cryptocurrency Prices
Zimmerman, Peter
SSRN
I present a model of cryptocurrency price formation that endogenizes both the financial market for coins and the fee-based market for blockchain space. A cryptocurrency has two distinctive features: a price determined by the extent of its usage as money, and a blockchain structure that restricts settlement capacity. Limited settlement space creates competition between users of the currency, so speculative activity can crowd out monetary usage. This crowding-out undermines the ability of a cryptocurrency to act as a medium of payment, lowering its value. Higher speculative demand can reduce prices, contrary to standard economic models. Crowding-out also raises the riskiness of investing in cryptocurrency, explaining high observed price volatility.

Brexit, the Irish Border and Social Security Rights
McKeever, G.
SSRN
This paper will examine the implications of Brexit for social security rights as they affect Northern Ireland. The paper begins with a consideration of the UK-wide implications for social security arising from Brexit including the potential impact on rights derived from EU law and the legal challenges inherent in reconciling existing EU laws with new domestic UK legislation, with concerns over the potential for parliamentary or independent scrutiny of this process. The general constitutional and political uncertainties for Northern Ireland arising from Brexit are considered in order to contextualise the examination of the most prominent social security implications for Northern Ireland â€" and the Republic of Ireland â€" stemming from the issue of cross-border working and the aggregation and exportability of social security entitlements. The paper brings together the positions of the UK, Northern Ireland, Republic of Ireland and the EU, as of January 2018, to provide a robust assessment of the cumulative issues that that will impact on social security rights in Northern Ireland post-Brexit.

Common Factor Augmented Forecasting Models for the US Dollar-Korean Won Exchange Rate
Kim, Hyeongwoo,Kim, Soohyon
SSRN
We propose factor-augmented out of sample forecasting models for the real exchange rate between Korea and the US. We estimate latent common factors by applying an array of data dimensionality reduction methods to a large panel of monthly frequency time series data. We augment benchmark forecasting models with common factor estimates to formulate out-of-sample forecasts of the real exchange rate. Major findings are as follows. First, our factor models outperform conventional forecasting models when combined with factors from the US macroeconomic predictors. Second, our factor models perform well at longer horizons when American real activity factors are employed, whereas American nominal/financial market factors help improve short-run prediction accuracy. Third, models with global PLS factors from UIP fundamentals overall perform well, while PPP and RIRP factors play a limited role in forecasting.

Default Ambiguity: Finding the Best Solution to the Clearing Problem
Pál András Papp,Roger Wattenhofer
arXiv

We study financial networks with debt contracts and credit default swaps between specific pairs of banks. Given such a financial system, we want to decide which of the banks are in default, and how much of their liabilities these defaulting banks can pay. There can easily be multiple different solutions to this problem, leading to a situation of default ambiguity and a range of possible solutions to implement for a financial authority.

In this paper, we study the general properties of the solution space of such financial systems, and analyze a wide range of reasonable objective functions for selecting from the set of solutions. Examples of such objective functions include minimizing the number of defaulting banks, minimizing the amount of unpaid debt, maximizing the number of satisfied banks, maximizing the equity of a specific bank, finding the most balanced distribution of equity, and many others. We show that for all of these objective functions, it is not only NP-hard to find the optimal solution, but it is also NP-hard to approximate this optimum: for each objective function, we show an inapproximability either to an $n^{1/2-\epsilon}$ or to an $n^{1/4-\epsilon}$ factor for any $\epsilon>0$, with $n$ denoting the number of banks in the system. Thus even if an authority has clear criteria to select a solution in case of default ambiguity, it is computationally intractable to find a solution that is reasonably good in terms of this criteria. We also show that our hardness results hold in a wide range of different model variants.

