Papers by Alexandra Malindi Colyer
Why consultation will not work and strategic reasoning for panel adjudication and the resulting s... more Why consultation will not work and strategic reasoning for panel adjudication and the resulting solution regarding the ongoing litigation of Canada-Measures Governing the Sale of Wine in Grocery Stores (DS520)
Carroll Round Source, 2018
The cost of developing a single pharmaceutical drug from discovery to final commercialization has... more The cost of developing a single pharmaceutical drug from discovery to final commercialization has skyrocketed over the past few decades without declines in risk or significant increases in revenues. The industry is thus faced with increased needs for capital funding; however, traditional methods of debt and equity financing remain volatile due to market characteristics. Thus, new financial vehicles have become commonplace in the industry, one of which is royalty stream licensing. As a relatively new financial vehicle, royalties and their associated impact on shareholder wealth has little been studied. This study finds that the announcement of royalty license transactions generates significant positive abnormal returns for the royalty receiver (licensor) but none for the royalty payer (licensee). Such findings indicate that these transactions are positive information signals for the licensor, while not for the licensee presenting further study possibilities regarding information asymmetry and optimism bias.
Drafts by Alexandra Malindi Colyer
. In what is known as the Long Telegram in 1946, the American charge d’affaires in Moscow, George... more . In what is known as the Long Telegram in 1946, the American charge d’affaires in Moscow, George Kennan, declared, “ in the long run there can be no permanent peaceful coexistence,” between the United States and Soviet Union, but was he right in that at some point these two great powers were bound to act hostilely towards each other and enter into conflict? As newly created superpowers sucked into World War II’s power vacuums, the United States and the Soviet Union were certain to confront each other, and likewise the emergence of contrasting new superpowers that creates a multipolar world and challenges hegemony will likely result in little cooperation and increased international conflict.
Theoretically, Napoleon’s Continental System represented an effective policy to force Great Brita... more Theoretically, Napoleon’s Continental System represented an effective policy to force Great Britain into acting according to French national interests; however, does a comprehensive embargo system actually work? During the Napoleonic Wars economic embargoes, particularly the Continental System, failed at attaining the French national interest of British defeat, and likewise, economic restrictions such as trade embargoes and sanctions continue to be ineffective at solely realizing their intended goals. Embargo systems are insufficient at achieving the national interest and often fail because such restrictions upset supply and demand thus harming the enforcing-nations’ own economies, most targeted states can mitigate economic impact through changes in internal policies, and it is impossible to guarantee universal adherence.
This analysis builds upon prior works into the clustering and network nature of financial data. T... more This analysis builds upon prior works into the clustering and network nature of financial data. This paper takes 40 different industrial sectors’ returns from food to insurance to identify and analyze correlation, clustering, and hierarchical structures’ presence. Through usage of unsupervised learning techniques - K-means clustering, hierarchical clustering, and principal components analysis – this study reveals the need for hierarchical structuring in portfolio models, groupings along high correlations, and some linkage along economic sector lines.
Bitcoin, the first cryptocurrency, was created in 2009 and ever since it has rattled the financia... more Bitcoin, the first cryptocurrency, was created in 2009 and ever since it has rattled the financial market. Soaring from $1,000 to just under $20,000 in 2017, Bitcoin was just the start of the cryptocurrency financial sphere piquing the interest from common man to Nobel laureates alike. With such a short history, and only gaining mainstream attention in 2017, the cryptocurrency assets have been little studied. Thus, this paper hopes to gain a better insight into the field’s risks, modeling, dependencies, and causalities by comparing a cryptocurrency to a financial equity portfolio and a portfolio of 10 cryptocurrencies.
The paper is broken into three key scopes of analysis to be explored and referenced throughout as Portfolio Statistics and Risk Profile, Time Scaling, and Cryptocurrency Dependency and Causality. The first area of exploration analyzes the statistics of two financial data portfolios, presenting the opportunity to gain a preliminary comparison of FX/equity portfolios and cryptocurrencies’ risk profiles. It aims via parametric and non-parametric calculations of Value-at-Risk and Conditional Value at Risk to gain the best overall insight of each respective portfolios’ risks. The second investigation analyzes the portfolios as random walks, signals, and time series to detect deviations from these analysis assumptions and evaluate financial returns at different time scales to more accurately model each’s intrinsic statistics and create better forecasts. The last consideration then breaks from these two portfolio comparisons, analyzing various cryptocurrency dependencies and causalities to better understand the returns of these “hot” financial products.
The Markowitz mean-variance portfolio theory posits that the optimal portfolio weights can be cho... more The Markowitz mean-variance portfolio theory posits that the optimal portfolio weights can be chosen based off an efficient tradeoff between profit modeled as the mean and risk measured as the variance-covariance matrix. These values must be historically estimated, occasionally leading to poor portfolio performance and asset misallocation. Moreover, as Brodie et. al. point out, the Markowitz optimization scheme, “is empirically unstable,” making, “the classic Markowitz portfolio optimization an ill-posed (or ill-conditioned) inverse problem.” Consequently, this paper formulates the global minimum variance portfolio as a regression problem with added penalties. This analysis thus utilizes penalty regularization on training data to more efficiently estimate global minimum variance portfolio weights and resolve many of the classic scheme’s issues.
