Efstathios Avdis

About

Welcome to my page. My full name is Efstathios, but I usually go by my nickname, Stathi. I am an associate professor of finance at the Alberta School of Business, which I joined after graduating from the Wharton School in 2012. My research focuses on information asymmetry, market inefficiencies, and financial time-series econometrics.

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Research

Publications

Maximum Likelihood Estimation of the Equity Premium, with J. Wachter, 2017, Journal of Financial Economics

• Online appendix

Information Tradeoffs in Dynamic Financial Markets, 2016, Journal of Financial Economics 

• Online appendix

Power Algorithms for Inverting Laplace Transforms, with W. Whitt, 2007, INFORMS Journal on Computing

Working papers and work in progress

Risk seekers: trade, noise, and the rationalizing effect of market impact on convex preferences

Rational-expectations Whiplash, with Masahiro Watanabe (Alberta)

Clear and liquid: The interaction of firm disclosure and trader competition, with Sanjay Banerjee (Alberta)

Trading in Large Markets

A Rumour-based Approach to Price Noise

Associative Expectations in Financial Markets

Video

Risk seekers: a light version of my work on how risk seeking can help us understand market inefficiency (about 19 min; see this paper for more.)

Research Interests

• Information in financial markets
• Financial time-series econometrics
• Artificial intelligence in financial markets
• Fintech
• Dynamic incentives

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Teaching

• Investments (undergraduate)
• Financial economics, continuous-time finance (PhD)

FIN 488 - Selected Topics in Finance

Normally restricted to third- and fourth-year Business students. Prerequisites: FIN 301 or consent of Department. Additional prerequisites may be required.

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Browse more courses taught by Efstathios Avdis

Standard models of rational learning in financial markets prescribe that economic agents learn according to Bayes's rule. This type of learning requires that agents learn consciously by fully incorporating all available information in observed price changes. It also implies that there is no room for preferences to directly affect how humans learn. Drawing on ideas from psychology and artificial intelligence, I propose a model of non-conscious learning, based on perceived changes in utility.

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In an economy where traders absorb information partially, we study the effect of disclosure accuracy and disclosure clarity on financial markets. Accuracy measures how precisely a disclosure identifies the firm's fundamentals, whereas clarity measures how well traders understand the disclosure. Increasing clarity promotes competition among informed traders more so than it increases the information asymmetry between informed traders and market makers. Increasing accuracy has the opposite effect. Trading volume increases monotonically in clarity, while liquidity has a U-shaped relationship with clarity if competition among traders is intense. Volume and liquidity both decrease in accuracy due to adverse selection among different market participants. Moreover, traders' attention to disclosure is hump-shaped in clarity, but increasing in accuracy. Trading profits mimic the patterns of attention, but only under intense competition. Overall, our results suggest that the current trend of increasing complexity in firm disclosures is detrimental to liquidity and volume.

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The value of information for a fully-dynamic economy in continuous time.

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A detailed derivation of the estimator for univariate, multivariate, and heteroskedastic settings.

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We present a financial market with investors who have nested private information. Small perturbations of price informativeness, coming from distortions of dividend expectations, can trigger an oscillating shock throughout the economy that destabilizes the feedback loop between prices and expectations. Moreover, decreasing the volatility of liquidity trading makes the equilibrium less stable. We investigate what the distribution of informed investors implies for equilibrium stability and for the risk premium of the asset. We find that different investor distributions have different implications, depending on whether adverse-selection or risk-sharing effects dominate in the economy.

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Long-held intuition dictates that information-based trade is impossible without exogenous noise. Risk seekers can resolve this conundrum. Even though such agents have negative risk aversion, they act as utility maximizers because they fully internalize their impact on prices. If their love of risk increases, information decreases in the aggregate, making prices noisier and returns more volatile. If public information becomes more precise, risk sharing decreases but welfare increases, contradicting the Hirshleifer effect. If private information becomes cheaper, liquidity always increases, rendering economies with risk seekers empirically distinct from economies with noise traders or random endowments.

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Efstathios Avdis, Jessica Wachter

Journal of Financial Economics. 2017 January; 125 (3):589-609

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Efstathios Avdis

Journal of Financial Economics. 2016 January; 122

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Efstathios Avdis, Ward Whitt

INFORMS Journal on Computing. 2007 January; 19 (3):341–355