What is science good for?

A nonbusiness discipline can provide a useful framework for thinking about old problems in new ways. People who study management, for instance, freely borrow from many fields of science to theorize about organizational behavior and business strategy. Evolutionary psychology and biology are especially popular sources of inspiration. But should they be? Evolutionary biologist Richard Dawkins has spent much of his career explaining science to the public. More than 20 years ago, his book The Selfish Gene shattered the popular belief that evolution necessarily favors altruism and self-sacrifice. In a conversation with HBR senior editor Diane Coutu, Dawkins discusses the role of science in our lives and identifies some of the more glaring public misperceptions of scientific theories. In particular, he disentangles the current notion that certain behaviors are in some way preprogrammed and explodes some contemporary myths about the Human Genome Project. Dawkins says much of the popular fear surrounding genetic manipulation is unfounded. "Humans have been practicing it for thousands of years, to no obvious ill effect," he says. Modern foot-long corncobs, the result of more than 1,000 years of artificial selection, are "quite Frankenstein-like" compared to their half-inch-long progenitors, he points out. He also touches on agriculture giant Monsanto and the media: "Part of the reason for Monsanto's troubles is that the company came up against an extraordinary amount of unfortunate, even malevolent, media hype," he says. "And people were more or less misled, by one scare story after another, into stampeding." A staunch defender of science as a haven of rational thought, Dawkins counsels businesspeople to recognize the limitations--as well as the beauty--of science.     

When is good news really good news?

The impression management literature suggests that managers often resort to biased disclosures. However, there is little systematic evidence on what types of strategies management uses to achieve this bias. Do managers simply lie? Or, do they use more subtle ways of introducing positive bias into corporate narratives, such as selecting specific information items which result in a more positive impression (‘selectivity’) or by keeping their narratives vague and general (‘vagueness’)? In order to differentiate between the two scenarios, I re-examine the positive forward-looking statements examined by Schleicher and Walker (2010) and compare, across firms with improving and deteriorating financial performance, the managerial choices made in relation to eight forecast attributes.

I make two observations. First, there are significant differences in the characteristics of good- and bad-news firms’ positive statements. In particular, bad-news firms’ positive statements involve more non-specific time horizons, more segmental forecasts, and more references to conditions and aims and objectives, but fewer directional forecasts, fewer numbers, and fewer reinforcing qualifiers. Second, the identified differences in good- and bad-news firms’ positive statements can be exploited for classification purposes: including into a classification model additional regressors that measure a positive forward-looking statement's level of selectivity and vagueness significantly increases the model's ability to separate firms with improving financial performance from firms with deteriorating financial performance. Overall, my results are consistent with (a) impression management operating predominantly through selectivity and vagueness and (b) selectivity and vagueness being an important signal for future financial performance.     

Is the realized volatility good for option pricing during the recent financial crisis?

The contributions of this paper are threefold. The first contribution is the proposed logarithmic HAR (log-HAR) option-pricing model, which is more convenient compared with other option pricing models associated with realized volatility in terms of simpler estimation procedure. The second contribution is the test of the empirical implications of heterogeneous autoregressive model of the realized volatility (HAR)-type models in the S&P 500 index options market with comparison of the non-linear asymmetric GARCH option-pricing model, which is the best model in pricing options among generalized autoregressive conditional heteroskedastic-type models. The third contribution is the empirical analysis based on options traded from July 3, 2007 to December 31, 2008, a period covering a recent financial crisis. Overall, the HAR-type models successfully predict out-of-sample option prices because they are based on realized volatilities, which are closer to the expected volatility in financial markets. However, mixed results exist between the log-HAR and the heterogeneous auto-regressive gamma models in pricing options because the former is better than the latter in times of turmoil, whereas it is worse during the rather stable periods.     

Does the market model provide a good counterfactual for event studies in finance?

Financial Markets and Portfolio Management - We provide a common framework that relates traditional event study estimation methods in finance to a modern approach for causal event studies. The...     