“Credit aggregates contain valuable information about the likelihood of future financial crises.”... more “Credit aggregates contain valuable information about the likelihood of future financial crises.”1 The seminal 2012 paper by Schularick and Taylor evaluated impacts of various macroeconomic indicators financial stability, discovering that rather than follow Friedman and Schwartz’s “money view” the post- WWII era has experienced credit decoupling from money supplies, creating more leveraged economies and thus new potential financial risks. Analyzing this fact, they found that lagged credit growth serves as a highly significant predictor of financial crises with virtually no explanatory power added by other macroeconomic indicators.
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Furthering their work on financial crises prediction, this paper utilizes machine learning classification techniques to evaluate and verify Schularick and Taylor’s findings’ explanatory and predictive power. With regards to credit growth’s financial impacts, this analysis verifies it as the primary macroeconomic indicator for financial crisis; however, further findings reveal that money growth, particularly narrow (M0 or M1) money aggregates, may hold additional explanatory and predictive power. Moreover, it is revealed that the traditional econometric logistic regression method is a superior model to most machine learning techniques, with only neural networks (H=3) having consistent, accurate performance.
Policy recommendations for USTR Lighthizer with regards to third-party rights in DS524.
Mauritius consistently ranks in the top 20 nations globally for its superior levels of democracy ... more Mauritius consistently ranks in the top 20 nations globally for its superior levels of democracy and economic freedom. For a landmass of only 2,030 sq. km, Mauritius represents an interesting microcosm of ethno-religious divisions that has surprisingly experienced consistent economic and political stability. No country, however, is perfect, and Mauritius is not an exception, suffering from a major deficit in female representation in parliament even with strong democratic and economic foundations. This paper quantitatively analyzes female parliamentarian representation deficits in Mauritius and prescribes a plausible electoral framework change on the national level to move forward to political gender equality.
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Papers by Alexandra Malindi Colyer
Drafts by Alexandra Malindi Colyer
The paper is broken into three key scopes of analysis to be explored and referenced throughout as Portfolio Statistics and Risk Profile, Time Scaling, and Cryptocurrency Dependency and Causality. The first area of exploration analyzes the statistics of two financial data portfolios, presenting the opportunity to gain a preliminary comparison of FX/equity portfolios and cryptocurrencies’ risk profiles. It aims via parametric and non-parametric calculations of Value-at-Risk and Conditional Value at Risk to gain the best overall insight of each respective portfolios’ risks. The second investigation analyzes the portfolios as random walks, signals, and time series to detect deviations from these analysis assumptions and evaluate financial returns at different time scales to more accurately model each’s intrinsic statistics and create better forecasts. The last consideration then breaks from these two portfolio comparisons, analyzing various cryptocurrency dependencies and causalities to better understand the returns of these “hot” financial products.
“
Furthering their work on financial crises prediction, this paper utilizes machine learning classification techniques to evaluate and verify Schularick and Taylor’s findings’ explanatory and predictive power. With regards to credit growth’s financial impacts, this analysis verifies it as the primary macroeconomic indicator for financial crisis; however, further findings reveal that money growth, particularly narrow (M0 or M1) money aggregates, may hold additional explanatory and predictive power. Moreover, it is revealed that the traditional econometric logistic regression method is a superior model to most machine learning techniques, with only neural networks (H=3) having consistent, accurate performance.
The paper is broken into three key scopes of analysis to be explored and referenced throughout as Portfolio Statistics and Risk Profile, Time Scaling, and Cryptocurrency Dependency and Causality. The first area of exploration analyzes the statistics of two financial data portfolios, presenting the opportunity to gain a preliminary comparison of FX/equity portfolios and cryptocurrencies’ risk profiles. It aims via parametric and non-parametric calculations of Value-at-Risk and Conditional Value at Risk to gain the best overall insight of each respective portfolios’ risks. The second investigation analyzes the portfolios as random walks, signals, and time series to detect deviations from these analysis assumptions and evaluate financial returns at different time scales to more accurately model each’s intrinsic statistics and create better forecasts. The last consideration then breaks from these two portfolio comparisons, analyzing various cryptocurrency dependencies and causalities to better understand the returns of these “hot” financial products.
“
Furthering their work on financial crises prediction, this paper utilizes machine learning classification techniques to evaluate and verify Schularick and Taylor’s findings’ explanatory and predictive power. With regards to credit growth’s financial impacts, this analysis verifies it as the primary macroeconomic indicator for financial crisis; however, further findings reveal that money growth, particularly narrow (M0 or M1) money aggregates, may hold additional explanatory and predictive power. Moreover, it is revealed that the traditional econometric logistic regression method is a superior model to most machine learning techniques, with only neural networks (H=3) having consistent, accurate performance.