The resource-based view of the firm attributes superior financial performance to organizational resources and capabilities. This paper develops the concept of IT as an organizational capability and empirically examines the association between IT capability and firm performance. Firm specific IT resources are classified as IT infrastructure, human IT resources, and IT-enabled intangibles. A matched-sample comparison group methodology and publicly available ratings are used to assess IT capability and firm performance. Results indicate that firms with high IT capability tend to outperform a control sample of firms on a variety of profit and cost-based performance measures.

A resource-based perspective on information technology capability and firm performance: an empirical investigation

https://www.mit.edu/~kothari/attach/KLW%20paper%20May%202001.pdf

Performance matched discretionary accrual measures

Theoretical models of financial markets built on the assumption that some investors are overconfident yield one central prediction: overconfident investors will trade too much. We test this prediction by partitioning investors on the basis of a variable that provides a natural proxy for overconfidence--gender. Psychological research has established that men are more prone to overconfidence than women. Thus, models of investor overconfidence predict that men will trade more and perform worse than women. Using account data for over 35,000 households from a large discount brokerage firm, we analyze the common stock investments of men and women from February 1991 through January 1997. Consistent with the predictions of the overconfidence models, we document that men trade 45 percent more than women and earn annual risk-adjusted net returns that are 1.4 percent less than those earned by women. These differences are more pronounced between single men and single women; single men trade 67 percent more than single women and earn annual risk-adjusted net returns that are 2.3 percent less than those earned by single women.

https://papers.ssrn.com/sol3/Delivery.cfm/98110615.pdf?abstractid=139415&rulid=175370&mirid=3

Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment

Corporate governance, chief executive officer compensation, and firm performance

Theoretical models predict that overconedent investors trade excessively We test this prediction by partitioning investors on gender Psychological research demonstrates that, in areas such as enance, men are more overconedent than women Thus, theory predicts that men will trade more excessively than women Using account data for over 35,000 households from a large discount brokerage, we analyze the common stock investments of men and women from February 1991 through January 1997 We document that men trade 45 percent more than women Trading reduces men’s net returns by 265 percentage points a year as opposed to 172 percentage points for women It’s not what a man don’t know that makes him a fool, but what he does know that ain’t so Josh Billings, nineteenth century American humorist It is difecult to reconcile the volume of trading observed in equity markets with the trading needs of rational investors Rational investors make periodic contributions and withdrawals from their investment portfolios, rebalance their portfolios, and trade to minimize their taxes Those possessed of superior information may trade speculatively, although rational speculative traders will generally not choose to trade with each other It is unlikely that rational trading needs account for a turnover rate of

/pdf/boys-will-be-boys-gender-overconfidence-and-common-stock-2ctwfizezu.pdf

Boys will be Boys: Gender, Overconfidence, and Common Stock Investment

https://leeds-faculty.colorado.edu/bhagat/barberlyon1997.pdf

Detecting long-run abnormal stock returns: The empirical power and specification of test statistics

We analyze tests for long-run abnormal returns and document that two approaches yield well-specified test statistics in random samples. The first uses a traditional event study framework and buy-and-hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewnessadjusted t-statistic or the empirically generated distribution of long-run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar-time portfolios and a time-series t-statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long-run abnormal returns is treacherous. COMMONLY USED METHODS TO TEST for long-run abnormal stock returns yield misspecified test statistics, as documented by Barber and Lyon ~1997a! and Kothari and Warner ~1997!. 1 Simulations reveal that empirical rejection levels routinely exceed theoretical rejection levels in these tests. In combination, these papers highlight three causes for this misspecification. First, the new listing or survivor bias arises because in event studies of long-run abnormal returns, sampled firms are tracked for a long post-event period, but firms that constitute the index ~or reference portfolio! typically include firms that begin trading subsequent to the event month. Second, the rebalancing bias arises because the compound returns of a reference portfolio, such as an equally weighted market index, are typically calculated assuming periodic ~generally monthly! rebalancing, whereas the returns of sample firms are compounded without rebalancing. Third, the skewness bias arises because the distribution of long-run abnormal stock returns is positively skewed, * Graduate School of Management, University of California, Davis. This paper was previously entitled “Holding Size while Improving Power in Tests of Long-Run Abnormal Stock Re

/pdf/improved-methods-for-tests-of-long-run-abnormal-stock-xzp39t83lx.pdf

Improved Methods for Tests of Long-Run Abnormal Stock Returns

https://leeds-faculty.colorado.edu/bhagat/BarberLyon1996.pdf

Detecting abnormal operating performance: The empirical power and specification of test statistics

Fama and French (1992) document a significant relation between firm size, book-to-market ratios, and security returns for nonfinancial firms Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results We document that the relation between firm size, book-to-market ratios, and security returns is similar for financial and nonfinancial firms In addition, we present evidence that survivorship bias does not significantly affect the estimated size or book-to-market premiums in returns Our results indicate data-snooping and selection biases do not explain the size and book-to-market patterns in returns

Firm size, book-to-market ratio, and security returns: a holdout sample of financial firms

We analyze the empirical power and specification of test- statistics in event studies designed to detect long-run (one to five-year) abnormal stock returns. We consider (1) the calculation of long-run abnormal returns by comparing summed monthly abnormal returns (cumulative abnormal returns) to holding period abnormal returns (buy-and-hold abnormal returns), (2) the construction of an appropriate return benchmark by considering the use of reference portfolios, control firms, and an application of the Fama-French three-factor model, and (3) the impact of sampling biases. When long-run abnormal returns are calculated as the buy-and-hold return of a sample firm less the buy-and-hold return of a reference portfolio (such as a market index), we document that test-statistics are significantly negatively biased. However, this negative bias is alleviated when buy-and-hold abnormal returns are calculated as returns of sample firms less returns of an appropriately selected control firm.

John D. Lyon

Papers

Detecting long-run abnormal stock returns: The empirical power and specification of test statistics

Improved Methods for Tests of Long-Run Abnormal Stock Returns

Detecting abnormal operating performance: The empirical power and specification of test statistics

Firm size, book-to-market ratio, and security returns: a holdout sample of financial firms

Detecting Long-Run Abnormal Stock Returns: The Empirical Power and Specification of Test Statistics