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We investigate the cross-sectional variation in the CDS-bond basis, which measures the difference between credit default swap (CDS) spread and cash-bond implied credit spread. We test several explanations for the violation of the arbitrage relation between cash bond and CDS contract, which states that the basis should be zero in normal conditions. The evidence is consistent with `limits to arbitrage' theories in that deviations are larger for bonds with higher frictions as measured by trading liquidity, funding cost, counterparty risk, and collateral quality. Surprisingly however, we find that the basis is more negative when the bond lending fee is higher, suggesting that arbitrageurs are unwilling to engage in a negative basis trade when short interest on the bond is high.

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

We review the literature studying interactions between climate change and financial markets. We first discuss various approaches to incorporating climate risk in macro-finance models. We then review the empirical literature that explores the pricing of climate risks across a large number of asset classes including real estate, equities, and fixed income securities. In this context, we also discuss how investors can use these assets to construct portfolios that hedge against climate risk. We conclude by proposing several promising directions for future research in climate finance.

We use transaction-level data and decompose the US equity premium into day (open to close) and night (close to open) returns. We document the striking result that the US equity premium over the last decade is solely due to overnight returns; the returns during the night are strongly positive, and returns during the day are close to zero and sometimes negative. This day and night effect holds for individual stocks, equity indexes, and futures contracts on equity indexes and is robust across the NYSE and Nasdaq exchanges. Night returns are consistently higher than day returns across days of the week, days of the month, and months of the year. The effect is driven in part by high opening prices which subsequently decline in the first hour of trading.

This paper proposes a modified version of the widely used price and moving average cross-over trading strategies. The suggested approach (presented in its 'long only' version) is a combination of cross-over 'buy' signals and a dynamic threshold value which acts as a dynamic trailing stop. The trading behavior and performance from this modified strategy is different from the standard approach with results showing that, on average, the proposed modification increases the cumulative return and the Sharpe ratio of the investor while exhibiting smaller maximum drawdown and smaller drawdown duration than the standard strategy.

Using the Bayesian posterior distribution of the market parameters we define self-adjusting uncertainty regions for the robust mean-variance problem. Under a normal-inverse-Wishart conjugate assumption for the market, the ensuing robust Bayesian mean-variance optimal portfolios are shrunk by the aversion to estimation risk toward the global minimum variance portfolio. After discussing the theory, we test robust Bayesian allocations in a simulation study and in an application to the management of sectors of the S&P 500. Fully commented code is available for download

We develop a dynamic asset pricing model of cryptocurrencies/tokens that allows users to conduct peer-to-peer transactions on digital platforms. The equilibrium value of tokens is determined by aggregating heterogeneous users' transactional demand rather than discounting cash flows, as is done in standard valuation models. Endogenous platform adoption builds on user network externality and exhibits an $S$-curve: it starts slow, becomes volatile, and eventually tapers off. The introduction of tokens lowers users' transaction costs on the platform by allowing users to capitalize on platform growth. The intertemporal feedback between user adoption and token price accelerates adoption and dampens user-base volatility.

The academic research is incontrovertible. On paper, value investing (at least as defined as investing in low PE and low price to book stocks) beats growth investing. Notwithstanding this finding, growth investing retains its allure with a large subset of investors, drawn by the payoff from investing in the “right’ growth companies. In this paper, we examine different strands of growth investing, ranging from passive screening (investing in small cap companies, initial public offerings or buying growth at a reasonable price (GARP)), to more activist growth investing, which is how we characterize venture capital investing. While growth investing under performs value investing, especially over long time periods, it is also true that there are sub-periods, where growth investing dominates and there is at least some evidence that active growth investors have better luck at beating passive growth investing strategies than active value investors do at beating passive value investing strategies.

Psychologists have studied human behavior for over a century and, as a result, have developed a robust set of theories regarding how people behave. Most financial accounting issues deal with matters of human behavior, such as the judgments and decisions of managers, investors, analysts, and auditors. Consequently, psychology offers a rich pool of theories from which financial accounting researchers can draw to motivate hypotheses and interpret results. Despite this, archival accounting researchers traditionally have relied almost solely on theories based in financial economics. We argue that two major obstacles to the use of psychology theories by archival researchers has been a lack of awareness about the theories that are available and when their use would be most productive. Our paper attempts to bridge this gap. Specifically, we describe a number of psychology theories that are applicable to financial accounting issues, lay out the circumstances where they may be especially useful to archival researchers, and provide a number of specific examples of how psychology theories provide new insights about financial accounting issues.

Most long-run empirical research on the historical risk premium has focused on the experience of the United States. However, the United States has been a remarkably successful economy, making it unlikely that the US risk premium is representative. Until recently, evidence on the risk premium in most other countries has typically been over only relatively brief intervals during the latter part of the twentieth century. We extend the evidence by examining equity, bond, and bill returns in 16 different countries over the 103-year period from 1900 to 2002. We show that the equity risk premium has typically been lower than most previous research has indicated. Finally, we argue that even this lower figure for the historical risk premium is still an overestimate of the likely future risk premium.

