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Quantitative Finance:

  1. A. M. Petersen
    How Much Did Pandemic Uncertainty Affect Real-Estate Speculation? Evidence from On-Market Valuation of For-Sale Versus Rental Properties (pdf)
    In press, Applied Economics Letters (2024). DOI:10.1080/13504851.2024.2302898 Abstract We exploit a panel of Zillow Inc. property valuations to estimate the excess real-estate price growth observed in three California cities that is attributable to speculation triggered by the COVID-19 pandemic. Our research design leverages the counterfactual comparison of properties listed for sale to properties listed for rent, with the latter property class being available for habitation – just not for purchase – and thus neutral to price speculation. We implement a pre/post-2020 difference-in-difference estimation, which utilizes unit-level matching of otherwise similar sale and rental properties within a 1/2-mile radius of each other to compare differences in: (a) 1-month valuation changes; and (b) spot valuation uncertainties. Results indicate post-2020 property valuations in Merced and San Jose featured an excess annual price estimate growth of 22 and 14.8 percentage points, respectively, whereas the Fresno market does not feature statistically significant excess growth.

  2. A. M. Petersen
    Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation (pdf)
    EPJ Data Science 13, 47 (2024). DOI:10.1140/epjds/s13688-024-00488-9 Abstract We exploit a timely city-level panel of individual house price estimates for both small and big real-estate markets in California USA to estimate the impact of COVID-19 on the housing market. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties – i.e., real estate entering the habitation market, just not for purchase and hence free of speculation – as an appropriate counterfactual to properties listed for sale. Combining unit-level matching and difference-in-difference approaches, we estimate that properties listed for sale after the pandemic featured an excess monthly price growth of roughly 1 percentage points, corresponding to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the annual growth observed across those regions in 2021. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counter-intuitive roles of uncertainty and interruptions in decision-making.

  3. A. M. Petersen.
    Applications of Statistical Physics to the Social and Economic Sciences (pdf)
    PhD Thesis, Boston University (2011). Thesis Advisor: H. Eugene Stanley

  4. B. Podobnik, D. Horvatic, A. M. Petersen, B. Urosevic, H. E. Stanley.
    Bankruptcy risk model and empirical tests (pdf)
    Proceedings of the National Academy of Sciences USA 107, 18325 (2010). DOI:10.1073/pnas.1011942107 Abstract We compare bankrupt companies with non-bankrupt companies using Zipf ranking techniques to analyze the debt-to-assets leverage ratio R. Using the distribution of R for bankrupt versus non-bankrupt companies, we estimate the bankruptcy risk of an existing company conditional on its current R value and find that the probability of bankruptcy P(B) ~ R.

    - The relationship between bankruptcy and relative debt for U.S. companies , PNAS Highlight

  5. A. M. Petersen, F, Wang, S. Havlin, H. E. Stanley.
    Market dynamics immediately before and after financial shocks: quantifying the Omori, productivity and Bath laws (pdf)
    Physical Review E 82, 036114 (2010). DOI:10.1103/PhysRevE.82.036114 Abstract Financial shocks (incoming information) can cause significant cascading (e.g. "market rallies"), so we use methods from earthquake physics to better understand the expected dynamics before and after shocks of characteristic main-shock magnitude M.

  6. A. M. Petersen, F. Wang, S. Havlin, H. E. Stanley.
    Quantitative law describing market dynamics before and after interest-rate change (pdf)
    Physical Review E 81, 066121 (2010). DOI:10.1103/PhysRevE.81.066121 Abstract We analyze the financial "earthquake" that occurs evey time the U.S. Federal Reserve makes an announcement to change the federal target interest rate, and estimate the magnitude of market `anticipation' and `surprise' using the fundamental relationship between the federal effective `overnight' interest rate and the 6-month Treasury Bill.

    - Bernanke Announcement Leaves Quake Like Aftershocks , Inside Science News Service

  7. A. M. Petersen, B. Podobnik, D. Horvatic, H. E. Stanley.
    Scale-invariant properties of public-debt growth (pdf)
    Europhysics Letters 90, 38006 (2010). DOI:10.1209/0295-5075/90/38006 Abstract Applying methods from macro-economic growth theory, we find 'convergence' in country debt-to-GDP leverage ratios over the last 30+ years.

  8. B. Podobnik, D. Horvatic, A. M. Petersen, M. Njavro, H. E. Stanley.
    Common scaling behavior in finance and macroeconomics (pdf)
    Eur. Phys. J. B 76, 487 (2010). DOI:10.1140/epjb/e2009-00380-3 Abstract We analyze the growth rates of worldwide stock indices and relate the market capitalization (MC) of the index to the gross domestic product (GDP) of the index country.

  9. B. Podobnik, D. Horvatic, A. M. Petersen, H. E. Stanley.
    Quantitative relations between risk, return, and firm size (pdf)
    Europhysics Letters 85, 50003 (2009). DOI:10.1209/0295-5075/85/50003 Abstract For individual companies comprising the Nasdaq (2002-2008) and S&P500 (2003-2008) indices, we analyze the logarithmic growth rate (return) R of the stock price. We also relate the annual market capitalization (MC) and the return-to-risk < R >/sigma(R) for each company and find interesting differences between the Nasdaq and S&P500.

  10. B. Podobnik, D. Horvatic, A. M. Petersen, H. E. Stanley.
    Cross-Correlations between Volume Change and Price Change (pdf)
    Proceedings of the National Academy of Sciences USA 106, 22079 (2009). DOI:10.1073/pnas.0911983106 Abstract In analogy to the analysis of price volatility in financial markets, we analyze the absolute logarithmic returns (volatility) of total volume at the 1-day time resolution for individual stocks as well as stock indices, and use Detrended Cross-Correlation Analysis (DCCA) to quantify the relation between price volatility and volume volatility.

Presentations: