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Tweet-level data from a social media platform reveals low average accuracy and high dispersion in the quality of advice by financial influencers, or “finfluencers”: 28% of finfluencers are skilled, generating 2.6% monthly abnormal returns, 16% are unskilled, and 56% have negative skill (“antiskill”) generating -2.3% monthly abnormal returns. Consistent with homophily shaping finfluencers’ social networks, antiskilled finfluencers have more followers and more influence on retail trading than skilled finfluencers. The advice by antiskilled finfluencers creates overly optimistic beliefs most times and persistent swings in followers’ beliefs. Consequently, finfluencers cause excessive trading and inefficient prices such that a contrarian strategy yields 1.2% monthly out-of-sample performance

This paper provides a numerical approach based on a Monte Carlo simulation for valuing dynamic capital budgeting problems with many embedded real options dependent on numerous state variables. We propose a way of decomposing a complex capital budgeting problem with many options into a set of simple options, suitably accounting for interaction and interdependence among them. The decomposition approach is numerically implemented using an extension of the Least Squares Monte Carlo algorithm, presented by Longstaff and Schwartz (2001) applied to our multi-option setting. We also provide a number of applications of our approach to well-known real options models and real life capital budgeting problems. Moreover, we present a set of numerical experiments to provide evidence for the accuracy of the proposed methodology.

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizeable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing.

The Basel Committee on Banking Supervision ("the Committee") released a consultative document that included a regulatory capital charge for operational risk. The complexity of the object "operational risk" led from the time of the document's release to vigorous and recurring discussions. We show that for a production unit of a bank with well-defined workflow processes where a comprehensive self-assessment based on six risk factors has been carried out, operational risk can be unambiguously defined and modelled. Using techniques from extreme value theory, we calculate risk measures for independent and dependent risk factors, re-spectively. The results of this modelling exercise are relevant for the implementation of a risk management framework: Frequency dependence among the risk factors only slightly changes the independency results, severity dependence on the contrary changes the independency results significantly, the risk factor "fraud" dominates all other factors and finally, only 10 percent of all processes have a 98 percent contribution to the resulting VaR. Since the definition and maintenance of processes is very costly, this last results is of major practical relevance. Performing a sensitivity analysis, it turns out that the key 10% of relevant processes is rather robust under this stress testing.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. In this course, we discuss scientifically sound ML tools that have been successfully applied to the management of large pools of funds.This material is part of Cornell University's ORIE 5256 graduate course at the School of Engineering.

Separation of ownership and control in firms creates information asymmetry problems between shareholders and managers that expose shareholders to a variety of agency risks. This paper investigates the extent to which governance attributes that are intended to mitigate agency risk affect firms' cost of equity capital. We examine governance attributes along four dimensions: (1) financial information quality, (2) ownership structure, (3) shareholder rights, and (4) board structure. We find that firms reporting larger abnormal accruals and less transparent earnings have a higher cost of equity, whereas firms with more independent audit committees have a lower cost of equity. We also find that firms with a greater proportion of their shares held by activist institutions receive a lower cost of equity, whereas firms with more blockholders have a higher cost of equity. Moreover, we find a negative relation between the cost of equity and the independence of the board and the percentage of the board that owns stock. Collectively, the governance attributes we examine explain roughly 8% of the cross-sectional variation in firms' cost of capital and 14 % of the variation in firms' beta. The results support the general hypothesis that firms with better governance present less agency risk to shareholders resulting in lower cost of equity capital.

We develop a new option pricing framework that tightly integrates with how institutional investors manage options positions. The framework starts with the near-term dynamics of the implied volatility surface and derives no-arbitrage constraints on its current shape. Within this framework, we show that just like option implied volatilities, realized and expected volatilities can also be constructed specific to, and different across, option contracts. Applying the new theory to the S&P 500 index time series and options data, we extract volatility risk and risk premium from the volatility surfaces, and find that the extracted risk premium significantly predicts future stock returns.

The Black-Litterman and related approaches modify the return distribution of a normally distributed market according to views or stress-test scenarios. We discuss how to broaden the range of applications of these approaches significantly by letting them act on the risk factors underlying the market, instead of the returns of the securities.

We present a neural network based calibration method that performs the calibration task withina few milliseconds for the full implied volatility surface. The framework is consistently applicable throughout a range of volatility models—including second generation stochastic volatilitymodels and the rough volatility family—and a range of derivative contracts. Neural networks inthis work are used in an off-line approximation of complex pricing functions, which are difficultto represent or time-consuming to evaluate by other means. The form in which informationfrom available data is extracted and used influences network performance: The grid-based algorithm used for calibration, inspired by representing the implied volatility and option prices as acollection of pixels is extended to include models where the initial forward variance curve is aninput. We highlight how this perspective opens new horizons for quantitative modelling. Thecalibration bottleneck posed by a slow pricing of derivative contracts is lifted, and stochasticvolatility models (classical and rough) can be handled in great generality. We demonstrate thecalibration performance both on simulated and historical data, on different derivative contractsand on a number of examples models of increasing complexity, and also showcase some of thepotentials of this approach towards model recognition. The algorithm and examples are provided in the Github repository GitHub: NN-StochVol-Calibrations.

