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This paper analyzes institutional investors' demand for stock characteristics and the implications of this demand for stock prices and returns. We find that "large" institutional investors nearly doubled their share of the stock market from 1980 to 1996. Overall, this compositional shift tends to increase demand for the stock of large companies and decrease demand for the stock of small companies. The compositional shift can, by itself, account for a nearly 50 percent increase in the price of large-company stock relative to small-company stock and can explain part of the disappearance of the historical small-company stock premium.

In the recent years green bonds became a popular example of climate finance instruments. Although the volume of the green bond market has been increasing steadily in the last years, the actual impact of the “green label” on the market of bonds is still poorly understood. This article investigates the yield term structures of green and brown (standard) bonds from the same set of issuers in the US American municipal bonds market. We show that, although returns on brown bonds are on average higher than for green bonds, this spread can to a large extent be explained by properties of the respective issuing entity and the bond. The “green nature” of the bond rather seems to be penalized by the market, as green bonds are traded at lower prices/higher yield than would be expected by their credit profiles.

We investigate the return predictability of corporate bonds using big data and machine learning. We find that machine learning models substantially improve the out-of-sample performance of stock and bond characteristics in predicting future bond returns. We also find a significant improvement in the performance of machine learning models when imposing a theoretically motivated economic structure from the Merton model, compared to the reduced-form approach without restrictions. Overall, our work highlights the importance of explicitly imposing the dependence between expected bond and stock returns via machine learning and Merton model when investigating expected bond returns.

We examine the relation between the cost of equity capital and seven attributes of earnings: quality, persistence, predictability, smoothness, value relevance, timeliness and conservatism. We refer to the first four attributes as accounting-based because measures of these constructs are typically based on accounting information only. We refer to the last three attributes as market-based because proxies for these constructs are typically based on relations between market data and accounting data. Our analysis of the cost of capital effects of these attributes is based on two distinct approaches to measuring the cost of capital: a cross-sectional approach which uses ex ante cost of capital estimates derived from analyst forecast data, and a time-series approach that uses realized returns and asset pricing regressions. Across both sets of tests, we find that firms with the most favorable values of each attribute, viewed individually, enjoy significantly lower costs of capital than firms with the least favorable values. The largest cost of capital effects are found for the accounting-based attributes; within this set, earnings quality has the strongest effects. Among the market-based attributes, value relevance dominates timeliness and conservatism. Considering all attributes together, the results show that investors consistently price earnings quality and earnings persistence, and to a lesser extent, value relevance.

We study how investor sentiment affects the cross-section of stock returns. We predict that a wave of investor sentiment has larger effects on securities whose valuations are highly subjective and difficult to arbitrage. Consistent with this prediction, we find that when beginning-of-period proxies for sentiment are low, subsequent returns are relatively high for small stocks, young stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme growth stocks, and distressed stocks. When sentiment is high, on the other hand, these stocks tend to earn relatively low subsequent returns.

Earnings of the overall market are typically viewed in the same perspective as earnings of individual companies. Conflicts between these perceptions are revealed with the use of Kalecki’s profit function to reveal the identification of negative characteristics with macro earnings, introduce the concept of “macro-earnings negativity”, and demonstrate the theoretical and statistical superiority of MV/GDP valuation measure versus earnings-based measures. Based on the MV/GDP metric, a multi-variable forecasting model is developed which utilizes both new and prior-researched variables, the most effective of which is a demographic measure. The resulting composite model is statistically superior to popular metrics, and, relative to popular benchmarks, forecasts considerably lower returns for the coming decade.

The Bankscope dataset is a popular source of bank balance sheet informations among banking economists, which covers the last 20 years for more than 30,000 worldwide banks. This technical paper intends to provide the critical issues one has to keep in mind as well as the basic arrangements which have to be undertaken if one intends to use this dataset. To that extent, we propose some straightforward ways to deal with data comparability, consolidation, duplication of assets or mergers, and provide Stata codes to deal with it.

This paper analyzes the asset pricing and portfolio implications of an important barrier to sustainable investing---uncertainty about the corporate ESG profile. In equilibrium, the market premium increases and demand for stocks declines under ESG uncertainty. In addition, the CAPM alpha and effective beta both rise with ESG uncertainty and the negative ESG-alpha relation weakens. Employing the standard deviation of ESG ratings from six major providers as a proxy for ESG uncertainty, we provide supporting evidence for the model predictions. Our findings help reconcile the mixed evidence on the cross-sectional ESG-alpha relation and suggest that ESG uncertainty affects the risk-return trade-off, social impact, and economic welfare.

The hedge-fund industry has grown rapidly over the past two decades, offering investors unique investment opportunities that often reflect more complex risk exposures than those of traditional investments. In this article we present a selective review of the recent academic literature on hedge funds as well as updated empirical results for this industry. Our review is written from several distinct perspectives: the investor's, the portfolio manager's, the regulator's, and the academic's. Each of these perspectives offers a different set of insights into the financial system, and the combination provides surprisingly rich implications for the Efficient Markets Hypothesis, investment management, systemic risk, financial regulation, and other aspects of financial theory and practice.

