Анализ аномалии премии за риск кризисных компаний: сектор компаний интернет и программного обеспечения в США
В процессе исследования мы проводим эмпирический эконометрический анализ и следуем подходу Кэмпбелла, Хильшера и Сзилажи (2008) для определения премии за риск несостоятельности (кризисности) компаний интернет, программного обеспечения, а также компьютерных услуг. Мы предполагаем, что индустрия компаний интернет, программного обеспечения, а также компьютерных услуг характеризуется другой структурой каналов распространения информации, а следовательно объяснение аномалии несостоятельности через неправильное ценообразование можем быть слабее для этого типа фирм. Мы проводим анализ двумя способами. В первом мы накладываем эмпирическую меру финансового стресса определённых фирм на совокупную доходность их акций к определенной дате, выбранной до наступления ситуации вхождения в стадию несостоятельности, и сортируем эти фирмы в квантили. Затем мы анализируем среднюю совокупную доходность каждого квантиля. Во втором способе мы составляем два портфеля акций, отобранных посредством сортировки компаний по квантилям эмпирической меры несостоятельности, в котором первый содержит акции высокого риска кризисности, а второй низкого. Затем мы анализируем среднюю доходность этих портфелей. В результате обоих способов мы находим доказательства положительной связи между риском несостоятельности (кризисности) и доходностью акций – то, что пытались найти исследователи в предыдущих работах. Мы предлагаем возможные причины такой связи, а также широко раскрываем статистические различия между нашими результатами и результатами, полученными в предыдущих исследованиях. Наши наблюдения помогают лучше понять корни кризиса доткомов и вносят дополнительную ценность в литературу, посвященную предсказанию несостоятельности фирм, а также предлагают эмпирические наблюдения, обращенные в сторону смещенного фокуса инвесторов на определённые особенности фирм интернет и программного обеспечения.
INTRODUCTION ………………………………………………………………………………………………………….. 6 LITERATURE REVIEW ………………………………………………………………………………………………… 9
1. Outline of the problematic of financial distress……………………………………………………………. 9
2. The financial distress prediction approaches……………………………………………………………… 11 2.1 The classical approach for distress studies …………………………………………………………… 11 2.2 Primary attempts to link distress and financial performance measures…………………….. 13 2.3 Hazard models …………………………………………………………………………………………………. 14
3. Distress anomaly…………………………………………………………………………………………………… 15 3.1 Score-based studies of distress anomaly ……………………………………………………………… 16 3.2 Variation in research questions and approaches for distress studies………………………… 18 3.3 Intensification of distress puzzle research ……………………………………………………………. 20 3.4 Suggestions for distress risk in internet, software and computer services firms………… 22
4. Conclusion on background review …………………………………………………………………………… 24 METHODOLOGY ……………………………………………………………………………………………………….. 25 1. Data description…………………………………………………………………………………………………….. 25 2. The logit model of distress prediction ………………………………………………………………………. 32 RESULTS……………………………………………………………………………………………………………………. 35
1. Construction of regressions …………………………………………………………………………………….. 35
2. Distress risk and stock returns …………………………………………………………………………………. 39 2.1 Total returns and distress risk premium ………………………………………………………………. 39 2.2 Portfolio analysis and distress risk premium………………………………………………………… 43
3. Limitations of results ……………………………………………………………………………………………… 47
CONCLUSION ……………………………………………………………………………………………………………. 48 REFERENCES …………………………………………………………………………………………………………….. 50
The interrelation of stock returns and distress risk has important implications for risk- premium trade-off in the world of financial markets. The market pricing of distress risk has attracted a lot of academic scrutiny beginning with Chan and Chen (1991) and Fama and French (1996) who at their time attributed higher returns to the firms that they named as relatively distressed. Generally, if the distress risk can be considered as systematic, investors should demand the premium for bearing such risk. The implementation of usual specification of capital asset pricing model (CAPM) fails to capture the distress risk-premium if corporate failures are correlated with deteriorating investment opportunities (Merton, 1973), or unmeasured components of wealth such as human capital (Fama and French, 1996), or debt securities (Ferguson and Shockley, 2003). A number of recent articles including Agarlwal and Taffler (2008), Campbell et al (2008), Chava and Purnanandam (2010), Garlappi and Yan (2011), O’Doherty (2012) find out that the stocks with elevated probabilities of financial distress in various forms (bankruptcy, default or delisting) earn anomalously low returns, compared with the intuitive rationale for risk-reward relation. In the academic literature this condition has received an informal name of “distress puzzle” or distress anomaly, which up to the best of our knowledge had not been explained empirically. The answer that had gained the biggest authority amongst the academicians is that market misprices the distressed firms. It can happen either by not considering possible positive future results due to limited information about the distressed firms (they are usually small and lack analyst coverage), or by noisy environment surrounding the firm.
