Essays on machining learning and big data in finance
Creators: name: Chernoff, Alan
Publisher: name: No Publisher Supplied
Year: 2024

description: Recent easy access to large amounts of data in finance has necessitated a growing number of techniques and methodologies that are capable of analyzing such large data sets. Statistical approaches and computational procedures are constantly being developed, updated, and applied in new ways to the flood of data that is finding its way into financial decision making. In particular, machine learning and big data aggregation methods have become extremely prominent in financial and econometric analysis. As the number of potential techniques that can be used to analyze these large data sets increases, the amount of situations where a particular model may exhibit greater predictive power or show superior performance versus another model grows as well. More data and more processes make it more difficult to find the parse through said data or find meaningful relations within a sea of noise, should they even exist. Even if we suppose there is a 'best' modeling technique, this idea is largely dependent on the situation in which one is conducting analysis. Differing methods of analysis will see superior performance only when they are applied in the ideal situation. Unsurprisingly, it is often not possible to assess which situation one is in until it has concluded, making hindsight one of the ultimate arbiters in the process of both analysis and data selection.In this dissertation, I examine the usage of big data methodologies and machine learning algorithms in finance through multiple avenues. In my first essay, I seek to apply modern machine learning and big data aggregation techniques to the estimation of integrated volatility from high-frequency financial stock data. In my second essay, I investigate whether machine learning and big data aggregation techniques are currently being adopted by the traditional banking sector, with this adoption being facilitated through partnerships with fintech firms. Chapter one presents a brief introduction to these topics.The usage of big data aggregation on high frequency stock data has led to large number of non-parametric volatility estimation techniques. Given this large amount of methods to estimate volatility, considering which estimation technique to apply becomes a non-trivial task. The second chapter of this dissertation synthesizes the work done in forecast combination with estimation methods for integrated volatility in high frequency financial data, in addition to using machine learning methods to estimate volatility. Given that integrated volatility is a latent variable, the utility and accuracy of estimates obtained through combination and machine learning is assessed by applying a trading strategy motivated by the volatility feedback effect, which highlights the negative relation between volatility and returns. I further motivate the use of machine learning with Monte-Carlo simulated data. By extending this to existing stock price data, I find that using by combining existing estimators through traditional forecast combination as well as newer machine learning techniques may both provide useful estimates of integrated volatility compared to existing methods. In the third chapter, I investigate how the use of modern machine learning and data aggregation techniques have affected the banking sector. Fintech firms tend to lend to under-served demographics using cutting edge algorithms and novel metrics. Given this, I hypothesize that banks that enter partnerships with these fintech firms would likely begin to use these tools and emulate their lending behavior. In particular, I investigate whether banks which enter partnerships with fintech firms see an increase in the probability of lending to the under-served demographics of consumers that previously had been overlooked by the traditional banking lending methods. I examine the lending activities of traditional banks by analyzing credit offers, credit originations, and credit limits extended to consumers with low credit ratings or no credit ratings on file, and see to what degree these activities change following partnerships with fintech firms.