Institutional Algorithmic Trading, Statistical Arbitrage and Technical Analysis
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Calibration of Bollinger Bands Parameters for Trading Strategy Development in the Baltic Stock Market
ISSN 1392 – 2785 Inzinerine Ekonomika-Engineering Economics, 2010, 21(3), 244-254 Calibration of Bollinger Bands Parameters for Trading Strategy Development in the Baltic Stock Market Audrius Kabasinskas, Ugnius Macys Kaunas University of Technology K. Donelaicio st. 73, LT-44029, Kaunas, Lithuania e-mail: [email protected], [email protected] In recent decades there was a robust boom in "Bollinger plotter" was developed using the most investment sector in Lithuania, as more people chose to popular mathematical toolbox MatLab in order to solve invest money in investment funds rather than keep money in stated problems. Application is capable of charting the closet. The Baltic States Market turnover has increased Bollinger Bands and 6 other technical indicators with from 721 MEUR in 2000 to 978 MEUR in 2008 (with peak desired period of time. This software is not a fully 2603 MEUR in 2005). When difficult period appeared in automated decision making system, as decisions are global markets, a lot of attention was dedicated towards the usually made based on value judgment. managing of investments. Investment management firms in Since the stock returns usually have distributions with Lithuania gain significance in personal as well as in fat tails, then less than 95% of data fit in the Bollinger business section increasingly; even though these firms are trading channels. However the Bollinger bands trading considerably young (the first one in Lithuania was signals were supported by additional indicators (e.g. %b), established in year 2000). so the loss of data is not significant. Successful investment begins with the financial Our calibration results show that short term investor analysis of stock, asset or index, which you are going to should apply 10 days moving average and use a trading invest. -
FOREX WAVE THEORY.Pdf
FOREX WAVE THEORY This page intentionally left blank FOREX WAVE THEORY A Technical Analysis for Spot and Futures Currency Traders JAMES L. BICKFORD McGraw-Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2007 by The McGraw-Hill Companies. All rights reserved. Manufactured in the United States of America. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. 0-07-151046-X The material in this eBook also appears in the print version of this title: 0-07-149302-6. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales pro- motions, or for use in corporate training programs. For more information, please contact George Hoare, Special Sales, at [email protected] or (212) 904-4069. TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc. (“McGraw-Hill”) and its licen- sors reserve all rights in and to the work. Use of this work is subject to these terms. -
What Happened to the Quants in August 2007?∗
What Happened To The Quants In August 2007?∗ Amir E. Khandaniy and Andrew W. Loz First Draft: September 20, 2007 Latest Revision: September 20, 2007 Abstract During the week of August 6, 2007, a number of high-profile and highly successful quan- titative long/short equity hedge funds experienced unprecedented losses. Based on empir- ical results from TASS hedge-fund data as well as the simulated performance of a specific long/short equity strategy, we hypothesize that the losses were initiated by the rapid un- winding of one or more sizable quantitative equity market-neutral portfolios. Given the speed and price impact with which this occurred, it was likely the result of a sudden liqui- dation by a multi-strategy fund or proprietary-trading desk, possibly due to margin calls or a risk reduction. These initial losses then put pressure on a broader set of long/short and long-only equity portfolios, causing further losses on August 9th by triggering stop-loss and de-leveraging policies. A significant rebound of these strategies occurred on August 10th, which is also consistent with the sudden liquidation hypothesis. This hypothesis suggests that the quantitative nature of the losing strategies was incidental, and the main driver of the losses in August 2007 was the firesale liquidation of similar portfolios that happened to be quantitatively constructed. The fact that the source of dislocation in long/short equity portfolios seems to lie elsewhere|apparently in a completely unrelated set of markets and instruments|suggests that systemic risk in the hedge-fund industry may have increased in recent years. -
Arbitrage Pricing Theory∗