The unique regulation of U.S. public pension funds links their liability discount rate to the expected return on assets, which gives them incentives to invest more in risky assets in order to report a better funding status. Comparing public and private pension funds in the United States, Canada, and Europe, we find that U.S. public pension funds act on their regulatory incentives. U.S. public pension funds with a higher level of underfunding per participant, as well as funds with more politicians and elected plan participants serving on the board, take more risk and use higher discount rates. The increased risk-taking by U.S. public funds is negatively related to their performance.

This paper studies trend filtering methods. These methods are widely used in momentum strategies, which correspond to an investment style based only on the history of past prices. For example, the CTA strategy used by hedge funds is one of the best-known momentum strategies. In this paper, we review the different econometric estimators to extract a trend of a time series. We distinguish between linear and nonlinear models as well as univariate and multivariate filtering. For each approach, we provide a comprehensive presentation, an overview of its advantages and disadvantages and an application to the S\&P 500 index. We also consider the calibration problem of these filters. We illustrate the two main solutions, the first based on prediction error, and the second using a benchmark estimator. We conclude the paper by listing some issues to consider when implementing a momentum strategy.

Corporate law generally makes voting power proportional to economic ownership. This serves several goals. Economic ownership gives shareholders an incentive to exercise voting power well. The coupling of votes and shares makes possible the market for corporate control. The power of economic owners to elect directors is also a core basis for the legitimacy of managerial authority. Both theory and evidence generally support the importance of linking votes to economic interest. Yet the derivatives revolution and other capital markets developments now allow both outside investors and insiders to readily decouple economic ownership of shares from voting rights. This decoupling, which we call the new vote buying, has emerged as a worldwide issue in the past several years. It is largely hidden from public view and mostly untouched by current regulation. Hedge funds have been especially creative in decoupling voting rights from economic ownership. Sometimes they hold more votes than economic ownership - a pattern we call empty voting. In an extreme situation, a vote holder can have a negative economic interest and, thus, an incentive to vote in ways that reduce the company's share price. Sometimes investors hold more economic ownership than votes, though often with morphable voting rights - the de facto ability to acquire the votes if needed. We call this situation hidden (morphable) ownership because the economic ownership and (de facto) voting ownership are often not disclosed. This Article analyzes the new vote buying and its potential benefits and costs. We set out the functional elements of the new vote buying and develop a taxonomy of decoupling strategies. We also propose a near-term disclosure-based response and outline a menu of longer-term regulatory choices. Our disclosure proposal would simplify and partially integrate five existing, inconsistent ownership disclosure regimes, and is worth considering independent of its value with respect to decoupling. In the longer term, other responses may be needed: we discuss strategies focused on voting rights, voting architecture, and supply and demand forces in the markets on which the new vote buying relies. ** This Article has two shorter companions. One is directed at an academic finance audience and the other is directed at legal practitioners and regulators. For the former, see Henry T.C. Hu & Bernard Black, Hedge Funds, Insiders, and Empty Voting: Decoupling of Economic and Voting Ownership in Public Companies, Journal of Corporate Finance, vol. 13, pp. 343-367 (2007), nearly final version at http://ssrn.com/abstract=874098. For the latter, see Henry T.C. Hu & Bernard Black, Empty Voting and Hidden (Morphable) Ownership: Taxonomy, Implications, and Reforms, Business Lawyer, vol. 61, pp. 1011-1070 (2006), also available at http://ssrn.com/abstract=887183 **

Algorithmic Trading (AT) and High Frequency (HF) trading, which are responsible for over 70\\% of US stocks trading volume, have greatly changed the microstructure dynamics of tick-by-tick stock data. In this paper we employ a hidden Markov model to examine how the intra-day dynamics of the stock market have changed, and how to use this information to develop trading strategies at high frequencies. In particular, we show how to employ our model to submit limit-orders to profit from the bid-ask spread and we also provide evidence of how HF traders may profit from liquidity incentives (liquidity rebates). We use data from February 2001 and February 2008 to show that while in 2001 the intra-day states with shortest average durations (waiting time between trades) were also the ones with very few trades, in 2008 the vast majority of trades took place in the states with shortest average durations. Moreover, in 2008 the states with shortest durations have the smallest price impact as measured by the volatility of price innovations.

