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We uncover new return predictability in the cross-section of delta-hedged equity options. Expected returns to writing delta-hedged calls are negatively correlated with stock price, profit margin and firm profitability, but positively correlated with cash holding, cash flow variance, new shares issuance, total external financing, distress risk, and dispersion of analyst forecasts. Our option portfolio strategies have annual Sharpe ratio above two and remain profitable after transaction costs. Their profits can be explained by two option factors while equity risk factors have no explanatory power. We find support for several economic channels at work, yet the option return predictabilities remain puzzling.

We introduce VolGAN, a generative model for arbitrage-free implied volatility surfaces. The model is trained on time series of implied volatility surfaces and underlying prices and is capable of generating realistic scenarios for joint dynamics of the implied volatility surface and the underlying asset. We illustrate the performance of the model by training it on SPX implied volatility time series and show that it is able to learn the covariance structure of the co-movements in implied volatilities and generate realistic dynamics for the (VIX) volatility index. In particular, the generative model is capable of simulating scenarios with non-Gaussian distributions of increments for state variables as well as time-varying correlations.  Finally, we illustrate the use of VolGAN to construct data-driven hedging strategies for option portfolios, and show that these strategies can outperform Black-Scholes delta  and delta-vega hedging.

La versión española de este artículo se puede encontrar en: http://ssrn.com/abstract=1125625.The return on investments depends on the effects of inflation. To analyze the effect of inflation, we shall use a case study of two companies engaging in the same business and in identical market conditions but in two countries with very different inflation rates. The problem of inflation and its consequences is expressed very clearly. And its solution is very simple. When inflation is high, company earnings are artificially high (i.e., not caused by an improvement in the company’s situation), which means that the tax paid is higher than if there was no inflation. Consequently, investments’ real return is smaller.

Using data from a large German brokerage, we find that individuals investing in passive exchange-traded funds (ETFs) do not improve their portfolio performance, even before transaction costs. Further analysis suggests that this is because of poor ETF timing as well as poor ETF selection (relative to the choice of low-cost, well-diversified ETFs). An exploration of investor heterogeneity shows that though investors who trade more have worse ETF timing, no groups of investors benefit by using ETFs, and no groups will lose by investing in low-cost, well-diversified ETFs.

Spanish Abstract: En esta monografía se describe el mercado financiero de renta variable o de acciones en concreto: el mercado primario, la oferta pública de venta (OPV), el mercado secundario: la Bolsa de Valores, fases de la contratación bursátil en el mercado continuo español, la negociación de alta frecuencia (HFT) y las plataformas oscuras (dark pools), los índices de Bolsa, la oferta pública de exclusión (OPE), una introducción a la valoración de acciones, y los ETF.English Abstract: In this monograph the financial market of variable income securities is analyzed. In short: the primary market, initial public offering (IPO), the secondary market: Stock Exchange, trading phases in the Spanish Continuous Stock Market, HFT and dark pools, Stock Exchange Indexes, an introduction to the stock valuation, and ETFs.

Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory, rather than on back-testing potential trading rules. The purpose of this monograph is to introduce Machine Learning (ML) tools that can help asset managers discover economic and financial theories.ML is not a black-box, and it does not necessarily over-fit. ML tools complement rather than replace the classical statistical methods. Some of ML’s strengths include: (i) Focus on out-of-sample predictability over variance adjudication; (ii) usage of computational methods to avoid relying on (potentially unrealistic) assumptions; (iii) ability to “learn” complex specifications, including non-linear, hierarchical and non-continuous interaction effects in a high-dimensional space; and (iv) ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