ARBITRAGE PRICING THEORY∗ Gur Huberman Zhenyu Wang† August 15, 2005 Abstract Focusing on asset returns governed by a factor structure, the APT is a one-period model, in which preclusion of arbitrage over static portfolios of these assets leads to a linear relation between the expected return and its covariance with the factors. The APT, however, does not preclude arbitrage over dynamic portfolios. Consequently, applying the model to evaluate managed portfolios contradicts the no-arbitrage spirit of the model. An empirical test of the APT entails a procedure to identify features of the underlying factor structure rather than merely a collection of mean-variance efficient factor portfolios that satisfies the linear relation. Keywords: arbitrage; asset pricing model; factor model. ∗S. N. Durlauf and L. E. Blume, The New Palgrave Dictionary of Economics, forthcoming, Palgrave Macmillan, reproduced with permission of Palgrave Macmillan. This article is taken from the authors’ original manuscript and has not been reviewed or edited. The definitive published version of this extract may be found in the complete The New Palgrave Dictionary of Economics in print and online, forthcoming. †Huberman is at Columbia University. Wang is at the Federal Reserve Bank of New York and the McCombs School of Business in the University of Texas at Austin. The views stated here are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of New York or the Federal Reserve System. Introduction The Arbitrage Pricing Theory (APT) was developed primarily by Ross (1976a, 1976b). It is a one-period model in which every investor believes that the stochastic properties of returns of capital assets are consistent with a factor structure. -
Statistical Arbitrage: Asset Clustering, Market-Exposure Minimization, and High-Frequency Explorations
Statistical Arbitrage: Asset clustering, market-exposure minimization, and high-frequency explorations. Aniket Inamdar([email protected]), Blake Jennings([email protected]), Bernardo Ramos([email protected]), Yiwen Chen([email protected]), Ben Etringer([email protected]) Stanford University MS&E 448, Spring 2016 Abstract In this paper we describe and implement two statistical arbitrage trading strategies. The first strategy models the mean-reverting residual of a cluster of assets whose weights are selected so as to minimize market exposure. The second strategy maintains a portfolio of pairs, each weighted proportional to a measure of mean-reversion speed. We present performance metrics for each strategy, discuss the difficulties of their application into high-frequency trading, and suggest a direction where future research could focus. 1 Introduction The technique of statistical arbitrage is the systematic exploitation of perceived mispricings of similar assets. A trading strategy built around statistical arbitrage involves three fundamental pillars: (1) a measure of similarity of assets, (2) a measure of pricing mismatch, and (3) a confidence metric for each mismatch. Traditional statistical arbitrage techniques, like \Pairs Trading", employ these three pillars, holding long-short positions in a pair of strongly \similar" assets. The covariance (or correlation) between two assets is a widely used metric of their \similarity". However, recent studies have used additional attributes such as co-integration tests to select asset pairs that are better suited for a statistical arbitrage trading strategy. We base our signal generation strategy on the seminal paper [3], whose theory is briefly described in Sec. 2. Next, Sec. 3.1 describes a strategy to select assets to perform statistical arbitrage on. -
Arbitrage and Price Revelation with Asymmetric Information And
Journal of Mathematical Economics 38 (2002) 393–410 Arbitrage and price revelation with asymmetric information and incomplete markets Bernard Cornet a,∗, Lionel De Boisdeffre a,b a CERMSEM, Université de Paris 1, 106–112 boulevard de l’Hˆopital, 75647 Paris Cedex 13, France b Cambridge University, Cambridge, UK Received 5 January 2002; received in revised form 7 September 2002; accepted 10 September 2002 Abstract This paper deals with the issue of arbitrage with differential information and incomplete financial markets, with a focus on information that no-arbitrage asset prices can reveal. Time and uncertainty are represented by two periods and a finite set S of states of nature, one of which will prevail at the second period. Agents may operate limited financial transfers across periods and states via finitely many nominal assets. Each agent i has a private information about which state will prevail at the second period; this information is represented by a subset Si of S. Agents receive no wrong information in the sense that the “true state” belongs to the “pooled information” set ∩iSi, hence assumed to be non-empty. Our analysis is two-fold. We first extend the classical symmetric information analysis to the asym- metric setting, via a concept of no-arbitrage price. Second, we study how such no-arbitrage prices convey information to agents in a decentralized way. The main difference between the symmetric and the asymmetric settings stems from the fact that a classical no-arbitrage asset price (common to every agent) always exists in the first case, but no longer in the asymmetric one, thus allowing arbitrage opportunities. -
Why Convertible Arbitrage Makes Sense in 2021
