伦敦宏观量化与衍生品会议：会议报告和客户调查总结

1、2019 London Macro Quantitative and Derivatives ConferenceSummary of Conference Presentations and Client SurveyOur 20th Annual Macro Quantitative & Derivatives Conference (London, Oct 11) was attended by 400 investors representing 250 institutions. The conference featured presentations from Alliance

2、Bernstein, Amundi, APG, Blueshift, CircleUp, Deka, First Quadrant, Man Group, QMAW, RAM, Robeco and Union. Speakers at the conference deliberated on various aspects of Quant Investing: Market Participants Shaping the Investing Environment, State of Liquidity, Macro- Economic Factors, Crisis Proofed

3、Strategies, New Investment Frontiers, Capacity and Hidden Risks of Factor Strategies, Harvesting Non-traditional Risk Premia (Merger Arbitrage, Credit etc.), Quants in the Private Markets, and Incorporating ML within the Fundamental Investment Process. In this report, we have summarized the conferen

4、ce presentations highlighting the key insights from each talk. Our next Quant Conference will take place in Hong Kong and Tokyo in early April 2020. Please let us know if you are interested in attending.We also conducted a asking our clients about their views on: implementation choices for their qua

5、nt strategies; top concerns for broader risk premia; current macro conditions and future expectations; along with application and adoption of big/alternative data and machine learning in investment decision making process. Investors believe Equity Multi-factor and Stat Arb / High Frequency strategie

6、s the best performance outlook, a remarkable shift 2018 when Selling Volatility was the most favored. Collapse in liquidity and heightened geopolitical risks are key risks to quant strategies, a belief largely unchanged a ago. On risk management, Volatility Targeting remains a key focus across vario

7、us types of investors with than 70% adoption rate. The most common lookback window is between 2 and 6 months applied typically at portfolio or individual asset level. Weighted realized volatility is the most popular measure used, followed by simple realized vol and mixed implied/realized vol. ESG in

8、vesting continues to gather adherents with higher adoption in Europe compared to the US. 46% of London attendees are already invested and 28% are evaluating ESG. Since ESG is aligned with Quality / Low Vol, an ESG bubble time as valuations favored ESG stocks get increasingly disconnected fromfundame

9、ntals.Alternative/Big Data and Machine Learning based strategies are primarily being used to enhance existing Quant approaches as well as portfolio construction and risk management. Only 13% of investors do not plan to use it extensively. Most investors have been trying out new data and ML technique

10、s. In the past a majority of the investors examined 1 to 3 signals. The key challenge for Alternative Data/ ML is the success rate. Two-thirds of investors said these signals have yielded no alpha, while one-third of the participants have value in 1-3 signals. Data vendors and aggregators are the ma

11、in source of Alternative Data and ML/AI capabilities though a significant number of investors primarily depend on internalsources.For a comprehensive overview on over 1000 alternative data providers, together with some case studies on applications of alternative data, see the latest J. P. Morgan rep

12、ort on Big Data and AI Strategies, 2019 Alternative Data Handbook.Global Quantitative and Derivatives Strategy29 November 2019Global Quantitative & Derivatives StrategyMarko Kolanovic, PhD AC(1-212) 622-3677 HYPERLINK mailto:marko.kolanovic marko.kolanovicJ.P. Morgan Securities LLCDubravko Lakos-Buj

13、as AC(1-212) 622-3601 HYPERLINK mailto:dubravko.lakos-bujas dubravko.lakos- HYPERLINK mailto:bujas bujasJ.P. Morgan SecuritiesLLCKhuram ChaudhryAC(44-20) 7134-6297 HYPERLINK mailto:khuram.chaudhry khuram.chaudhryBloomberg JPMA CHAUDHRY J.P. Morgan Securities plcAyub Hanif, PhD HYPERLINK mailto:ayub.