Systemic risk is commonly used to describe the possibility of a series of correlated defaults among financial institutions - typically banks - that occur over a short period of time, often caused by a single major event. However, since the collapse of Long Term Capital Management in 1998, it has become clear that hedge funds are also involved in systemic risk exposures. The hedge-fund industry has a symbiotic relationship with the banking sector, and many banks now operate proprietary trading units that are organized much like hedge funds. As a result, the risk exposures of the hedge-fund industry may have a material impact on the banking sector, resulting in new sources of systemic risks. In this paper, we attempt to quantify the potential impact of hedge funds on systemic risk by developing a number of new risk measures for hedge funds and applying them to individual and aggregate hedge-fund returns data. These measures include: illiquidity risk exposure, nonlinear factor models for hedge-fund and banking-sector indexes, logistic regression analysis of hedge-fund liquidation probabilities, and aggregate measures of volatility and distress based on regime-switching models. Our preliminary findings suggest that the hedge-fund industry may be heading into a challenging period of lower expected returns, and that systemic risk is currently on the rise.

Strong regulatory actions are needed to combat climate change, but climate policy uncertainty makes it difficult for investors to quantify the impact of future climate regulation. We show that such uncertainty is priced in the option market. The cost of option protection against downside tail risks is larger for firms with more carbon-intense business models. For carbon-intense firms, the cost of protection against downside tail risk is magnified at times when the public’s attention to climate change spikes, and it decreased after the election of climate change skeptic President Trump.

I describe a model of earnings and earnings growth and I demonstrate how this model may be used to obtain estimates of the expected rate of return on equity capital. These estimates are compared with estimates of the expected rate of return implied by commonly used heuristics - viz., the PEG ratio and the PE ratio. Proponents of the PEG ratio (which is the price-earnings (PE) ratio divided by the short-term earnings growth rate) argue that this ratio takes account of differences in short-run earnings growth providing a ranking that is superior to the ranking based on PE ratios. But even though the PEG ratio may provide an improvement over the PE ratio, it is arguably still too simplistic because it implicitly assumes that the short-run growth forecast also captures the long-run future. I provide a means of simultaneously estimating the expected rate of return and the rate of change in abnormal growth in earnings beyond the (short) forecast horizon - thereby refining the PEG ratio ranking. The method may also be used by researchers interested in determining the effects of various factors (such as disclosure quality, cross-listing, etc.,) on the cost of equity capital. Although the correlation between the refined estimates and estimates of the expected rate of return implied by the PEG ratio is high supporting the use of the PEG ratio as a parsimonious way to rank stocks, the estimates of the expected rate of return based on the PEG ratio are biased downwards. This correlation is much lower and the downward bias is much larger for estimates of the expected rate of return based on the PE ratio. I provide evidence that stocks for which the downward bias is higher can be identified a priori.

Using a comprehensive sample of 164 domestic equity Smart Beta (SB) ETFs during 2003-2014 period, I analyze whether these funds beat their benchmarks by tilting their portfolios to well-known factors such as size, value, momentum, quality, beta and volatility. I then test if Smart Beta funds harvest factor premiums more efficiently than their traditional cap-weighted benchmarks by periodic trading against price movements. While 60% of SB fund categories have beaten their raw passive benchmarks, I find no conclusive empirical evidence to support the hypothesis that SB ETFs outperform their risk-adjusted benchmarks over the studied period. Performance of SB funds is also insignificant when compared with the risk-adjusted blended benchmark that uses existing cap-weighted funds to provide low-cost passive exposure to market, size and value factors. SB ETFs exhibit potentially unintended factor tilts which may work to offset the return advantage from intended factor tilts. After decomposing total allocation component of SB funds into static and dynamic effects, I find that the benefit from dynamic factor allocation is neutral at best. This is consistent with the hypothesis that static factor exposure rather systematic rule-based rebalancing is the main driver of SB ETFs performance.