For UBS marketing purposes Convertible arbitrage as a hedge fund strategy should continue to work well in 2021. (Keystone) Investment strategies Why convertible arbitrage makes sense in 2021 19 January 2021, 8:52 pm CET, written by UBS Editorial Team The current market environment is conducive for convertible arbitrage hedge funds with elevated new issuance, attractive equity volatility levels and convertible valuations all likely to support performance in 2021. Investors should consider allocating to the strategy within a diversified hedge fund portfolio. The global coronavirus pandemic resulted in significant equity market drawdowns in March last year, followed by an almost unparalleled recovery over the next few months. On balance, 2020 was a good year for hedge funds focusing on convertible arbitrage strategies which returned 12.1% for the year, according to HFR data. We believe that all drivers remain in place for a solid performance in 2021. And while current market conditions resemble those after the great financial crisis, convertible arbitrage today is a far less crowded strategy with more moderate leverage levels. The market structure is also more balanced between hedge funds and long only funds, and has after a long period of outflows, recently witnessed renewed investor interest. So we believe there are a number of reasons why convertible arbitrage remains attractive for investors in 2021 based on the following assumptions: Convertible arbitrage traditionally performs well in recovery phases. Historically, convertible arbitrage funds have generated their best returns during periods of economic recovery and expansion. Improving risk sentiment, tightening credit spreads, and attractively priced new issues are key contributors to returns in such periods. -
Modeling and Analyzing Stock Trends
Modeling and Analyzing Stock Trends A Major Qualifying Project Submitted to the Faculty of Worcester Polytechnic Institute in partial fulllment of the requirements for the Degree in Bachelor of Science Mathematical Sciences By Laura Cintron Garcia Date: 5/6/2021 Advisor: Dr. Mayer Humi This report represents work of WPI undergraduate students submitted to the faculty as evidence of a degree requirement. WPI routinely publishes these reports on its web site without editorial or peer review. For more information about the projects program at WPI, see http://www.wpi.edu/Academics/Projects 1 Abstract Abstract The goal of this project is to create and compare several dierent stock prediction models and nd a correlation between the predic- tions and volatility for each stock. The models were created using the historical data, DJI index, and moving averages. The most accurate prediction model had an average of 5.3 days spent within a predic- tion band. A correlation of -0.0438 was found between that model an a measure of volatility, indicating that more prediction days means lower volatility. 2 2 Acknowledgments Without the help of some people, it would have been signicantly more di- cult to complete this project without a group. I want to extend my gratitude to Worcester Polytechnic Institute and the WPI Math Department for their great eorts and success this year regarding school and projects during the pandemic. They did everything they could to ensure these projects was still a rich experience for the students despite everything. I would also like to thank my MQP advisor, Professor Mayer Humi for his assistance and guidance on this project, for allowing me to work indepen- dently while always being willing to meet with me or answer any questions, and for continuously encouraging me to do what I thought was best for the project. -
Hedge Fund Returns: a Study of Convertible Arbitrage
Hedge Fund Returns: A Study of Convertible Arbitrage by Chris Yurek An honors thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science Undergraduate College Leonard N. Stern School of Business New York University May 2005 Professor Marti G. Subrahmanyam Professor Lasse Pedersen Faculty Adviser Thesis Advisor Table of Contents I Introductions…………………………………………………………….3 Related Research………………………………………………………………4 Convertible Arbitrage Returns……………………………………………....4 II. Background: Hedge Funds………………………………………….5 Backfill Bias…………………………………………………………………….7 End-of-Life Reporting Bias…………………………………………………..8 Survivorship Bias……………………………………………………………...9 Smoothing………………………………………………………………………9 III Background: Convertible Arbitrage……………………………….9 Past Performance……………………………………………………………...9 Strategy Overview……………………………………………………………10 IV Implementing a Convertible Arbitrage Strategy……………….12 Creating the Hedge…………………………………………………………..12 Implementation with No Trading Rule……………………………………13 Implementation with a Trading Rule……………………………………...14 Creating a Realistic Trade………………………………….……………....15 Constructing a Portfolio…………………………………………………….16 Data……………………………………………………………………………..16 Bond and Stock Data………………………………………………………………..16 Hedge Fund Databases……………………………………………………………..17 V Convertible Arbitrage: Risk and Return…………………….…...17 Effectiveness of the Hedge………………………………………………...17 Success of the Trading Rule……………………………………………….20 Portfolio Risk and Return…………………………………………………..22 Hedge Fund Database Returns……………………………………………23 VI -
Arbitraging the Basel Securitization Framework: Evidence from German ABS Investment Matthias Efing (Swiss Finance Institute and University of Geneva)
