OptiRisk Systems

Offering ALMs on... Getting Old and Women

Issue 2

This year’s magic number is 7424. A woman retiring in 2010 will receive this much less than a man on average each year in pounds; this magic number is also known as the pension gap.

The survey, which was done by Prudential (www.pru.co.uk/pdf/presscenter/pension_gap_grows.pdf), found that not only is the gap worth £7,424 but that it has also been increasing over the years; in 2009 it was £6,642 so there is an increase of £782 this year.

Some possible reasons for the disparity are as follows:

1.  Women need to take career breaks for various reasons, in particular to have children.
2. They generally earn less than men.
3. And women also live longer than men, so more years after retirement means a lower pension per year.

The report suggests that women can get Home Responsibilities Protection (http://www.direct.gov.uk/en/MoneyTaxAndBenefits/BenefitsTaxCreditsAndOtherSupport/Caringforsomeone/DG_10018691) and also make up National Insurance contributions to avoid receiving less money during their pension years. Moreover, they should consider company pension schemes and even try to save sufficiently by private schemes.

There is a wealth of information available about The Pension Gap. You can simply type it on Google search and you will find a variety of documents that date back for years, implying that the gap has been a problem for a while now. So why then, is it still widening at such a high rate? Maybe a lot of women are not aware of such obstacles until they actually retire -- by which time it is too late.

At this point Asset-Liability Management plays an important role. On a corporate level ALM models can be used to guarantee a pension to the retired members while keeping the contributions low for the active members in case of a defined benefit plan, or by guaranteeing a sufficient level of pension benefit payments in case of a defined contribution scheme. Individuals can also opt for individual ALM models that take into account tax, salary, fixed expenses (i.e. children’s tuition fees), current and possible future real estate and marital status.

It's not like what it used to be

Considering the current economic environment, the question has to be raised as to which roles within a financial corporation are more important to the restoration of the company. Applying this to the quantitative analyst role, the question translates to: “do we want personnel who can quickly improve the implementation process of models that are already in place producing results that have immediate effects, or a role in which time is needed to design and test new innovative methods?” The former is referred to as a quant developer and the latter is a “traditional” quant, and this time it’s the traditional quant who lose out to the quant developers I’m afraid. 

A glance through any forum or job advertisement for quants’ and you get the perception that they are referring to programmers, with all talk surrounding Java, VB, C++, C# etc. In fact, it has now become a standard requirement for quants to be familiar with programming languages. Quite an intimidating concept really for all those aspiring quants who have come out of higher education with a finance/maths/physics degree expecting their knowledge to be sufficient for the job. The reality is quants need to be well rounded with a variety of skills and knowledge including awareness of the indications of their work to traders, which does no harm for their own career progression either. So a new language is on the agenda, and it must be learnt.

Could this mean the end of “traditional” quants? Quite possibly. With a revolutionary model already available (the position taken by the Black-Scholes equation), it is not likely that the same will happen anytime soon.  What financial institutions demand for now, is immediate results.

Luckily, there is also another side of the story. It seems that there is a visible trend in quants leaving their big institutions and starting it out alone, once they obtain a certain amount of experience. They take with them the models and tricks of the trade learnt over the years, becoming entrepreneurs in their own rights and consequently abandoning the traditional image of a quant sat with their eyes glued to a computer in a dark corner somewhere in the building. The “tricks” comprise of the application and practical side of the quantitative work, allowing them to morph into the role of a trader. Moreover, trading positions are becoming more and more technical with a high degree of quantitative skills required as complex trading securities become increasingly popular in trading activities. The understanding of such instruments is not simple, to say the least, not to mention the validation and back testing processes behind it. Quants hold a great advantage in this respect as they already have the quantitative skills and, from experience in the industry, the practical side can also be obtained. For courses that provide practical applications of mathematical theory to the finance sector, see http://www.optirisk-systems.com/newsandevents_events.asp.

With the evolving condition of our financial markets, roles of quantitative analysts are also changing alongside it. Gone are the traditional stereotypes and in come the practical appliers.

Offering ALMs on... Lost in Translation

Issue 1

Loans, interest rates, debt, mortgages, cash flows, current holdings, scenario generation, liabilities…  STOP!  Too much information.  

