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Investors face a number of challenges when seeking to estimate the prospective performance of a long-only investment in commodity futures. For instance, historically, the average annualized excess return of individual commodity futures has been approximately zero and commodity futures returns have been largely uncorrelated with one another. However, the prospective annualized excess return of a rebalanced portfolio of commodity futures can be equity-like. Certain security characteristics, such as the term structure of futures prices, and some portfolio strategies have historically been rewarded with above average returns. Avoiding naïve extrapolation of historical returns and striking a balance between dependable sources of return and possible sources of return is important. This is the unabridged version of our 2006 publication in the Financial Analysts Journal.

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. We begin this paper by looking at the economic determinants of equity risk premiums, including investor risk aversion, information uncertainty and perceptions of macroeconomic risk. 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 are 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 also look at the relationship between the equity risk premium and risk premiums in the bond market (default spreads) and in real estate (cap rates) and how that relationship can be mined to generated expected equity risk premiums. 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.

This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2022 for 95 countries. We got answers for 99 countries, but we only report the results for 95 countries with more than 6 answers.Many respondents use for European countries a RF higher than the yield of the 10-year Government bonds. The coefficient of variation (standard deviation / average) of RF is higher than the coefficient of variation of MRP for the Euro countries.The paper also contains the links to previous years surveys, from 2008 to 2021.

The empirical aim of this paper is motivated by the anecdotal belief among the professional and non-professional investment community, that a “low” reading in the CBOE Volatility Index (VIX) or large decline alone are ample reasons to believe that volatility will spike in the near future. While the Volatility Index can be a useful tool for investors and traders, it is often misinterpreted and poorly used. This paper will demonstrate that the dispersion of the Volatility Index acts as a better predictor of its future VIX spikes.

Relative to quantitative methods traditionally used in accounting and finance, textual analysis is substantially less precise. Thus, understanding the art is of equal importance to understanding the science. In this survey we describe the nuances of the method and, as users of textual analysis, some of the tripwires in implementation. We also review the contemporary textual analysis literature and highlight areas of future research.

The “shadow” banking system played a major role in the financial crisis, but was not a central focus of the recent Dodd-Frank Law and thus remains largely unregulated. This paper proposes principles for the regulation of shadow banking and describes a specific proposal to implement those principles. We first document the rise of shadow banking over the last three decades, helped by regulatory and legal changes that gave advantages to three main institutions of shadow banking: money-market mutual funds (MMMFs) to capture retail deposits from traditional banks, securitization to move assets of traditional banks off their balance sheets, and repurchase agreements (“repo”) that facilitated the use of securitized bonds in financial transactions as a form of money. A central idea of this paper is that the evolution of a bankruptcy “safe harbor” for repo has been a crucial feature in the growth and efficiency of shadow banking, and so regulators can use access to this safe harbor as the lever to enforce new rules. As for the rules themselves, history has demonstrated two successful methods for the regulation of privately created money: strict guidelines on collateral (used to stabilize national bank notes in the 19th century), and government-guaranteed insurance (used to stabilize demand deposits in the 20th century). We propose the use of insurance for MMMFs combined with strict guidelines on collateral for both securitization and repo as the best approach for shadow banking, with regulatory control established by chartering new forms of narrow banks for MMMFs and securitization and using the bankruptcy safe harbor to incent compliance on repo.

I examine corporate green bonds, whose proceeds finance climate-friendly projects. These bonds have become more prevalent over time, especially in industries where the environment is financially material to firm operations. I document that investors respond positively to the issuance announcement, a response that is stronger for first-time issuers and bonds certified by third parties. The issuers improve their environmental performance post issuance (i.e., higher environmental ratings and lower CO2 emissions), and experience an increase in ownership by long-term and green investors. Overall, the findings are consistent with a signaling argument—by issuing green bonds, companies credibly signal their commitment towards the environment.

We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems – such as those presented in designing and pricing securities, constructing portfolios, and risk management – often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full economic model. Applying deep learning methods to these problems can produce more useful results than standard methods in finance. In particular, deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.

Hedge funds are often cited as attractive investments because of their diversification benefits and distinctive risk profiles - in contrast to traditional investments such as stocks and bonds, hedge-fund returns have more complex risk exposures that yield complementary sources of risk premia. This raises the possibility of creating passive replicating portfolios or clones using liquid exchange-traded instruments that provide similar risk exposures at lower cost and with greater transparency. Using monthly returns data for 1,610 hedge funds in the TASS database from 1986 to 2005, we estimate linear factor models for individual hedge funds using six common factors, and measure the proportion of the funds' expected returns and volatility that are attributable to such factors. For certain hedge-fund style categories, we find that a significant fraction of both can be captured by common factors corresponding to liquid exchange-traded instruments. While the performance of linear clones is often inferior to their hedge-fund counterparts, they perform well enough to warrant serious consideration as passive, transparent, scalable, and lower-cost alternatives to hedge funds.