Diversifying Private Equity
Gredil, Oleg,Liu, Yan,Sensoy, Berk A.
SSRN
Uncertainty about manager skill and diversification constraints are hallmarks of investing in alternative assets. This paper quantifies the utility losses to private equity investors arising from these frictions. When calibrated to the data on institutional allocations to private equity and across-fund diversification, our analysis reveals that certainty equivalent returns in PE fund investing are 2-to-8% lower than if inferred from average fund performance levels. The results provide new perspectives on the value of funds-of-funds that operate in the alternative investment space, as well as on manager selection and retention decisions.

ESG investments: Filtering versus machine learning approaches
Carmine de Franco,Christophe Geissler,Vincent Margot,Bruno Monnier
arXiv

We designed a machine learning algorithm that identifies patterns between ESG profiles and financial performances for companies in a large investment universe. The algorithm consists of regularly updated sets of rules that map regions into the high-dimensional space of ESG features to excess return predictions. The final aggregated predictions are transformed into scores which allow us to design simple strategies that screen the investment universe for stocks with positive scores. By linking the ESG features with financial performances in a non-linear way, our strategy based upon our machine learning algorithm turns out to be an efficient stock picking tool, which outperforms classic strategies that screen stocks according to their ESG ratings, as the popular best-in-class approach. Our paper brings new ideas in the growing field of financial literature that investigates the links between ESG behavior and the economy. We show indeed that there is clearly some form of alpha in the ESG profile of a company, but that this alpha can be accessed only with powerful, non-linear techniques such as machine learning.

Fast Hybrid Schemes for Fractional Riccati Equations (Rough is not so Tough)
Callegaro Giorgia,Grasselli Martino,Pagès Gilles
arXiv

We solve a family of fractional Riccati differential equations with constant (possibly complex) coefficients. These equations arise, e.g., in fractional Heston stochastic volatility models, that have received great attention in the recent financial literature thanks to their ability to reproduce a rough volatility behavior. We first consider the case of a zero initial value corresponding to the characteristic function of the log-price. Then we investigate the case of a general starting value associated to a transform also involving the volatility process. The solution to the fractional Riccati equation takes the form of power series, whose convergence domain is typically finite. This naturally suggests a hybrid numerical algorithm to explicitly obtain the solution also beyond the convergence domain of the power series representation. Our numerical tests show that the hybrid algorithm turns out to be extremely fast and stable. When applied to option pricing, our method largely outperforms the only available alternative in the literature, based on the Adams method.

Have the New Factor Models Improved on the Theory Front? Evidence from Decomposing Asset Growth
Li, Yuan
SSRN
Contrary to the theoretical foundations of the asset-growth factor in both the five-factor model of Fama and French (2015) and the q-factor model of Hou, Xue, and Zhang (2015), I find that investment level is not the reason why asset growth is negatively associated with future stock returns. Instead, the source is investment structure â€" the division of total investments between those on and off the balance sheet. In particular, (1) asset growth (âˆ†A/LagA) is not associated with future stock returns among âˆ†A < 0 firms, and (2) decomposing asset growth of âˆ†A > 0 firms into two components âˆ' investment structure (âˆ†A/TINV, increase in assets divided by total investments) and investment level (TINV/LagA, total investments divided by one-year-lagged assets) âˆ' reveals that the component negatively associated with future stock returns is not investment level but rather investment structure. Furthermore, the associations of asset growth with both systematic risk and fundamental uncertainty exhibit similar patterns to its association with future stock returns, as described in (1) and (2).

How Banks Respond to Distress: Shifting Risks in Europeâ€™s Banking Union
Mink, Mark,Ramcharan, Rodney ,van Lelyveld, Iman
SSRN
This paper uses granular bond portfolio data to study how banking systems across the European Union (EU) adjust their asset holdings in response to regulatory solvency shocks. We also study the impact of these shocks at financial intermediaries on the prices of bonds in their portfolio. Despite the creation of a Single Supervisory Mechanism (SSM) in the EU, we find that risk-shifting interacts with regulatory arbitrage motives to explain how banks adjust their portfolios after adverse solvency shocks. After regulatory solvency declines, banks increase their exposure to domestic bonds, including higher yielding but zero risk-weight sovereign bonds. The increase in banking system risk might therefore be even larger than the decline in risk-weighted solvency ratios suggests. Distress in the banking system also feeds back onto bond prices. Bonds owned by less-well capitalized banking systems trade at a discount relative to otherwise similar bonds owned by better capitalized intermediaries.