However, more recent studies suggest that not all distressed firms exhibit the same results. Walker and Wu (2019) show that the distress anomaly is weaker among firms that have institutional investors in their capital structure, which is in line with the idea that investment and hedge funds apply some efforts into understanding the future prospects of the firm before investing in it.
Gopalan and Xie (2011) investigate if different industries experience the same or not impact of financial distress. Their findings provide two major points to consider: (1) conglomeration weakens the effect of financial distress in the overall industry, primarily due to the internal capital market (ICM) and more constructive information spread between the market players. These industries during distress have higher sales growth and higher investments in research and development, compared with single-segment industry; (2) industries that are characterised with higher past performance and that are more competitive experience less negativity in financial results even when in distress. This suggests that some industries naturally are less prone to distress, and some are more prone.
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Lastly, Kolay, Lemmon and Tashjian (2016) study either financial distress can be spread along the value chain. The authors’ results exhibit that working with economically distressed firms greatly negatively influences their suppliers, creating higher selling, general and administrative (SG&A) expenses and decreasing their margins due to the replacement of contactors. These findings reflect that, intuitively, sectors and industries that are less burdened by the bargaining power of their suppliers or are less linked to the asset specificity may experience less distress when the overall industry goes downward, because distress firm being independent would not significantly harm other external stakeholders other than its customers.
If we would look at the composition of the industries in the United States, we would suggest that technological sector, namely internet, software and computer services companies’ subset of technological sector complies to the most extent for the description of being (1) relatively independent (supply chain is not as long as in, e.g. oil and gas industry), (2) conglomerate industry with high competition (historically, the technological sector is a mixture of hardware and software companies with infrastructure and equipment providers that surround the firms). Technological firms are often associated with venture capital (as opposed to institutional investments), that shows similar traits as in Walker and Wu (2019), according to Megginson et al. (2016) that find that VC- backed IPOs experience less financial distress risk post-offering than do comparable non-VC- backed IPOs companies. Moreover, companies backed by more reputable venture capitalists exhibit higher levels of financial distress risk even when they show superior operating performance, due to their highly levered capital structure and investment in relatively illiquid assets (Megginson et al, 2016).
Based on this rational we would suppose that internet, software and computer services companies’ might be characterised by the decreased extent of mispricing, due to the decreased informational asymmetry circulating within this subsector. If the mentioned parameters are applicable for technological industry, we may perceive the information dissemination channels to differ from other sectors, and contain more complete information for market to correct, fully or partially, pricing inefficiency.
Considering that, I define the research questions of the paper as: do the mispricing explanation is weaker for distressed subset of technological firms represented by internet, software and computer services companies? Can we identify the positive relation between distress and risk premium for them? How can we differentiate empirical measure of distress of technological firms from other firms?
Answering these questions would shed a new light to the topic of distress risk anomaly into several directions. Mainly, we can differentiate them as academic implications and managerial implications.
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In terms of academic, we would observe either the equity market consistently misprices the distressed firms on the industrial scale, to say either we can observe the similar anomalously low returns for both accentuated by the paper subsector of technological firms and non- technological firms. To accomplish that we would compare the results of the analysis with the previous papers’ findings following the same approach to discriminate distressed firms and for their subsequent returns calculations. We would discuss these issues in-depth in the methodology section of the paper.
Another point for academic considerations includes empirically observing the phenomenon of distress risk premium for the mentioned firms. The major implication in this field would contain the answer for the question: can we observe the positive relation between distress characteristic and return, measured as realized returns? This is a fundamental question for the previous research, which would be put from the industrial perspective of specified sample.
The managerial implications would include, again, two central points. The first influences the financial and credit risk analysts within institutional and non-institutional financial organisations, which could apply the findings of the paper for the future development of empirical approaches for distress risk measurement and connected to it pricing options.
The second point could provide a soil for elaborating on new investment strategies for both institutional and non-institutional investors, particularly interested in distress investing.
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