The availability of high-frequency data on transactions, quotes and order flow in electronic order-driven markets has revolutionized data processing and statistical modeling techniques in finance and brought up new theoretical and computational challenges. Market dynamics at the transaction level cannot be characterized solely in terms the dynamics of a single price and one must also take into account the interaction between buy and sell orders of different types by modeling the order flow at the bid price, ask price and possibly other levels of the limit order book. We outline the empirical characteristics of high-frequency financial time series and provide an overview of stochastic models for the continuous-time dynamics of a limit order book, focusing in particular on models which describe the limit order book as a queuing system. We describe some applications of such models and point to some open problems.

We find that four well-known characteristics (carry, defensive, momentum and value) explain a significant portion of the cross-sectional variation in corporate bond excess returns. These characteristics have positive risk-adjusted expected returns and are not subsumed by traditional market premia or respective equity anomalies. The returns are economically significant, not explained by macroeconomic exposures, and there is some evidence that mispricing plays a role, especially for momentum.

We examine the role of high-frequency traders (HFTs) in price discovery and price efficiency. Overall HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors, both on average and on the highest volatility days. This is done through their liquidity demanding orders. In contrast, HFTs’ liquidity supplying orders are adversely selected. The direction of buying and selling by HFTs predicts price changes over short horizons measured in seconds. The direction of HFTs’ trading is correlated with public information, such as macro news announcements, market-wide price movements, and limit order book imbalances.

We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. We find that all four sectors have become highly interrelated over the past decade, likely increasing the level of systemic risk in the finance and insurance industries through a complex and time-varying network of relationships. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power in out-of-sample tests. Our results show an asymmetry in the degree of connectedness among the four sectors, with banks playing a much more important role in transmitting shocks than other financial institutions.

Der deutsche Aktienmarkt sah sich in den letzten 15 Jahren substantiellen Veränderungen gegenüber, welche unter anderem in eine zunehmende Internationalisierung und deutlich erhöhten Streubesitz mündeten. In der vorliegenden Arbeit untersuchen wir, inwieweit dies die aus klassischen Multifaktormodellen bekannten Risikofaktoren beeinflusste. Basierend auf den Renditen der CDAX-Unternehmen von Juli 1996 bis Dezember 2011 dokumentieren wir vier wesentliche Ergebnisse. Erstens finden wir eine insignifikant (positive) Marktrisikoprämie, eine signifikant negative Größenprämie (Size Premium), eine signifikant positive Substanzprämie (Value Premium) und eine signifikant positive Momentumprämie (Momentum Premium). Zweitens zeigen sich alle vier Faktoren untereinander nur schwach bzw. negativ korreliert und teilweise mit internationalen Gegenstücken nur schwach korreliert. Drittens zeigt sich, dass Renditen von Aktienportfolios, sortiert nach Marktkapitalisierung und Buch-Marktwert-Verhältnis, durch ein Dreifaktorenmodell nach Fama French (1993) substantiell besser erklärt werden, als durch ein Einfaktormodell in Anlehnung an das klassische Capital Asset Pricing Model. Der zusätzliche Erklärungsbeitrag des Momentumfaktors in Anlehnung an Carhart (1997) ist hingegen marginal. Letztendlich argumentieren wir daher vor dem Hintergrund der bekannten Literatur und unserer Ergebnisse für eine länderspezifische Erweiterung des Capital Asset Pricing Models. For the last 15 years, the German stock market has been facing substantial changes that resulted in increasing internationalization and a higher free float. In this paper, we investigate to what extent these changes influenced the well-known risk factors of standard multi-factor models. Based on the returns of all stocks listed in the German composite index CDAX (all domestic companies of the Prime and General Standard of the Frankfurt Stock Exchange) from July 1996 to December 2011, we document four major results: First, we find an insignificant (positive) market risk premium, a significant negative size premium, a significant positive value premium and a significant positive momentum premium. Second, the correlation within all four risk factors is only weakly positive or even negative and with international counterparts only weak. Third, we find that returns of portfolios, sorted by market capitalization and book-to-market equity, are captured substantially better by multi-factor models by Fama/French (1993) or Carhart (1997) than by the one-factor model based on the standard Capital Asset Pricing Model. Finally, after comparing our findings for the last 15 years with the existing literature, we conclude for a country specific extension of the Capital Asset Pricing Model.