Since the financial crisis of 2008 and the recent (end of 2015) pull back, investors are searching for less risky investments. Therefore, there is a growing demand for low risk/absolute return portfolios. In this paper we describe a simple dual-momentum model (called Protective Asset Allocation or PAA) with a vigorous “crash protection” which might fit this bill. It is a tactical variation on the traditional 60/40 stock/bond portfolio where the optimal stock/bond mix is determined by multi-market breadth using dual momentum. We backtested the model with several global multi-asset ETF-proxies. Starting from Dec 1970 allows us to investigate the behavior of PAA in periods with rate hikes as well. The in-sample (Dec 1970-Dec 1992) and out-of-sample returns of the most protective variant of our PAA strategy satisfy our absolute return requirement without compromising high returns. This makes PAA an appealing alternative for a 1-year term deposit.

Using a novel dataset, we show that components of firms' GAAP earnings stemming from ancillary business activities or transitory shocks are significant in frequency and magnitude. These components have grown over time and are dispersed across various sections of the 10-K. Excluding them from GAAP earnings yields a core earnings measure that distinguishes between the recurring and non-recurring components of net income and forecasts future performance. Analysts and market participants are slow to impound these earnings components' implications, particularly the amounts disclosed in footnotes. Trading strategies that exploit non-core earnings produce abnormal returns of 8% per year.

Complete Lists of Authors, Institutions, and Papers and the Citations They Have Received in the Social Science Citation Index.The paper "The Remarkable Growth in Financial Economics, 1974-2020" is available here: https://ssrn.com/abstract=3722311.

A Cross Currency Swap (CCS) is a financial instrument that allows investors to exchange a set of cashflow liabilities for an equivalent set in another currency, often USD. Investors trade CCS to secure cheaper funding, hedge FX exposures, manage liquidity risk and of course for speculative purposes.In this paper we review the CCS product, its features and risks. We show how to price CCS and provide the mathematical formulae with examples & illustrations. Furthermore we outline how to calculate the CCS Basis Spread, which is how CCS are quoted in the financial marketplace.

This paper explores the discounted cash flow valuation methods. We start the paper with the simplest case: no-growth, perpetual-life companies. Then we will study the continuous growth case and, finally, the general case. The different concepts of cash flow used in company valuation are defined: equity cash flow (ECF), free cash flow (FCF), and capital cash flow (CCF). Then the appropriate discount rate is determined for each cash flow depending on the valuation method used. Our starting point will be the principle by which the value of a company's equity is the same, whichever of the four traditional discounted cash flow formulae is used. This is logical: given the same expected cash flows, it would not be reasonable for the equity's value to depend on the valuation method.

Corporate Social Responsibility (CSR) refers to the incorporation of Environmental, Social, and Governance (ESG) considerations into corporate management, financial decision making, and investors’ portfolio decisions. Socially responsible firms are expected to internalize the externalities (e.g. pollution) they create, and are willing to be accountable to shareholders as well as a broader group of stakeholders (employees, customers, suppliers, local communities,…). Over the past two decades, various rating agencies developed firm-level measures of ESG performance, which are widely used in the literature. A problem for past and a challenge for future research is that these ratings show inconsistencies, which depend on the rating agencies’ preferences, weights of the constituting factors, and rating methodology.CSR also deals with sustainable, responsible, and impact investing (SRI). The return implications of investing in the stocks of socially responsible firms, the search for an EGS factor, as well as the performance of SRI funds are the dominant topics. SR funds apply negative screening (exclusion of ‘sin’ industries), positive screening, as well as activism through proxy voting or direct engagement. In this context, one wonders whether responsible investors are willing to trade off financial returns with a ‘moral’ dividend (the return given up in exchange for an increase in utility driven by the knowledge that one invests ethically). A recent literature concentrates on green financing (the financing of environmentally friendly investment projects by means of green bonds) and on how to foster economic de-carbonization as climate change affects financial markets and investor behavior.

Spanish Abstract: Se analiza un ejemplo real de valoración: la adquisición de RJR Nabisco en 1988. El objetivo principal es comprender por qué las acciones de la empresa se compraron a 108 $/acción cuando cotizaban en el mercado a 55,875 $/acción. También sirve para profundizar en las distintas valoraciones que incluye el documento. English Abstract: We value RJR Nabisco in 1988 and discuss the different valuation methods and the premium paid.

Portfolio allocation aims to optimize the risk/return ratio in investment management. Traditional methods based on modern portfolio theory have been widely used for this purpose. However, the emergence of deep reinforcement learning (DRL) offers an alternative approach. This article conducts a comprehensive comparative analysis of traditional portfolio allocation methods and DRL, examining their principles, methodologies, and performance in maximizing risk-return profiles. It demonstrates that a basic version of DRL converges to traditional methods, while a myopic agent driven by immediate rewards represents the dynamic version of traditional methods. Experimental results indicate some improvement of DRL over traditional methods.\\keywords{Deep RL \\and Portfolio allocation.

This paper investigates various machine learning trading and portfolio optimisation models and techniques. The notebooks to this paper are Python based. By last count there are about 15 distinct trading varieties and around 100 trading strategies. Code and data are made available where appropriate. The hope is that this informal paper will organically grow with future developments in machine learning and data processing techniques. Changes can be tracked on the GitHub repository. This draft paper has been repackaged for the Journal of Financial Data Science.

