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Statistical arbitrage Stanford

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More than 2,900 Journals. Springer Offers Many Opportunities for Authors to Publish. Let the World Learn About Your Work. Publish Your Research in Our Journals Statistical arbitrage = short-term trading strategy that bets on mean-reversion of asset baskets (more later) The intuition of statistical arbitrage is based on the idea that the di erence between what an equities' price is and what it should be is driven by idiosyncratic shocks Statistical arbitrage requires 3 steps: 1Finding asset baskets Statistical Arbitrage 2017 Stanford University care must be taken to avoid bias resulting from forward- lled data 2 or from failures in data processing 3. Given 14 full years of data (2003-2016), we have elected to reserve the most recent 4 years for out-of-sample testing; our model will be tted and trained on at most the years 2002-2012 Statistical Arbitrage: Asset clustering, market-exposure minimization, and high-frequency explorations. Aniket Inamdar(ainamdar@stanford.edu), Blake Jennings(bmj@stanford.edu), Bernardo Ramos(bramos@stanford.edu), Yiwen Chen(yiwen15@stanford.edu), Ben Etringer(etringer@stanford.edu) Stanford University MS&E 448, Spring 2016 Abstrac Statistical arbitrage requires 3 steps: 1 Finding asset baskets 2 Prediction based on mean-reversion 3 Portfolio construction Shane Barratt, Russell Clarida, Mert Esencan, Francesco Insulla, Cole Kiersznowski, Andrew Perry (Stanford University)Statistical Arbitrage May 5, 20203/1

Join this webinar with Markus Pelger, Assistant Professor of Management Science & Engineering at Stanford University to explore a general framework for statistical arbitrage. The approach generalizes the idea of pairs trading and mean reversion by finding commonality and time-series patterns in a flexible way The main idea in statistical arbitrage is to exploit short-term deviations in returns from a long-term equilibrium across several assets. This kind of strategy heavily relies on the assumption of mean-reversion of idiosyncratic returns, reverting to a long-term mean after some time

Stanford Home; Maps & Directions; Search Stanford; Emergency Info; Terms of Use; Privacy; Copyright; Trademarks; Non-Discrimination; Accessibility © Stanford University, Stanford, California 94305 will do well in statistical arbitrage if the residual can be modelled as an OU process: dX t= (m X t)dt+ ˙dW t: (3) This can be discretized and modelled as an AR(1) process: X n+1 = a+ bX n+ n+1 b= e t;a= m(1 b);var( ) = ˙2 1 b2 2 Given the clusters of stocks, we t their residual dX t(gotten from linear regression) with an AR(1 In the eld of investment, statistical arbitrage refers to attempting to pro t from pricing ine -ciencies identi ed through mathematical models. The basic assumption is that prices will move to-wards a historical average. The most commonly used and simplest case of statistical arbitrage is pairs trading. If stocks Pand Qare in the sam Greg Zanotti Stanford University, School of Engineering, Management Science & Engineering Abstract : Statistical arbitrage identifies and exploits temporal price differences between similar assets. We propose a unifying conceptual framework for statistical arbitrage and develop a novel deep learning solution, which finds commonality and time-series patterns from large panels in a data-driven and flexible way

In finance, statistical arbitrage (often abbreviated as Stat Arb or StatArb) is a class of short-term financial trading strategies that employ mean reversion models involving broadly diversified portfolios of securities (hundreds to thousands) held for short periods of time (generally seconds to days). These strategies are supported by substantial mathematical, computational, and trading platforms Statistical & financial consulting by a Stanford PhD. Expertise includes data mining, time series, arbitrage, derivative pricing, risk management, biostatistics, R, SPSS, SAS, Matlab, Stata, Python. Help with data analysis, dissertations, analytics development and business projects About Our Department. Education in the Statistics discipline acquaints students with the role played by probabilistic and statistical ideas and methods in the many fields of science, medicine, technology, and even the humanities. We provide instruction in the theory and application of techniques that have been found to be commonly useful, and offer. Statistical Arbitrage for Metatrader MT4 - V3. Statistical arbitrage trading techniques (sometimes knows as convergence or pairs trading) are based on the concept of mean reversion. The system continuously monitors the performance of two historically highly correlated instruments which the trader defines. When the correlation between the two. This repository contains three ways to obtain arbitrage which are Dual Listing, Options and Statistical Arbitrage. These are projects in collaboration with Optiver and have been peer-reviewed by staff members of Optiver

