SSRN Viewer

Measuring and managing exchange rate risk exposure is important for reducing a firm's vulnerabilities from major exchange rate movements, which could adversely affect profit margins and the value of assets. This paper reviews the traditional types of exchange rate risk faced by firms, namely transaction, translation and economic risks, presents the VaR approach as the currently predominant method of measuring a firm's exchange rate risk exposure, and examines the main advantages and disadvantages of various exchange rate risk management strategies, including tactical versus strategical and passive versus active hedging. In addition, it outlines a set of widely accepted best practices in managing currency risk and presents some of the main hedging instruments in the OTC and exchange-traded markets. The paper also provides some data on the use of financial derivatives instruments, and hedging practices by U.S. firms.

Over the last forty years there has been a strong positive trend in the magnitude of amortization charges due to both economic and accounting changes. Concurrent with this trend, managers and external users of financial statements increasingly discuss operating performance focusing on earnings metrics that exclude amortization but include depreciation. This study compares earnings before interest, taxes, and amortization (EBITA) with its two more common alternatives-EBIT and EBITDA. Throughout the sample period, EBITDA performed substantially better than both EBITA and EBIT in explaining market values using industry multiples, and EBITA performed better than EBIT. Consistent with the amortization trend, EBITA's advantage over EBIT has gradually increased over time. In terms of predicting stock returns, the three operating income measures performed well until the financial crisis, but not since then.

Does the globalization process of the past 25 years obviate the need to segregate global equities into developed and emerging market buckets? We argue the answer is no. Emerging equity markets differ in a statistically significant fashion from developed markets, featuring much lower levels of GDP per capita and equity integration. They also have significantly lower stock market development levels and, on average, feature lower valuation ratios. Emerging markets have morphed into high-beta investments that are highly correlated with developed markets. The historical performance of emerging market investing is much improved by replacing value-weighted indices with alternative weighting schemes, including equal weights, valuation-based weights, and GDP weights.

This paper studies the asset pricing implications of industrial pollution. A long-short portfolio constructed from firms with high versus low toxic emission intensity within a given industry generates an average return of 4.42% per annum, which remains significant after controlling for risk factors. This pollution premium cannot be explained by several explanations, including existing systematic risks, investors' preference, market sentiment, political connections, and corporate governance. We propose and model a new systematic risk related to environmental policy uncertainty. We use the growth of environmental litigation penalties to measure regime change risk, and find that it helps price the cross-section of emission portfolios' returns.

We provide the first comprehensive analysis of the properties of investment recommendations generated by “Robo-Analysts,” which are human-analyst-assisted computer programs conducting automated research analysis. Our results indicate that Robo-Analyst recommendations differ from those produced by traditional “human” research analysts across several important dimensions. First, Robo-Analysts produce a more balanced distribution of buy, hold, and sell recommendations than do human analysts and are less likely to recommend “glamour” stocks and firms with prospective investment banking business. Second, automation allows Robo-Analysts to revise their recommendations more frequently than human analysts and incorporate information from complex periodic filings. Third, while Robo-Analysts’ recommendations exhibit weak short-window return reactions, they have long-term investment value. Specifically, portfolios formed based on the buy recommendations of Robo-Analysts significantly outperform those of human analysts. Overall, our results suggest that automation in the sell-side research industry can benefit investors.

We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher-labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short-term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT’s advantage over other algorithms, and Google’s original bidirectional encoder representations from transformers (BERT) model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18%, compared with FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.

The conventional wisdom in finance is that competition is fierce among investors: if a group changes its behavior, others adjust their strategies such that nothing happens to prices.We estimate a demand system with flexible strategic responses for institutional investors in the US stock market. When less aggressive traders surround an investor, she adjusts by trading more aggressively.However, this strategic reaction only counteracts two thirds of the impact of the initial change in behavior.In light of these estimates, the rise in passive investing over the last 20 years has made the demand for individual stocks 11% more inelastic.

Better proxies for the information about future returns contained in firm characteristics such as size, book-to-market equity, cash flow-to-price, percent change in employees, and various past return measures are obtained by breaking these explanatory variables into two industry-related components. The components represent (1) the difference between firms' own characteristics and the average characteristics of their industries (within-industry variables), and (2) the average characteristics of firms' industries (across-industry variables). Each variable is reliably priced within-industry and measuring the variables within-industry produces more precise estimates than measuring the variables in their more common form. Contrary to Moskowitz and Grinblatt [1999], we find that within-industry momentum (i.e., the firm's past return less the industry average return) has predictive power for the firm's stock return beyond that captured by across-industry momentum. We also document a significant short-term (one-month) industry momentum effect which remains strongly significant when we restrict the sample to only the most liquid firms.

This paper seeks to identify why stock prices behave the way they do. We find that conventional economic theories are unable to explain why there are so many long bull and bear markets. We suggest that it is investor expectations of future market movements, based on their views of other investors' opinions, that determine stock prices. Where there is aggregate optimism, stock prices will rise and vice versa. We extend the work of the Rational Belief Hypothesis by suggesting that today's extremely high P/E ratios are associated with a substantial dislocation between investment horizons and economic reality.

