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If investors have limited attention, then accounting outcomes that saliently highlight positive aspects of a firm's performance will promote high market valuations. When cumulative accounting value added (net operating income) over time outstrips cumulative cash value added (free cash flow), it becomes hard for the firm to sustain further earnings growth. When the balance sheet is 'bloated' in this fashion, we argue that investors with limited attention will overvalue the firm, because naïve earnings-based valuation disregards the firm's relative lack of success in generating cash flows in excess of investment needs. The level of net operating assets, the difference between cumulative earnings and cumulative free cash flow over time, is therefore a measure of the extent to which operating/reporting outcomes provoke excessive investor optimism. Therefore, if investor attention is limited, net operating assets will negatively predict subsequent stock returns. In our 1964-2002 sample, net operating assets scaled by beginning total assets is a strong negative predictor of long-run stock returns. Predictability is robust with respect to an extensive set of controls and testing methods.

A two-stage stock valuation model derived from Wilcox's (1984) P/B-ROE approach is shown to be a surprisingly effective tool for a broad variety of uses, including the explanation of current prices and the prediction of future return differences. This applies both to the cross-section of individual US stocks and the time-series of the S&P 500 index. In addition to offering a new closed-form expression for firm value, the model measures the investment horizon over which the market predicts exceptional profitability, demonstrates the dependence of expectations for future profitability on past volatility of return on equity, and quantifies the extent of the recent US stock price bubble. Perhaps its most persuasive result is its no-look-ahead statistical support for tactical asset allocation.

Market efficiency has an influence on the investment strategy of an investor because if market is efficient, trying to pickup winners will be a waste of time. In an efficient market there will be no undervalued securities offering higher than deserved expected returns, given their risk. On the other hand if markets are not efficient, excess returns can be made by correctly picking the winners. In this paper, an analysis of three popular stock indices is carried out to test the efficiency level in Indian Stock market and the random walk nature of the stock market by using the run test and the autocorrelation function ACF (k) for the period from January 1996 to June 2002. The study carried out in this paper has presented the evidence of the inefficient form of the Indian Stock Market. From autocorrelation analyses and runs test we are able to conclude that the series of stock indices in the India Stock Market are biased random time series. The auto correlation analysis indicates that the behavior of share prices does not confirm the applicability of the random walk model in the India stock market. Thus there are undervalued securities in the market and the investors can always excess returns by correctly picking them.

The emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modelling approaches. In this paper, we discuss the promises and pitfalls of applying machine learning to asset management, by reviewing the existing ML literature from the perspective of a prudent practitioner. The focus is on the methodological design choices that can critically affect predictive outcomes and on an evaluation of the frequent claim that ML gives spectacular performance improvements. In light of the practical considerations, the apparent advantage of ML is reduced, but still likely to make a difference for investors who adhere to a sound research protocol to navigate the intrinsic pitfalls of ML.

I developed in a previous working paper the Sidre and Most-Strategy. The strategy relies on the typical termstructure of VIX futures. The Calvados is a refined and condensed version of these strategies. The starting point was a paper of Simon and Campasano. The authors demonstrate that the VIX futures basis does not have significant forecast power for the change in the VIX spot index, but does have forecast power for subsequent VIX futures returns. It is especially profitable to short VIX futures contracts when the basis is in contango. The original Calvados working paper presented improved metrics and parameter settings of the Simon&Campasano approach. The current working paper improves the original work in several points and extends the historic backtest. The overall patterns of the original results are reassured and improved upon. The Calvados is traded in the Sybil-Fund. It is so far the pick of the bunch. One gets a lot of fun for a medium dose of risk.

A selection of twenty common ratios used in financial statement analysis covering five key aspects: Liquidity; Activity; Profitability; Leverage; and Equity Valuation. This summary includes a brief description of the variables as well as the construction of the formulae.

The hybrid approach combines the two most popular approach to VaR estimation: RiskMetrics and Historical Simulation. It estimates the VaR of a portfolio by applying exponentially declining weights to past returns and then finding the appropriate percentile of this time-weighted empirical distribution. This new approach is very simple to implement. Empirical tests show a significant improvement in the precision of VaR forecasts using the hybrid approach relative to RiskMetrics and Historical Simulation. It is especially appropriate for calculating the VaR of fat-tailed and highly skewed data, with rapidly changing moments. As an aside, we also introduce a new method for testing the perfomance of various VaR forecasts.