In the good ol’ days managing your wealth was not so technical; people were exposed to it as a part of regular life. They grew up learning the ALM language having to handle their farms, animals, families and the likes. Needless to say, we are all exposed to a certain degree of ALM expertise in our everyday life (Credit to all our lovely stay-at-home mothers and fathers; managing a home and family is not a layman’s job!)

But with the ever-changing laws, increasing expertise, numerous options and resources available to us, our inherent ability to manage does not cover all aspects of our financial needs. Life has moved on from being just about crops and cows, causing the ALM language to widen, too.

Formalizing a proper description of ALM…

On the positive side of it, we have assets. To plainly describe this, it is a resource in your possession that will ultimately give you some benefit and in most cases is generally monetary. These resources come in various different forms like property, stocks, bond, gold and so on.

On the negative side - often characterized by the fact that we are all closet shopaholics to a certain extent - we have liabilities. This is exactly the opposite of assets; resources that need to be paid off such as loans, debts, mortgages, taxes.

Finally, asset-liability management tries to find a perfect balance between the two such that your assets ultimately exceed your liabilities; hence it is also known as surplus management. ALM must take into account the amount of risk you are willing to take since your assets can be invested in high risk resources as they generally have a higher return in the future.

Apart from the fact that you need thorough methods of managing your assets, why is this high level of professionalism so important?

The answer was taken care of more than 2300 years ago by Socrates in Oeconomicus written by Xenophon, his follower. In a conversation between Socrates and Critobulus on wealth management, Socrates asks whether a flute in one’s possession can be considered an asset. Critobulus smartly replies back that it is only valuable if one knows how to play it and for the unskilled, it can be sold to make it profitable. But Socrates argues back by saying,

“And I understand you to concur in the truth of our proposition so far: wealth is that, and that only, whereby a man may be benefited. Obviously, if a man used his money to buy himself a mistress, to the grave detriment of his body and soul and whole estate, how is that particular money going to benefit him now? What good will he extract from it?” (Oeconomicus by Xenophon translated by H. G. Dakyns)

This is exactly how we understand an asset to be described, a resource that benefits you in some way, and Socrates suggests that an asset can become a liability (in this case bad health may lead to medical expenses) unless one knows exactly how to manage the asset for a positive benefit. An ALM precisely manages your assets in a manner that benefits you not just by putting extra money in your wallet.

ALMs do not only cater to individuals but also institutions such companies, organizations etc. There are also numerous amounts of algorithms for different sectors such as pension, insurance and banking, neatly summarized in the ALM Handbook which can be found at http://www.optirisk-systems.com/publications/ALM_Handbook.asp. Optirisk Systems itself is in the progress of creating one such software: LDIOpt (Liability Driven Investment Optimization). The ALM models, created by Schwaiger et al. (2010, Journal of Asset Management), are detailed to manage pension funds.

For further information, there are also three workshops available: Practical Asset and Liability Management Workshop (http://www.optirisk-systems.com/events/practicalalm.asp), Asset and Liability Management Hands-On Workshop (http://www.optirisk-systems.com/events/almws.asp) and Bank Asset & Liability Management (http://www.optirisk-systems.com/events/bankalm.asp).

The World of High Frequency Trading: We can’t live with 'em, can't live without 'em

High Frequency trading (HFT), also known as algorithmic trading, is a procedure for automated trading which is mainly focussed on detecting market idiosyncrasies/irregularities and exploiting these all within a fraction of a second. This is done by dedicated powerful computers constantly analysing the market data and driven by algorithms which utilize the trading opportunity. These computers operate in a cycle of milliseconds, outperforming the human brain which usually cannot cope with very low-latency information, digest it, accurately resolve and act upon it.

HFT operates on powerful computers co-located by (sitting next to) the trading platforms in the stock exchange.  These non-human traders process millions of orders at lightning speed and reap huge profits.  Since computers seldom get ill, are easily replaceable, have no emotions and have become really cheap, HFT is a highly lucrative business. It is the hottest thing in the equity market and governs almost 60% of volume; in essence HFT drives the market.

Trading at the highest frequencies with well-designed and implemented strategies produces double-digit Sharpe ratios while real-life Sharpe ratios for well-executed daily strategies tend to fall in the 1-2 range.

High-Frequency Trading Strategies:

Each quote, or “tick,” carries unique information about market conditions. High-frequency traders look for small gains at tick level on short-term market arbitrage, mostly trying to take advantage of temporary market inefficiencies. The traders move in and out of the positions several times in a day.

Broadly speaking, HFT strategies make their profits through:

• Market-making: makes money on the "bid-ask spread" by selling and buying on both sides of the trade. Exchanges offer discount of about one-third of a penny per share as these firms are willing to step up and provide shares when needed.
• Ticker tape reading
• Event arbitrage
• High-frequency statistical arbitrage

Programming: 

Mostly C++/Python/Matlab and some Java

Firms:

Getco, Lime Brokerage/Tower Capital, Goldman Sachs, Citadel Investment Group LLC, Renaissance Technologies LLC, Jane Street Capital LLC, Hudson River Trading LLC, Wolverine Trading LLC and Jump Trading LLC.

Issues with HFT:

HFT practitioners claim to have lowered fees, boosted liquidity and massively increased volume, but the problem is that, unlike a traditional market maker, they have no requirements.  No minimum size to display, no minimum time to display a quote and no capital commitment to a client.  The market is unregulated and most high-frequency traders feel they have no obligation to help maintain an orderly market.

HFT’s simply shut down their computers and walk away when their model corrupts,  creating a panic in the market (Biggest unexplained crash in the market 6^th May 2010);  if these HFT firms decide to shut down, what will happen to that 60% of the volume that they now control?

Traditional traders are at a disadvantage, in that unless until you have instant access to data across markets and the ability to place your own trades in a nano-second, you cannot compete.

It’s an arms race with only the biggest, fastest computers making money consistently. HFT causes mass-correlation and, ironically, encourages more volatility as opposed to more stability. 

Summary Position:

The problem is not that we can't live without HFT liquidity; the problem is that the traders using HFT eat up all other sources of liquidity and when they shut down their shops, basically no one is left.

A Demand for Quants

The thirst for quants nowadays in industry is resulting in a higher number of students enrolling on financial mathematics courses. In turn, the knowledge and training that is expected from employers is forever increasing. Therefore, to stand a chance of triumphing in the sea of competition, potential employees have to take it upon themselves to sign up for supplementary courses and workshops to obtain that extra certificate or award. With lists trailing long and hard of such qualifications, complied with strings of capital letters that seem to have been assembled by a toddler with his toys, e.g. CQF, CFA, MFE, MFM, it is quite a task knowing which one to choose given no set guidelines. To make things even more difficult, it has been known for financial designations to fade away, potentially rendering the qualification worthless. Therefore, it is essential that the correct amount of preparatory work and research is done before the decision is made on which course should be taken.

Another factor to keep in mind is the span of mathematical knowledge that is required for a quant role, ranging widely from stochastic calculus to programming languages. Not all courses can provide such a wide range of topics, or at least not to the right degree of detail. Here at OptiRisk, we hold workshops running on popular topics such as Monte Carlo methods, Stochastic calculus, time series and derivatives. These workshops illustrate the applications of mathematical theory to the practical financial world through case studies, hands-on practice and in-depth presentations. Participation will enable you to reach a greater understanding on subject areas explored by quants. For more detailed information see http://optirisk-systems.com/newsandevents_events.asp .

 “It is essential for the aspiring super-quant to overlay theoretical knowledge with pragmatic common sense.” Quote by Emanuel Derman, Professor at Columbia University.

News Analytics for Electronic Trading

In an article “Sentimental education” published in the The Trade, Issue 24, April to June 2010, Professor Gautam Mitra, Director of OptiRisk Systems, was interviewed and argued that “ News data can give alternative investment managers advantages of speed, scope and better risk control than models driven purely by market data. Portfolio models essentially can take more factors into account and respond more reliably with this additional perspective to drive correlations. The usage of news in trading is likely to grow for both low and high-latency strategies.” 

