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Prediction in Economics: a Case Study of Economists’ Views on the 2008 Financial Crisis

By Weiran Zeng

Prediction in economics is the focal point of debate for the future of economics, ever since economists were burdened with the failure to “predict” the 2008 Financial Crisis. This paper discusses positions held by philosophers and economic methodologists regarding what kinds of predictions there are and creates a taxonomy of prediction. Through evaluation of those positions, this paper presents different senses of prediction that can be expected of economics, and assess economists’ reflections according to those senses.

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Advisor: Kevin Hoover | JEL Codes: B41, N1, G17

Evaluating Asset Bubbles within Cryptocurrencies using the LPPL Model

By Rafal Rokosz

The advent of blockchain technology has created a new asset class named cryptocurrencies that have experienced tremendous price appreciation leading to speculation that the asset class is experiencing an asset bubble. This paper examines the novelty and functionality of cryptocurrencies and potential factors that may lead to conclude the existence of an asset bubble. To empirically evaluate whether the asset class is experiencing an asset bubble the LPPL model is used. The LPPL model was able to successfully identify two of the four crashes within the data set signifying that cryptocurrencies are within an asset bubble.

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Advisors: Ed Tiryakian and Grace Kim | JEL Codes: G12, Z00, C60

Effect of Sentiment on Bitcoin Price Formation

By Brian Perry-Carrera

With the recent growth in the investment of cryptocurrencies, such as bitcoin, it has become increasingly relevant to understand what drives price formation. Given that investment in bitcoin is greatly determined by speculation, this paper seeks to find the econometric relationship between public sentiment and the price of bitcoin. After scraping over 500,000 tweets related to bitcoin, sentiment analysis was performed for each tweet and then aggregated for each day between December 1st, 2017 and December 31st, 2017. This study found that both gold futures and market volatility are negatively related to the price of bitcoin, while sentiment demonstrates a positive relationship.

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Advisor: Grace Kim | JEL Codes: G12, G41, Z00

Multi-Horizon Forecast Optimality Based on Related Forecast Errors

By Christopher G. MacGibbon

This thesis develops a new Multi-Horizon Moment Conditions test for evaluating multi-horizon forecast optimality. The test is based on the variances, covariances and autocovariances of optimal forecast errors that should have a non-zero relationship for multi-horizon forecasts. A simulation study is conducted to determine the test’s size and power properties. Also, the effects of combining the Multi-Horizon Moment Conditions test and the well-known Mincer-Zarnowitz and zero autocorrelation tests into one forecast optimality test are examined. Lastly, an empirical study evaluating forecast optimality for four multi-horizon forecasts made by the Survey of Professional Forecasters is included.

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Advisors: Andrew Patton, Grace Kim and Kent Kimbrough | JEL Codes: G1, G17, G00

An Analysis of Passive and Active Bond Mutual Fund Performance

By Michael J. Kiffel

The literature on the performance differential between passively and actively managed equity mutual funds is thorough: passively managed funds generally outperform their active counterparts except in the rare presence of highly-skilled managers. However, there exists limited academic research regarding fixed income mutual funds. This study utilizes the Fama-French bond risk factors, TERM and DEF, in a dual-step multivariate linear regression analysis to determine this performance differential between passively and actively managed bond mutual funds. The funds are comprised of either corporate or government bonds, spanning three categorizations of average maturities. Overall, it is determined that passively managed bond funds offer higher net returns than those offered by actively managed funds. Additionally, the regressions demonstrated that DEF possesses a high degree of predictive power and statistical
significance.

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Advisor: Edward Tower | JEL Codes: C55, G10, G11

Modeling Variation in U.S. Bank Holding Companies’ Net Interest Margins

By Daniel Dorchuck

This study explores variation in US bank holding companies’ (BHCs) net inter-est margins (NIMs) and the effects of interest rate risk exposure on NIMs. Interest rate risk (IRR) is intrinsic in maturity transformation and financial intermediation as banks take on short-term liabilities in the form of deposits and create assets in the form of loans with longer maturities and different repricing profiles. Accordingly, interest rate risk is necessary for bank holding companies (BHCs) to be profitable in financial intermediation, and net interest margins are chosen as a variable of inter-est because they are an isolated measure of bank’ profitability from interest earning assets. Naturally, BHCs employ maturity pairing and derivative hedging to mitigate IRR and ultimately increase and smooth earnings. Synthesizing banks’ balance sheet and income statement data, macroeconomic variables, credit conditions, and interest rate environment variables, this study hopes to expand on existing work by provid-ing insight on the determinants of NIMs as well as interest rate derivatives’ efficacy in increasing and stabilizing net interest margins. The models presented establish links between long term rate exposure, risk-averse capital positions, and increased margins. Additionally, the models suggest that banks earn smaller spreads (NIMs) in higher interest rate environments but benefit from steeper yield curves.