14、hanif ay HYPERLINK mailto:ub.hanif ub.hanifWilliam Summer HYPERLINK mailto:william.summer william.summerDobromir Tzotchev,PhD HYPERLINK mailto:dobromir.tzotchev dobromir.tzotchevDavide Silvestrini HYPERLINK mailto:davide.silvestrini davide.silvestriniNarendra Singh HYPERLINK mailto:narendra.x.singh

15、narendra.x.singhPeng Cheng, CFA HYPERLINK mailto:peng.cheng peng.chengArun Jain HYPERLINK mailto:arun.p.jain arun.p.jainRobert Smith, PhD HYPERLINK mailto:robert.z.smith robert.z.smithAda Lau HYPERLINK mailto:ada.lau ada.lauBerowne Hlavaty HYPERLINK mailto:berowne.d.hlavaty berowne.d.hlavatySee page

16、 31 for analyst certification and important disclosures, including non-US analyst disclosures.J.P.Morgandoesandseekstodobusinesswithcompaniescoveredinitsresearchreports.Asaresult,investorsshouldbeawarethatmay a of the of as a HYPERLINK / Table of Contents HYPERLINK l _bookmark0 Morning Session I3 HY

17、PERLINK l _bookmark1 How Market Participants Shape the Environment for Macro andEquityInvesting3 HYPERLINK l _bookmark2 The Best of Strategies for the Worst of Times: Can Portfolios beCrisisProofed?5 HYPERLINK l _bookmark3 Macro-Economic Factors and Relative Performance ofDiversifyingStrategies7 HYP

18、ERLINK l _bookmark4 Quants in the Private Markets: Dont Hunt Unicorns, EatGranolaBars9 HYPERLINK l _bookmark5 MorningSessionII11 HYPERLINK l _bookmark6 The State of Liquidity inEquityMarkets11 HYPERLINK l _bookmark7 NewInvestmentFrontiers13 HYPERLINK l _bookmark8 The Capacity ofFactorStrategies15 HY

19、PERLINK l _bookmark9 Survival of the Fittest: An Unbiased Investment Approach Based on HYPERLINK l _bookmark9 EvolutionaryBiology16 HYPERLINK l _bookmark10 Evening Session18 HYPERLINK l _bookmark11 SystematicMergerArbitrage18 HYPERLINK l _bookmark12 Hidden Risks behind FactorInvestingStrategies20 HY

20、PERLINK l _bookmark13 Extending Equity Risk Premia into the CorporateBondMarket22 HYPERLINK l _bookmark14 Incorporating Machine Learning within the Investment Process of a HYPERLINK l _bookmark14 FundamentalAssetManager23 HYPERLINK l _bookmark15 Panel Discussion onAlternativeData25 HYPERLINK l _book

21、mark16 AttendeesSurveyResults26Morning Session IHow Market Participants Shape the Environment for Macro and Equity InvestingThe investment behavior of market participants can give valuable insights to determine which market environment one is in, this in turn allows one to draw conclusions as to the

22、 importance of fundamentals as a price driver for assets. focus on market participants is seen as an important point of differentiation compared to conventional trend following strategies.SummaryIn academia the notion of the market participant is mainly reflected the field of behavioral finance wher

23、e the main thrust of research focuses on biases, errors, herding and similar phenomena. Market practitioners, too, tend to focus on herding biases which is expressed through conventional trend following strategies, CTAs etc. The speakers firm attempts to identify different market participants and th

24、e objectives a specific subset of investors is expected to pursue (e.g. bond investor, central banker, options trader, producers, andcompanies).Given that objectives and investment behavior of a market participant evolve as market regimes change, it is crucial to determine in which market environmen

25、t one is at any point in time. At the polar extremes are the states of fragile and resilient market regimes. The behavior market participants gives a clue as to what market environment one is in. How does a market participant react to the emergence of negative news? Does it trigger a buy on dip (res

26、ilient market regime) or a propensity to liquidate a position (fragile market regime)? The underlying fundamental picture of the economy influences the likelihood of a market participant being in a fragile or resilientstate.As part of the process each market is broken down into its market participan

27、ts, their motives (from profit-maximizing in a resilient market environment to needs-driven investment decisions in a fragile state) which in turn is divided into various categories of preferences and needs that are pursued (e.g. asset fundamentals, protection, policy objectives) derived from a rang