How Do Expectations Affect Learning About Fundamentals? Some Experimental Evidence
Kieran Marray,Nikhil Krishna,Jarel Tang
arXiv

Individuals' output often depends not just on their ability and actions, but also on external factors or fundamentals, whose effect they cannot separately identify. At the same time, many individuals have incorrect beliefs about their own ability. Heidhues et al. (2018) characterise overconfident and underconfident individuals' equilibrium beliefs and learning process in these situations. They argue overconfident individuals will act sub-optimally because of how they learn. We carry out the first experimental test their theory. Subjects take incorrectly marked tests, and we measure how they learn about the marker's accuracy over time. We use machine learning to identify heterogeneous effects. Overconfident subjects have lower beliefs about the fundamental, as Heidhues et al. predict, and thus would make sub-optimal decisions. But we find no evidence it is because of how they learn.

Impossible Inference in Econometrics: Theory and Applications
Marinho Bertanha,Marcelo J. Moreira
arXiv

This paper studies models in which hypothesis tests have trivial power, that is, power smaller than size. This testing impossibility, or impossibility type A, arises when any alternative is not distinguishable from the null. We also study settings in which it is impossible to have almost surely bounded confidence sets for a parameter of interest. This second type of impossibility (type B) occurs under a condition weaker than the condition for type A impossibility: the parameter of interest must be nearly unidentified. Our theoretical framework connects many existing publications on impossible inference that rely on different notions of topologies to show models are not distinguishable or nearly unidentified. We also derive both types of impossibility using the weak topology induced by convergence in distribution. Impossibility in the weak topology is often easier to prove, it is applicable for many widely-used tests, and it is useful for robust hypothesis testing. We conclude by demonstrating impossible inference in multiple economic applications of models with discontinuity and time-series models.

Investment Proximity and Venture Capital Returns
Krishnan, C. N. V.,Nguyen, Daniel
SSRN
Using a large dataset of 82,818 investments in start-up firms from December 1965 through August 2019, we document detailed descriptive statistics. The biggest group of investors are venture capital firms (about 79% of all observations). The number of all venture investments are very few in the 60â€™s and 70â€™s, but peaked in the 90â€™s. The majority of in-state investments are found clustered in California (with the San Francisco VC Hub), Massachusetts (with the Boston VC Hub) and New York. Out-of-state investments are more widely spread-out, suggesting that regardless of VC clusters, VCs do make investments over a wide range of region in the United States. The trend of returns generated from early round investing to late round decreases generally: early investors get a higher return than late stage investors, and IPO exits entail the highest return. We find that geographical proximity is significantly and positively associated with returns - cumulative returns or annualized returns. This results holds whether we use actual distance (in km) between the VC investment firm HQ and portfolio firm HQ, or a cross-region indicator variable when the portfolio firm HQ is out-of-state as compared to VC investment firm HQ. Using both univariate as well as multivariate tests that control for other possible determinants of returns, we find that cross-region variable is a strong indicator of returns, being negative and statistically significant across all exits or only successful exits, across VC investor types, across entry timing â€" whether early stage or late stage, whether the VC firm HQ location is in a previously-documented VC hub or not, and whether the investment is to a previously-documented favored VC industry or not. The distance variable is also negative and statistically significant in the full sample, and in many such sub-samples. The relation between investment proximity and returns continues to be robust even when we include additional portfolio company variables, and in a matched-sample analysis where we pair each proximate investment with a matched distant investment. We find that VC Director and other outside directors as a proportion of all Directors on the boards on portfolio companies is significantly higher, on average, for in-state and short distance investments as compared to out-of-state or longer distance investments, indicating better involvement by VCs in their more proximate investments. The percentage of VC-appointed and other outside directors in a portfolio company is also significantly and positively associated with both cumulative and annualized returns, indicating better governance, and better monitoring by VCs in their more proximate investments.

