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Research Notebook –

Comparing “Explanations” for the iVol Puzzle. May 5, 2015. A stock’s idiosyncratic-return volatility is the root-mean-squared error,. From the daily regression. Ang, Hodrick, Xing, and Zhang (2006). Shows that stocks with lots of idiosyncratic-return volatility in the previous month have extremely low returns in the current month. To quantify just how low these returns are, I run a cross-sectional regression each month,. Has an average excess returns of. You can find all the code here. The standard appro...

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Research Notebook – | alexchinco.com Reviews
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Comparing “Explanations” for the iVol Puzzle. May 5, 2015. A stock’s idiosyncratic-return volatility is the root-mean-squared error,. From the daily regression. Ang, Hodrick, Xing, and Zhang (2006). Shows that stocks with lots of idiosyncratic-return volatility in the previous month have extremely low returns in the current month. To quantify just how low these returns are, I run a cross-sectional regression each month,. Has an average excess returns of. You can find all the code here. The standard appro...
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Research Notebook – | alexchinco.com Reviews

https://alexchinco.com

Comparing “Explanations” for the iVol Puzzle. May 5, 2015. A stock’s idiosyncratic-return volatility is the root-mean-squared error,. From the daily regression. Ang, Hodrick, Xing, and Zhang (2006). Shows that stocks with lots of idiosyncratic-return volatility in the previous month have extremely low returns in the current month. To quantify just how low these returns are, I run a cross-sectional regression each month,. Has an average excess returns of. You can find all the code here. The standard appro...

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Comparing “Explanations” for the iVol Puzzle – Research Notebook

http://www.alexchinco.com/comparing-explanations-for-the-ivol-puzzle

Comparing “Explanations” for the iVol Puzzle. May 5, 2015. A stock’s idiosyncratic-return volatility is the root-mean-squared error,. From the daily regression. Ang, Hodrick, Xing, and Zhang (2006). Shows that stocks with lots of idiosyncratic-return volatility in the previous month have extremely low returns in the current month. To quantify just how low these returns are, I run a cross-sectional regression each month,. Has an average excess returns of. You can find all the code here. The standard appro...

2

Alex – Research Notebook

http://www.alexchinco.com/author/admin

Inferring Trader Horizons from Trading Volume. July 13, 2016. This post shows that, if traders face convex. Ie, it costs them more per share to buy. Shares of stock than to buy. In the past, when researchers have studied traders’ investment horizons, they have used data on portfolio positions rather than trading volume. Some have measured trading activity at a couple of investment horizons for a small number of stocks. e.g., Brogaard et al. (2014). Use NASDAQ data on a randomly selected sample of. Period...

3

Research Notebook –

http://www.alexchinco.com/notebook

Inferring Trader Horizons from Trading Volume. July 13, 2016. This post shows that, if traders face convex. Ie, it costs them more per share to buy. Shares of stock than to buy. In the past, when researchers have studied traders’ investment horizons, they have used data on portfolio positions rather than trading volume. Some have measured trading activity at a couple of investment horizons for a small number of stocks. e.g., Brogaard et al. (2014). Use NASDAQ data on a randomly selected sample of. Period...

4

Uncategorized – Research Notebook

http://www.alexchinco.com/category/uncategorized

Inferring Trader Horizons from Trading Volume. July 13, 2016. This post shows that, if traders face convex. Ie, it costs them more per share to buy. Shares of stock than to buy. In the past, when researchers have studied traders’ investment horizons, they have used data on portfolio positions rather than trading volume. Some have measured trading activity at a couple of investment horizons for a small number of stocks. e.g., Brogaard et al. (2014). Use NASDAQ data on a randomly selected sample of. Period...

5

About – Research Notebook

http://www.alexchinco.com/about

I am an assistant professor of Finance at the University of Illinois at Urbana-Champaign. My research interests center on the areas of behavioral finance, macroeconomics, and real estate finance. Scale is a fundamental feature of modern asset markets. e.g., in a single midtown Manhattan office suite, traders sitting next to one another might be trading anything from currencies to commodity futures to stocks at horizons ranging from. Order per millisecond to. I am an assistant professor of Finance at the ...

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QuickLaTeX

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Applied mathematics and beyond. Cubature formulas for the unit disk. Stable Newton-Cotes Formulas (Open Type). Noise Robust Gradient Operators. Sites Powered by QuickLaTeX. August 3, 2015. 383 Image size reduction using OptiPNG, minimized memory usage. July 4, 2015. 382 New server, compatibility with WP-LaTeX. December 24, 2014. 381 Switched to non-blocking connections to server. November 15, 2012. 380 Added rendering to SVG format. September 22, 2011. 379 Fixed semi-critical bug in parsing. July 25, 2011.

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Fitness Landscape Analysis for Computational Finance

http://www.turingfinance.com/fitness-landscape-analysis-for-computational-finance

Turing Finance August 21, 2016. Fitness Landscape Analysis for Computational Finance. On June 29, 2015. Some of the most interesting new research coming out of the Computational Intelligence Research Group (CIRG). Which is applicable to numerous computational finance and machine learning optimization problems, is the development of fitness landscape analysis techniques. Fitness landscape analysis aims to characterize high dimensional fitness landscapes. By Dr Katherine Malan. For a specific algorithms wi...