To value shares there are two usual methods that, if properly applied, provide the same value: 1/ Present value of expected free cash flows (FCF) discounted with the WACC rate and then, subtract the value of debt; and 2/ Present value of expected equity cash flows (ECF) discounted with the Ke rate (required return to equity). Both valuations must provide the same result because both methods analyze the same reality under the same hypotheses; they differ only in the cash flows taken as the starting point for the valuation.But in many valuations performed by investment banks, analysts, consultants, finance professors… both methods do not provide the same value.This paper presents a real valuation performed by a well-known investment bank, with two very different values: €6,9 million using method 1/, and €4,2 million using method 2/.

The increasing popularity of factor investing has led to valuation concerns among some contrarian-minded investors, and fears of imminent mean-reversion and underperformance. In this paper, the authors find that despite their recent popularity the most common factors or styles, namely the value, momentum and defensive styles, are not, in general, markedly over-valued as measured by their value spreads. More broadly, tactical timing, whether of markets or factors, always seems to hold appeal for many. The authors look at the general efficacy of value spreads in predicting future returns to styles. At first glance, valuation-based timing of styles appears promising. This is not surprising as it is a simple consequence of the efficacy of the value strategy itself. Yet when the authors implement value timing in a multi-style framework that already includes the value style, they find somewhat disappointing results. As value timing of factors is correlated to the standard value factor, it adds further value exposure, but as compared to an explicit risk-targeted strategic allocation to value, value timing provides an intermittent and sub-optimal amount of value exposure. Thus, according to the authors, tactical value timing can reduce diversification and detract from the performance of a multi-style strategy that already includes value. Finally, the authors explore whether value timing works better at longer holding periods or at extremes, still finding fairly weak results. Contrarian value timing of factors is, generally, a weak addition for long-term investors holding well-diversified factors including value and, specifically, not sending a strong signal on stretched valuations today.

We present conclusive evidence on the performance of private equity, using a high quality dataset of fund cash flows that covers about 85 percent of capital ever raised by U.S. buyout funds. For almost all vintage years since 1980, U.S. buyout funds have significantly outperformed the S&P 500. Liquidated funds from 1980 to 2000 have delivered excess returns of about 450 basis points per year. Adding partially liquidated funds up to 2005, excess returns rise to over 800 basis points. The cross-sectional variation is considerable with just over 60% of all funds doing better than the S&P, and excess returns being driven by top-decile rather than top-quartile funds. We document an extreme cyclicality in returns with much higher figures for funds set up in the first half of each of the past three decades, and correspondingly lower returns towards the end of each decade. However, we find a significant downward trend in absolute returns over all 29 vintage years. Our results are robust to measuring excess returns via money multiples instead of IRRs, and are essentially unchanged when pricing residual values at observed secondary market discounts.

Equity risk premiums are a central component of every risk and return model in finance and are a key input into estimating costs of equity and capital in both corporate finance and valuation. Given their importance, it is surprising how haphazard the estimation of equity risk premiums remains in practice. In the standard approach to estimating equity risk premiums, historical returns are used, with the difference in annual returns on stocks versus bonds over a long time period comprising the expected risk premium. We note the limitations of this approach, even in markets like the United States, which have long periods of historical data available, and its complete failure in emerging markets, where the historical data tends to be limited and volatile. We look at two other approaches to estimating equity risk premiums - the survey approach, where investors and managers ar asked to assess the risk premium and the implied approach, where a forward-looking estimate of the premium is estimated using either current equity prices or risk premiums in non-equity markets. We close the paper by examining why different approaches yield different values for the equity risk premium, and how to choose the "right" number to use in analysis. (In an addendum, we also look at equity risk premiums during the market crisis, starting on September 12, 2008 through October 16, 2008.)