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Statistical arbitrage is a class of financial trading strategies using mean reversion models. The corresponding techniques rely on a number of assumptions which may not hold for general non. Statistical arbitrage, also known as stat arb, refers to any trading strategy that uses statistical and econometric techniques to profit with an element of market risk reduction. Arbitrage opportunities occur both in the long-term and short term Founded index arbitrage group. Hired and supervised traders and programmers. Managed index portfolios. Developed algorithms and design specifications for cutting-edge trading system. Negotiated with clearing firm to reduce financing costs. Traded statistical arbitrage, agency baskets, pairs, and individual stocks. Executed VWAP and contingent orders Statistical arbitrage (SA) is a complex word used to refer to pairs trading. It is a simple way of using hedging as a strategy. In SA, you take two assets and trade them in the opposite direction. For instance in a normal silent day, one without major news coming in: two similar assets will trade in the same direction Statistical arbitrage is a collection of trading algorithms that are widely used today but can have very uneven performance, depending on their detailed implementation. I will introduce these methods and explain how the data used as trading signals are prepared so that they depend weakly on market dynamics but have adequate statistical regularity

Statistical Arbitrage: A profit situation arising from pricing inefficiencies between securities. Investors identify the arbitrage situation through mathematical modeling techniques Statistical Arbitrage or Stat Arb has a history of being a hugely profitable algorithmic trading strategy for many big investment banks and hedge funds. Statistical arbitrage originated around 1980's, led by Morgan Stanley and other banks, the strategy witnessed wide application in financial markets. The popularity of the strategy continued.

Morgan Stanley and the Birth of Statistical Arbitrage. Team Latte August 28, 2011. Richard Bookstaber, the first market risk manager at Morgan Stanley in the mid-1980s and the author of the 2006 book, A Demon of Our Own Design, writes that Statistical arbitrage is now past its prime.In mid-2002 the performance of stat arb strategies began to wane, and the standard methods have not recovered Statistical arbitrage is a trading strategy that allows one to trade two highly correlated assets for a high probability mean reversion. This video delves in.. Yes, statistical arbitrage works for FOREX, but it uses completely different principles and patterns, in contrast to similar strategies that use correlation. Many tests have been done to exploit the correlation and convergence on which these FOREX strategies are based. This strategy will certainly lead to losses and very large ones Stanford's Introduction to Statistics teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts

Statistical arbitrage trading or pairs trading as it is commonly known is defined as trading one financial instrument or a basket of financial instruments - in most cases to create a value neutral basket. It is the idea that a co-integrated pair is mean reverting in nature. There is a spread between the instruments and the further it deviates. Statistical arbitrage is one of the most influential trading strategies ever devised. Learn how it is leveraged by investors and traders seeking profits

Deep Learning Statistical Arbitrage - Nvidi

Statistical Arbitrage offers a rare glimpse of insights into the otherwise opaque world of short-term trading strategies. The book provides an excellent balance conceptualizing the mathematics of short-term technical trading strategies with more practical discussions on the recent performance of such strategies Look at the Key Mistakes That Businesses Make in Machine Learning and Predictive Analytics. Download the Whitepaper to Get Greater Value from Your Analytics with TIBCO® Data Science Lu, Anran, et al. Cluster-Based Statistical Arbitrage Strategy. Stanford University, (June, 2018) About. Statistical arbitrage of cointegrating currencies with pair trading where the signal for the next day is predicted using LSTM Topics. lstm-neural-networks cointegration mean-reversion pair-trading pair-trading-strateg statistical arbitrage in the US equity market by proposing a factor model with mean-reverting residuals and a threshold-based bang-bang strategy. This model is further analyzed and ex-tended by Papanicolaou and Yeo (2017), who discuss risk control and develop an optimization method to allocate the investments given the trading signals