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Advisor: Mary Beth Fisher, Kent Kimbrough |  JEL Codes: E44, G20, G21 | Tagged: Depository Institutions, Interest Rate Derivatives, Interest Rate Risk, Net Interest Margins, US Commercial Banking 

Evaluating Stock and Bond Portfolio Allocations using CAPER and Tobin’s Q

By Jayanth Ganesan

I test whether an investor can increase the returns on their portfolio over the long-term by timing the market using measures of market value, such as the Tobin’s q ratio and the Cyclically Adjusted Price Earnings (CAPE or Shiller-CAPE). To test this proposition, I examine contrarian investor strategies proposed by Smithers and Wright (2000) and investor strategies based on different equity-fixed income combination portfolios. I seek to determine whether these strategies produce higher risk-adjusted returns than buy-and-hold equity strategies such as those proposed by Siegel (2014) for long-term portfolios. I also examine whether Siegel’s theory that stocks are better investment vehicles than bonds for investment horizons greater than 20 years. In my study, buy-and-hold portfolios composed of the S&P 500 have additional annualized returns of 1.5% than portfolios which reallocate funds in alternative securities based on CAPE and q thresholds. I conclude that for long-term investment horizons, an investor is unlikely to increase portfolio returns by reallocating funds to an alternative asset class when stocks are overvalued. However, I do not find that stocks are better investment vehicles compared to bonds as portfolio with bonds have a lower portfolio risk in my sample. I believe that the effectiveness q ratios for market timing is likely to be independent of how the q ratio is calculated. As suggested by Asness (2015), I find that portfolios that utilize both value and trend investing principles with CAPE and q may outperform portfolios that utilize only value-based market timing strategies. I conclude that CAPE and q based timing strategies are difficult to implement without detailed knowledge of future stock valuations.

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Advisor: Edward Tower | JEL Codes: G11, G14 | Tagged: Information on Market Efficiency, Investment Decisions, Portfolio Choice

Where Did The Liquidity Go? The Cost of Financial Regulation to Foreign Exchange Markets

By James Stevenson

In financial markets, the terms “bull” and “bear” markets are used to describe the cyclicality of asset prices. Similar to asset price cycles, there are cycles in regulatory scrutiny. Beginning in the 1980’s, regulatory scrutiny diminished, cumulating in the repeal of the Glass-Steagall Act in 1999, allowing commercial banks and securities firms to be housed under the same roof for the first time since the 1930’s. In the aftermath of the global financial crisis in 2008 and 2009, the tides have reversed on financial regulation. With the Dodd-Frank reforms in the United States, and similar regulation being signed into law around the world, it is unknown how new regulation will affect financial markets. Legislators wrote the new rules in hopes that they would create safer financial institutions, but at what cost?

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Advisor: Connel Fullenkamp | JEL Codes: G1, G12, G18 | Tagged: Dodd-Frank, Financial Regulation, Foreign Exchange, Market Liquidity, Volcker Rule

Deterring Ineffcient Gambling in Risk-Taking Agents

By Ryan Westphal

This paper proposes a model describing the incentive issues faced by prin-
cipals and agents when the agent has limited liability and is capable of un-
dertaking unidentifiable, inefficient risky behavior. We propose a contract
structure by which the principal deters risk by deferring payment to the
agent until she reaches an absorbing steady-state in which promised equity
alone deters inefficient behavior. The paper discusses the effect of exogenous
parameters on the tradeoffs facing the principal as well as the implications
they have on the efficient choice of contract. We also outline extensions to
the model in which the principal has access to a costly monitoring technology
to identify inefficient risk taking. The theoretical results have implications
for real-world employment contracts and practices in financial firms such as
investment banks and private equity funds.

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Advisor: Curtis Taylor | JEL Codes: D82, D86, G32, L14 | Tagged: Contract Theory, Moral Hazard., Optimal Contracts, Risk Management

Google Search Volume Index: Predicting Returns, Volatility and Trading Volume of Tech Stocks

By Rui Xu

This paper investigates the efficacy of using Google Search Volume Index (SVI), a publicly available tool Google provides via Google Trends, to predict stock movements within the tech sector. Relative changes in weekly search volume index are recorded from April 2004 to March 2015 and correlated with weekly returns, realized volatility and trading volume of 10 actively traded tech stocks. Correlations are drawn for three different time periods, each representing a different stage of the financial business cycle, to find out how Search Volume Index correlates with stock market movements in economic recessions and booms. Google SVI is found to be significantly and positively correlated with trading volume and weekly closing price across 2004 to 2015, and positively correlated with realized volatility from 2009-2015. There exists a positive correlation between weekly stock returns and SVI for half of the stocks sampled across all 3 periods. The regression model was a better fit before and during the recession, suggesting the possibility of stronger “herding” behavior during those periods than in recent years.

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Advisor: Edward Tower | JEL Codes: G1, G14, G17 | Tagged: Analysis, Information, market efficiency, Stock Returns

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