Bitcoin was the first cryptocurrency using blockchain and has been the market leader since the first bitcoin was mined in 2009. After the birth of bitcoin in the Genesis Block, more than 1000 altcoins and crypto-tokens have been created with at least 919 trading actively on unregulated or registered exchanges. This entire class of cryptocurrency and tokens has been classified by some tax authorities as having the same status as commodities. If cryptocurrency is viewed in the same class as commodities, how different it is in terms of its risk and return structure? This paper sets out to help the readers to understand cryptocurrencies, and to explore the risk and return characteristics using a portfolio of cryptocurrency represented by the CRIX Index. Substantial discussions are centred on bitcoin and its close variants. Some questions are raised about the potential of cryptocurrencies as an investment class. Results show that the correlations between the cryptocurrencies and traditional assets are low, and incorporation of CRIX index will improve the performance of the portfolio that consists mainly of mainstream assets. Sentiment analysis also indicates the CRIX index has a relatively high Sharpe ratio. While we may view the results with care, a new form of financing for crypto and blockchain start-ups is born. The disruption brought about by bitcoin may be felt beyond payments through what is known as Initial Crypto-Token Offering (ICO) or Initial Token Sales (ITS).

We document that unweighted indexes have outperformed weighted indexes and that the S&P400 and the S&P600 have outperformed the S&P500. $100 invested in the S&P500 in January 2000 became $252.6 in April 2018, but invested in the S&P600 became $577.2, invested in the 30% smallest companies (equal weight) of Kenneth French became $617.9 and invested in the portfolio of [smallest companies and highest Book to Market] (equal weight) of Kenneth French became $1,640. Then, we can conclude that it has been very easy to beat the S&P500. Kenneth French data show (exhibits 5 and 6) that it has been so since 1927.When a rational investor invests for the long-term, he cares about how much money he will have at the end (retirement, endowment…) and he diversifies to avoid a concentration of risk in some of the individual investments. The rational investors (at least the ones we know) do not care about using “the best model”, “the most popular model”…They do not care neither about maximizing some ratio (Sharpe…) nor about minimizing the volatility of his portfolio (most rational investors we know like volatility: volatility does not measure the risk they want to avoid).The objectives of this paper are neither number crunching, neither to maximize anything nor to provide recipes on how to invest, but to provide with some data (facts) that help the reader to analyze his investments and, perhaps, to change his investment criteria. We include a final advice: apply the logic principle “Never buy a hair growth lotion from a man with no hair” to your investment advisors.

This study examines the association between measures of earnings quality and auditor industry specialization. Prior work has examined the association between auditor brand name and earnings quality, using auditor brand name to proxy for audit quality. Recent work has hypothesized that auditor industry specialization also contributes to audit quality. Extending this literature, we compare the absolute level of discretionary accruals (DAC) and earnings response coefficients (ERC) of firms audited by industry specialists with those of firms not audited by industry specialists. We restrict our study to clients of Big 6 (and later Big 5) auditors to control for brand name. Because industry specialization is unobservable, we use multiple proxies for it. After controlling for variables established in prior work to be related to DAC and the ERC, we find clients of industry specialist auditors have lower DAC and higher ERC than clients of non-specialist auditors. This finding is consistent with clients of industry specialists having higher earnings quality than clients of non-specialists.

This paper considers extensions of the Libor market model (Brace et al (1997), Jamshidian (1997), Miltersen et al (1997)) to markets with volatility skews in observable option prices. We expand the family of forward rate processes to include diffusions with non-linear forward rate dependence and discuss efficient techniques for calibration to quoted prices of caps and swaptions. Special emphasis is put on generalized CEV processes for which exact closed-form expressions for cap prices are derived. We also discuss modifications of the CEV process which exhibit appealing growth and boundary characteristics. The proposed models are investigated numerically through Crank-Nicholson finite difference schemes and Monte Carlo simulations.

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own-signal predictability, assuming an equal strength across securities, our framework is flexible and includes cross-predictability-leading to three main results. First, we derive the optimal strategy in closed form. It consists of eigenvectors of a “prediction matrix,” which we call “principal portfolios.” Second, we decompose the problem into alpha and beta, yielding optimal strategies with, respectively, zero and positive factor exposure. Third, we provide a new test of asset pricing models. Empirically, principal portfolios deliver significant out-of-sample alphas to standard factors in several data sets.

