I am a Ph.D. student in Economics at Goethe University Frankfurt. Since 2016, I have been a research assistant at the chair of Banking and Finance. I did my undergraduate studies in Petroleum Engineering at Sharif University of Technology (SUT), Iran.
My research is focused on the topics related to the information contents of earnings conference calls, corporate disclosures, and corporate governance.
Teaching has always been a top interest in my life, and I have taught as an assistant of Master course Mergers and Acquisitions at Goethe University Frankfurt, Master course on Game Theory and bachelor courses of Mathematics1 and Microecnonimics 1 & 2 at EBS University Wiesbaden. I have also been a teacher assistant of the Undergraduate course of computer programming python at SUT.
I’m interested in the world of machine learning, and NLP coding is my number one hobby. In addition to reading books, other hobbies are bouldering, hiking, and swimming.
Abstract:Sustainable Entrepreneurship (SE) targets profitability and sustainability goals. A major research gap concerns SE's economic attractiveness for entrepreneurs and investors. The question is ambiguous because sustainability orientation creates costly constraints, while startups cannot fully appropriate the rents from their positive externalities. We propose a machine-learning approach to measure Environment, Society, and Governance (ESG) properties from text data, and relate these properties to startup valuation and performance. First, startups with salient ESG goals achieve higher valuations, suggesting that sustainability orientation is financially attractive for the entrepreneur. Second, long-term investor returns are lower than in conventional startups, reflecting investors' willingness-to-pay for sustainability-related, non-financial returns. Third, consistent with the notion that sustainability orientation creates costly constraints, we find that valuation and performance effects are weaker in startups with high degrees of technological, network, and governance formalization.
Abstract:Initial Coin Offerings (ICOs) provide an opportunity to study the role of information frictions for financial markets in a setting with relatively little regulation. This paper analyzes the role of freelancing human experts (ICO analysts) as information intermediaries. ICO analyst assessments vary in quality and exhibit biases due to the reciprocal interactions of ICO analysts with ICO team members. Investors assign more weight to ratings by high-quality analysts, and they discount reciprocal ratings. Overall, the findings suggest that on this market, too, market discipline works to some extent and intermediaries can play an important role in mitigating information asymmetries.
Abstract: In this study, using a comprehensive dataset on business media coverage and textual analysis of the discussions in firms’ quarterly earnings conference calls, we show that when management fails to satisfy the demand for information, ceteris paribus, their firms receive less media coverage. Poor information environment hurts the information-creation capacity of the media, while such an environment does not show a similar association with the media’s information-dissemination role. Furthermore, this association is more prominent for professional business media, compared to their non-professional counterparts such as blogs and alternative articles. Our results add nuance to the literature on media coverage bias by showing that supply-side factors, i.e. the factors affecting the suppliers of the coverage, mainly drive the coverage of firms, not the demand.
Abstract: It is relatively easy for us humans to detect when a question we asked has not been answered – we teach this skill to a computer. Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,027 trigrams that signal non-answers. We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings conference calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. Our metric is designed to be of general applicability for Q&A situations, and hence, is capable of identifying non-answers outside the contextual domain of financial earnings conference calls.
Abstract: Context specific language or jargon helps, by definition, to efficiently and precisely transfer information. However, due to its complex nature, jargon might also be a tool to obfuscate information. This paper studies whether jargon is used in verbal firm disclosures to obfuscate or to efficiently transfer information. We observe that, within the Q&A of earnings conference calls, managers use less jargon in responses to tougher questions, and in calls after a quarter of bad success. Moreover, markets interpret the lack of precise information as a bad signal: we find lower cumulative abnormal returns and a higher implied volatility following earnings calls where managers use less jargon. These results support the argument that an excessive use of non-factual language is perceived as blathering that retards the reduction of information asymmetries.
Here you can find more details about my teachings.
You can find the soloutions' notebook here.
You can find the soloutions' notebook here.
Please feel free to contact me to discuss possible bachelor/master thesis topics. You can find the guidelines for writing a thesis in our chair in this document.