Trend Following
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Trend Following Algorithms in Automated Derivatives Market Trading ⇑ Simon Fong , Yain-Whar Si, Jackie Tai
Expert Systems with Applications 39 (2012) 11378–11390 Contents lists available at SciVerse ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa Trend following algorithms in automated derivatives market trading ⇑ Simon Fong , Yain-Whar Si, Jackie Tai Department of Computer and Information Science, University of Macau, Macau article info abstract Keywords: Trend following (TF) is trading philosophy by which buying/selling decisions are made solely according to Trend following the observed market trend. For many years, many manifestations of TF such as a software program called Automated trading system Turtle Trader, for example, emerged in the industry. Surprisingly little has been studied in academic Futures contracts research about its algorithms and applications. Unlike financial forecasting, TF does not predict any mar- Mechanical trading ket movement; instead it identifies a trend at early time of the day, and trades automatically afterwards by a pre-defined strategy regardless of the moving market directions during run time. Trend following trading has been popular among speculators. However it remains as a trading method where human judgment is applied in setting the rules (aka the strategy) manually. Subsequently the TF strategy is exe- cuted in pure objective operational manner. Finding the correct strategy at the beginning is crucial in TF. This usually involves human intervention in first identifying a trend, and configuring when to place an order and close it out, when certain conditions are met. In this paper, we evaluated and compared a col- lection of TF algorithms that can be programmed in a computer system for automated trading. In partic- ular, a new version of TF called trend recalling model is presented. -
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. -
Global Macro–Why Now?
Global Macro–Why Now? SOLUTIONS & MULTI ASSET | AIP HEDGE FUND TEAM | INVESTMENT INSIGHT | 2018 Global Macro is an investment CO-AUTHORS style that is highly opportunistic and has the potential to generate MARK VAN DER ZWAN, CFA strong risk-adjusted returns in Chief Investment Officer challenging markets. Against a backdrop and Head of AIP Hedge Fund Team of geopolitical uncertainty and potentially increased volatility,1 we felt it would be timely to share our insights on the space and explain ROBERT RAFTER, CFA Head of Discretionary Global why we believe now could be an opportune Macro, Sovereign Fixed time to make an allocation to Global Macro. Income Relative Value and Emerging Markets Strategies Both equity and fixed income markets have exhibited strong performance in recent years, leading to fully valued stock markets, historically low yields and tight credit spreads. However, substantial U.S. fiscal stimulus through a $1.5 trillion tax cut and a $300 billion increase in government spending,2 amid robust economic growth and inflation readings, have made it difficult for investors to adopt a more cautious, late- cycle posture in their portfolios. Importantly, increasing market volatility levels may result from any of a litany of potential catalysts, including escalating protectionist policies, U.S. midterm elections, central bank policy missteps, Italian debt sustainability, tensions in the Middle East, and political unrest in key emerging markets. We believe that these dynamics have 1 Please see Glossary for definitions. 2 Source: AIP Hedge Fund Team. The statements above reflect the opinions and views of Morgan Stanley Investment Management as of the date hereof and not as of any future date and will not be updated or supplemented. -
Absolute Momentum: a Simple Rule-Based Strategy and Universal Trend-Following Overlay
Absolute Momentum: a Simple Rule-Based Strategy and Universal Trend-Following Overlay Gary Antonacci Portfolio Management Associates, LLC1 February 28, 2013 Abstract There is a considerable body of research on relative strength price momentum but relatively little on absolute, time series momentum. In this paper, we explore the practical side of absolute momentum. We first explore its sole parameter - the formation, or look back, period. We then examine the reward, risk, and correlation characteristics of absolute momentum applied to stocks, bonds, and real assets. We finally apply absolute momentum to a 60-40 stock/bond portfolio and a simple risk parity portfolio. We show that absolute momentum can effectively identify regime change and add significant value as an easy to implement, rule-based approach with many potential uses as both a stand- alone program and trend following overlay. 1 http://optimalmomentum.com 1 1. Introduction The cross-sectional momentum effect is one of the strongest and most pervasive financial phenomena (Jegadeesh and Titman (1993), (2001)). Researchers have verified its value with many different asset classes, as well as across groups of assets (Blitz and Van Vliet (2008), Asness, Moskowitz and Pedersen (2012)). Since its publication, momentum has held up out-of-sample going forward in time (Grundy and Martin (2001), Asness, Moskowitz and Pedersen (2012)) and back to the Victorian Age (Chabot, Ghysels, and Jagannathan (2009)). In addition to cross-sectional momentum, in which an asset's performance relative to other assets predicts its future relative performance, momentum also works well on an absolute, or time series basis, in which an asset's own past return predicts its future performance. -