This paper contains the statistics of a survey about the Risk-Free Rate (RF) and the Market Risk Premium (MRP) used in 2024 for 96 countries. We got answers for 104 countries, but we only report the results for 96 countries with more than 6 answers.The paper also contains the links to previous years surveys, from 2008 to 2023.

Compared with the market, value, or size factors, momentum has offered investors the highest Sharpe ratio. However, momentum has also had the worst crashes, making the strategy unappealing to investors who dislike negative skewness and kurtosis. We find that the risk of momentum is highly variable over time and predictable. Managing this risk virtually eliminates crashes and nearly doubles the Sharpe ratio of the momentum strategy. Risk-managed momentum is a much greater puzzle than the original version.

This paper is intended as a risk management primer for senior managers. It discusses the integrated risk management framework, emphasizing the connections between the three fundamental ways a company can implement its risk management objectives: modifying the firm's operations, adjusting its capital structure, and employing targeted financial instruments. "Integration" refers both to the combination of these three risk management techniques, and to the aggregation of all risks faced by the firm. The paper offers a functional analysis of integrated risk management using a wide set of illustrative situations to show how the risk management process influences, and is influenced by, the overall business activities and the strategy of the firm. Finally, the paper provides a risk management framework for formulating and designing a risk management system for the firm, concluding with a perspective on the future evolution of risk management.

The equity premium designates four different concepts: Historical Equity Premium (HEP); Expected Equity Premium (EEP); Required Equity Premium (REP); and Implied Equity Premium (IEP).We highlight the confusing message of the textbooks and academic articles regarding the equity premium and its evolution. The confusion arises from not distinguishing among the four concepts and from not recognizing that although the HEP is equal for all investors, the REP, the EEP and the IEP are different for different investors. There is a kind of schizophrenic approach to valuation: while all authors admit different expectations of equity cash flows, most authors look for a unique discount rate. It seems as if the expectations of equity cash flows are formed in a democratic regime, while the discount rate is determined in a dictatorship. A unique IEP requires assuming homogeneous expectations for the expected growth (g), but we show that there are several pairs (IEP, g) that satisfy current prices. We claim that different investors have different REPs and that it is impossible to determine the REP for the market as a whole, because it does not exist.We also investigate the relationship between (IEP - g) and the risk free rate.

Planning in financial markets is a difficult task as the method needs to dramatically change its behavior when facing very rare black swan events like crises that shift market regime. In order to address this challenge, we present a gradient boosting decision trees (GBDT) approach to predict large price drops in equity indexes from a set of 150 technical, fundamental and macroeconomic features. We report an improved accu-racy of GBDT over other machine learning (ML) methods on the S&P 500 futures prices. We show that retaining fewer and carefully selected features provides improvements across all ML approaches. We show that this model has a strong predic-tive power. We train the model from 2000 to 2014, a period where various crises have been observed and use a validation period of 3 years to find hyperparameters. The fitted model timely forecasts the Covid crisis giving us a planning method for early detection of potential future crises.

This paper presents several applications of cointegration based trading strategies: a classic index tracking strategy, a long-short equity market neutral strategy and a number of strategies combining index tracking and long-short market neutral. As opposed to other traditional index tracking or long-short equity strategies, the portfolio optimisation is based on cointegration rather than correlation. The first strategy aims to replicate a benchmark accurately in terms of returns and volatility, while the other seeks to minimise volatility and generate steady returns under all market circumstances. The combinations of index tracking and long-short market neutral are designed as to enhance the properties of the basic strategies. To validate the applicability of the cointegration technique to asset allocation, pioneered by Lucas (1997) and Alexander (1999), and explain how and why it works, we have employed a panel data on DJIA and its constituent stocks. When applied to constructing trading strategies in the DJIA, the cointegration technique produced encouraging results. For example, between January 1995 and December 2001 the most successful self-financing statistical arbitrage strategies returned (net of transaction and repo costs) approximately 10% with roughly 2% annual volatility and negligible correlation with the market. The comprehensive set of back-test results reported is meant to offer a detailed picture of the cointegration mechanism, and to emphasise its practical implementation issues. Its key characteristics, i.e. mean reverting tracking error, enhanced weights stability and better use of the information contained in stock prices, allow a flexible design of various funded and self-financing trading strategies, from index and enhanced index tracking, to long-short market neutral and alpha transfer techniques. Further enhancement of the strategy should target first, the identification of successful stock selection rules to supplement the simple cointegration results and second, the investigation of the potential benefits of applying optimal rebalancing rules.

I sort domestic all-equity mutual funds into different categories of active management using Active Share and tracking error. I find that over my sample period until the end of 2009, the most active stock pickers have outperformed their benchmark indices even after fees and transaction costs. In contrast, closet indexers or funds focusing on factor bets have lost to their benchmarks after fees. The same long-term performance patterns held up over the 2008-2009 financial crisis, and they also hold within market cap styles. Closet indexing increases in volatile and bear markets and has become more popular after 2007. Cross-sectional dispersion in stock returns positively predicts average benchmark-adjusted performance by stock pickers.