Liquidity suppliers lean against the wind. We analyze whether high-frequency traders (HFTs) lean against large institutional orders that execute through a series of child orders. The alternative is HFTs trading "with the wind," that is, in the same direction. We find that HFTs initially lean against these orders but eventually change direction and take position in the same direction for the most informed institutional orders. Our empirical findings are consistent with investors trading strategically on their information. When deciding trade intensity, they seem to trade off higher speculative profit against higher risk of detection by HFTs and being preyed on.

We offer a general equilibrium analysis of cryptocurrency pricing. The fundamental value of the cryptocurrency is its stream of net transactional benefits, which depend on its future prices. This implies that, in addition to fundamentals, equilibrium prices reflect sunspots. This, in turn, implies there are multiple equilibria and extrinsic volatility, that is, cryptocurrency prices fluctuate even when fundamentals are constant. To match our model to the data, we construct indices measuring the net transactional benefits of bitcoin. In our calibration, a fraction of the variations in bitcoin returns reflects changes in net transactional benefits, but a larger fraction reflects extrinsic volatility.

Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology.

This paper derives the optimal dynamic strategy for arbitrageurs with a finite horizon and non-myopic preferences facing a mean-reverting arbitrage opportunity (e.g. an equity pairs trade). We find that intertemporal hedging demands play an important role in determining how aggressively arbitrageurs trade against the mispricing and account for a large fraction of the total allocation to the arbitrage opportunity. While arbitrageurs typically bet against the mispricing, we analytically show that there is a critical level of mispricing beyond which further divergence precipitates a reduction in the allocation. When applied to Siamese twin shares our optimal strategy delivers a significant improvement in the realized Sharpe ratio and welfare relative to a simple threshold rule.

We develop a comprehensive and parsimonious measure of the extent to which a firm is raising (distributing) capital from (to) capital market participants. We show that the relation between our measure of net external financing and future stock returns is stronger than has been documented in previous research focusing on individual categories of financing transactions. Decompositions of our measure reveal additional insights. First, the weaker results of previous research are attributable to 'refinancing' transactions having no change on net external financing. Second, after controlling for refinancing transactions, there is a consistently strong and negative relation between all major categories of external financing transactions and future stock returns. Third, the negative relation between external financing and future stock returns is most consistent with a combination of over-investment and aggressive accounting.

The “practitioner Black-Scholes delta” for hedging options is a delta calculated from the Black-Scholes-Merton model (or one of its extensions) with the volatility parameter set equal to the implied volatility. As has been pointed out by a number of researchers, this delta does not minimize the variance of changes in the value of a trader’s position. This is because there is a non-zero correlation between movements in the price of the underlying asset and implied volatility movements. The minimum variance delta takes account of both price changes and the expected change in implied volatility conditional on a price change. This paper determines empirically a model for the minimum variance delta. We test the model using data on options on the S&P 500 and show that it is an improvement over stochastic volatility models, even when the latter are calibrated afresh each day for each option maturity. We present results for options on the S&P 100, the Dow Jones, individual stocks, and commodity and interest-rate ETFs.

These lectures notes have been written for the course in Sustainable Finance given at the University of Paris-Saclay. The slides cover the following topics: 1. Introduction, 2. ESG Scoring,3. Impact of ESG Investing on Asset Prices and Portfolio Returns, 4. Sustainable Financial Products,5. Impact Investing, 6. Engagement & Voting Policy, 7. Extra-financial Accounting, 8. Awareness of Climate Change Impacts, 9. The Ecosystem of Climate Change, 10. Economic Models \& Climate Change, 11. Climate Risk Measures, 12. Transition Risk Modeling, 13. Climate Portfolio Construction. 14. Physical Risk Modeling and 15. Climate Stress Testing & Risk Management

This paper models firms' choices between alternative means of presenting information, and the effects of different presentations on market prices when investors have limited attention and processing power. In a market equilibrium with partially attentive investors, we examine the effects of alternative: levels of discretion in pro forma earnings disclosure, methods of accounting for employee option compensation, and degrees of aggregation in reporting. We derive empirical implications relating pro forma adjustments, option compensation, the growth, persistence, and informativeness of earnings, short-run managerial incentives, and other firm characteristics to stock price reactions, misvaluation, long-run abnormal returns, and corporate decisions.

Growth-Trend (GT) timing from Philosophical Economics is a brilliant timing strategy which only signals a bear market when both the trend in the unemployment (UE) rate and the SP500 index are bearish. As a result, it captures most market downturns while switching to cash in less than 15% of the time. In this sense, its crash protection is much less drastic than our own “canary” protection in our DAA strategy (25% in cash) or the breadth protection in our VAA strategy (around 50% in cash). In this paper we apply GT timing to the well-known 60-40 static benchmark (60% SPY - 40% IEF), and search in-sample for variations on 60-40 with GT timing. For these variations, we in particular consider risky portfolios which are also agnostic for inflation and yield, inspired by the various static portfolio like the Permanent Portfolio and its siblings. Our final strategy switches between two static portfolios based on GT timing. This strategy is called the Lethargic Asset Allocation (LAA).