Ex-Commonwealth PM Set to Launch $500M Macro Fund LAUNCH
The long and the short of it www.hfmweek.com ISSUE 497 3 MAY 2018 INFRAHEDGE CEO BRUCE KEITH DEPARTS AFTER 7 YEARS HFM EUROPEAN 2018 $30bn MAP co-founder to be replaced by Andrew Allright PEOPLE MOVES 03 PERFORMANCE AWARDS DEUTSCHE PUTS PRIME FINANCE BUSINESS UNDER REVIEW HF head Tarun Nagpal to leave bank after 15 years PRIME BROKERAGE 07 EX-GRUSS CAPITAL PROS PREP EVENT-DRIVEN FUND HFMWEEK REVEALS ALL Indar Capital expected to launch later this year LAUNCHES 10 THE WINNERS AWARDS 23 Ex-CommonWealth PM set to launch $500m macro fund Christopher Wheeler readies between 2013 and 2016. London-based CJW Capital CommonWealth closed BY SAM MACDONALD down last year as Fisher depart- ed to join $26bn Soros Fund FORMER CITADEL AND Management. CommonWealth Opportunity From November 2016 until Capital portfolio manager Chris- March this year, Wheeler is topher Wheeler is set to launch a understood to have traded a sub- LAUNCH macro fund with at least $500m stantial macro sleeve for Citadel. initial investment, HFMWeek He previously spent five years has learned. with London-based liquid multi- ANALYSIS Wheeler is starting London- asset business Talisman Global NUMBERS SURGE IN 2017 based CJW Capital Management Asset Management. He earlier with backing from a large asset worked at Morgan Stanley. manager and is looking to begin CJW Capital could become trading this year, HFMWeek one of this year’s largest HFM Global’s annual survey shows understands. European start-ups, amid a num- He registered the firm with ber of prominent macro hedge equity strategies remained most in UK Companies House on 23 fund launches. -
A Hard Day's Knight: the Global Financial Market Confronts
GLOBAL PERSPECTIVE S A Hard Day’s Knight: The Global Financial Market Confronts Uncertainty, Not Just Risk (and the Difference is Important) October 2007 Richard H. Clarida Global Strategic Advisor PIMCO “A variety of asset-backed securities have led to disruption around the world.” Bank of England, September 4, 2007 “Markets for a wide range of securities have de facto disappeared.” Financial Times, September 20, 2007 1 ”Practical men, who believe themselves to be quite exempt from any intellectual influence, are usually the slaves of some defunct economist.” John Maynard Keynes It is a truism to observe that financial markets clear at asset prices that balance the demand for reward against the supply of risk that is on offer at any point in time. Textbook models — even the most sophisticated, Nobel prize winning ones — typically begin with the assumption that there is a known underlying distribution from which asset returns are drawn. Given a fixed- and known-return distribution, investors can price options, identify relative values on yield curves, and decide on optimal hedge ratios for currencies. Of course, in practice the art of investing comes down to making informed judgments about distributions of returns that are not ‘known’ in advance. Indeed, getting the ex ante distribution of returns right is often the most important element of successful ‘risk’ manage- ment in actual financial markets, where the distribution of returns is an equilibrium outcome, not a preordained physical constant. However, under certain circumstances, it is crucially important to distinguish between the risk of undertaking an investment and the possibility of substantial uncertainty regarding the range of possible distributions of investment outcomes that an investor may confront. -
The Global Securitized Debt Market Opportunities Often Reside in the Debris of Crisis