We find, as predicted, that the differential ability of accrual and cash flow components of earnings to help forecast future abnormal earnings and the persistence of the components results in the components having different valuation implications. We base our tests on Ohlson (1999) applied to fourteen industries. We find: (1) Accruals and cash flows aid in forecasting future abnormal earnings incremental to abnormal earnings and equity book value. (2) Accruals and cash flows provide explanatory power for equity market value incremental to equity book value and abnormal earnings. (3) There is evidence that accruals and cash flows valuation coefficients are consistent with the Ohlson model.

Warren Buffett is a long-term investor, but is required by law to disclose his trades on a quarterly basis. The market seems to under-react to the revelation of his trades. From 1980 to 2006, it has been possible to achieve investment results similar to Buffett’s own simply by following his trades disclosed by Berkshire Hathaway. We consider overconfidence by sophisticated market participants as a contributing factor to the apparent under reaction to information contained in public disclosures of changes in Berkshire Hathaway’s holdings of stocks. Net sales of corporate insiders of stocks held by Berkshire Hathaway tend to decrease when those holdings increase consistent with shared private information. However, financial analysts’ recommendations tend to downgrade and institutions tend to sell at those times. This behavior by analysts and fund managers is consistent with pejorative experts displaying overconfidence by over estimating their stock picking abilities or precision of their independent private information and, as a consequence, underweighting public information in making their decisions.

This paper expands the debate about "active vs. passive" management using data from active and passive ETFs listed in the U.S. market. The results reveal that the active ETFs underperform both the corresponding passive ETFs and the market indexes. With respect to risk-adjusted returns, both active and passive ETFs provide investors with no positive excess returns, an expectable finding for the passive ETFs but not for the active ETFs which are aimed at beating the market. Going further, the underperformance of active ETFs is depicted to the low performance rates such as the Sharpe or the Treynor ratios they receive relative to the passive ETFs and the indexes. Furthermore, regression analysis on the selectivity and market timing skills of ETF managers indicate that the managers of both the active and passive ETFs are lacking in such skills. However, the passive managers are not expected to have such skills. Finally, tracking error estimates indicate that the discrepancy between ETF and index returns is greater for active ETFs. However, this result is to be expected as the active ETFs do not target to replicate the performance of the indexes.

I identify issues that bank regulators need to consider if fair value accounting is used for determining bank regulatory capital and when making regulatory decisions. In financial reporting, US and international accounting standard setters have issued several disclosure and measurement and recognition standards for financial instruments and all indications are that both standard setters will mandate recognition of all financial instruments at fair value. To help identify important issues for bank regulators, I briefly review capital market studies that examine the usefulness of fair value accounting to investors, and discuss marking-to-market implementation issues of determining financial instruments' fair values. In doing so, I identify several key issues. First, regulators need to consider how to let managers reveal private information in their fair value estimates while minimising strategic manipulation of model inputs to manage income and regulatory capital. Second, regulators need to consider how best to minimise measurement error in fair values to maximise their usefulness to investors and creditors when making investment decisions, and to ensure bank managers have incentives to select investments that maximise economic efficiency of the banking system. Third, cross-country institutional differences are likely to play an important role in determining the effectiveness of using mark-to-market accounting for financial reporting and bank regulation.

Existing research indicates that firms with high accruals are more likely to experience future earnings reversals and SEC enforcement actions for GAAP violations, but that investors do not appear to anticipate these consequences. In this paper, we directly examine the published opinions of two types of professional investor intermediaries to see if they anticipate the consequences of high accruals. First, we examine the earnings forecasts of sell-side analysts. We show that analysts' earnings forecasts do not anticipate the future earnings reversals associated with high accruals. Second, we examine the audit opinions of independent auditors. We find no evidence that auditors signal the higher likelihood of GAAP violations through their audit opinions. Overall, our evidence indicates that even professional investor intermediaries act as if they do not anticipate the consequences of high accruals.

We present a dialogue on Counterparty Credit Risk touching on Credit Value at Risk (Credit VaR), Potential Future Exposure (PFE), Expected Exposure (EE), Expected Positive Exposure (EPE), Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA), DVA Hedging, Closeout conventions, Netting clauses, Collateral modeling, Gap Risk, Re-hypothecation, Wrong Way Risk, Basel III, inclusion of Funding costs, First to Default risk, Contingent Credit Default Swaps (CCDS) and CVA restructuring possibilities through margin lending. The dialogue is in the form of a Q&A between a CVA expert and a newly hired colleague.