Are good financial advisors really good? The performance of investment banks in the M&;A market

The study examines whether prestigious investment banks deliver quality gains to their clients in a sample of 6,379 US M&A deals. It finds that acquirers advised by tier-one advisors lost more than $42 billion, whereas those advised by tier-two advisors gained $42 billion, whereas those advised by tier-two advisors gained 13.5 billion at the merger announcement. The results were mainly driven by the large loss deals advised by tier-one advisors. The evidence indicates that investment banks might have different incentives when they advise on large deals vs. small deals. The results imply that market share based reputation league tables, could be misleading and therefore, the selection of investment banks should be based on their track record in generating gains to their clients. The findings were consistent with the superior deal hypothesis as tier-one target advisors outperformed tier-two advisors and the existence of a prestigious advisor on at least one side of an M&A transaction resulted in higher wealth gains to the combined entity. Target advisors were able to extract more wealth gains for their clients, which led to higher combined gains at the expense of the acquirer.     

How good can heuristic-based forecasts be? A comparative performance of econometric and heuristic models for UK and US asset returns

This paper systematically investigates the sources of differential out-of-sample predictive accuracy of heuristic frameworks based on internet search frequencies and a large set of econometric models. The volume of internet searches helps gauge the degree of investors’ time-varying interest in specific assets. We use a wide range of state-of-the-art models, both of linear and nonlinear type (regime-switching predictive regressions, threshold autoregressive, smooth transition autoregressive), extended to capture conditional heteroskedasticity through GARCH models. The predictor variables investigated are those typical of the literature featuring a range of macroeconomic and market leading indicators. Our out-of-sample forecasting exercises are conducted with reference to US, UK, French and German data, both stocks and bonds, and for 1- and 12-months-ahead horizons. We employ several forecast performance metrics and predictive accuracy tests. Internet-search-based models are found to perform better than the average of all of the alternative models. For several country-asset-horizon combinations, particularly for UK bond returns, our heuristic models compare favourably with sophisticated econometric methods. The heuristic models are also shown to perform well in forecasting realized volatility. The baseline results are supported by several extensions and robustness checks, such as using alternative search keywords, controlling for Fama–French and Cochrane–Piazzesi factors, and implementing heuristic-based trading strategies.     

Are CEOs incentivized to shelter good information?

Prior theoretical studies on the agency problem hold different opinions from the empirical literature on two questions: (a) Are CEOs incentivized to shelter good information? (b) Are CEOs incentivized to evenly shelter good and bad information? This paper demonstrates that CEOs with high pay‐performance incentives tend to successfully shelter good information rather than bad information. Furthermore, CEOs with high pay‐performance incentives shelter good information by using real earnings management and textual manipulation but not accrual‐based earnings management. These asymmetric information manipulation behaviors help to decrease corporate cash flow volatility as well as the jump and crash risk on the stock market.     

Are you a good boss--or a great one?

Private moments of doubt and fear come even to managers who have spent years on the job. Any number of events can trigger them: an initiative going poorly, a lukewarm performance review, a daunting new assignment. HBS professor Hill and executive Lineback have long studied the question of how manager grow and advance. Their experience brings them to a simple but troubling observation: Most bosses reach a certain level of proficiency and stay there--short of what they could and should be. Why? Because they stop working on themselves. The authors offer what they call the three imperatives for managers who seek to avoid this stagnation. First, manage yourself--who you are as a person, the beliefs and values that drive your actions, and especially how you connect with others all matter to the people you must influence. Second, manage your network. Effective managers know that they cannot avoid conflict and competition among organizational groups; they build and nurture ongoing relationships. Third, manage your team. Team members need to know what's required of them collectively and individually and what the team's values, norms, and standards are. The authors include a useful assessment tool to help readers get started.     

Retirement income adequacy: good news or bad?

