/pdf/the-theory-of-industrial-organization-19bejph0ej.pdf

The Theory of Industrial Organization

An empirical q-factor model consisting of the market factor, a size factor, an investment factor, and a profitability factor largely summarizes the cross section of average stock returns. A comprehensive examination of nearly 80 anomalies reveals that about one-half of the anomalies are insignificant in the broad cross section. More importantly, with a few exceptions, the q-factor model's performance is at least comparable to, and in many cases better than that of the Fama-French (1993) 3-factor model and the Carhart (1997) 4-factor model in capturing the remaining significant anomalies.

/pdf/digesting-anomalies-an-investment-approach-3oub45mmsf.pdf

Digesting Anomalies: An Investment Approach

Motivated from investment-based asset pricing, we propose a new factor model that consists of the market factor, a size factor, an investment factor, and a return-on-equity factor The new model [i] outperforms the Carhart (1997) four-factor model in pricing portfolios formed on earnings surprise, idiosyncratic volatility, financial distress, equity issues, as well as on investment and return-on-equity; [ii] performs similarly as the Carhart model in pricing portfolios on momentum as well as on size and book-to-market; but [iii] underperforms in pricing the total accrual deciles Our model's performance, combined with its clear economic intuition, suggests that it can serve as a new workhorse model for academic research and investment management practice

We test for firm-level asset investment effects in returns by examining the crosssectional relation between firm asset growth and subsequent stock returns. Asset growth rates are strong predictors of future abnormal returns. Asset growth retains its forecasting ability even on large capitalization stocks. When we compare asset growth rates with the previously documented determinants of the cross-section of returns (i.e., book-to-market ratios, firm capitalization, lagged returns, accruals, and other growth measures), we find that a firm’s annual asset growth rate emerges as an economically and statistically significant predictor of the cross-section of U.S. stock returns. ONE OF THE PRIMARY FUNCTIONS OF CAPITAL MARKETS is the efficient pricing of real investment. As companies acquire and dispose of assets, economic efficiency demands that the market appropriately capitalizes such transactions. Yet, growing evidence identifies an important bias in the market’s capitalization of corporate asset investment and disinvestment. The findings suggest that corporate events associated with asset expansion (i.e., acquisitions, public equity offerings, public debt offerings, and bank loan initiations) tend to be followed by periods of abnormally low returns, whereas events associated with asset contraction (i.e., spinoffs, share repurchases, debt prepayments, and dividend initiations) tend to be followed by periods of abnormally high returns. 1 In

https://www.krannert.purdue.edu/faculty/hgulen/asset_growth.pdf

Asset Growth and the Cross-Section of Stock Returns

A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff-Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provides a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. In an application to the DM/$ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from non-jump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process.

/pdf/roughing-it-up-including-jump-components-in-the-measurement-35xg2w5blh.pdf

Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility

An investment factor, long in low-investment stocks and short in high-investment stocks, helps explain the new issues puzzle. Adding the investment factor into standard factor regressions reduces the SEO underperformance by about 75%, the IPO underperformance by 80%, the underperformance following convertible debt offerings by 50%, and Daniel and Titman’s (2006) composite issuance effect by 40%. The reason is that issuers invest more than nonissuers, and the investment factor earns a significantly positive average return of 0.57% per month. Equity and debt issuers underperform matching nonissuers with similar characteristics in the post-issue years (e.g., Ritter, 1991; Loughran and Ritter, 1995; and Spiess and Affleck-Graves, 1995, 1999). We explore empirically the investment-based hypothesis of this underperformance. The q-theory of investment and the real options theory imply a negative relation between real investment and expected returns. If the proceeds from equity and debt issues are used to finance investment, then issuers should invest more and earn lower average returns than matching nonissuers.

The New Issues Puzzle: Testing the Investment-Based Explanation

Theoretical models predict that the value of a real option should be increasing in the volatility of the underlying asset. Thus, if real options are economically important, then firm values should be positively related to volatility. Consistent with this prediction, we find evidence that stock returns are contemporaneously positively correlated with changes in volatility. Moreover, this positive relation is stronger for firms that are more likely to have more real options and for firms with more irreversible investment opportunities. Most importantly, we find that the sensitivity of firm values to changes in volatility declines significantly after firms exercise their real options. These results indicate that real options constitute an economically meaningful component of firm values.

Real Options, Volatility, and Stock Returns

Trade credit and supplier competition

This paper develops a theory of financing of entrepreneurial ventures via crypto tokens, which is not limited to platform-based ventures. We compare token financing with traditional equity financin...

A Theory of ICOs: Diversification, Agency, and Information Asymmetry

We demonstrate theoretically and empirically that strategic considerations are important in shaping the cash policies of innovative firms. In our model, firms compete in product markets with uncertain structure using cash as a commitment device to invest in innovation. We show that firms’ equilibrium cash holdings are related to the expected intensity of competition. The sign and magnitude of this relation depends on firms’ financial constraints. Consistent with the strategic motive for hoarding cash, we show that firms’ cash holdings are negatively affected by their rivals’ cash-holding choices, even more so when competition is expected to be intense.

Cash Holdings, Competition, and Innovation

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