Stock market | EPFL Graph Search

A stock market, equity market, or share market is the aggregation of buyers and sellers of stocks (also called shares), which represent ownership claims on businesses; these may include securities listed on a public stock exchange, as well as stock that is only traded privately, such as shares of private companies which are sold to investors through equity crowdfunding platforms. Investment is usually made with an investment strategy in mind. Size of the market The total market capitalization of all publicly traded securities worldwide rose from US

2.5 trillion in 1980 to US

93.7 trillion at the end of 2020. , there are 60 stock exchanges in the world. Of these, there are 16 exchanges with a market capitalization of $1 trillion or more, and they account for 87% of global market capitalization. Apart from the Australian Securities Exchange, these 16 exchanges are all in North America, Europe, or Asia. By country, the largest stock markets as of January 2022 are in the Un

The triangular value of patents

Albina Khairullina

This thesis addresses the question of a patent value from three different angles. It comprises three papers on the patent valuation methods. The patent valuation issues are well-known to the world of research and practice. However, the debates over what the patent contributes to the business and the economy overall remain open. The main contribution of my dissertation is that I present novel ways of addressing the value of patents from both micro- and macroeconomic perspectives. I bridge the theoretical modeling with real-world estimations, which sheds light on the legitimacy of certain hypothetical assumptions in patent valuation. In the first paper, I develop a factor model to value patents in a corporate patent portfolio. According to the results, the patent value is determined by five factors, such as forward and backward citations, family size, the number of oppositions received, and the number of claims. I contribute to the literature by introducing an interaction of a quantitative measure of a patent value with its qualitative assessment derived from a survey of patent managers. The results show that model estimates are weakly consistent with an expert opinion on the value of patents. In the second paper, we collaborate with a large multinational company and analyze internal and highly confidential data on product sales, revenues, and other corporate performance measures by tracing each patent down to its product market. To establish causality, we design a randomized controlled trial together with the management of the company, in which some randomly chosen patents are dropped from the portfolio, while others are kept for the duration of the study. To our knowledge, this is the first study that conducts a randomized controlled trial to measure the real market effect of patent protection. In the third paper, we study whether the regulator expands production possibilities of the economy by assigning a standard-essential status to patents. As we show, the innovatorsâ risk of losing the standard-setting game ex-ante attenuates the anticipation of a larger market share. Secondly, since the discovery of new technologies is typically slower than the discounting rate for the monopoly profits, standards with a positive contribution to productivity tend to be growth-reducing. Our results have significant policy implications. Regulators impose Fair, Reasonable, And Non-Discriminatory (FRAND) pricing on standard-essential technologies to compensate for the larger market-share the innovators get. We demonstrate how FRAND regulation of mark-ups has an unambiguously negative effect on growth and long-term growth-destroying consequences.

EPFL2020

Essays in Empirical Asset Pricing

Alexis Arilès Marchal

This thesis consists of three applications of machine learning techniques to empirical asset pricing.In the first part, which is co-authored work with Oksana Bashchenko, we develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short-term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the two. Then it is applied to out-of-sample simulated data and delivers results that considerably differ from the benchmark: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach for jump screening allows to extract a more precise signal about future volatility.In the second part, which is co-authored work with Oksana Bashchenko, we develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.In the third part, I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This chapter can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.

EPFL2022

Essays in Financial Economics

Jan Peter Kulak

In the first chapter of this thesis, I empirically show that the time delay firms face in raising outside capital affects cash holdings. I exploit the 2005 US Securities Offering Reform (the Reform) as a quasi-natural experiment. For a subset of large public US firms, the Reform relaxed the requirement to undergo the standard review process with the Securities and Exchange Commission (SEC) before raising new public capital, leading to a reduction in regulatory delay of approximately 1-1.5 months on average and exceeding half a year in extreme cases. Difference-in-differences estimates based on the event suggest a causal channel from time delay to cash holdings. Over the course of the year following the Reform, affected firms reduced their cash holdings by approximately 2-3% as a fraction of book assets relative to unaffected control-group firms. As predicted by theory, the effect is strongest among firms in cash flow volatile industries. The second chapter investigates whether seasoned equity offerings (SEOs) are accompanied by price pressure effects that models of investor inattention and slow-moving capital predict. In line with theoretical predictions, I find that the share prices of issuing firms exhibit a drop in the pre-offer week, and a positive trend in the first four to eight weeks thereafter. The pattern is systematically related to the size of an offering and to measures of the amount of attention the issuing firm receives by market participants. The resulting costs to the issuing firm are on the order of 2% as a fraction of offer proceeds for the average firm, and nearly twice as large for the tercile of firms issuing the largest quantities of illiquid stock. The price impact is so sizable and long-lasting that simple monthly trading strategies that purchase SEO stocks in the first calendar month after an offering yield monthly five-factor alphas exceeding 1.5 percentage points. The abnormal returns are most pronounced in periods of low aggregate stock market liquidity, as measured by the Pástor and Stambaugh (2003) liquidity index. The results are consistent with the interpretation that the return pattern represents a compensation to temporary liquidity providers, who require the largest compensation during periods in which funding liquidity is scarce. The third chapter is joint work with Cornelius Schmidt from the University of Lausanne. We propose a novel setting to test how the presence of attention constraints affects stock prices. Our setting exploits the particular industry exposure of vertically integrated firms. Because of inter-segment sales of goods, the relative size of individual segments within such firms, in an accounting sense, is a misleading indicator for the firm's overall exposure to industry shocks. We hypothesize that attention constrained investors will tend to neglect this detail, leading to systematic pricing mistakes after confounding, industry-specific news shocks. In line with this hypothesis, we find evidence of predictable price corrections in post-news periods – in particular in the time around firms' earnings announcements. A fully implementable long-short equity strategy based on the phenomenon leads to significant risk-adjusted excess returns. Security analysts' earnings forecasts exhibit a predictable error in the same direction as stock prices.

EPFL2012