Uncertainty in Pension benefits and Liability Driven Investment

After UK Chancellor George Osborne passed the budget couple of weeks back, it has created quite a stir in the financial community, especially for retired pensioners. Pension Minister Steve Webb, has announced that the government is trying to develop a model where-in the increased burden of pension on the employers is done away with and also to reduce the uncertainty of pension incomes to retired people.

Finally salary pension schemes are attractive to pensioners as they guarantee income linked to inflation. However as people are living longer and with increased inflation, and stagnant economy, companies see these schemes to have increased costs affecting their bottom line.

In an interview on the BBC Radio 4 Today program, Mr. Webb said: “Firms would like to offer their employees some sort of certainty but without all the costs and burden they already face.”

“We are pretty clear that employers are going to be very nervous about anything that involves guarantees,” Malcolm Small of the Institute of Directors told the BBC.

Many firms have introduced defined benefit schemes where employees pay into their pension fund. However, those plans rely on performance of financial markets and hence it is difficult for workers to predict their incomes.

Steve Webb, has announced Defined Ambition plan to balance uncertainty of future income and increase cost of final salary pensions.

Given the scenario, my understanding is that more pension funds need to introduce intelligent software tools that can predict stable income given various uncertainties involved. Liability Driven Investment is one such concept where, the pension funds focus to balance liabilities with assets and try to generate constant predicted income as opposed to maximizing incomes over a period of time. Stable income is the name of the game and more pension funds that provide this balance stand to win in the long run.

LDI Opt is a software that we have developed at OptiRisk Systems, which involves high degree of Sochastic Optimisation and Risk Modeling under various uncertainties (such as systematic and unsystematic risks). We feel that our software will be a great tool for pension funds to balance the employer and employee needs and generate optimum income for retired pensioners. Please have a look at the presentation of tool below. Please contact us for any queries:

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Liability Driven Investment – Variant of Asset Liability Management

Undoubtedly, the pension systems are in a crisis. Corporations, governments and regulators need to adopt new approaches before individuals are left alone with their pension and healthcare planning. An increasing dependency ratio (ratio of pensioners versus workers) has forced governments to decrease their retirement benefits; while at the same time corporate pension schemes have disappointed with their low returns (not to speak about their deficits) due to their current financial planning. The perceived view of experts is that regulators and pension fund trustees need to identify the pension schemes funding difficulties and take corrective actions quickly. From an asset management side the investment strategy needs to protect a plan’s surplus in
downward market conditions.

Mulvey et al. propose an integrated pension trust and corporate planning system; enterprise-wide risk management, which is a generalisation of the LDI/ALM framework. The pension plan can be closely linked to the economic path of the sponsoring company as well as the sponsoring company can be closely linked with the overall economy.

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Portfolio Optimisation – OptiRisk Systems

This white paper introduces Markowitz mean-variance model with a
general overview and sets out to explain why and how the finance
industry has fully embraced this as method of choice for portfolio
planning.
The main focus of the white paper is to bring out many aspects of
the portfolio planning problem which are addressed by enhanced
mean-variance models that meet the growing requirements of the
finance industry.
Portfolio analysis is a leading issue with fund managers who apply
such models in many situations such as index tracking, performance
evaluation and historical data/backtesting.
The technical underpinning of these methods are described in Part II.
A number of currently available software systems are also reviewed
together with a broad overview of usage of these systems in Part I.
This white paper will be of interest to:
·  Fund managers
·  Trading desk staff
·  Back office staff
·  Quantitative analysts who wish to know the general
development in the market place.

Continue to the White Paper

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Workshops on Quantitative Finance – London

OptiRisk Systems in collaboration with UNICOM Seminars Ltd is pleased to bring to you its series of yearly events on Hidden Markov Models, Interest Rate Modeling, Portfolio Analysis and Asset Liability Management. The workshops are delivered by subject experts and provide valuable tools and fresh thinking that can be applied to industry.

