Risk Model Shops and Industrial Risk Models

Northfield Infomation Services: Northfield risk model here, publications here

adjust for sensitivity of estimation, considering constrain(tax, cost, fee, turnover etc.), translate RR model into easily comprehensible factors. chain optimization, backtesting w rebalancing.auto identify management style(normalize leverage, ?.) CUSUM(cumulated IR for exsample, claim to detect structure change), dispersion comparison to detect(skill v.s. luck).

APT , advanced portfolio technologies. using stats factor model, robust port optimization. acquired by Sungard. webinar, whitepaper etc. here

Barra: equity risk model details (fundamental model), including a short term trading model(predict vol mainly).

Quantal: acquired by reuters, app available in capital IQ

Optimal Trading Strategy

[2-1-10] read up to chp 8, so far nothing solid, but start touch something interesting with dealer model and timing risk.

Robert Kissell( one of the author of OTS) is actually a ED at JPM quant trading team, here is an internal paper. see also ‘expanded implementation shortfall’ below.

’05 articles about pre-trade analytics offered by citi, feature almgren

digging gold in footnote – 4

Barr Rosenberg, factor model ( Swanson had a few page discuss about Barr’s fund performance, except for initial success, the performance post fee is sub par).

Rosenberg, Persuasive evidence of market inefficiency.

Rosenberg, Factor related and specific returns of common stock.

BARRA on campus: kit should be available at SOM, check

BARRA webinars

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Advanced Trading, a tech trading magazine

Automated Trader, anther algo trading magazine

Andre Perold: HBS prof. one of the most important transaction cost paper: Implementation Shortfall (jpm version), (’88), article about GS new algo to address it (’08)

Andre Perold (very well connected, worked with E. Schulman, Black, Sharpe, Markowitz etc. , also the author of batterymarch case, and many other investment management/trading related case ( where can I get all these cases??), some interesting work as well.

Wayne Wagner, LA based Plexus Group founder, Stanford Alum, acquired by JPM then by ITG. ‘whole cost measurement industry originated by Plexus according to Leiweber.

Wayne Wagner, The Incredible Story of Transaction Cost Management: A Personal Recollection. Journal of Trading 3, no. 3. Summer 2008. (Go get it!)

ALL THE AI TECHNIQUES, PARTICULARLY GA/TEXT FRONT, SOCIAL MEDIA/COLLECTIVE INTELLIGENCE.

digging gold in footnote – 3

Ben Rosen, MS analyst, VC, first one to spreadsheet (VISICALC), his blog here

Vernon Smith, ’02 Noble Laureate for experimental economics, reflection on behavior, work, wiki / Charles plott Smith’s college at Caltech(?) foundation paper, exp econ handbook with Veron.

Ross Miller, GE quant finance head, authors of Paving Wall Street: Experimental Economics and the Quest for the Perfet Market ( ordered from amazon on 2/2/10)

Blake LeBaron, Agent based market model, work, a good summary

Terrence Hendershott, CIFT stuff, Haas a.p.  some paper shares my interest: e.g. mm inventory, mm algo etc. etc.  / SEE ALSO David Easley (Cornell), Taron Ramadori (Oxford)

Dimitris Bertsimas/Andew Lo: Books: cont time method and mkt microstr(Lo), stat method non standard finance(Lo). Course materials and paper could be interesting as well. The transaction cost paper is HERE.

Robert Almgren, another cost pioneer in Courant, the optimal trading paper is HERE, the idea is more close to real environment by taking risk preference into consideration.

IBM article, the trader is dead, long live the trader 06

2015 Prognostication: Bearingpoint, 1 2 Datamonitor 1,

SEC: TRW(?), General Dynamics, BAE system, Northrop Grumman, [ different kind: AICC (10b ’17) , China Poly Group, China New Era Group. ]

NYSE: abasco service report in 57 to automate the trading floor. abasco acquired by Raytheon (5th defence contractor).

INQTEL: CIA’s VC arm

WOMBAT Fin Software: acquired by NYSE in 08, Adnan Khashoggi of low-latency finance(means infrastructure i guess).

Wall Street and Technology: the magazine

digging gold in footnote – 2

David Whitcomb

[0] papers

[1] Transaction Costs and Institutional Investor Trading Strategies, Schwartz, Whitcomb, nomoograph 1988-23

[2] The Microstructure of Securities Markets, Kalman J Cohen, Robert A. Schwartz, David Whitcomb 1986 ( available in yale social science lib)

[3] Trading and Exchange: Market Microstructure for Practitioner, Larry Harris

[4] Empirical Market Microstructure: The Institutions, Economics, Econometrics of Security Trading, Joel Hasbrouck (available in yale social science lib)

Robert A. Schwartz:

personal page

research ( a few interesting paper before ’88)

tradex ( experimental market, here offers a glimpse of what tradex is about)

digging gold in footnote – 1

CIFT [adviser]

Steven Skiena: Stony Brook CS faculty. interesting book on betting, and internet datamining.

Toby Segaran: author of programming collective intelligence, data mining, visualization.

Richard Rosenblatt: CEO of Rosenblatt Security. Equity market structure expert, runs NYSE floor.

William Janeway: Warburg Pincus TMT advisor, experience includes  FORTENT(finance crime, surveillance merge with Actimize), O’Reiley media, Roubini Global Economics, Nuance( voice recog), NYFIX/Wallstreet System(order management, transaction service acquired by NYSE EURONEXT).

Roger Ehrenberg:  ex boss of of DB hedge fund, bloggist of Information Arbitrage. now running TMT focused IA capital. founded Kinetic Trading Strategy( comp fin, textual analytics, alt data, ’08) Portfolio here.

David Whitcomb: true entrepreneur, ATD founder, research on microstructure and trading cost in 80s. top 100 economist(?). ATD focused on limit order execution. Whitcomb SEC testimony.

Evan Schuleman: father of quant trading, batterymarch autotrading system in ’70s, lattice trading (order matching, routing, VWAP execution, sold to state street in ’97).

Richard Lindsey: author of how I become a quant. former SEC director of mkt and chief economist, former president of bear security, former SOM professor. market microstructurerist.

Henry Lichstein:  citibank IT chief. doing VC now.

William Hart: data communication background, Black’s team member for 4 years in developing algo trading strategy and infrastructure. Lehman, Soloman Strategic Equity B dev for trading infrastructure. EVP Nasdaq, BOA equity strategy. board of ECN, ATS.

Bill Aronin: founder of  quantitative analytics acquired by Reuters.

Peter Dickson: Algo solution for Dowjones.

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CIFT [stuff]

Mahesh Krishnan – microstructurist

Max Dama – the bloggist

Jike Chong – infrastructure

Organization – Process – People

The last two chapter of ‘Pioneering Portfolio Management’ conveyed a clear message, one of a recurring theme of Swanson’s idea, which is a relentless emphasis of Process Process Process! The structure of an organization defined the its behavior and behavior of its people, and henceforth determines the characters and performance. Swanson outlined in his book his ideal investment management organization:

1) Investment Committee

• diversified
• judgment over professional qualification
• goal is to manage the process not the investment portfolio

This is similar to emphasize policy portfolio and asset allocation over tactical decision making and market timing. As the investment committee suppose to oversee the higher order attributes of the organization.

2) Investment stuff

• encourage honesty guarantees disclosure
• disciplined process, coherent intellectual framework guards against informal casual decision
• scheduled meeting institutionalize the decision process. Written memo encourage logical thorough thinking over oral discussion
• guard against group dynamics, limit consensus building in the process, encourage independent thinking and contrarian view. provide supportive environment for loner (this is much more like a university department than a corporate)
• constantly renewal of young professional, train through apprenticeship is much more effective than outside hiring or seek training through academic program

3) Organizational character

• high quality people are attracted to cutting edge issues, embrace global strategy and focus on mentorship
• operates on the PERIPHERY of standard institutional norm
• aware of the fact that standard selection process exclude interesting manager. embrace non-standard approach
• aware of the fact that conventional unimaginative bureaucracy ensure poor result.
• avoid assigning blame, encourage discussion of failure opening (by emphasizing the process over individual decision)

4) Decision making Challenge

• misalignment of interest between individual and institution, between agency and principal.
• fear, seek of instant gratification, peer pressure
• contrarian pitfall ( NYU too far away from norm, Boston U too concentrated, strike the right balance is the name of the art).

