THIS paper is concerned with those actions of business firms which have harmful effects on others. The standard example is that of a factory the smoke from which has harmful effects on those occupying neighbouring properties. The economic analysis of such a situation has usually proceeded in terms of a divergence between the private and social product of the far' ory, in which economists have largely followed the treatment of Pigou in The Economics of Welfare. The conclusions to which this kind of analyris seems to have led most economists is that it would be desirable to make the owner of the factory liable for the damage caused to those injured by the smoke, or alternatively, to place a tax on the factory owner varying with the amount of smoke produced and equivalent in money terms to the damage it would cause, or finally, to exclude the factory from residential districts (and presumably from other

闡釋《社會成本問題》[The Problem of Social Cost]

Testing Trade-Off and Pecking Order Predictions about Dividends and Debt

Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the forecasting ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample forecasting power, matching or exceeding that of macroeconomic variables. Furthermore, technical indicators and macroeconomic variables provide complementary information over the business cycle: technical indicators better detect the typical decline in the equity risk premium near business-cycle peaks, while macroeconomic variables more readily pick up the typical rise in the equity risk premium near cyclical troughs. Consistent with this behavior, we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone. Overall, the substantial countercyclical fluctuations in the equity risk premium appear well captured by the combined information in technical indicators and macroeconomic variables.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1807225_code16976.pdf?abstractid=1787554&mirid=3

Forecasting the Equity Risk Premium: The Role of Technical Indicators

Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample predictive power, matching or exceeding that of macroeconomic variables. Furthermore, technical indicators and macroeconomic variables provide complementary information over the business cycle: technical indicators better detect the typical decline in the equity risk premium near business-cycle peaks, whereas macroeconomic variables more readily pick up the typical rise in the equity risk premium near cyclical troughs. Consistent with this behavior, we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone. Overall, the substantial countercyclical fluctuations in the equity risk premium appear well captured by the combined information in technical indicators and macroeconomic variables.

Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1838 .

This paper was accepted by Wei Jiang, finance.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4062&context=lkcsb_research

I build a dynamic capital structure model that demonstrates how business-cycle variations in expected growth rates, economic uncertainty, and risk premia influence firms' financing and default policies. Countercyclical fluctuations in risk prices, default probabilities, and default losses arise endogenously through firms' responses to the macroeconomic conditions. These comovements generate large credit risk premia for investment grade firms, which helps address the "credit spread puzzle" and "under-leverage puzzle" in a unified framework. The model generates interesting dynamics for financing and defaults, including "credit contagion" and market timing of debt issuance. It also provides a novel procedure to estimate state-dependent default losses.

Macroeconomic Conditions and the Puzzles of Credit Spreads and Capital Structure

I estimate the market's valuation of the net benefits to leverage using panel data from 1994 to 2004, identified from market values and betas of a company's debt and equity. The median firm captures net benefits of up to 5.5% of firm value. Small and profitable firms have high optimal leverage ratios, as predicted by theory but in contrast to existing empirical evidence. Companies are on average slightly underlevered relative to the optimal leverage ratio at refinancing. This result is mainly due to zero leverage firms. I also look at implications for financial policy.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1370445_code340494.pdf?abstractid=1370445&mirid=1&type=2

The Net Benefits to Leverage

I estimate the market's valuation of the net benefits to leverage using panel data from 1994 to 2004, identified from market values and betas of a company's debt and equity. The median firm captures net benefits of up to 5.5% of firm value. Small and profitable firms have high optimal leverage ratios, as predicted by theory, but in contrast to existing empirical evidence. Companies are on average slightly underlevered relative to the optimal leverage ratio at refinancing. This result is mainly due to zero leverage firms. I also look at implications for financial policy.

https://www.jstor.org/stable/info/23324407

This paper uses a randomized field experiment to identify which start-up characteristics are most important to investors in early stage firms. The experiment randomizes investors’ information sets of fund-raising start-ups. The average investor responds strongly to information about the founding team, but not to firm traction or existing lead investors. In contrast, inexperienced investors respond to all information categories. Our results suggest that information about human assets is causally important for the funding of early stage firms, and hence, for entrepreneurial success.

Attracting Early-Stage Investors: Evidence from a Randomized Field Experiment

Valuations of entrepreneurial companies are only observed occasionally, albeit more frequently for well-performing companies. Consequently, estimators of risk and return must correct for sample selection to obtain consistent estimates. We develop a general model of dynamic sample selection model and estimate it using data from venture capital investments in entrepreneurial companies. Our selection correction leads to markedly lower intercepts and higher estimates of risks compared to previous studies. The methodology is generally applicable to estimating risk and return in illiquid markets with endogenous trading.

/pdf/risk-and-return-characteristics-of-venture-capital-backed-5efoekts9j.pdf

Risk and Return Characteristics of Venture Capital-Backed Entrepreneurial Companies

This paper finds statistically and economically significant out-of-sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. Investors must account for estimation risk, and incorporate an ensemble of important features, including time-varying volatility, and time-varying expected returns driven by payout yield measures that include share repurchase and issuance. Prior research documents a lack of benefits to return predictability, and our results suggest that this is largely due to omitting time-varying volatility and estimation risk. We also document the sequential process of investors learning about parameters, state variables, and models as new data arrive.

/pdf/sequential-learning-predictability-and-optimal-portfolio-2oh81dlr4m.pdf

Arthur G. Korteweg

Papers

The Net Benefits to Leverage

The Net Benefits to Leverage

Attracting Early-Stage Investors: Evidence from a Randomized Field Experiment

Risk and Return Characteristics of Venture Capital-Backed Entrepreneurial Companies

Sequential Learning, Predictability, and Optimal Portfolio Returns