The basic financial purpose of a firm is to maximize its value. An inventory management system should also contribute to realization of this basic aim. Many current asset management models currently found in financial management literature were constructed with the assumption of book profit maximization as basic aim. However these models could lack what relates to another aim, i.e., maximization of enterprise value. This article presents a modified value-based inventory management model.

We document the widespread nature and structure of missing observations of firm fundamentals and show how to systematically handle them. Missing financial data affects more than 70% of firms that represent about half of the total market cap. Firm fundamentals have complex systematic missing patterns, invalidating traditional approaches to imputation. We propose a novel imputation method to obtain a fully observed panel of firm fundamentals that exploits both time-series and cross-sectional dependency of data to impute missing values and allows for general systematic patterns of missingness. We document important implications for risk premiums estimates, cross-sectional anomalies, and portfolio construction. (JEL C14, C38, C55, G12)

This paper examines the value relevance of goodwill impairment and the information content of impairment announcements with the introduction of Financial Reporting Standard (FRS) 11 in the UK in 1998 which allowed an annual impairment review as an alternative to capitalisation and subsequent systematic amortisation of goodwill. In contrast to systematic amortisation of capitalised goodwill, our results suggest that goodwill impairment is associated with economically significant reductions in market value. We also find evidence of a significant negative market reaction to goodwill impairment announcements. The negative impact is greater for firms with a higher proportion of assets carried as goodwill and for firms that release little information prior to the announcement. Our results also confirm previous findings that capitalised goodwill is significantly associated with market value in the year of acquisition, but weakens thereafter; and that systematic amortisation of capitalised goodwill is not value-relevant.

We show how to implement a Rubinstein (1994) implied binomial tree using an Excel spreadsheet, but without having to use visual basic in Excel (VBA). We demonstrate both the optimization needed to generate implied ending risk-neutral probabilities from a set of actual option prices and the backwards recursion needed to solve for the entire implied tree. By using only standard Excel functions, and not resorting to VBA, we make this option pricing technique immediately accessible to both practitioners and academics. With minimal preparation, this technique can also be introduced to the undergraduate classroom.

In this paper, we collect individual stock prices for NYSE stocks over the period 1815 to 1925 and individual dividend data over the period 1825 to 1870. We use monthly price and dividend information on more than 600 individual securities over the period to estimate a stock price index and total return series that extends virtually to the beginning of the New York Stock Exchange. We use this data to estimate the power of past returns and dividend yields to forecast future long-horizon returns. We find some evidence of predictabiity in sub-periods but little predictability over the long term. We estimate the time-varying volatility of the U.S. market over the period 1815 to 1925 and find evidence of a leverage effect on risk. This new database will allow future researchers to test a broad range of hypotheses about the U.S. capital markets in a rich, untouched sample.

The real option valuation method is often presented as an alternative to the conventional discounted cash flow (DCF) approach because it is able to recognize additional project value due to the presence of management flexibility. However, these two valuation methods can be separated on a more fundamental level by their differences in risk discounting. Real option valuation applies the risk-adjustment to the source of uncertainty in the cash flow while the DCF method adjusts for risk at the aggregate level of net cash flow. This seemingly small difference is the reason why the real option method is able to differentiate between projects according to each project's unique risk characteristics while the conventional DCF approach cannot. This paper provides an overview of the real options and DCF valuation frameworks and discusses the differences in risk discounting that exist between the two methods. Using grade-school mathematics, this paper clearly demonstrates how, with real options, a unique project risk discount can be calculated which is directly linked to the project's unique risk profile. It also highlights why the DCF method fails in this regard and shows why a call to increase the Risk-Adjusted Discount Rate is an incomplete solution at best. Finally, a heap-leach project and satellite reserve development project are valued with both techniques and the difference in investment conclusions is explained in terms of the risk-discounting concepts discussed here.