Is Mutual Fund Family Retirement Money Smart?
SSRN
Using data on investments of fund family employees in their 401(k) plans, I show that employee flows predict fund performance up to two years. The predictive power is stronger when fund family employees are located close to fund managers, pointing to employees exploiting their proximity to managers to learn about the managersâ€™ skill. The results are not driven by plan design, portfolio managersâ€™ ownership, or cross-subsidization. The top quintile of funds in terms of employee flows outperforms the bottom quintile by 1.6% annually in terms of Carhart Alpha, suggesting that other investors can benefit by mimicking fund employees.

Life After Default. Private and Official Deals
Marchesi, Silvia,Masi, Tania
SSRN
This paper studies the relationship between sovereign debt default and annual GDP growth distinguishing between private and official deals. Using the Synthetic Control Method to analyze 23 official and private defaulters from 1970 to 2017, we find that private defaults generate output losses both during the crisis and persisting over time. Conversely, official defaulters do not show a permanent drop in GDP per capita, neither during the crisis nor in its aftermath. Using panel data analysis to control for the creditors' loss (haircut), we confirm that official and private defaults may have different effects on GDP growth.

Market Power in Convex Hull Pricing
Jian Sun,Chenye Wu
arXiv

The start up costs in many kinds of generators lead to complex cost structures, which in turn yield severe market loopholes in the locational marginal price (LMP) scheme. Convex hull pricing (a.k.a. extended LMP) is proposed to improve the market efficiency by providing the minimal uplift payment to the generators. In this letter, we consider a stylized model where all generators share the same generation capacity. We analyze the generators' possible strategic behaviors in such a setting, and then propose an index for market power quantification in the convex hull pricing schemes.

Modelling and Forecasting Stock Volatility and Return: A New Approach Based on Quantile Rogers-Satchell Volatility Measure With Asymmetric Bilinear CARR Model
Tan, Shay Kee,Chan, Jennifer,Ng, Kok Haur
SSRN
Rogers-Satchell (RS) measure is an efficient volatility measure. This paper proposes quantile RS (QRS) measure to ensure robustness and correct the downward bias of RS measure with an additive term. Moreover scaling factors are provided for different interquantile ranges to ensure unbiasedness. Simulation studies confirm the efficiency of QRS measure relative to the intraday (open-to-close) squared returns and RS measures in the presence of intraday extreme prices. To smooth out the noises, QRS measures are fitted to the conditional autoregressive range (CARR) model with different asymmetric mean functions and error distributions. These fitted volatilities are then incorporated into return models to capture the heteroskedasticity of returns. Different value-at-risk (VaR) and conditional VaR return forecasts are provided and tested. Results based on Standard and Poor 500 and Dow Jones Industrial Average indices show that volatility estimates using QRS measures, asymmetric bilinear mean function and generalised beta type II distribution provide the best in-sample model-fit and out-of-sample forecast. For return models, the constant mean structure with Student-t errors and QRS volatility estimates provides the best in-sample fit. Different performance measures including Kupiec test for VaRs based on the best return model are evaluated to confirm the accuracy of the VaR forecasts.

Optimal Entry and Consumption under Habit Formation
Yue Yang,Xiang Yu
arXiv

We formulate a composite problem involving the decision making of the optimal entry time and dynamic consumption afterwards: in stage-1, the investor has access to full market information subjecting to some information costs and needs to choose an optimal stopping time to initiate stage-2; in stage-2 starting from the chosen stopping time, the investor terminates the costly full information acquisition and starts dynamic investment and consumption under partial observations of free public stock prices. The habit formation preference is employed, in which the past consumption affects the investor's current decisions. The value function of the composite problem is proved to be the unique viscosity solution of some variational inequalities.