Cluster-Based Statistical Arbitrage Strategy Abstract In this paper, we study and develop the classical statistical arbitrage strategy developed by Avellaneda and Lee [1]. Classical statistical arbitrage picks two highly correlated risky assets, such as two stocks in a same sector, and generates trading signals when one of the stocks is mispriced In Statistical Arbitrage, Pole has given his audience a didactic tour of the basic principles of statistical arbitrage, eliminating opacity at the Statistical Arbitrage 101 level. In the 1980s and early 1990s, Stat. Arb. 101 was, for the most part, DOWNLOAD NOW » Author: Andrew Pole. Publisher: John Wiley & Sons ISBN: 9781118160732 Category: Business & Economic

Statistical Consulting: data mining, time series, statistical arbitrage, risk analysis.Statistical & Financial Consulting by Stanford PhD. OVERVIEW. I am a professional offering services in the areas of statistical and financial consulting Joint work: Deep-Learning Statistical Arbitrage, Cryptocurrency Arbitrage Former Doctoral Students: Luyang Chen, Ph.D. 2019, Computational and Mathematical Engineering (co-advised with George Papanicolaou) Thesis: Studies in Stochastic Optimization and Application

Statistical Arbitrage Department of Statistic

  1. Deep Learning Statistical Arbitrage (with J. Guijarro-Ordonez and G. Zanotti) Deep Learning in Asset Pricing (with L. Chen and J. Zhu) Internet Appendix Best Paper Award at the Utah Winter Finance Conference 2020 Best Paper Award at the Asia-Pacific Financial Markets Conference 2020 CQA Academic Paper Competition, 2nd Prize, 202
  2. With our paper, we have successfully transferred an advanced machine-learning-based statistical. arbitrage approach from the U.S. equities markets to a large universe of 40 cryptocurrency coins.
  3. Machine learning research has gained momentum—also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., Takeuchi and Lee (2013); Moritz and Zimmermann (2014); Krauss et al. (2017). With our paper, we pose the question how such a statistical arbitrage approach would fare in the.
  4. High Frequency Statistical Arbitrage Tyler Coleman, Cedrick Argueta, Vidushi Singhi, Luisa Bouneder, Dottie Jones A project using HFT techniques and statistical arbitrage on stocks in the NASDAQ-100 index. Work done for Stanford MS&E 448
  5. ar, UC Berkeley April 26, 2018 with Joongyeub Yeo Risk Control of Mean-Reversion Time in Statistical Arbitrage, J. Yeo and G. Papanicolaou, Risk and Decision Analysis, vol 6, 2017, p. 263-290. G. Papanicolaou, CDAR-UCB Risk Control 1/24
  6. Abstract. This article introduces the concept of a statistical arbitrage opportunity (SAO). In a finite-horizon economy, a SAO is a zero-cost trading strategy for which (i) the expected payoff is positive, and (ii) the conditional expected payoff in each final state of the economy is nonnegative. Unlike a pure arbitrage opportunity, a SAO can.

George Papanicolaou, Stanford Statistical arbitrage is a collection of trading algorithms that are widely used today but can have very uneven performance, depending on their detailed implementation. I will introduce these methods and explain how the data used as trading signals are prepared so that they depend weakly on market dynamics but have adequate statistical regularity Stanford University. Cluster-Based Statistical Arbitrage Strategy, Page 2. Accessed June 1, 2020. Journal of Finance. Do ETFs Increase Volatility? Page 91. Accessed June 1, 2020 Corpus ID: 7617604. Statistical Arbitrage in High Frequency Trading Based on Limit Order Book Dynamics @inproceedings{Ahmed2009StatisticalAI, title={Statistical Arbitrage in High Frequency Trading Based on Limit Order Book Dynamics}, author={M. Ahmed and Anwei Chai and Xiaowei Ding and Y. Jiang and Yunting Sun}, year={2009}

Reinforcement learning can interact with the environment and is suitable for applications in decision control systems. Therefore, we used the reinforcement learning method to establish a foreign exchange transaction, avoiding the long-standing problem of unstable trends in deep learning predictions. In the system design, we optimized the Sure-Fire statistical arbitrage policy, set three. Dynamic Asset Pricing FINANCE 632: Empirical Dynamic Asset Pricing This course explores the interplay between dynamic asset pricing theory, statistical assumptions about sources of risk, and the choice of econometric methods for analysis of asset return data. Therefore, the lectures will be a blend of theory, econometric method, and critical review of empirical studies Statistical arbitrage is a natural application field for big data and machine learning. Lo (2010) recalls it involves a large number of securities and substantial computational, trading and information technology infrastructure