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What Drives Real GDP Growth?

http://www.turingfinance.com/what-drives-real-gdp-growth-part-one

Turing Finance August 20, 2016. What Drives Real GDP Growth? What socioeconomic factors drive long term real GDP growth? What better ways are there of grouping countries together than geographically? Which countries of the world are going to experience real GDP growth from now till 2019? Update - I have completed the second part of this three part series entitled:. Monte Carlo K-Means Clustering of Countries with Python. Constructing the Data Sets. Three data sets containing absolute values were construc...

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Stuart Gordon Reid

http://www.turingfinance.com/author/StuartReid

Turing Finance August 26, 2016. Posts ByStuart Gordon Reid. Lossless Compression Algorithms and Market Efficiency? April 18, 2016 StuartReid. Hacking The Random Walk Hypothesis. We applied the NIST suite of cryptographic tests for randomness to binarized daily market returns. Overall the NIST suite failed on the data. This result was taken to mean that markets are not quite the . Read More. Stock Market Prices Do Not Follow Random Walks. February 8, 2016 StuartReid. A Non-Random Walk down Wall Street.

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Random walks down Wall Street, Stochastic Processes in Python

http://www.turingfinance.com/random-walks-down-wall-street-stochastic-processes-in-python

Turing Finance August 20, 2016. Random walks down Wall Street, Stochastic Processes in Python. On April 7, 2015. And asked them to build an interactive educational web application for learning about stochastic processes. Update - I got quite a few questions regarding the usage of this code, as per some of the suggestions from readers I have created an IPython Notebook. Hope that helps 🙂. A random event is any event which has a chance of happening. Probability is the measure of that chance. Rando...Are f...

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Computational Finance at IEEE WCCI 2014

http://www.turingfinance.com/computational-finance-updates-ieee-world-congress-computational-intelligence

Turing Finance August 20, 2016. Computational Finance at IEEE WCCI 2014. I recently had the awesome opportunity to present my honours research at this years IEEE World Congress for Computational Intelligence conference (IEEE-WCCI) in Beijing. My trip was sponsored by the University of Pretoria's Computational Intelligence Research Group. CIRG) so I would like to send out a special thanks to supervisor, Dr. Katherine Malan, and my Professor, Andries Engelbrecht, for making the trip possible. Some of the p...

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Simulated Annealing for Portfolio Optimization

http://www.turingfinance.com/simulated-annealing-for-portfolio-optimization

Turing Finance August 31, 2016. Simulated Annealing for Portfolio Optimization. This article applies the Simulated Annealing (SA) algorithm. Basic Simulated Annealing Algorithm. This real-world concept may, at first, seem quite difficult to translate into an optimization algorithm, however, with some 'algorithmic license' the resulting algorithm is actually quite simple. The simulated annealing algorithm for portfolio optimization essentially only consists of eleven steps, only two of which are reall...

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Computational Decision Making Methods

http://www.turingfinance.com/computational-decision-making-for-finance

Turing Finance August 21, 2016. Computational Decision Making Methods. Artificial intelligence is broadly defined as the ability of an agent or a model to make either optimal or satisficing decisions. How should I allocate my portfolio? Should I buy this stock or not? What should I pay for this security? This article will discuss the applications and challenges of computational decision making and present a taxonomy of computational decision making methods, their strengths, and their weaknesses. I ho...

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A Recipe for the 2008 Financial Crisis

http://www.turingfinance.com/recipe-for-the-financial-crisis

Turing Finance August 20, 2016. A Recipe for the 2008 Financial Crisis. On May 5, 2015. In 2008 when the market crashed I was 16-years old and visiting London for the very first time. At that age I was already obsessed with the markets. Feeling confident that I could understand the crash after having read the classic investment books such as. Common Stocks and Uncommon Profits,. What are credit derivatives? What does insurance have to do with the stock market? The Greatest Trade Ever Made. They've got al...

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All Models are Wrong, 7 Sources of Model Risk

http://www.turingfinance.com/perils-optimization-in-investment-management

Turing Finance August 21, 2016. All Models are Wrong, 7 Sources of Model Risk. The 2008 financial crisis. Revealed to the world (in spectacular fashion) the fragility of financial models. Since the financial crisis two words have come up time and time again:. Models are used to represent some object in the real world. We build models so that we can. There are many types of financial models but they are all used to represent. From the world of finance. An interesting challenge with financial modelling...

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Research Notebook –

Comparing “Explanations” for the iVol Puzzle. May 5, 2015. A stock’s idiosyncratic-return volatility is the root-mean-squared error,. From the daily regression. Ang, Hodrick, Xing, and Zhang (2006). Shows that stocks with lots of idiosyncratic-return volatility in the previous month have extremely low returns in the current month. To quantify just how low these returns are, I run a cross-sectional regression each month,. Has an average excess returns of. You can find all the code here. The standard appro...

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