Spanish Abstract: Se presenta la valoración de bonos (activos del mercado de deuda). Para ello se definirán los parámetros utilizados para su valoración, y la sensibilidad de su valor con respecto a las oscilaciones de los tipos de interés. Se aborda el "precio de un punto básico" y la estructura temporal de los tipos de interés. Se termina realizando ejercicios sobre el valor actual neto (VAN) y la tasa interna de rentabilidad (TIR) de cuatro bonos con distintas características. English Abstract: We value Government bonds. For that purpose, we define present value (PV) and internal rate of return (IRR). We show the meaning of "price of a basis point" and the structure of interest rates. We finish with exercises of present value (PV) and internal rate of return (IRR) of four bonds with different features.

Islamic lending transactions are governed by the precepts of the shariah, which bans interest and stipulates that income must be derived as return from entrepreneurial investment. Since Islamic finance is predicated on asset backing and specific credit participation in identified business risk, structuring shariah-compliant securitization seems straightforward. This paper explains the fundamental legal principles of Islamic finance, which includes the presentation of a valuation model that helps distil the essential economic characteristics of shariah-compliant synthetication of conventional finance. In addition to a brief review of the current state of market development, the examination of pertinent legal and economic implications of shariah compliance on the configuration of securitization transactions informs a discussion of the most salient benefits and drawbacks of Islamic securitization.

This paper explores the role of liquidity risk in the pricing of corporate bonds. We show that corporate bond returns have signifcant exposures to fluctuations in treasury bond liquidity and equity market liquidity. Further, this liquidity risk is a priced factor for the expected returns on corporate bonds, and the associated liquidity risk premia help to explain the credit spread puzzle. In terms of expected returns, the total estimated liquidity risk premium is around 0.6% per annum for US long-maturity investment grade bonds. For speculative grade bonds, which have higher exposures to the liquidity factors, the liquidity risk premium is around 1.5% per annum. We find very similar evidence for the liquidity risk exposure of corporate bonds for a sample of European corporate bond prices.

The document starts with 100 questions that students, alumni and other persons (judges, arbitrageurs, clients…) have posed to me over the past years. They were recompiled so as to help the reader remember, clarify and, in some cases, discuss some useful concepts in finance. Most of the questions have a clear answer but others can receive several emphasis. A short answer to all of the questions is provided in section 2. It continues with 104 questions related to valuation, with 86 definitions very useful to clarify concepts and 49 questions related to differentiate concepts.

We show how a market maker employs information about the momentum in the price of the asset (i.e., alpha signal) to make decisions in her liquidity provision strategy in an order driven electronic market. The momentum in the midprice of the asset depends on the execution of liquidity taking orders and the arrival of news. Buy (resp. sell) market orders (MOs) exert a short-lived upward (resp. downward) pressure on the midprice. We employ Nasdaq high-frequency data to estimate model parameters and to illustrate the performance of the market making strategy. The market maker employs the alpha signal to minimise adverse selection costs, execute directional trades in anticipation of price changes, and to manage inventory risk. As the market maker increases her tolerance to inventory risk, the expected profits that stem from the alpha signal increase because the strategy employs more speculative MOs and performs more roundtrip trades with limit orders.

This paper focuses on exits by venture capitalists from their portfolio companies. Using a unique self-collected data set, we provide new stylized facts about the venture capital industry in Europe and in the US. Although there are numerous similarities between the US and Europe, there are also important differences, in particular with respect to the duration of exit stage, the use of convertible securities, the replacement of former management and deal syndication. Much of these differences can be brought to a common denominator, namely that European venture capitalists face a less liquid market for the human resources that go into the ventures as well as for the exit opportunities. The most striking difference is with respect to the use of convertible securities, which are by far less often used in Europe as compared to the US. Overall, we show that European venture capitalists monitor less. Finally, the paper analyzes the impact of venture capital firms' characteristics and the use of different monitoring devices (stage financing, board representation, use of convertible securities and reporting of activities) on the exit route. Some aspects of close monitoring seem to significantly affect the venture's likelihood of going public.

The goal of this paper is to assist the trader in answering two questions: 1) "What is a reasonable performance estimate of the long-run edge of the trading system?" and, 2) "What worst-case contingencies must be tolerated in short-run performance in order to achieve the long-run expectation?" With this information, the trader can make probabilistic, data-driven decisions on whether to allocate capital to the system and once actively trading, whether the system is "broken" and should cease trading. Traditional trading system development leads not only to positively biased performance estimates due to the data mining bias, but much valuable information is lost in the process. The result for many traders is frustration due to poor realized trading system performance that does not live up to expectations. This paper explores how to leverage the optimization inherent in typical system development via a method named System Parameter Permutation (SPP) and to extract information that enables realistic contingency planning based on probabilities. Many traders and system developers go to great lengths to avoid the effects of randomness in trading results, knowing the large impact it may cause. In contrast, SPP embraces randomness as a tool to help uncover what may probabilistically be expected from a trading system in the future. The method is simple to apply yet very effective. The method is applied to an example rotational trading system based on relative momentum and the results are compared to traditional out-of-sample testing. The example shows how SPP fully leverages available historical data to enable deep understanding of potential risks and rewards prior to allocating capital to a trading system.

In a financial system in which balance sheets are continuously marked to market, asset price changes appear immediately as changes in net worth, eliciting responses from financial intermediaries who adjust the size of their balance sheets. We document evidence that marked-to-market leverage is strongly procyclical. Such behavior has aggregate consequences. Changes in dealer repos—the primary margin of adjustment for the aggregate balance sheets of intermediaries—forecast changes in financial market risk as measured by the innovations in the Chicago Board Options Exchange Volatility Index (VIX). Aggregate liquidity can be seen as the rate of change of the aggregate balance sheet of the financial intermediaries.

This paper demonstrates a simple way of deriving both the Capital Asset Pricing Model (CAPM) and a capital asset’s beta value from the Capital Market Line (CML). The CML model is extended to include a series of isocorrelation curves along which the returns of any portfolio can be plotted according to its total risk and the degree to which its return correlates to that of the market. This approach is simpler than methods currently available in the relevant literature and may be useful for teaching purposes.

Prior to SFAS 142, goodwill was subject to periodic amortization and a recoverability-based impairment test. SFAS 142 eliminates periodic amortization and imposes a fair-value-based impairment test. We examine the impact of this standard on the accounting for and valuation of goodwill. Our results indicate that the new standard has resulted in relatively inflated goodwill balances and untimely impairments. We also find that investors do not appear to fully anticipate the untimely nature of post-SFAS 142 goodwill impairments. Overall, our results suggest that, in practice, some managers have exploited the discretion afforded by SFAS 142 to delay goodwill impairments, causing earnings and stock prices to be temporarily inflated.

During the period 1991-1999, stock returns were correlated with the G-Index based on twenty-four governance provisions (Gompers, Ishii, and Metrick (2003)) and the E-Index based on the six provisions that matter most (Bebchuk, Cohen, and Ferrell (2009)). This correlation, however, did not persist during the subsequent period 2000-2008. We provide evidence that both the identified correlation and its subsequent disappearance were due to market participants’ gradually learning to appreciate the difference between firms scoring well and poorly on the governance indices. Consistent with the learning hypothesis, we find that: (i) The disappearance of the governance-return correlation was associated with an increase in the attention to governance by a wide range of market participants; (ii) Until the beginning of the 2000s, but not subsequently, stock market reactions to earning announcements reflected the market’s being more positively surprised by the earning announcements of good-governance firms than by those of poor-governance firms; (iii) Stock analysts were also more positively surprised by the earning announcements of good-governance firms than by those of poor-governance firms until the beginning of the 2000s but not afterwards; (iv) While the G-Index and E-Index could no longer generate abnormal returns in the 2000s, their negative association with Tobin’s Q and operating performance persisted; and (v) The existence and subsequent disappearance of the governance-return correlation cannot be fully explained by additional common risk factors suggested in the literature for augmenting the Fame-French-Carhart four-factor model. ___________________________________ Gompers, Ishii, and Metrick (2003) is available on SSRN at: http://ssrn.com/abstract=278920 Bebchuk, Cohen, and Ferrell (2009) is available on SSRN at: http://ssrn.com/abstract=593423

We document a new phenomenon in bond and equity markets that we call cross-asset time series momentum. Using data from 20 countries, we show that past bond market returns are positive predictors of future equity market returns, and past equity market returns are negative predictors of future bond market returns. We use this predictability to construct a diversified cross-asset time series momentum portfolio that yields a Sharpe ratio 45% higher than a standard time series momentum portfolio. We present evidence that time series momentum and cross-asset time series momentum are driven by slow-moving capital in bond and equity markets.

This paper introduces a new measure of a firm's negative impact on biodiversity, the corporate biodiversity footprint, and studies whether it is priced in an international sample of stocks. On average, the corporate biodiversity footprint does not explain the cross-section of returns between 2019 and 2022. However, a biodiversity footprint premium (higher returns for firms with larger footprints) began emerging in October 2021 after the Kunming Declaration, which capped the first part of the UN Biodiversity Conference (COP15). Consistent with this finding, stocks with large footprints lost value in the days after the Kunming Declaration. The launch of the Taskforce for Nature-related Financial Disclosures (TNFD) in June 2021 had a similar effect. These results indicate that investors have started to require a risk premium upon the prospect of, and uncertainty about, future regulation or litigation to preserve biodiversity.