The prestige, Christopher Nolan

Watched ‘The Prestige’ for a second time tonight. The movie had Christopher Nolan’s hallmark of cutting edge filming techniques, nonlinear narrative structure and psychological themes. Interestly, the two rival magician, robert angier played by hugh jackman and alfred borden played by christian bale, and the female leads, rebacca hall and scarlet johnanson were also cast chosen in woody alan’s scoop and vicky cristina bacelona.  the plot exploit symetry on multiple levels embodying the rivalry between angier and borden: both incessantly seeking to deciper the secret through each others diary, the sacrifice, the love affairs of aniger and borden and the tragic love of borden and his decoy. The open scene of hats and bird trick mimics the overally theme of duplication of self and sacrifices both magicians made to entertain. so much to expect for Inception written and directed by Nolan and casting dicarprio this summer.

The source of alpha

illiquid (long term, less marketable)

unconventional/inconvenience (geographical IFC/OPIC, out-of-run issues, ultra low latency)

complexity (regulation, hard to price)

value (evaluation, margin of safety, patience, this words in combination with liquidity)

value creation ( improved management(forestry, corporate, urban planing, (potential)celebrity, (potential) network effect, prop technology)

size ( fill bathtub with teaspoon)

out-of-favor, contrarian ( GMO out of favor, this is not in strict sense a source of alpha, but …)

outright inefficiency (Sallie Mae stock conversion)

Information ( cap < 1b, no coverage)

Asset class

As a starting point, the first step for a good portfolio manager is to identify the asset class. So exactly what is asset class? According to MPT, from a numerical perspective, an asset class differentiates from one to another wrt return, volatility of return and more importantly covariance. But merely treat asset class with respect to its numerical attributes is missing the deeper meaning of the concept and hence bounded to be misguided when choice asset classes and pursuing diversification. The basic concept of diversification is to construct a basket of asset whose return does not correlate with one another hence one factor of general environment affects a limited part of the whole portfolio. Invest benefits by trying to understand the source of value creation when analyzing their portfolio. Value can be created through a variety of ways.

Taking forest land for example, the value creation process in this case is nature biological growth of tree. The asset itself does not diminish during the value creation process, and better forestry management enhance the timber yield in a sustainable way. Other auxiliary value could be created in the process, for example, better resource management might improve the environmental quality of the surrounding area and therefore increase the desirability of property near the forest land. So roughly speaking, there are three components of value creation in this case: 1) the natural growth rate, which is the reward for planting the seed and create a desirable resource 2) direct, observable value enhancement through better management 3) indirect, hard to monetize enhancement through step two.

Taking this analogy one step way into equity market. The first layer of value creation is through accumulation of capital. As a scarce resource, capital is rewarded with approximately risk free interest rate. For putting the capital in a risky position as oppose to risk free asset, a premium is demanded which should be linear wrt to the risk assumed, this can be considered as merely enhance the return on investment through leverage, which should constitutes part of the conventional equity risk premium. Equity market as a whole wrt to debt demands another type of reward, which is for the business activities private sector going through with incorporate and organization in general sense. This part of reward(1~2%) together with the reward for assuming the capital risk constitute the equity risk premiums. For U.S. market debt ( 2% return v.s. 10% risk) is consistent with equity ( 6%  return v.s. 20% ; assume 2% premium for business activities), the result is consistent with this assumption.  Through better management and operation (buy out in pure sense), propitiatory technology (GOOG, 70% return v.s. 50% vol, which implies, after normalize with debt vol, 2%(capital)+1%(corp)+9%(management+prop)), the equity return can be improved wrt to benchmark.

Another source of value generation is liquidity. Due to the behavioral and capital constrain, patient investor is rewarded for holding illiquid asset over long period of time. According to Yale endowment data, the adjusted liquidity premium should be around 2% in long term. Besides, absolute return provide another source of value through enforcing market efficiency. The caveat for this two strategy is that historical data might paint an overoptimistic picture for the future. Real asset vol is normally underestimated due to low observation frequency; absolute return data normally suffer from back filing and survival bias.

Applying this conceptual framework, many diversified portfolio might not be as what investor expected. For example, Private equity (buy out and VC) in purity extract value through improving management and spotting promising prop technologies. However, if, and in most case, the main component of PE’s PnL is generate through leverage and late stage IPO financing, investor will not have much true diversification compare to boarder equity market.

Also, most traditional 70/30 equity/debt portfolio actually over weighted in capital component and over concentrated on corporation activity component. a comparison of best endowment portfolios and mean portfolio illustrate the case:

Components                                   Yale:                                                              Mean

cap                                                     33%                                                                   50%

corp                                                   20%                                                                   38%

mange+prop                                  13%                                                                    3%

liquidity                                           20%                                                                    3%

absolute                                           16%                                                                     6%

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