We provide large sample evidence on whether the equity and debt markets impound information about the quality of earnings. We examine eight proxies for earnings quality (four based on the modified Jones approach to estimating abnormal accruals; three based on the Dechow and Dichev [2002] approach which relates working capital accruals to cash flows; and one based on a factor analysis of the other seven). Across all eight metrics, we find that firms with lower quality earnings have higher costs of capital as evidenced by lower debt ratings, larger realized costs of debt, larger industry-adjusted earnings-price ratios, larger equity betas, and positive loadings on an earnings quality factor added to one-factor and three-factor asset pricing regressions. The documented effects are statistically significant and economically meaningful: The results show, for example, that firms with the best earnings quality enjoy discounts of 80-160 basis points in their costs of debt and 150-300 basis points in their costs of equity relative to firms with the poorest earnings quality.

It is common practice to assess the average effect of some type of announcement on stock prices by performing event studies on a large sample of firms and then averaging or otherwise combining the results. One benefit of this procedure is that the event window length can be standardized across observations because the errors from having too long or short an event window should have a small impact on the average by the Law of Large Numbers. Here, we examine various potential rules for determining the length of an event window when looking at a limited number of observations. We find that rules based on continuing price movements yield window lengths that correlate with the "size" of the news, as measured by the magnitude of earnings surprises, while a rule based on abnormally high volume does not have this property.

The globalization of accounting standards as seen through the proliferation of IFRS worldwide is one of the most important developments in corporate governance over the last decade. I offer an analysis of some international political dynamics of countries’ IFRS harmonization decisions. The analysis is based on field studies in three jurisdictions: Canada, China, and India. Across these jurisdictions, I first describe unique elements of domestic political economies that are shaping IFRS policies. Then, I inductively isolate two principal dimensions that can be used to characterize the jurisdictions’ IFRS responses: proximity to existing political powers at the IASB; and own potential political power at the IASB. Based on how countries are classified along these dimensions, I offer predictions, ceteris paribus, on countries’ IFRS harmonization strategies. The analysis and framework in this paper can help broaden the understanding of accounting’s globalization.

We extend a popular binomial model to allow for option pricing using real-world rather than risk-neutral world probabilities. There are three benefits. First, our model allows direct inference about relevant real-world probabilities (e.g. of success in a real-option project, of default on a corporate bond, or of an American-style option finishing in the money). Second, practitioners using our model for corporate real-option applications completely avoid managerial anxiety that competing risk-neutral models generate when they use risk-free discount rates for risky cash flows. Third, our model simplifies option pricing when higher moments (e.g., skewness and kurtosis) appear in asset pricing models.

This case presents the valuation of the shares of an electric company in an emerging country. It permits the reader to identify errors and recalculate the valuation.ElectraBul was expropriated by the government in April 2010. The government said that ElectraBul shareholders should be paid a fair amount: the value of the shares according to a recognized valuator.

The literature on long-run asset returns has continued to grow steadily, particularly since the start of the new millennium. We survey this expanding body of evidence on historical return premia across the major asset classes-stocks, bonds, and real assets-over the very long run. In addition, we discuss the benefits and pitfalls of these long-run data sets and make suggestions on best practice in compiling and using such data. We report the magnitude of these risk premia over the current and previous two centuries, and we compare estimates from alternative data compilers. We conclude by proposing some promising directions for future research.

In this paper, we revisit the myths regarding the superior performance of market timing strategies based on moving average and time-series momentum rules. These active timing strategies are very appealing to investors because of their extraordinary simplicity and because they promise substantial advantages over their passive counterparts (see, for example, the paper by M. Faber (2007) "A Quantitative Approach to Tactical Asset Allocation" published in the Journal of Wealth Management). However, the ``too good to be true" reported performance of these market timing rules raises a legitimate concern as to whether this performance is realistic and whether investors can expect that future performance will be the same as the documented historical performance. We argue that the reported performance of market timing strategies usually contains a considerable data-mining bias and ignores important market frictions. To address these issues, we perform out-of-sample tests of these two timing models in which we account for realistic transaction costs. Our findings reveal that the performance of market timing strategies is highly overstated, to say the least.

"Support" and "resistance" levels - points at which an exchange rate trend may be interrupted and reversed - are widely used for short-term exchange rate forecasting. Nevertheless, the levels' ability to predict intraday trend interruptions has never been rigorously evaluated. This article undertakes such an analysis, using support and resistance levels provided to customers by six firms active in the foreign exchange market. The author offers strong evidence that the levels help to predict intraday trend interruptions. However, the levels' predictive power is found to vary across the exchange rates and firms examined.

This paper examines the factors contributing to the rapid collapse of Silicon Valley Bank, once regarded as one of the best banks. We show that the bank invested heavily in debt securities during a period of low interest rates, and the subsequent surge in interest rates in 2022 resulted in significant unrealized losses. Additionally, the bank's deposits were heavily concentrated among a small group of venture capitalists, which increased the likelihood of a bank run. Furthermore, the bank held less equity capital and had an inefficient risk management system, exacerbating the impact of the risk. Overall, the mismanagement of assets and liabilities led to the bank’s failure.