Deep Learning Statistical Arbitrage : algotradin

Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more. The theory and practice of investment management : asset allocation, valuation, portfolio construction, and strategies in SearchWorks catalo About Stanford GSB. About Our Degree Programs. Faculty & Research Working Papers The Emprical Foundations of The Arbitrage Pricing Theory I: The Empirical Tests. revealed few violations of the zero beta model which could not be ascribed to statistical problems Statistical arbitrage algorithms. MATH 238. Mathematical Finance. 3 Units. Stochastic models of financial markets. Forward and futures contracts. European options and equivalent martingale measures. This is the only course at Stanford whose syllabus includes nearly all the math background for CS 229,. Join us on 6/1 for a #webinar with Stanford University to learn about the approach to a general framework for statistical arbitrage and optimal #trading strategies.

Have done the same in other parts of the U.S. for 4 years. Worked in industry for 10 years, focusing on projects pertaining to data mining, cluster analysis, factor analysis, time series analysis, stochastic volatility modeling / derivative pricing, statistical arbitrage / development of proprietary trading strategies, and so on Stanford University, Other statistical The arbitrage pricing theory (APT), introduced by Ross (1976), involves neither a market portfolio nor a risk-free asset and states that a multifactor model of the form (2.1) should hold approximately in the absence of arbitrage for sufficiently large m Risk adjustment and trading strategies, Review of Financial Studies 16, 459-485. Testing market efficiency using statistical arbitrage with applications to momentum and value strategies, Journal of Financial Economics 73, 525-565.. DOWNLOAD NOW » Author: . Publisher: John Wiley & Sons ISBN: 9780470035498 Category: Mathematics Page: 2176 View: 687 Leading the way in this field, the. Using Brent and TD3 data, synthetic floating storage positions are constructed, which are shown to be cointegrated with Brent futures prices of common maturity. A comprehensive model specification analysis of the optimal statistical arbitrage trading model of Bertram (2010) is performed on this data

Posts about Statistical Arbitrage written by thonier. Recently, I have read interesting book - Lean Startup from Eric Ries. In addition, I regularly listen to DFJ Thought Leader Series from Stanford (ecorner.stanford.edu) (2019). High-dimensional Statistical Arbitrage with Factor Models and Stochastic Control. Applied Mathematical Finance: Vol. 26, No. 4, pp. 328-358 Experienced professionals GAO is led by a trusted team of highly qualified investment professionals, combining decades of investment experience gained in major international banks including JPMorgan, Standard Chartered, Citibank, Barclays, and UBS. This experience is further complemented by team' D. E. Shaw & Co., L.P. is a multinational investment management firm founded in 1988 by David E. Shaw and based in New York City.The company is known for developing complicated mathematical models and sophisticated computer programs to exploit anomalies in the market Algorithmic Finance is a high-quality academic research journal that seeks to bridge computer science and finance, including high frequency and algorithmic trading, statistical arbitrage, momentum and other algorithmic portfolio management strategies, machine learning and computational financial intelligence, agent-based finance, complexity and market efficiency, algorithmic analysis on.