Estimating replacement rate targets, and using them to assess the current state of retirement savings adequacy, has been the focus of much attention and analysis. Building on his earlier work published in Benefits Quarterly, the author conceptually defines retirement income adequacy, estimates replacement rate targets and reviews research on the current state of baby boomers' retirement savings. He concludes that, despite existing data limitations, researchers have made considerable strides in recent years in thinking about saving for retirement and the adequacy of workers' preparation for it. These advances suggest that singular rules of thumb for replacement rates are naive and that estimates should take into account the unforeseen risks that individuals face.     

Do analysts overreact to extreme good news in earnings?

We provide an alternative explanation for the previous finding of analysts’ overreaction to extreme good news in earnings. We show that such finding could be a result of analysts’ rational behavior in the face of high earnings uncertainty rather than their cognitive bias. Extreme earnings performance tends to be associated with higher earnings uncertainty that generally leads to more forecast optimism. Once this effect is accounted for, the univariate result of analysts’ overreaction to extreme good news in earnings is subsumed, leaving only their underreaction in general.

International tax competition: do public good spillovers matter?

We study the impact of public good spillovers on tax competition between two imperfectly integrated countries with different levels of productivity. We show that international public good spillovers, by reducing the tax gap between countries, strengthen the agglomeration of firms in the most productive country. Then we carry on a welfare analysis. We first assume that governments are engaged in a redistributive tax policy. At the non-cooperative equilibrium, the tax level in the high-productivity country is inefficiently high while it is inefficiently low in the other country. A different conclusion emerges when tax revenues are recycled in a public good provision: taxes are inefficiently low in both countries and public good spillovers increase the global welfare. Finally, for a given amount of total tax revenues, public good provision in the high-productivity country is inefficiently high compared to its level in the low-productivity country.     

Are equities good inflation hedges? A frequency domain perspective

By using industry level data, we examine the relation between equity returns and inflation in a frequency dependent framework. Our analysis shows that a positive relation in fact exists between equity returns and high frequency inflation shocks for commodity and technology related industries. Since higher frequency shocks are independent from trend and are transitory in nature, our findings imply a positive relation between stock returns and the unexpected component of inflation. Furthermore, we show that the results are robust to firm-level data by using a sample from the oil industry. Hence, our study provides a new look at the impact of inflation on equities by showing the sensitivity of conclusions in prior work to frequency dependence in data.     

Acquisitions driven by stock overvaluation: Are they good deals?

Theory and recent evidence suggest that overvalued firms can create value for shareholders if they exploit their overvaluation by using their stock as currency to purchase less overvalued firms. We challenge this idea and show that, in practice, overvalued acquirers significantly overpay for their targets. These acquisitions do not, in turn, lead to synergy gains. Moreover, these acquisitions seem to be concentrated among acquirers with the largest governance problems. CEO compensation, not shareholder value creation, appears to be the main motive behind acquisitions by overvalued acquirers.     

Rating the ratings: How good are commercial governance ratings?

Proxy advisory and corporate governance rating firms (such as RiskMetrics/Institutional Shareholder Services, GovernanceMetrics International, and The Corporate Library) play an increasingly important role in U.S. public markets. They rank the quality of firm corporate governance, advise shareholders how to vote, and sometimes press for governance changes. We examine whether commercially available corporate governance rankings provide useful information for shareholders. Our results suggest that they do not. Commercial ratings do not predict governance-related outcomes with the precision or strength necessary to support the bold claims made by most of these firms. Moreover, we find little or no relation between the governance ratings provided by RiskMetrics with either their voting recommendations or the actual votes by shareholders on proxy proposals.     

Bank efficiency and foreign ownership: Do good institutions matter?

This paper contributes to the literature on foreign ownership and bank efficiency by examining whether the efficiency of foreign banks depends on the institutional quality of the host country and on institutional differences between the home and host country. Using stochastic frontier analysis for a sample of 2095 commercial banks in 105 countries for the years 1998–2003, we find that foreign ownership negatively affects bank efficiency. However, in countries with good governance this negative effect is less pronounced. We also find that higher quality of the institutions in the home country and higher similarity between home and host country institutional quality reduce foreign bank inefficiency.     