Quantum Implementation of Risk Analysis-relevant Copulas
Janusz Milek
arXiv

Modern quantitative risk management relies on an adequate modeling of the tail dependence and a possibly accurate quantification of risk measures, like Value at Risk (VaR), at high confidence levels like 1 in 100 or even 1 in 2000. Quantum computing makes such a quantification quadratically more efficient than the Monte Carlo method; see (Woerner and Egger, 2018) and, for a broader perspective, (Or\'us et al., 2018). An important element of the risk analysis toolbox is copula, see (Jouanin et al., 2004) regarding financial applications. However, to the best knowledge of the author, no quantum computing implementation for sampling from a risk modeling-relevant copula in explicit form has been published so far. Our focus here is implementation of simple yet powerful copula models, capable of a satisfactory capturing the joint tail behaviour of the modelled risk factors. This paper deals with a few simple copula families, including Multivariate B11 (MB11) copula family, presented in (Milek, 2014). We will show that this copula family is suitable for the risk aggregation as it is exceptionally able to reproduce tail dependence structures; see (Embrechts et al., 2016) for a relevant benchmark as well as necessary and sufficient conditions regarding the ultimate feasible bivariate tail dependence structures. It turns out that such a discretized copula can be expressed using simple constructs present in the quantum computing: binary fraction expansion format, comonotone/independent random variables, controlled gates, and convex combinations, and is therefore suitable for a quantum computer implementation. This paper presents design behind the quantum implementation circuits, numerical and symbolic simulation results, and experimental validation on IBM quantum computer. The paper proposes also a generic method for quantum implementation of any discretized copula.

Regulatory Capital Regime, Competition and Stability: The Case Study of Banking Sector of Pakistan
SSRN
Regulatory capital requirements serve an important objective of ensuring financial stability. However higher capital requirements may increase risk of financial instability and even lead to accumulation of market power by banks thus affecting competition in the process through concentration of pricing power.Using data of 30 banks over the period 2011Q1 till 2018Q2 for Pakistan, we find that higher capital requirements are associated with, improved financial stability of overall banking sector. Results vary depending on type (private or public sector banks) and size (big, medium or smaller banks). The findings for Big6 and Small9 banks indicate that imposing higher capital requirements might increase instability. The study also finds that higher capital requirements mildly increase pricing power of the banks with bigger banks accumulating more power compared to medium and smaller-sized banks in Pakistan.

Robo-Advisors - Market Impact and Fiduciary Duty of Care to Retail Investors
Clarke, Demo
SSRN

Satellite reveals age and extent of oil palm plantations in Southeast Asia
Olha Danylo,Johannes Pirker,Guido Lemoine,Guido Ceccherini,Linda See,Ian McCallum,Hadi,Florian Kraxner,Frédéric Achard,Steffen Fritz
arXiv

In recent decades, global oil palm production has shown an abrupt increase, with almost 90% produced in Southeast Asia alone. Monitoring oil palm is largely based on national surveys and inventories or one-off mapping studies. However, they do not provide detailed spatial extent or timely updates and trends in oil palm expansion or age. Palm oil yields vary significantly with plantation age, which is critical for landscape-level planning. Here we show the extent and age of oil palm plantations for the year 2017 across Southeast Asia using remote sensing. Satellites reveal a total of 11.66 (+/- 2.10) million hectares (Mha) of plantations with more than 45% located in Sumatra. Plantation age varies from ~7 years in Kalimantan to ~13 in Insular Malaysia. More than half the plantations on Kalimantan are young (<7 years) and not yet in full production compared to Insular Malaysia where 45% of plantations are older than 15 years, with declining yields. For the first time, these results provide a consistent, independent, and transparent record of oil palm plantation extent and age structure, which are complementary to national statistics.

Startup Success Factors in the Capital Attraction Stage: Foundersâ€™ Perspective
Prohorovs, Anatolijs,Bistrova, Julija
SSRN
Only a small percentage of startups attract capital from venture capital investors. To determine the factors which owners of startups consider the most important for attracting seed venture investments, the founders of 40 startups in Latvia and Russia were questioned. The researchers compared organizational and financial factorsâ€™ importance for two groups of entrepreneurs: those who succeeded and those who failed in attracting funding. The results of the study indicate certain differences between the viewpoints of founders and investors regarding success factors. Based on the factor and regression analysis, the authors developed a model to forecast success in capital attraction.

TV Media Sentiment, Mutual Fund Flows and Investment Decisions: They Donâ€™t Put Their Money Where Their Sentiment Is
Naumer, Hans-JÃ¶rg
SSRN
The central role of the media for peopleâ€™s minds and for capital markets has been analyzed by a broad range of literature, nourished from several strands of academic research. Applying a vector autoregression on a unique set of TV news, consumer sentiment and excess flows of mutual funds, I find evidence that daily TV news is reflected in consumer sentiment and that this reflection varies with the news topics. However, I uncover no evidence of an effect on viewersâ€™ allocation decisions. Mutual fund investors seem to put their money neither where their newly won insights from TV news are, nor where their sentiment is. The findings are robust to different measures of the fund flows and an alternative indicator for the news sentiment. The results indicate a direction for further studies on a more micro level.

The Market Price of Risk for Delivery Periods: Pricing Swaps and Options in Electricity Markets
Annika Kemper,Maren D. Schmeck,Anna Kh. Balci
arXiv

In electricity markets futures deliver the underlying over a period and thus function as a swap contract. In this paper we introduce a market price of risk for delivery periods of electricity swaps. In particular, we suggest a weighted geometric average of an artificial geometric electricity futures price over the corresponding delivery period. This leads to a geometric electricity swap price dynamics without any approximation requirements. Our framework allows to include typical features as the Samuelson effect, seasonalities as well as a stochastic volatility in the absence of arbitrage. We show that our suggested model is suitable for pricing options on electricity swaps using the Heston method. Especially, we illustrate the related pricing procedure for electricity swaps and options in the setting of Arismendi et al. (2016), Schneider and Tavin (2018) and Fanelli and Schmeck (2019).

The More Illiquid, The More Expensive: A Search-Based Explanation of Illiquidity Premium
Choi, Jaewon,Han, Jungsuk,Shin, Sean Seunghun ,Yoon, Ji Hee
SSRN
Using a search-based trading model, we show that either illiquidity price premium or discount can arise endogenously for two assets with identical fundamentals. Liquidity difference between the two assets may diverge with trades concentrating in one market in a self-reinforcing manner depending on the relative strength of buying or selling pressure. When buyers are marginal investors due to strong buying pressure, prices are determined by buyers' tradeoff between immediacy and trading gains, generating illiquidity price discount. When sellers are marginal investors due to strong selling pressure, however, illiquidity price premium arises. Using an identification strategy that exploits same-issuer bonds but with differing liquidity, we confirm these theoretical predictions by showing that illiquid bonds have higher prices than liquid bonds during fire-sale episodes, while liquid bonds carry higher prices in normal periods.

The New ICO Intermediaries
Villanueva Collao, Vanessa,Winship, Verity
SSRN
Smart contracts promise a world without intermediaries. However, that promise has quickly proved elusive, including in the context of Initial Coin Offerings (ICOs), a vehicle for funding startups built on smart contracts and blockchain. Particularly as ICOs attract retail investors who are not code-literate, the question arises: is there a role for new intermediaries? This article assesses the possibility of an ICO auditor, providing a framework for understanding potential audit functions. In particular, it identifies three main roles: to translate the code for retail investors who are not code-sophisticates, to reconcile the code with promises made in other materials aimed at ICO participants, and to verify offline activity and identity where these remain important to the transactions. It then maps these functions onto emerging models.