28、e of insights. The aggregate of these insights allows to back-out what objectives are being pursued by a given market participant, how closely these objectives align with profit maximization or needs-driveninvestments and, ultimately, what market regime thatimplies.In a resilient environment investo

29、rs are optimistic, forward looking, focus on analyst forecasts, valuations matter, investors take leaps of faiths, follow trends, momentum and growth. Generally, the objectives can be summarized as rational and profit maximizing - contrasted with those in a fragile environment where objectives such

30、as protection, yield, liquidity take precedence. Similar differentiations can be made in the macrospace.If one is to understand which market environment one is in, one can draw conclusions on asset prices and asset allocation. In a resilient market environment we can expect the CAPM to hold; risk re

31、turn relationship to work as expected; volatility, tail risk and herding risk to be drawdowns to be shallow and correlations to remain low - the opposite is true in fragile environments. In fragile environments diversification is advisable, while in resilient environments concentration in a portfoli

32、o canwork.What market environment are we in currently then? Various indicators are being considered grouped into three buckets: equity risk appetite, economic and financial data. The picture that emerges is that we are currently close to a fragile marketenvironment.Audience Q&AQ1. How has increased

33、regulation and the withdrawal of proprietary capital and liquidity post the GFC affected the strategy?A1. The impact of regulation has been minimal. Instead one of the biggest changes has been the involvement of central banks in rates markets, especially on the short end of the curve.Q2. How has the

34、 current environment with Trump tweets etc. affected the strategy?A2. The speaker has noticed the biggest impact to be in FX markets, with a significant increase in noise, as currency markets react strongly to these headlines reflecting a changing short-term investment mentality. The impact on the s

35、trategy is low when signals are judged on a scale of days and weeks and, indeed, in other asset classes.Q3. Are you seeing benefits from alternative data or machine learning?A3. The strategys edge is not seen to stem from new technologies but asking the right and differentiated set of questions at t

36、he outset of the investment process even if new and innovative technologies including machine learning are being used. Especially, random forest models have been found to be effective. Alternative data is used, albeit only as part of the confirming evidence process to cross check if internal assumpt

37、ions hold and market participants behave as anticipated. Once confirmed the focus swings back to observable catalysts that market participants have access to and that may trigger a change in behavior.Q4. How does the model affect turnover?A4. The strategys edge is seen to lie in identifying the turn

38、 of cycles and as such the turnover tends to be low. In equities the portfolio is turned over every two months and in the macro process every three months.Key TakeawaysThe investment behavior of market participants can give valuable insights to determine which market environment one is in; this in t

39、urn allows one to draw conclusions as to the importance of fundamentals as a price driver for assets.Determining which market regime one is in on a spectrum from fragile to resilient, plays an important role in the firms portfolio construction. The focus on market participants is seen as an importan

40、t point of differentiation compared to conventional trend following strategies. While the theoretical concept purported is intuitively cogent, more details on identifying signals and broader execution of the concept would be useful. The firms models see the current market environment to lean towards

41、 a fragile state, which suggests unreliable risk-rewards, high volatility, tail risk, herding and correlation with less consideration given to fundamentals. The risk of large draw-downs is heightened.The Best of Strategies for the Worst of Times: Can Portfolios be Crisis Proofed?A number of research

42、 papers were discussed focusing on various risk management aspects during crises. The findings highlighted that traditional CTA momentum (12M momentum) and medium-term trend offer positive gains during worst equity periods. Quality L/S strategies have historically performed well during crises, thoug

43、h the flight to quality has been challenged more recently. Also, they show how the negative convexity of rebalancing can be countered by positive convexity of a trend following overlay.SummaryThespeakers presentationreviewedanumberoftheirrecentpapersonstrategicriskmanagementinthepresenceof trending

44、markets. began the conversation with the characterization of periods of market stress that often go from bad to worse. Traditional risk metrics often dont account for the resulting price trendiness during crises. This, in authors opinion, is quite a profound observation and observes the investment r