Discussion Paper Deutsche Bundesbank No 40/2015 Arbitraging the Basel securitization framework: evidence from German ABS investment Matthias Efing (Swiss Finance Institute and University of Geneva) Discussion Papers represent the authors‘ personal opinions and do not necessarily reflect the views of the Deutsche Bundesbank or its staff. Editorial Board: Daniel Foos Thomas Kick Jochen Mankart Christoph Memmel Panagiota Tzamourani Deutsche Bundesbank, Wilhelm-Epstein-Straße 14, 60431 Frankfurt am Main, Postfach 10 06 02, 60006 Frankfurt am Main Tel +49 69 9566-0 Please address all orders in writing to: Deutsche Bundesbank, Press and Public Relations Division, at the above address or via fax +49 69 9566-3077 Internet http://www.bundesbank.de Reproduction permitted only if source is stated. ISBN 978–3–95729–207–0 (Printversion) ISBN 978–3–95729–208–7 (Internetversion) Non-technical summary Research Question The 2007-2009 financial crisis has raised fundamental questions about the effectiveness of the Basel II Securitization Framework, which regulates bank investments into asset-backed securities (ABS). The Basel Committee on Banking Supervision(2014) has identified \mechanic reliance on external ratings" and “insufficient risk sensitivity" as two major weaknesses of the framework. Yet, the full extent to which banks actually exploit these shortcomings and evade regulatory capital requirements is not known. This paper analyzes the scope of risk weight arbitrage under the Basel II Securitization Framework. Contribution A lack of data on the individual asset holdings of institutional investors has so far pre- vented the analysis of the demand-side of the ABS market. I overcome this obstacle using the Securities Holdings Statistics of the Deutsche Bundesbank, which records the on-balance sheet holdings of banks in Germany on a security-by-security basis. -
Multi-Strategy Arbitrage Hedge Fund Qualified Investor Hedge Fund Fact Sheet As at 31 May 2021
CORONATION MULTI-STRATEGY ARBITRAGE HEDGE FUND QUALIFIED INVESTOR HEDGE FUND FACT SHEET AS AT 31 MAY 2021 INVESTMENT OBJECTIVE GENERAL INFORMATION The Coronation Multi-Strategy Arbitrage Hedge Fund makes use of arbitrage Investment Structure Limited liability en commandite partnership strategies in the pursuit of attractive risk-adjusted returns, independent of general Disclosed Partner Coronation Management Company (RF) (Pty) Ltd market direction. The fund is expected to have low volatility with a very low correlation to equity markets. Stock-picking is based on fundamental in-house research. Factor- Inception Date 01 July 2003 based and statistical arbitrage models are used solely for screening purposes. Active Hedge Fund CIS launch date 01 October 2017 use of derivatives is applied to reduce risk and implement views efficiently. The risk Year End 30 September profile of the fund is expected to be low due to its low net equity exposure and focus on arbitrage-related strategies. The portfolio is well positioned to take advantage of Fund Category South African Multi-Strategy Hedge Fund low probability/high payout events and will thus generally be long volatility through Target Return Cash + 5% the options market. The fund’s target return is cash plus 5%. The objective is to achieve Performance Fee Hurdle Rate Cash + high-water mark this return with low risk, providing attractive risk-adjusted returns through a low fund standard deviation. Annual Management Fee 1% (excl. VAT) Annual Outperformance Fee 15% (excl. VAT) of returns above cash, capped at 3% Total Expense Ratio (TER)† 1.38% INVESTMENT PARAMETERS Transaction Costs (TC)† 1.42% ‡ Net exposure is capped at 30%, of which 15% represents true directional exposure in Fund Size (R'Millions) R303.47 the alpha strategy. -
Earth & Sky Trading System
Earth & Sky Trading System By Pierre Du Plessis 1 Copyright © Forex Mentor Pro 2011. All rights reserved. Any redistribution or reproduction of part or all of the contents in any form is prohibited other than the following: You may print or download to a local hard disk copies for your personal and non- commercial use only; You may not, except with our express written permission, distribute or commercially exploit the content. Nor may you transmit it or store it in any other website or other form of electronic retrieval system. Risk Disclosure Statement The contents of this e-Book are for informational purposes only. No part of this publication is a solicitation or an offer to buy or sell any financial market. Examples are provided for illustration purposes only and should not be construed as investment advice or strategy. All trade examples are hypothetical. No representation is made that any account or trader will or is likely to achieve profits or loses similar to those discussed in this e-Book. By purchasing this e-Book, and/or subscribing to our mailing list you will be deemed to have accepted these and all other terms found on our web page ForexMentorPro.com in full. The information found in this e-Book is not intended for distribution to, or use by any person or entity in any jurisdiction or country where such distribution or use would be contrary to the law or regulation or which would subject us to any registration requirement within such jurisdiction or country. CFTC RULE 4.41 HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS.