We extract firms’ cyber risk with a machine learning algorithm measuring the proximity between their disclosures and a dedicated cyber corpus. Our approach outperforms dictionary methods, uses full disclosure and not devoted-only sections, and generates a cyber risk measure uncorrelated with other firms’ characteristics. We find that a portfolio of US-listed stocks in the high cyber risk quantile generates an excess return of 18.72% p.a. Moreover, a long-short cyber risk portfolio has a significant and positive risk premium of 6.
Along with the increasing frequency and severity of cyber incidents, understanding their economic implications is paramount. In this context, listed firms’ reactions to cyber incidents are compelling to study since they (i) are a good proxy to estimate the costs borne by other organizations, (ii) have a critical position in the economy, and (iii) have their financial information publicly available. We extract listed firms’ cyber incident dates and characteristics from newswire headlines.
This article introduces TechRank, a recursive algorithm based on a bipartite graph with weighted nodes that the authors developed to link companies and technologies based on the reflection method. They allow the algorithm to incorporate exogenous variables that reflect an investor’s preferences and calibrate the algorithm in the cybersecurity sector. First, their results help estimate each entity’s influence and explain companies’ and technologies’ ranking. Second, the results provide investors with an optimal quantitative ranking of technologies and thus help them design their optimal portfolio.
n this paper, the authors estimate the risk and return of venture capital investments with selection bias correction. They use an up-to-date dataset and enhance it to account for missing valuations using machine learning. They infer, with a median error of less than 4%, the true log value of the firm for a total of nearly 120,000 observations from 2010 to 2022. They find an annualized expected return of around 38%, an annualized CAPM alpha of 32.
Early-stage firms play a significant role in driving innovation and creating new products and services, especially for cybersecurity. Therefore, evaluating their performance is crucial for investors and policymakers. This work presents a financial evaluation of early-stage firms’ performance in 19 cybersecurity sectors using a private-equity dataset from 2010 to 2022 retrieved from Crunchbase. We observe firms, their primary and secondary activities, funding rounds, and pre and post-money valuations. We compare cybersecurity sectors regarding the amount raised over funding rounds and post-money valuations while inferring missing observations.
We study security-development patterns in computer-science technologies through (i) the security attention among technologies, (ii) the relation between technological change and security development, and (iii) the effect of opinion on security development. We perform a scientometric analysis on arXiv e-prints (𝑛 = 340,569) related to 20 computer-science technology categories. Our contribution is threefold. First, we characterize both processes of technological change and security development: while most technologies follow a logistic-growth process, the security development follows an AR(1) process or a random walk with positive drift.
This paper investigates the effect of the “financialization” of commodity markets in terms of pricing. I explore whether the emergence of commodity index traders affects weekly returns and turn-over during the roll periods. I split the sample (1994–2017) into the pre-financialization (1994–2003) and the post-financialization (2004–2017). I directly test whether the CIT market share (CIT/Open Interest) contributes to commodity returns and whether risk adjustments (based on momentum, basis, basis-momentum, open interest, crowding, and average factors) alter liquidity and insurance premiums documented in Kang, Rouwenhorst, and Tang (2020).
This paper explores empirically whether the supply or the demand uncertainty, the time to maturity, and the slope of the term structure (storage), explain the realized volatility of nearby commodity futures 5-minute returns. I find support for the “uncertainty resolution” and the “theory of storage” hypotheses while the “time to maturity” hypothesis is rejected. These results are robust to the inclusion of autoregressive terms in the baseline model. Next, I evaluate the in- and out-of-sample forecasting ability of models including these economic variables and find mixed results.
This paper studies the valuation effect of the SP-GSCI roll on commodity contracts. We identify a surge of investment tracking commodity futures indices in December 2003. Before 2004, the roll period generated average cumulative abnormal price changes amounting to 115 bps for the nearby contract and 146 bps for the first deferred contract. From 2004 to 2010, the average cumulative abnormal price changes of the nearby (first deferred) is equal to -60 bps (-40 bps).
This dissertation is constituted of three distinct chapters on commodity markets, which cover different aspects of finance. The first chapter focuses on the consequences of the financialization of commodity markets, an important topic from a regulatory perspective. Using a multivariate change-point algorithm, I statistically date when the financialization materializes, with a narrow confidence interval. Using this date as a reference point, I use panel data methods to statistically assess the impact of the financialization on commodity futures prices.