Difference between revisions of "Document:MIT-MFin-essay"

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For decades, proponents of the random walk hypothesis have asserted that the market as unpredictable, while Black attributed price movements to random noise trades and informed trades which move market price towards the unobservable intrinsic "shadow" price. More recently, evidence was presented (including by Professor Lo) that goes against the random walk hypothesis. Meanwhile, Mandelbrot has criticized the assumptions of portfolio theory, and proposed using fractals for modelling instead. Considering the implications, one would do well to heed the debate being waged in the literature, but they are largely out of my current mathematical reach, although I do have a qualitative appreciation for the simpler ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. To properly reason about financial theory and methods, I must first establish a mathematical understanding for them, and that is what the core of the MFin programme is about. The study of non-independent time series data, general random processes, and the statistical methods involved, would also be very useful even in other areas of data science. Academically, my long term goal is to investigate the applications of nonparametric methods in modelling financial data. In particular, neural nets are interesting because the assumptions they make is largely only based on their own structure - perhaps they could offer novel solutions, or complement conventional models in situations when the usual assumptions break down.
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For decades, proponents of the random walk hypothesis have asserted that the market as unpredictable, while Black attributed price movements to random noise trades and informed trades which move market price towards the unobservable intrinsic "shadow" price. More recently, evidence was presented (including by Professor Lo) that goes against the random walk hypothesis. Meanwhile, Mandelbrot has criticized the assumptions of portfolio theory, and proposed using fractals for modelling instead. Considering the implications, one would do well to heed the debate being waged in the literature, but they are largely out of my current mathematical reach, although I do have a qualitative appreciation for the simpler ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. To properly reason about financial theory and methods, I must first establish a mathematical understanding for them, and that is what the core of the MFin programme is about. The study of non-independent time series data, general random processes, and the statistical methods involved, would also be very useful even in other areas of data science. My current academic goal is to investigate the applications of nonparametric methods in modelling financial data. In particular, neural nets are interesting because the assumptions they make is largely only based on their own structure - perhaps they could offer novel solutions, or complement conventional models in situations when the usual assumptions break down.
  
  

Revision as of 09:46, 2 January 2016


Finance permeates everything in a modern society (just like computers); having a sound understanding of its principles, limitations, and potential, is paramount, as individual and organizations stand to gain and lose a great deal, sometimes not even from their own mismanagement. To me, finance embodies rigorous treatment of risk and payoff under uncertainty, through applying advanced statistical methods on lots and lots of data. Having just finished an undergraduate degree in MEng Computer Science with a focus on machine learning, most of my experience is with (un)supervised learning, data mining, and working with various forms of data, such as search or advertising data, ontologies, images, DNA sequences etc. My background, as well as my personal interest in becoming an educated participant in the open market, makes this a very appealing field of study.


For decades, proponents of the random walk hypothesis have asserted that the market as unpredictable, while Black attributed price movements to random noise trades and informed trades which move market price towards the unobservable intrinsic "shadow" price. More recently, evidence was presented (including by Professor Lo) that goes against the random walk hypothesis. Meanwhile, Mandelbrot has criticized the assumptions of portfolio theory, and proposed using fractals for modelling instead. Considering the implications, one would do well to heed the debate being waged in the literature, but they are largely out of my current mathematical reach, although I do have a qualitative appreciation for the simpler ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. To properly reason about financial theory and methods, I must first establish a mathematical understanding for them, and that is what the core of the MFin programme is about. The study of non-independent time series data, general random processes, and the statistical methods involved, would also be very useful even in other areas of data science. My current academic goal is to investigate the applications of nonparametric methods in modelling financial data. In particular, neural nets are interesting because the assumptions they make is largely only based on their own structure - perhaps they could offer novel solutions, or complement conventional models in situations when the usual assumptions break down.


While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, while on the research side, I have been solving problems specific to the tech industry. I have mostly worked in teams of 2 to 6 on the development front, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research were self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.


I share MIT's belief in lifelong learning, and I firmly believe that having an understanding in multiple disciplines provides the necessary insight and expertise to comprehend and ultimately solve today's most difficult problems. I seek out challenging environments and new perspectives to change the way I think; that is when I make the most important mistakes, learn the most useful lessons, and develop fastest both personally and professionally, and so I actively work towards putting myself in such situations every opportunity I get. I think the combination of both MIT and the Master of Finance programme is the best fit at this stage in my career, and I look forward to learning and contributing with the very best this summer.