The standard approach (e.g. Dupire (1994) and Rubinstein (1994)) to fitting stock processes to observed option prices models the underlying stock price as a one-factor diffusion process with state- and time-dependent volatility. While this approach is attractive in the sense that market completeness is maintained, the resulting model is often highly non-stationary, difficult to fit to steep volatility smiles, and generally is not well supported by empirical evidence. In this paper, we attempt to overcome some of these problems by overlaying the diffusion dynamics with a jump-process, effectively assuming that a large part of the observed volatility smiles can be explained by fear of sudden large market movements ("crash-o-phobia"). The first part of this paper derives a forward PIDE (Partial Integro-Differential Equation) satisfied by European call option prices and demonstrates how the resulting equation can be used to fit the model to the observed volatility smile/skew. In the second part of the paper, we discuss efficient methods of applying the calibrated model to the pricing of contingent claims. In particular, we develop an ADI (Alternating Directions Implicit) finite difference method that is shown to be unconditionally stable and, if combined with FFT (Fast Fourier Transform) methods, computationally efficient. The paper also discusses the usage of Monte Carlo methods, and contains several detailed examples from the S&P500 market. We compare pricing results obtained by the jump-diffusion approach with those of pure diffusion, and find significant differences for a range of popular contracts.

Since the intensification of the crisis in September 2008, all euro area long-term government bond yields relative to the German Bund have been characterized by highly persistent processes with upward trends for countries with weaker fiscal fundamentals. Looking at the daily period 1 September 2008 - 4 August 2011, we find that three factors can explain the recorded developments in sovereign spreads: an aggregate regional risk factor, the country-specific credit risk and the spillover effect from Greece. Specifically, higher risk aversion has increased the demand for the Bund and this is behind the pricing of all euro area spreads, including those for Austria, Finland and the Netherlands. Country-specific credit ratings have played a key role in the developments of the spreads for Greece, Ireland, Portugal and Spain. Finally, the rating downgrade in Greece has contributed to developments in spreads of countries with weaker fiscal fundamentals: Ireland, Portugal, Italy, Spain, Belgium and France.

This study contributes to the age-old question of whether stock market returns are predictable, by studying the relationship of VIX futures term structure and future S&P500 returns. The objective of this empirical analysis is to verify if the shape of the volatility futures term structure has signaling effects regarding future equity prices movements, as several investors believe. Our findings generally support the hypothesis that the term structure of VIX futures can be employed as a contrarian market timing indicator for the equity market. The empirical analysis of this study has important practical implications for financial market practitioners, as it shows that they can use the VIX futures term structure not only as a proxy of market expectations on forward volatility, but also as a stock market predictive tool.

We use a comprehensive data set of funds-of-funds to investigate performance, risk, and capital formation in the hedge fund industry from 1995 to 2004. While the average fund-of-funds delivers alpha only in the period between October 1998 and March 2000, a subset of funds-of-funds consistently delivers alpha. The alpha-producing funds are not as likely to liquidate as those that do not deliver alpha, and experience far greater and steadier capital inflows than their less fortunate counterparts. These capital inflows attenuate the ability of the alpha producers to continue to deliver alpha in the future.

Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation. We formalize this argument with a conditional factor pricing model. Using instrumented principal components analysis, we estimate latent factors with time-varying factor loadings that depend on observable firm characteristics. We show that factor loadings vary significantly over time, even at short horizons over which the momentum phenomenon operates (one year), and this variation captures reliable conditional risk premia missed by other factor models commonly used in the literature. Our estimates of conditional risk exposure can explain a sizable fraction of momentum and long-term reversal returns and can be used to generate even stronger return predictions.

Many papers in the empirical finance literature implement tests of asset pricing models either via Fama-French time-series regressions or via Fama-Macbeth cross-sectional regressions. This short paper explains their conceptual relationships. There is a time-series equivalent method to implementing Fama-Macbeth regressions (in a stable world). This correspondence also helps to clarify the interpretation of the estimates from the two methods: The Fama-Macbeth test is better suited for APT tests, while the plain Fama-French test is better suited for equilibrium tests. (Of course, all equilibrium model must be arbitrage-free, but not vice-versa.) It is possible to test not only whether factors can price portfolios in an equilibrium framework, but also the less restrictive requirement that the factors should not allow for arbitrage. For example, this short paper shows that the Fama-French 3-factor model fails the weaker arbitrage pricing restriction for the the 2x3 Fama-French portfolios, and not just the stronger equilibrium pricing restriction.

Should investors be willing to pay higher prices for more liquid assets than for otherwise similar assets that are less liquid? If the answer is yes, how much should the premium be for liquid assets? Conversely, how do we estimate the discount for illiquid assets? In this paper, we argue that it is a mistake to think of some assets as illiquid and others as liquid and that liquidity is a continuum, where some assets are more liquid than others. We then examine why liquid assets may be priced more highly than otherwise similar illiquid assets and why some investors value liquidity more than others. We follow up be presenting the empirical evidence that has accumulated over time and across different assets - financial and real - on the cost of illiquidity. Finally, we consider how we can use the theory and evidence on illiquidity to estimate the effect of illiquidity on the value of an asset or business.