Statistical arbitrage - Wikipedi

  1. Mr. Tulchinsky is the Founder, Chairman and CEO of WorldQuant, LLC, which he established in 2007 following 12 years as a statistical arbitrage portfolio manager at Millennium Management. Prior to joining Millennium Management, Mr. Tulchinsky was a venture capitalist, scientist at AT&T Bell Laboratories, video game programmer and author
  2. Ken has a successful track record as a technologist and innovator in financial services, transportation and other industry sectors. From the mid-90's until 2006, he was the technical lead for Morgan Stanley's highly successful PDT proprietary trading group (now PDT Partners) where he architected their statistical arbitrage trading system
  3. Statistical Arbitrage and Systematic Trading Strategies Option Pricing, Stanford University PhD and MS Economics and Financial Mathematics. 2004 - 2008. Language
  4. This project implements a high frequency trading strategy that utilizes Support Vector Machines to capture statistical arbitrage in the pricing of Class A and Class C Google stocks. - alexdai186/HighFrequencyTradingSVM
  5. View Avellaneda_StatisticalArbitrageUSEquity from SEEM 2520 at CUHK. This article was downloaded by: [Chinese University of Hong Kong] On: 01 November 2013, At: 19:19 Publisher: Routledge Informa Lt
  6. Mathematics with Statistical Modeling Ling Department of Statistics, Stanford University Tiong Wee Lim Department of Statistics and Applied Probability, National University of Singapore Keywords Option pricing, Substantive models, Nonparametric regression arbitrage condition to derive closed-form pricing formulas for European options.

SAC Capital Advisors was a group of hedge funds founded by Steven A. Cohen in 1992. The firm employed approximately 800 people in 2010 across its offices located in Stamford, Connecticut and New York City, and various offices. It reportedly lost many of its traders in the wake of various investigations by the Securities and Exchange Commission (SEC) Speaker: George Papanicolaou, Stanford. Statistical arbitrage is a collection of trading algorithms that are widely used today but can have very uneven performance, depending on their detailed implementation

Statistical Consulting: data mining, time - Stanford Ph

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CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We apply machine learning methods to obtain an index arbitrage strategy. In particular, we employ linear regression and support vector regression (SVR) onto the prices of an exchange-traded fund and a stream of stocks. By using principal component analysis (PCA) in reducing the dimension of feature space, we observe. Forex Statistical Arbitrage Software, simpatia para ganhar dinheiro ano novo - ledger wallet nano s idealo, pivot trading strategy for nifty, stock options quicken mac. Dave. I find that both markets are. Please allow us 24-72 hours to review your comment

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Looking for abbreviations of SCF? It is Statistical Computing Facility. Statistical Computing Facility listed as SCF. Statistical Computing Facility - How is Statistical Computing Facility Stanford Computer Forum: SCF: Supervisory and Control Function: SCF: Statistical arbitrage; Statistical arbitrage; Statistical arbitrage; Statistical. The mission of the Stanford Graduate School of Business is to create ideas that deepen and advance the understanding of management, Alphanomics: Informational Arbitrage in Equity Markets. Statistical Experimentation in Businesses Risk control of mean-reversion time in statistical arbitrage. J. Yeo and G. Papanicolaou. Risk and Decision Analysis, Volume 6 (2017), 263-290. PCA for Implied Volatility Surfaces, Marco Avellaneda, Brian Healy, Andrew Papanicolaou and George Papanicolaou, The Journal of Financial Data Science, DOI: 10.3905/jfds.2020.1.032, (2020). SIGNAL ANALYSI

Arbitrage Simply put, arbitrage is risk-free profit. That is, when we invest some amount of money, we will lose money with probability 0 and have some positive chance of gaining money. A market that is free of arbitrage opportunities is called an Efficient Market, and markets with arbitrage opportunities are called Inefficient Market A Stanford undergrad, he has an MBA from UCLA. Tom Aiello. He was also founder of Pearl Investments, a proprietary statistical arbitrage trading firm based in Portland, Maine. He was also co-founder and managing partner of the Washington, DC based hedge fund Brochet Capital Partners, LP GAR (Global Arbitration Review) is the world's leading international arbitration journal and news service. GAR provides breaking news, daily updates and in-depth monthly features covering international arbitration in countries around the world. GAR also features guest commentary and articles from the world's leading international arbitration practitioners I am currently a Master's student in Stanford University studying Computer Science. I finished my Bachelor's degree with a double-major in Computer Science and Mathematics in Hong Kong University of Science and Technology, with a GPA of 4.012/4.3. I also had an exchange semester in Georgia Institute of Technology, with a GPA of 4.0/4.0 Ed Thorp: Statistical Arbitrage, Wilmott Magazine, June 2008 (, This page was last edited on 31 December 2020, at 21:36. So it's fair to say that VVIX follows separate price dynamics which is different from the VIX. Graph below shows the 20-day rolling correlation between SPX and VIX prices for the last year. In China, quantitative investment including statistical arbitrage is not the.