Professor Mitra’s view is based on the OptiRisk Systems Publication “News Analytics in Finance Handbook”, which will be published by John Wiley in 2010. In this handbook, Professor Gautam Mitra and Dr. Leela Mitra reviewed the news analytics and its applications in finance in the first chapter, in particular the multiple facets of current research and some of the major categories of applications. It is widely recognised news plays a key role in financial markets. The sources and volumes of news continue to grow. New technologies that enable automatic or semi-automatic news collection, extraction, aggregation and categorisation are emerging. Further machine learning techniques can be used to process the textual input of news stories to determine quantitative sentiment scores. Various types of news available and how these can be processed to form inputs to financial models are considered. Applications of news, for prediction of abnormal returns, for trading strategies, for diagnostic applications as well as the use of news for risk control are also reported in this review chapter.

To download the content page of this handbook, please click  Download Contents page.

News Analytics

There’s an old joke on Wall Street. Invest in the markets and you’ll sleep like a baby: You’ll wake up every two hours crying. Follow the news and you’ll see why. With so much technological innovations, news that keeps streaming in incessantly at milli-seconds level, is very hard for anyone in the market to visualize and understand it all. News Analytics is a method of solving this problem by processing and quantifying it fast.

We all know market reacts to news within fraction of seconds, so any model and the underlying machine learning algorithm which captures the effect faster than the human, is highly sought after on the trading floors. 

RavenPack and Thomson Reuters have come with a product to solve such problems. The basic idea behind their system is to quantify the market sentiment as soon as the news is released in the market. The quant factors which come out of these systems can be further used in the trading models to generate the alpha.

We are at the leading edge of the development of models for news analytics systems. We are compiling the research knowledge in news analysis and publishing with John Wiley a volume on News Analytics in Finance: Handbook. This is due for publication in November this year.

The Handbook brings together in one volume - products, services, developments and research knowledge in the field of news analysis for trading and risk management. This will be a highly regarded source book for the technocrats and key decision makers in the quantitative finance industry and investment community. The contributors to this Handbook are leading researchers from academia and industry and to name a few – Sanjiv Das, David Leinweber, Andrew Lo, Terance Odean, Dan diBartolomeo and Marc Rieger. Our chairman, Professor Gautam Mitra contributes to this volume and also edits the Handbook.

The target readership for the Handbook are decision makers in Banking, Finance & Insurance Services industry. In particular: Asset managers; Quantitative fund managers; Hedge Fund managers; Algorithmic Traders; Proprietary (Program) Trading desks; sell-side firms; brokerage houses; Risk Managers and Research departments. These industry players will be interested in this unique book as there are no other publication focussed on this new and pertinent area of financial modelling.

OptiRisk will present & exhibit at LQG’s Investment Technology Day, 21 June

OptiRisk Systems is delighted to be attending the Investment Technology Event organised by London Quant Group (LQG) once again. It is the second such event, returning due to popular demand from quant users/buy side community and a very successful first event. We feel that this event will provide a forum for technology providers and buy-side communities who can learn more about financial analytic products and services.

This year, we are pleased to be offered the opportunity to present our recent work on “Enhanced Indexation Based on Second-Order Stochastic Dominance”.

*Our presentation

Enhanced Indexation Based on Second-Order Stochastic Dominance

    -Professor Gautam Mitra, Director of OptiRisk Systems

Second order Stochastic Dominance (SSD) has a well recognised importance in portfolio selection, due to its connection to risk-averse behaviour theory. Recently proposed SSD based portfolio models assume that a reference distribution is available; then, a portfolio is constructed, whose return distribution dominates the reference with respect to SSD. The purpose of this paper is to analyse the effectiveness of such strategies in the context of enhanced indexation. Several datasets, drawn from FTSE 100, SP 500 and Nikkei 225 were used for backtesting in a rebalancing frame; three main conclusions were drawn. First, the portfolios chosen by the SSD based models consistently outperformed the indices and the traditional index trackers. Secondly, the SSD based models do not require imposition of cardinality constraints since naturally a small number of stocks are selected. Thus, they do not present the computational difficulty normally associated to index tracking models. Finally, the SSD based models are robust with respect to changes in the scenario set and thus little or no rebalancing is necessary.

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