Pseudonymous attacks on public companies are followed by stock price declines and sharp reversals. I find these patterns are likely driven by manipulative stock options trading by pseudonymous authors. Among 1,720 pseudonymous attacks on mid- and large-cap firms from 2010-2017, I identify over $20.1 billion of mispricing. Reputation theory suggests these reversals persist because pseudonymity allows manipulators to switch identities without accountability. Using stylometric analysis, I show that pseudonymous authors exploit the perception that they are trustworthy, only to switch identities after losing credibility with the market.

Using data from a new field experiment in South Korea, we study how information from virtual communities such as stock message boards influences investors’ trading decisions and investment performance. Motivated by recent studies in psychology, we conjecture that investors would use message boards to seek information that confirms their prior beliefs. This confirmation bias would make them more overconfident and adversely affect their investment performance. An analysis of 502 investor responses from the largest message board operator in South Korea supports our conjecture. We find that investors exhibit confirmation bias when they process information from message boards. We also demonstrate that investors with stronger confirmation bias exhibit greater overconfidence. Those investors have higher expectations about their performance, trade more frequently, but obtain lower realized returns. Collectively, these results suggest that participation in virtual communities increases investors’ propensity to commit investment mistakes and is likely to be detrimental to their investment performance.

This paper examines the effects of disclosure and other corporate governance mechanisms on the cost of equity capital in Asia's emerging markets with newly released surveys from Credit Lyonnais Securities Asia (CLSA). We find that both disclosure and non-disclosure corporate governance mechanisms have a significantly negative effect on the cost of equity capital. In addition, the effect of non-disclosure governance mechanisms is more profound than that of disclosure on the cost of equity capital. Specifically, after controlling for beta and size, when a firm improves its aggregate non-disclosure corporate governance ranking from the 25th percentile to the 75th percentile, its cost of equity capital is reduced roughly by 1.26 percentage points, while the corresponding reduction in the cost of equity capital for the same improvement in disclosure is 0.47. Finally, we find that country-level investor protection and firm-level corporate governance are both important in reducing the cost of equity capital. Our findings suggest that, in emerging markets where infrastructural factors, such as the legal protection of investors and the overall level of corporate governance, are not well established, reducing the expropriation risk by strengthening overall corporate governance appears to be more important in reducing the cost of equity capital than adopting a more forthright disclosure policy.

We inspect the price volatility before, during, and after financial asset bubbles in order to uncover possible commonalities and check empirically whether volatility might be used as an indicator or an early warning signal of an unsustainable price increase and the associated crash. Some researchers and finance practitioners believe that historical and/or implied volatility increase before a crash, but we do not see this as a consistent behavior. We examine forty well-known bubbles and, using creative graphical representations to capture robustly the transient dynamics of the volatility, find that the dynamics of the volatility would not have been a useful predictor of the subsequent crashes. In approximately two-third of the studied bubbles, the crash follows a period of lower volatility, reminiscent of the idiom of a “lull before the storm”. This paradoxical behavior, from the lenses of traditional asset pricing models, further questions the general relationship between risk and return.

This paper proposes a simple back testing procedure that is shown to dramatically improve a panel data model's ability to produce out of sample forecasts. Here the procedure is used to forecast mutual fund alphas. Using monthly data with an OLS model it has been difficult to consistently predict which portfolio managers will produce above market returns for their investors. This paper provides empirical evidence that sorting on the estimated alphas populates the top and bottom deciles not with the best and worst funds, but with those having the greatest estimation error. This problem can be attenuated by back testing the statistical model fund by fund. The back test used here requires a statistical model to exhibit some past predictive success for a particular fund before it is allowed to make predictions about that fund in the current period. Another estimation problem concerns the use of a single statistical model for all available mutual funds. Since mutual funds often, but not always, employ dynamic trading strategies their betas move over time in a ways that differ from fund to fund. Since no one statistical model is likely to fit every fund, the result is a great deal of misspecification error. This paper shows that the combined use of an OLS and Kalman filter model increases the number of funds with predictable out of sample alphas by about 60%. Overall, a strategy that uses very modest ex-ante filters to eliminate funds whose parameters likely derive primarily from estimation errors produces an out of sample risk adjusted return of over 4% per annum.