Are RiskMetrics forecasts good enough? Evidence from 31 stock markets

Academic research has highlighted the inherent flaws within the RiskMetrics model and demonstrated the superiority of the GARCH approach in-sample. However, these results do not necessarily extend to forecasting performance. This paper seeks answer to the question of whether RiskMetrics volatility forecasts are adequate in comparison to those obtained from GARCH models. To answer the question stock index data is taken from 31 international markets and subjected to two exercises, a straightforward volatility forecasting exercise and a Value-at-Risk exceptions forecasting competition. Our results provide some simple answers to the above question. When forecasting volatility of the G7 stock markets the APARCH model, in particular, provides superior forecasts that are significantly different from the RiskMetrics models in over half the cases. This result also extends to the European markets with the APARCH model typically preferred. For the Asian markets the RiskMetrics model performs well, and is only significantly dominated by the GARCH models for one market, although there is evidence that the APARCH model provides a better forecast for the larger Asian markets. Regarding the Value-at-Risk exercise, when forecasting the 1% VaR the RiskMetrics model does a poor job and is typically the worst performing model, again the APARCH model does well. However, forecasting the 5% VaR then the RiskMetrics model does provide an adequate performance. In short, the RiskMetrics model only performs well in forecasting the volatility of small emerging markets and for broader VaR measures.     

Iterated VaR or CTE measures: A false good idea?

The purpose of this paper is twofold. Firstly, we consider different risk measures in order to determine the solvency capital requirement of a pension fund. Secondly, we illustrate the impact of the time horizon of long-term guarantee products on these capital. We consider a financial market modelled by a common Black–Scholes–Merton model. We neglect the mortality and underwriting risks by assuming that the pension fund is fully hedged against these risks, which allows us to keep understandable and tractable formulæ (the longevity risk will be a part of future researches). A portfolio is built in this market according to different strategies and the pension fund offers a fixed guaranteed rate on a certain time horizon. We begin with well-known static risk measures (value at risk and conditional tail expectation measures) and then we consider their natural dynamic generalization. In order to be time consistent, we consider their iterated versions by a backward iterations scheme. Within the dynamic setting, we show that solvency capital can be expensive and that attention must be paid to the safety level considered.     

Innovation versus complexity: what is too much of a good thing?

What's the number of product or service offerings that would optimize both your revenues and your profits? For most firms, it's considerably lower than the number they offer today. The fact is, companies have strong incentives to be overly innovative in new product development. But continual launches of new products and line extensions add complexity throughout a company's operations, and as the costs of managing that complexity multiply, margins shrink. To maximize profit potential, a company needs to identify its innovation fulcrum, the point at which an additional offering destroys more value than it creates. The usual antidotes to complexity miss their mark because they treat the problem on the factory floor rather than at its source: in the product line. Mark Gottfredson and Keith Aspinall of Bain & Company present an approach that goes beyond the typical Six Sigma or lean-operations program to root out complexity hidden in the value chain. The first step is to ask, What would our company look like if it made and sold only a single product or service? In other words, you identify your company's equivalent of Henry Ford's one-size-fits-all Model T-for Starbucks, it might be a medium-size cup of coffee; for a bank, a simple checking account-and then determine the cost of producing that baseline offering. Next, you add variety back into the business system, product by product, and carefully forecast the resulting impact on sales as well as the cost implications across the value chain. When the analysis shows the costs beginning to overwhelm the added revenues, you've found your innovation fulcrum. By deconstructing their companies to a zero-complexity baseline, managers can break through organizational resistance and deeply entrenched ways of thinking to find the right balance between innovation and complexity.     

Too much of a good incentive? The case of executive stock options

Using a utility-maximization framework, I show that the incentive to increase stock price does not always increase as more options are granted. Keeping the total cost of his compensation fixed, granting more options creates greater incentives to increase stock price only if option wealth does not exceed a certain fraction of total wealth. Beyond this critical level, granting more options actually reduces incentive effects and becomes counterproductive. In addition, stock options also create incentive to reduce (increase) idiosyncratic (systematic) risk. These incentive effects are sensitive to the choice of exercise price.