Advanced Statistical Arbitrage for MetaTrader MT

statistical-arbitrage · GitHub Topics · GitHu

Find great deals for Fixed-Income Arbitrage: Analytical Techniques and Strategies: By Wong, M. Ant.... Shop with confidence on eBay Statistical Arbitrage is a very important asset class for investors seeking distribution of risk. Why? Because Statistical Arbitrage (if done properly) is an absolute return strategy. Its low correlation to stocks and other assets means it is not hostage to the vagaries of the market,. Machine Learning. Algorithmic Trading. Blockchain Technology. Data Science. Artificial Intelligence. Business Analytics. Conclusion. The role of a business & finance professional is evolving rapidly with the impending Fourth Industrial Revolution. Technical skills have been identified consistently as the most in-demand skill for success in.

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No-arbitrage is the fundamental principle of economic rationality which unifies normative decision theory, game theory, and market theory. In economic environments where money is available as a medium of measurement and exchange, no-arbitrage is synonymous with subjective expected utility maximization in personal decisions, competitive equilibria in capital markets and exchange economies, and. Leadership. Contact. Sign In. Leadership. NYDIG's leadership team brings industry-leading experience in institutional asset management, trading, technology, and regulation. Executive Leadership. Ross Stevens. Founder and Executive Chairman. Robert Gutmann Looking for abbreviations of SAS? It is Statistical Analysis System. Statistical Analysis System listed as SAS. Statistical Analysis System - How is Statistical Analysis System Stanford Astronomical Society (Stanford, CA) SAS: Secondary Alarm Station: SAS: Statistical arbitrage; Statistical arbitrage; Statistical arbitrage; Statistical.

Statistical Arbitrage: Defined & Strategies Analyzing Alph

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A model in which international calorie‐arbitrage plays a central role is shown to be consistent with available statistical data on the grain trade of the People's Republic of China. Making allowanc.. Ve el perfil de Eriz Zárate en LinkedIn, la mayor red profesional del mundo. Eriz tiene 3 empleos en su perfil. Ve el perfil completo en LinkedIn y descubre los contactos y empleos de Eriz en empresas similares Arbitrage opportunities based on fundamental and statistical logic; Stanford Chemist • Oct. 11 Arbitrage Trader is always in the chat room with real time trading ideas and is armed with. Statistical Arbitrage FRE-GY7121, 1.5 Credits Forensic Financial Technology and Regulatory Systems FRE-GY7211, 1.5 Credits Big Data in Finance FRE-GY7221, 1.5 Credit

MFin Curriculum. Putting ideas into action is fundamental to MIT's approach to knowledge creation, education, and research. In the MFin program, this presents itself in a rigorous, hands-on curriculum that offers students the chance to build a deep reservoir of finance knowledge and immediately put that knowledge to work in the world There are obvious counterarguments to this requirement of explainability. For example, the blackness of an investment model is the product of a specific historical epoch; option pricing models, technical analysis, program trading, optimization programs, and statistical arbitrage programs were the black boxes of their day

How Maximize Returns with a good Statistical Arbitrage

  1. The Unconventional Guide To The Best Websites For Quants. Jobs & Skills. Feb 06, 2018. By Nitin Thapar. Technology moves at a startling speed and it has been the same case in the algorithmic and quantitative trading domain. Traders around the world are making use of Machine Learning, Artificial Intelligence, Blockchain, Neural Networks, Deep.
  2. The arbitrage pricing theory is an alternative to the CAPM that uses fewer assumptions and can be harder to implement than the CAPM. While both are useful, many investors prefer to use the CAPM, a.
  3. e the value of derivatives

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  2. CiteSeerX - Scientific documents that cite the following paper: Integrating Arbitrage Pricing Theory and Artificial Neural Networks to Support Portfolio Management. Decision Support System
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(PDF) Statistical Arbitrage in S&P500Statistical Arbitrage Archives - QUANTITATIVE RESEARCH ANDCointegration in the Forex market | Mechanical ForexSharp Sports Betting by Stanford Wong - Book - Read Online
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