Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Entire DC Network
Essays On The Minimum Wage, Immigration, And Privatization, Doruk Cengiz
Essays On The Minimum Wage, Immigration, And Privatization, Doruk Cengiz
Doctoral Dissertations
This dissertation empirically examines effects of the minimum wage, immigration, and privatization; three of the most crucial policies that impact workers worldwide using recent advances in statistics and econometrics to provide causally interpretable results, and to reconcile controversies in the literature. In the first chapter, titled “Seeing Beyond the Trees: Using machine learning to estimate the impact of minimum wages on affected individuals”, I identify minimum wage workers prior to estimating its effects using machine learning tools, and provide highly representative demographically-based groups that capture as much as 73.4% of all likely minimum wage workers. I find that there is …
Can Machine Learning On Economic Data Better Forecast The Unemployment Rate?, Aaron S. Kreiner
Can Machine Learning On Economic Data Better Forecast The Unemployment Rate?, Aaron S. Kreiner
Honors Papers
This paper examines different machine learning methods to project the U.S. unemployment rate one year ahead. The forecasts include a naive forecast equal to the current unemployment plus the change of unemployment over the last year, along with forecasts from a Lasso regression and a neural network model. The last two models, which can be quickly run using an SQL database, select data from the Federal Reserve Economic Database (FRED) and are fitted (trained) in-sample from 1970 to 2000 to forecast quarterly unemployment rates over 2001 to 2018. The training window is updated in each forecast quarter to include new …
Multi-Step Forecast Of The Implied Volatility Surface Using Deep Learning, Nikita Medvedev
Multi-Step Forecast Of The Implied Volatility Surface Using Deep Learning, Nikita Medvedev
Electronic Theses and Dissertations
Implied volatility is an essential input to price an option. Machine learning architectures have shown strengths in learning option pricing formulas and estimating implied volatility cross-sectionally. However, implied volatility time series forecasting is typically done using the univariate time series and often for short intervals. When a univariate implied volatility series is forecasted, important implied volatility properties such as volatility skew and the term structure are lost. More importantly, short term forecasts can’t take advantage of the long term persistence in the volatility series. The thesis attempts to bridge the gap between machine learning-based implied volatility modeling and multivariate multi-step …
Less Is More: Beating The Market With Recurrent Reinforcement Learning, Louis Kurt Bernhard Steinmeister
Less Is More: Beating The Market With Recurrent Reinforcement Learning, Louis Kurt Bernhard Steinmeister
Masters Theses
"Multiple recurrent reinforcement learners were implemented to make trading decisions based on real and freely available macro-economic data. The learning algorithm and different reinforcement functions (the Differential Sharpe Ratio, Differential Downside Deviation Ratio and Returns) were revised and the performances were compared while transaction costs were taken into account. (This is important for practical implementations even though many publications ignore this consideration.) It was assumed that the traders make long-short decisions in the S&P500 with complementary 3-month treasury bill investments. Leveraged positions in the S&P500 were disallowed. Notably, the Differential Sharpe Ratio and the Differential Downside Deviation Ratio are risk …