Brandywine Global Investment Management, LLC Topical Insight | May 23, 2014 The Global Securitized Debt Market Opportunities Often Reside in the Debris of Crisis New issuance of private-label securitizations remains at nearly non-existent levels today despite more than five years of improving investor risk appetite since the Financial Crisis. While other spread- product markets reclaimed considerable new issuance momentum shortly after 2008, many investors today are still scarred from the miserable failures of private-label securitization during the housing crisis. Convolutedly packaged collateralized debt obligations (CDOs), backed by poorly underwritten subprime collateral, not only delivered steep price declines during the Financial Crisis, but market liquidity dried up quickly which impeded investors from exiting positions at reasonable valuations. Despite recent price gains in legacy securitized issues—those issued prior to 2008—neither the economics nor the tarnished reputation of securitization will justify a vigorous rebirth of the private- label new issue market anytime soon. As a result of the slowdown in new issuance and investors’ past trouble with these complex securities, many are choosing to fully avoid the legacy securitized market—eschewing exposure to both the private-label market and the more complex areas of the government-securitized market, called “agencies.” For that reason a large, under-owned, and under- followed collection of legacy securitized issues are available at attractive valuations. The legacy securitized market exhibits many of the inefficiencies that exemplify a distressed market. Those inefficiencies include all kinds of dislocations in the form of rating disparities, pricing anomalies, information asymmetry, ongoing regulatory uncertainties, unresolved legal settlements, abnormal servicer behavior, and an unclear future for housing finance. -
Adoptive Trend Following Strategy in Financial Time Series with Multi-Objective Function
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 12, DECEMBER 2019 ISSN 2277-8616 Adoptive Trend Following Strategy In Financial Time Series With Multi-Objective Function Ajit Kumar Rout, Satish Muppidi Abstract: Given the complex nature of the stock market, determining the stock market timing - when to buy or sell is a challenge for investors. There are two basic methodologies used for prediction in financial time series; fundamental and technical analysis. The fundamental analysis depends on external factors such as economic environment, industry and company performance. Technical analysis utilizes financial time series data such as stock price and trading volume. Trend following (TF) is an investment strategy based on the technical analysis of market prices. Trend followers do not aim to forecast nor do they predict specific price levels. They simply jump on the uptrend and ride on it until the end of this uptrend. Most of the trend followers determine the establishment and termination of uptrend based on their own rules. In this paper, we propose a TF algorithm that employs multiple pairs of thresholds to determine the stock market timing. The experimental result on 7 stock market indexes shows that the proposed multi-threshold TF algorithm with multi-objective function is superior when it is compared to static, dynamic, and float encoding genetic algorithm based TF. Index Terms: financial time series, technical analysis, trend following Algorithm. —————————— —————————— 1. INTRODUCTION identify the establishment of an uptrend and determine It is believed that the future behavior can be predicted by whether the uptrend could persist for a certain period of time studying the past behaviors. -
~Do NOT Compare Your Results with Other People, How They Trade Has
.~I have no ego with my system, I just take the signals.. I have no expectations as I know the losses will be 60% but when the market decides to trend then I know all losses will be recovered as WINNERS ARE BIGGER than LOSERS~ ~Do NOT compare your results with other people, how they trade has nothing to do with how you trade or your system, comparing results will just cause you to lose faith in your PERFECTLY TESTED SYSTEM.~ ~The problem is not this trade it’s about the 1000 Trades to come, the problem is if you take profit early and it works out to your advantage, you're fucked for following rules in the future and your Model will not be REWARDED as it should be based on your well BACK-TESTED results for over 7 years~ ~Most intelligent way to begin managing money when trading is to confess you don't have a clue where the markets are going - Hugh Johnson~ ~Holy Grail of trading is finding a strategy where you manage INEVITABLE LOSSES whilst MAXIMIZING PROFITS- Big Money little Effort Mark Shipman~ ~Going against any system rules means that you are not trading the system. Long-term investment system has PERFORMED VERY WELL over the years without your help or interference and it doesn’t need any of that input now. ~ ~The first stage is the mechanical stage. In this stage, you: 1. Build the self-trust necessary to operate in an unlimited environment. 2. Learn to flawlessly execute a trading system. 3. Train your mind to think in probabilities 4. -
Second Quarter 2017 Earnings Release