Pairs trading is a popular trading strategy that tries to take advantage of market inefficiencies in order to obtain profit. The idea is simple: find two stocks that move together and take long/short positions when they diverge abnormally, hoping that the prices will converge in the future. From the academic point of view of weak market efficiency theory, pairs trading strategy shouldn’t present positive performance since, according to it, the actual price of a stock reflects its past trading data, including historical prices. This leaves us with a question, does pairs trading strategy presents positive performance for the Brazilian market? The main objective of this research is to verify the performance and risk of pairs trading in the Brazilian financial market for different frequencies of the database, daily, weekly and monthly prices for the same time period. The main conclusion of this simulation is that pairs trading strategy was a profitable and market neutral strategy at the Brazilian Market. Such profitability was consistent over a region of the strategy’s parameters. The best results were found for the highest frequency (daily), which is an intuitive result.

We calculate the present value of state employee pension liabilities as of June 2009 using discount rates that reflect the risk of the payments from a taxpayer perspective. If benefits have the same default and recovery characteristics as state general obligation debt, the national total of promised liabilities based on current salary and service is $3.20 trillion. If pensions have higher priority than state debt, the present value of liabilities is much larger. Using zero-coupon Treasury yields, which are default-free but contain other priced risks, promised liabilities are $4.43 trillion. Liabilities are even larger under broader concepts that account for projected salary growth and future service.

Correlation matrices are ubiquitous in finance. Some key applications include portfolio construction, risk management, and factor/style analysis. Correlation matrices are usually estimated from historical empirical observations or derived from historically estimated factors. It is widely acknowledged that empirical correlation matrices: (a) have poor numerical properties that lead to unreliable estimators; and (b) have poor predictive power. Additionally, factor-based correlation matrices have their own caveats. In particular, estimated factors are typically non-hierarchical and do not allow for interactions at different levels. This contravenes the fact that financial instruments typically exhibit a nested cluster structure (e.g., MSCI’s GICS levels 1-4).This paper introduces a machine learning (ML) algorithm to estimate forward-looking correlation matrices implied by economic theory. Given a particular theoretical representation of the hierarchical structure that governs a universe of securities, the method fits the correlation matrix that complies with that theoretical representation of the future. This particular use case demonstrates how, contrary to popular perception, ML solutions are not black-boxes, and can be applied effectively to develop and test economic theories.

The current financial crisis has highlighted the growing importance of the 'shadow banking system,' which grew out of the securitization of assets and the integration of banking with capital market developments. This trend has been most pronounced in the United States, but it has had a profound influence on the global financial system. In a market-based financial system, banking and capital market developments are inseparable: Funding conditions are closely tied to fluctuations in the leverage of market-based financial intermediaries. Growth in the balance sheets of these intermediaries provides a sense of the availability of credit, while contractions of their balance sheets have tended to precede the onset of financial crises. Securitization was intended as a way to transfer credit risk to those better able to absorb losses, but instead it increased the fragility of the entire financial system by allowing banks and other intermediaries to 'leverage up' by buying one another’s securities. In the new, post-crisis financial system, the role of securitization will likely be held in check by more stringent financial regulation and by the recognition that it is important to prevent excessive leverage and maturity mismatch, both of which can undermine financial stability.

We address the valuation of bitcoins and other blockchain tokens in a new type of production economy: a decentralized financial network (DN). An identifying property of these assets is that contributors to the DN trust (miners) receive units of the same asset used by consumers of DN services. Therefore, the overall production (hashrate) and the bitcoin price are jointly determined. We characterize the demand for bitcoins and the supply of hashrate and show that the equilibrium price is obtained by solving a fixed-point problem and study its determinants. Price-hashrate “spirals” amplify demand and supply shocks.

This study develops novel text-based proxies of corporate exposure to the low-carbon transition and applies them to study investor responses to major events. US stocks with greater regulatory transition risk outperformed in response to the Russia-Ukraine war, though firms with renewable energy opportunities also benefited temporarily. The US Inflation Reduction Act (IRA) also favored both types of firms, suggesting that the pricing of climate risk does not generally follow a one-dimensional “brown vs. green” framework. In Europe, if anything, high-transition risk firms suffered. These results indicate an international divergence in the pace of energy transition.

This paper introduces the concept of statistical arbitrage, a long horizon trading opportunity that generates a riskless profit and is designed to exploit persistent anomalies. Statistical arbitrage circumvents the "joint hypothesis" dilemma of traditional market efficiency tests because its definition is independent of any equilibrium model and its existence is incompatible with market efficiency. We provide a methodology to test for statistical arbitrage and then empirically investigate whether momentum and value trading strategies constitute statistical arbitrage opportunities. Despite controlling for transaction costs and the influence of small stocks, we find evidence that these strategies generate statistical arbitrage. Furthermore, their profitability does not appear to decline over time.

My paper, “Does the Fed control interest rates?” is in the 2013 Review of Asset Pricing Studies (Volume 3, pp. 180-199). The paper finds that the Fed controls the Federal Funds (FF) rate (the overnight rate on interbank borrowing of reserves). Other short-term rates are related to FF, but they take long swings away from FF, and the relations between FF and other rates weaken quickly for longer maturities.