Credit (CVA), Debit (DVA) and Funding Valuation Adjustments (FVA) are now familiar valuation adjustments made to the value of a portfolio of derivatives to account for credit risks and funding costs. However, recent changes in the regulatory regime and the increases in regulatory capital requirements has led many banks to include the cost of capital in derivative pricing. This paper formalises the addition of cost of capital by extending the Burgard-Kjaer (2013) semi-replication approach to CVA and FVA to include an addition capital term, Capital Valuation Adjustment (KVA). The utilization of the capital for funding purposes is also considered. The use of the semi-replication approach means that the exibility around the treatment of self-default is carried over into this analysis. The paper further considers the practical calculation of KVA with reference to the Basel II (BCBS-128 2006) and Basel III (BCBS-189 2011) capital regimes and their implementation via CRD IV (EU 2013b; EU 2013a). The paper also assesses how KVA may be hedged, given that any hedging transactions themselves lead to regulatory capital requirements and hence capital costs. Finally a number of numerical examples are presented to gauge the cost impact of KVA on vanilla derivative products.

We document that investors derive nonpecuniary utility from investing in dual-objective VC funds, thus sacrificing returns. Impact funds earn 4.7 percentage points (ppts) lower IRRs ex post than traditional VC funds. In random utility/willingness-to-pay (WTP) models investors accept 2.5-3.7 ppts lower IRRs ex ante for impact funds. The positive WTP result is robust to fund access rationing and investor heterogeneity in fund expected returns. Development organizations, foundations, financial institutions, public pensions, Europeans, and UNPRI signatories have high WTP. Investors with mission objectives and/or facing political pressure exhibit high WTP; those subject to legal restrictions (e.g., ERISA) exhibit low WTP.

In both corporate finance and asset pricing empirical work, researchers are often confronted with panel data. In these data sets, the residuals may be correlated across firms or across time, and OLS standard errors can be biased. Historically, the two literatures have used different solutions to this problem. Corporate finance has relied on clustered standard errors, while asset pricing has used the Fama-MacBeth procedure. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct) standard errors and when they diverge. The intent is to provide intuition as to why the different approaches sometimes give different answers and give researchers guidance for their use.

To promote fair and sustainable capitalism and help business and labor work together to build an American economy that works for all, this paper presents a comprehensive proposal to reform the American corporate governance system by aligning the incentives of those who control large U.S. corporations with the interests of working Americans who must put their hard-earned savings in mutual funds in their 401(k) and 529 plans. The proposal would achieve this through a series of measured, coherent changes to current laws and regulations, including: requiring not just operating companies, but institutional investors, to give appropriate consideration to and make fair disclosure of their policies regarding EESG issues, emphasizing “Employees” and not just Environmental, Social, and Governance” factors; giving workers more leverage by requiring all societally-important companies to have board level committees charged with ensuring fair treatment of employees, authorizing companies to use European-style works’ councils to increase employee voice, and reforming labor laws to make it easier for workers to join a union and bargain for fair wages and working conditions; reforming the corporate election system so that voting occurs on a more rational, periodic, and thoughtful basis supportive of sustainable business practices and long-term investment; improving the tax system to encourage sustainable, long-term investment and discourage speculation, with the resulting proceeds being used to revitalize and green America’s infrastructure, tackle climate change, invest in American workers’ skills, transition workers from carbon-intensive industries to jobs in the clean energy sector; and taking other measures, such as reform of corporate political spending and forced arbitration, to level the playing field for workers, consumers, and ordinary investors.

The Vasicek model (1977) is one of the earliest stochastic models of the term structure of interest rates. This model, though it has it's shortcomings, has many advantages, such as analytical tractability and mean reversion features, and may be viewed as a short rate model template. Several short rate models have their foundations rooted in the Vasicek model. The classical Hull-White model (1990a), for example, is an extension of the Vasicek model with time dependent parameters. In the work that follows we derive the short rate implied by the Vasicek model using the integrating factor method and provide an overview of this method and it's shorthand. Secondly we consider the model dynamics and finally we apply the model to zero-coupon bond pricing and provide a detailed derivation. Finally in reviewing the Vasicek model we outline it's disadvantages, consider other short rate models and look at the Hull-White extension to this model. The aim of the paper is to provide an overview of the Vasicek model and an introduction into short rate modelling.

We characterize the distribution of long-term equity returns based on the historical record of stock market performance in a broad cross section of 39 developed countries over the period from 1841 to 2019. Our comprehensive sample mitigates concerns over survivor and easy data biases that plague other work in this area. A bootstrap simulation analysis implies substantial uncertainty about long-horizon stock market outcomes, and we estimate a 12% chance that a diversified investor with a 30-year investment horizon will lose relative to inflation. The results contradict the conventional advice that stocks are safe investments over long holding periods.

In this paper we use the hedge fund return replication technique recently introduced in Kat and Palaro (2005) to evaluate the net-of-fee performance of 485 funds of hedge funds. The results indicate that the majority of funds of funds have not provided their investors with returns, which they could not have generated themselves by trading S&P 500, T-bond and Eurodollar futures. Purely in terms of returns therefore, most funds of hedge funds have failed to add value.