Fortress Reports Second Quarter 2017 Results New York, NY. August 3, 2017 – Fortress Investment Group LLC (NYSE: FIG) (“Fortress” or the “Company”) today reported its second quarter 2017 financial results. ∗ FINANCIAL SUMMARY • Management Fee Paying Assets Under Management (“AUM”) of $72.4 billion as of June 30, 2017, up 3% compared to the previous quarter • GAAP net income of $32 million, or $0.06 per diluted Class A share, for the second quarter of 2017, compared to a GAAP net loss of $27 million, or a $0.07 loss per diluted Class A share, for the second quarter of 2016 • GAAP net income of $25 million, or $0.05 per diluted Class A share, for the first half of 2017, compared to a GAAP net loss of $43 million, or a $0.11 loss per diluted Class A share, for the first half of 2016 • Pre-tax distributable earnings (“DE”) of $86 million, or $0.22 per dividend paying share, for the second quarter of 2017, compared to pre-tax DE of $101 million, or $0.26 per dividend paying share, for the second quarter of 2016 • Pre-tax DE of $160 million, or $0.40 per dividend paying share, for the first half of 2017, compared to pre-tax DE of $165 million, or $0.42 per dividend paying share, for the first half of 2016 • Net cash and investments of $1.0 billion, or $2.55 per dividend paying share, as of June 30, 2017 • $1.4 billion of gross embedded incentive income across funds and permanent capital vehicles as of June 30, 2017, that has not yet been recognized in DE • Total uncalled capital, or “dry powder,” of $7.5 billion as of June 30, 2017, including -
First Quarter 2017 Earnings Release
Fortress Reports First Quarter 2017 Results Announces Dividend of $0.09 per Share New York, NY. May 8, 2017 – Fortress Investment Group LLC (NYSE: FIG) (“Fortress” or the “Company”) today reported its first quarter 2017 financial results. ∗ FINANCIAL SUMMARY • Fortress declared a cash dividend of $0.09 per dividend paying share for the first quarter 2017 • Management Fee Paying Assets Under Management (“AUM”) of $70.2 billion as of March 31, 2017, up 1% compared to the previous quarter • GAAP net loss of $7 million, or a $0.02 loss per diluted Class A share, for the first quarter of 2017, compared to a GAAP net loss of $16 million, or a $0.04 loss per diluted Class A share, for the first quarter of 2016 • Pre-tax distributable earnings (“DE”) of $74 million, or $0.19 per dividend paying share, for the first quarter of 2017, compared to pre-tax DE of $64 million, or $0.16 per dividend paying share, for the first quarter of 2016 • Net cash and investments of $1.0 billion, or $2.45 per dividend paying share, as of March 31, 2017 • $1.4 billion of gross embedded incentive income across funds and permanent capital vehicles as of March 31, 2017, that has not yet been recognized in DE • Total uncalled capital, or “dry powder,” of $7.5 billion as of March 31, 2017, including $4.8 billion available for general investment purposes BUSINESS HIGHLIGHTS • Raised $1.6 billion of capital across alternative investment businesses in the first quarter of 2017 • Investment performance summary as of March 31, 2017: o Annualized inception-to-date net IRRs for Credit Opportunities Fund (“FCO”), FCO II and FCO III of 23.3%, 16.1% and 10.5%, respectively o First quarter 2017 net returns of 2.4% for the Drawbridge Special Opportunities Fund (“DBSO”) LP o All 16 Logan Circle strategies outperformed respective benchmarks in the first quarter of 2017 PROPOSED ACQUISITION BY SOFTBANK • On February 14, 2017, Fortress announced that it had entered into a definitive merger agreement pursuant to which it will be acquired by SoftBank Group Corp. -
Newcastle Investment Corp
FORTRESS INVESTMENT GROUP LLC Contact: Gordon E. Runté 212-798-6082 Fortress Reports Third Quarter 2011 Financial Results New York, NY. November 3, 2011 – Fortress Investment Group LLC (NYSE: FIG) today reported its third quarter 2011 results. THIRD QUARTER 2011 HIGHLIGHTS • Assets under management of $43.6 billion as of September 30, 2011, down slightly compared to September 30, 2010. • Pre-tax distributable earnings (DE) of $43 million in the third quarter of 2011, down from $78 million in the third quarter of 2010; pre-tax DE of $0.08 per dividend paying share in the third quarter of 2011, down 47% from $0.15 per dividend paying share in the third quarter of 2010. • Fund management DE of $51 million in the third quarter of 2011, down 28% from $71 million in the third quarter of 2010. • GAAP net loss, excluding principals agreement compensation, of $102 million in the third quarter of 2011, compared to a net loss of $32 million in the third quarter of 2010; GAAP net loss attributable to Class A shareholders in the third quarter of 2011 was $142 million, compared to a net loss of $95 million in the third quarter of 2010. • Total cash and cash equivalents plus investments net of debt obligations payable of $2.08 per dividend paying share, and GAAP book value per share of $1.95 as of September 30, 2011. “Fortress delivered steady, profitable results in a quarter that saw double-digit declines in broad market indices and in which volatility spiked to its highest levels since early 2009,” said Daniel Mudd, Fortress Chief Executive Officer.