45、isk (& opportunities) accounting pricetrendiness.The first paper reviewed was Trend Following: Equity and Bond Crisis Alpha (2016). The paper tested given Momentum strategies performed positively before both the 1985 (bond bear market) and after 1985 (bond bull market); if we do not have a bond bull

46、 market how can Momentumwork?They build a momentum strategy using monthly data: 13 commodities, 6 equities and 5 bonds (futures proxy), 9 currencies (after Bretton Woods 73). Four strategies are tested CTA momentum (based past 11 months), medium- term trend (based on past 4 months returns), mom (5,

47、8) (based on returns from 5-8 months ago), and seasonal effect (based on returns from 9-11 months ago). All strategies work; however, the medium-term trend offers best performance.Simulating returns of the CTA momentum strategy from worst to best equity environments, they find an equity smile: best

48、returns in the worst equity periods also good in the best equity periods. When tested against Bonds, they find a bond smiletoo.The second paper discussed followed on from the above 2016 paper, The Best of Strategies for the Worst of Times: Can Portfolios be Crisis Proofed? (2019). Essentially, the p

49、aper looks to see what other strategies work well during equitycrises.Since 1985, there have been eight drawdowns in the S&P 500 -15%. Bonds do not do well during Equity crises. Bonds were an unreliable crisis hedge 100 years before 2000. Post-2000 the bond-equity correlation was persistently negati

50、ve;notthe100yearsbeforethat.Goldseemstohavedonewellmorerecently.Credit/longputshaveanegative average return though do pay off duringcrises.Are there dynamic strategies that perform well during crises? We look to economic intuition as well. Two strategies were evaluated: 1) futures time-series moment

51、um, and 2) Quality L/S stocks. Futures time-series momentum performs better during extended sell-offs, with 12M momentum too-fast. Quality stocks have historically benefitted from a flight to quality during equity crises though the largest amount of gains were during the tech bubble - more recently

52、this feature seemschallenged.The last paper reviewed was Strategic Rebalancing (2019). In trending markets, monthly rebalancing of a 60-40 equity-bond portfolio led to larger drawdowns than with buy-and-hold over periods back tested. A trend following overlay counters the impact on drawdowns of the

53、mechanical rebalancing strategy. Alternatively, one can use a trend- based rebalancing rule e.g. delay rebalancing if stock markets are downwardstrending.Audience Q&AQ1. On Short credit risk, what do you use?A1. You could use a CDX - a basket of synthetic credit notes.Q2. What do you see the percent

54、age correlation to pre-2000 levels?A2. Most equities do work better during positive inflation regimes. If inflation comes roaring back, then equities beat bonds and the correlation goes back to positive.Q3. What are your t-costs in these analyses?A3. There is no real way to model t-costs historicall

55、y. They have been roughly 4bps recently and we have taken this historically.Q4. How do you calculate / account for yields being 0 or 0?A4. The ZIRP / NIRP environments do not alter calculations, we just use returns.Key TakeawaysThe speakers presented a number of papers discussing various risk manage

56、ment aspects during crises. They found that traditional CTA momentum (12M momentum) and medium-term trend offer positive gains during worst equity periods. Quality L/S strategies have historically performed well during crises though the flight to quality has been challenged more recently. And they a

57、lso show how the negative convexity of rebalancing can be countered by positive convexity of a trend following overlay.Macro-Economic Factors and Relative Performance of Diversifying StrategiesBoth trend and non-trend macro strategies offer diversification. The non-trend strategies have the advantag

58、e of being less crowded and could continue to outperform trend strategies should the current low rate environment persist.SummaryThespeakersfirminvests$130Bn inassetsentirelyquantitatively. Hisobjectivetodayistorelatetheperformanceof quantitative strategies to the various macroenvironments.If we com

59、pare the relative performance of trend following indices and other systematic macro indices relative to 60/40 portfolios, we can see that with the exception of 2014 and this year the performance of trend following has declined substantially. We believe that the macro environment over the last decade

60、 depressed the performance of trend following strategies, but helped other risk mitigating strategies. The talk is going to focus precisely onthat.Returns have diverged meaningfully post-GFC and some non-trend strategies have performed well. The same macro frameworkcanexplainboththeunderperformanceo