Stock market price/earnings ratios should be influenced by demography. Since demography is predictable, stock returns should be as well. We provide a simple stochastic OLG model with a cyclical structure which generates cyclical P/E ratios. We calibrate the model to roughly fit the cyclical features of historical P/E ratios.

How are job satisfaction and firm value linked? I tackle this long-standing management question using a new methodology from finance. I study the effect on firm-level value, rather than employee-level productivity, to take into account the cost of increasing job satisfaction. To address reverse causality, I measure firm value by using future stock returns, and control for risk, firm characteristics, industry performance, and outliers. Companies listed in the "100 Best Companies to Work For in America" generated 2.3-3.8%/year higher stock returns than their peers from 1984-2011. These results have three main implications. First, consistent with HRM theories, job satisfaction is beneficial for firm value. Second, corporate social responsibility can improve stock returns. Third, the stock market does not fully value intangible assets, and so it may be necessary to shield the manager from short-term stock prices to encourage long-run growth.

The popular press is replete with commentary seeking to damn the behavior of corporate managers in handing free cash flow back into the hands of shareholders. These criticisms are often, even regularly, without merit (at least merit that can be demonstrated), sometimes glaringly so. Aggregate share repurchase activity has not been at historical highs when measured properly, and when netted against debt issuance is almost a non-event, does not mechanically create earnings (EPS) growth, does not stifle aggregate investment activity, and has not been the primary cause for recent stock market strength. These myths should be discarded.

Daniel Kahneman is widely considered the most influential psychologist in the world today. He is best known in the financial realm for pioneering work that helped to lay the foundation for behavioral economics, which studies the psychology of judgment and economic decision making and its impact on the financial markets. Together with his long-time collaborator Amos Tversky, Dr. Kahneman explored the ways in which human judgment systematically departs from the basic principles of decision theory when evaluating economic risk, consequently creating the concept of prospect theory. Their findings challenged fundamental economic assumptions and expanded the boundaries of research by introducing psychologically realistic models into economic theory. In 2002, Dr. Kahneman’s work was recognized with the Nobel Memorial Prize in Economic Sciences for his integration of insights from psychological research into economic science. In February 2012, Dr. Kahneman spoke with members of the Editorial Advisory Board of the Journal of Investment Consulting about his investigations into decision making in the context of a dual-process model, loss aversion and risk tolerance, adversarial collaboration, and financial advisors’ impact on investors’ well-being.

“Investor Behavior: An Overview” is the introduction chapter for the book Investor Behavior: The Psychology of Financial Planning and Investing edited by H. Kent Baker and Victor Ricciardi that presents a historical perspective of investor psychology and theory. The field of investor behavior attempts to understand and explain investor decisions by combining the topics of psychology and investing on a micro level (i.e., the decision process of individuals and groups) and a macro perspective (i.e., the role of financial markets). The decision-making process of investors incorporates both a quantitative (objective) and qualitative (subjective) aspect that is based on the specific features of the investment product or financial service. Investor Behavior examines the cognitive factors (mental processes) and affective (emotional) issues that individuals, financial experts, and traders reveal during the financial planning and investment management process. In practice, individuals make judgments and decisions that are based on past events, personal beliefs, and preferences. The chapter also provides an overview of the emerging research topics covered in Investor Behavior and the abstract descriptions for the remaining 29 chapters of the book.

An accounting-based model has strong out-of-sample power not only to detect fraud, but also to predict cross-sectional returns. Firms with a higher probability of manipulation (MSCORE) earn lower returns in every decile portfolio sorted by: Size, Book-to-Market, Momentum, Accruals, and Short-Interest. We show that the predictive power of MSCORE is related to its ability to forecast the persistence of current-year accruals, and is most pronounced among low-accrual (ostensibly high earnings-quality) stocks. Most of the incremental power derives from measures of firms’ predisposition to manipulate, rather than their level of aggressive accounting. Our evidence supports the investment value of careful fundamental analysis, even among public firms.

Among high Active Share portfolios – whose holdings differ substantially from their benchmark – only those with patient investment strategies (with holding durations of over 2 years) on average outperform, over 2% per year. Funds trading frequently generally underperform, including those with high Active Share. Among patient funds, separating closet index from high Active Share funds matters, as low Active Share funds on average underperform even with patient strategies. Our results suggest that U.S. equity markets provide opportunities for longer-term active managers, perhaps because of the limited arbitrage capital devoted to patient and active investment strategies. The online appendix to this paper is available at http://ssrn.com/abstract=2498743.

Technical analysis is the study for forecasting future asset prices with past data. In this survey, we review and extend studies on not only the time-series predictive power of technical indicators on the aggregated stock market and various portfolios, but also the cross-sectional predictability with various firm characteristics. While we focus on reviewing major academic research on using traditional technical indicators, but also discuss briefly recent studies that apply machine learning approaches, such as Lasso, neural network and genetic programming, to forecast returns both in the time-series and on the cross-section.