SSRN Author: Kesheng WuKesheng Wu SSRN Content
http://www.ssrn.com/author=1724981
http://www.ssrn.com/rss/en-usTue, 17 May 2016 01:01:43 GMTeditor@ssrn.com (Editor)Tue, 17 May 2016 01:01:43 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Solving the Optimal Trading Trajectory Problem Using a Quantum AnnealerWe solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.
http://www.ssrn.com/abstract=2649376
http://www.ssrn.com/1496671.htmlMon, 16 May 2016 01:10:38 GMTNew: Predicting Baseline for Analysis of Electricity PricingTo understand the impact of new pricing structure on residential electricity demands, we need a baseline model that captures every factor other than the new price. The standard baseline is a randomized control group, however, a good control group is hard to design. This motivates us to devlop data-driven approaches. We explored many techniques and designed a strategy, named LTAP, that could predict the hourly usage years ahead. The key challenge in this process is that the daily cycle of electricity demand peaks a few hours after the temperature reaching its peak. Existing methods rely on the lagged variables of recent past usages to enforce this daily cycle. These methods have trouble making predictions years ahead. LTAP avoids this trouble by assuming the daily usage profile is determined by temperature and other factors. In a comparison against a well-designed control group, LTAP is found to produce accurate predictions.
http://www.ssrn.com/abstract=2773991
http://www.ssrn.com/1493262.htmlTue, 03 May 2016 11:25:23 GMTREVISION: Intraday Patterns in Natural Gas Futures: Extracting Signals from High-Frequency Trading DataHigh Frequency Trading is pervasive across all electronic financial markets. As algorithms replace an increasing number of tasks previously performed by humans, cascading effects similar to the Flash Crash of May 6th 2010 become more likely. In this study, we bring together a number of different data analysis tools to improve our understanding of natural gas futures trading activities. We focus on Fourier analysis and cointegration between weather forecasts and natural gas prices. From the Fourier analysis of Natural Gas futures market, we see strong evidences of High Frequency Trading in the market. The Fourier components corresponding to high frequencies (1) are becoming more prominent in the recent years and (2) are much stronger than could be expected from the overall trading records. Additionally, significant amount of trading activities occur in the first second of every minute, which is a telltale sign of the Time-Weighted Average Price (TWAP) execution algorithms. To ...
http://www.ssrn.com/abstract=2657224
http://www.ssrn.com/1476517.htmlMon, 07 Mar 2016 10:04:58 GMTREVISION: Solving the Optimal Trading Trajectory Problem Using a Quantum AnnealerWe solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.
http://www.ssrn.com/abstract=2649376
http://www.ssrn.com/1433110.htmlFri, 02 Oct 2015 09:23:39 GMTREVISION: Intraday Patterns in Natural Gas Futures Trading: A Financial Big Data ApplicationHigh Frequency Trading is pervasive across all electronic financial markets. As algorithms replace an increasing number of tasks previously performed by humans, cascading effects similar to the Flash Crash of May 6th 2010 become more likely. In this study, we bring together a number of different data analysis tools to improve our understanding of natural gas futures trading activities. We focus on Fourier analysis and cointegration between weather forecasts and natural gas prices. From the Fourier analysis of Natural Gas futures market, we see strong evidences of High Frequency Trading in the market. The Fourier components corresponding to high frequencies (1) are becoming more prominent in the recent years and (2) are much stronger than could be expected from the overall trading records. Additionally, significant amount of trading activities occur in the first second of every minute, which is a telltale sign of the Time-Weighted Average Price (TWAP) execution algorithms. To ...
http://www.ssrn.com/abstract=2657224
http://www.ssrn.com/1431867.htmlMon, 28 Sep 2015 12:27:45 GMTREVISION: Understanding Natural Gas Futures Trading Through Data AnalysisHigh Frequency Trading is pervasive across all electronic financial markets. As algorithms replace an increasing number of tasks previously performed by humans, cascading effects similar to the Flash Crash of May 6th 2010 become more likely. In this work, we bring together a number of different data analysis tools to improve our understanding of natural gas futures trading activities. We focus on Fourier analysis and correlation between weather forecasts and natural gas prices. These tools and techniques are not widely used in the current research literature, and therefore, are more likely to yield new insights. From the Fourier analysis of Natural Gas futures market, we see strong evidences of High Frequency Trading in the market. The Fourier components corresponding to high frequencies (1) are becoming more prominent in the recent years and (2) are much stronger than could be expected from the structure of the market. Additionally, significant amount of trading activities occur in ...
http://www.ssrn.com/abstract=2657224
http://www.ssrn.com/1426960.htmlWed, 09 Sep 2015 19:34:37 GMTREVISION: Solving the Optimal Trading Trajectory Problem Using a Quantum AnnealerWe solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.
http://www.ssrn.com/abstract=2649376
http://www.ssrn.com/1424066.htmlSat, 29 Aug 2015 13:41:36 GMTREVISION: Solving the Optimal Trading Trajectory Problem Using a Quantum AnnealerWe solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.
http://www.ssrn.com/abstract=2649376
http://www.ssrn.com/1422509.htmlMon, 24 Aug 2015 08:32:17 GMTREVISION: Statistical Overfitting and Backtest PerformanceIn the field of mathematical finance, a “backtest” is the usage of historical market data to assess the performance of a proposed trading strategy. It is a relatively simple matter for a present-day computer system to explore thousands, millions or even billions of variations of a proposed strategy, and pick the best performing variant as the “optimal” strategy “in sample” (i.e., on the input dataset). Unfortunately, such an “optimal” strategy often performs very poorly “out of sample” (i.e., on another dataset), because the parameters of the invest strategy have been overfit to the in-sample data, a situation known as “backtest overfitting”.
While the mathematics of backtest overfitting has been examined in several recent theoretical studies, here we pursue a more tangible analysis of this problem, in the form of an online simulator tool. Given a input random walk time series, the tool develops an “optimal” variant of a simple strategy by exhaustively exploring all integer ...
http://www.ssrn.com/abstract=2507040
http://www.ssrn.com/1399066.htmlSat, 23 May 2015 13:37:10 GMT