Target Audience: Risk Analysts, Risk Managers, Quant Analysts, Traders, Asset Managers, Fund Managers, Consultants, Bankers, Researchers and Academics

Following are the events which are jointly organised by OptiRisk Systems, CARISMA, and Fraunhofer ITWM and most of these carry CFA PDUs.

1)      Application of Hidden Markov Models & Filters to Financial Time Series Data, 23 – 24 April, 2012

Here are few testimonials from last year’s workshop attendees:

“It was inspiring to learn about HMM from a different perspective (my background is speech recognition)

-          Bangor University

“The simplicity and efficiency by which Korn explained MC plus the examples provided were brilliant”

-          Brunel University

2)      Practical Asset and Liability Management, 15 – 16 May 2012, London

Attendees receive a copy of the recently published “Handbook on ALM” (list price £105)

3)      Portfolio Optimisation: Basics and Advances in Continuous-time and Discrete-time Models, 30 -31 May 2012, London

4)      Interest Rate Modelling  and Applications in Practice, 31 May – 1 June 2012, London

5)       News Analytics Applied to Trading, Fund Management and Risk Control, Birkbeck College, London: TBA

Speakers:

The events are led by Professor Gautam Mitra, director of OptiRisk Systems and distinguished researcher in Mathematical Modeling and Optimisation. The list of speakers includes Moorad Choudhry (RBS); Dan diBartolomeo (Northfield Information Services); Michael Dempster & Elena Medova (Centre for Financial Research, Cambridge University); Teemu Pennnanen (King’s College London); Enza Messina (University of Milano-Bicocca); Peter Ruckdeschel, Ralf Korn & Joerg Wenzel (Fraunhofer ITWM).

For industry participants: GBP1,025 + VAT

Previous year’s attendees include:

Citi Group; Credit Suisse; Deutsche Boerse AG; Dow Jones & Co; ETH Zurich, Goldman Sachs; Misys; Standard Life Investments; SunGard-APT; Morgan Stanley; Thomson Reuters; UBS Investment Bank.

For more information please contact:

Email: info@optirisk-systems.com

Telephone: +44 (0) 1895 819 486

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With High-Frequency Trading, Financial Firms Face New Challenges

Reblogged from Cisco Blog

In recent years, the financial industry has witnessed a revolution. To discuss, debate, and seek a bit of consensus on the crucial issues impacting the industry, I met earlier this year in New York with a team of experts at the Electronic Trading Innovation Council. For the event, Cisco partnered with the founders of the council, Julio Gomez and Clay Booma. I was joined by my Cisco colleagues Aron Dutta, co-managing director for financial markets, Cisco IBSG; Chris O’Connell, Cisco’s head of strategy for alternative investment markets; and Dave Malik, Cisco’s technology & architecture lead. The other participants represented a wide range of financial and tech-based firms, including BNY Mellon, Citi, Credit Suisse, Lazard Freres, Morgan Stanley, Nomura, State Street, UBS, Equinix, Savvis, and Tervela.

It was a great team, and the roundtable meetings benefited from a vast body of knowledge and a high level of participation.

Three main points of discussion emerged:

  1. Defining High-Frequency Trading: How is this defined and what role should government (regulation) play? Is the issue the automation of trading by computer, or is it position and execution management? If I buy and sell a position in five milliseconds, is that high-frequency trading? What if I sell in seven milliseconds, or for that matter, two hours or two weeks. At what point does the government feel it can regulate trading strategies and activities, and what impact does it have for our capital markets on both the client and proprietary side of trading? These questions drove a spirited debate. And while there was no final consensus among the participants on how to fully define a high-frequency trade, there was clear agreement on the need to find a consensus. Since it is not clarified in regulations like Dodd-Frank, the industry needs to agree on its own definition, along with other measures to deal with emerging regulatory burdens.
  2. Fragmentation of Liquidity: The “hunt for liquidity” is at the center of the recent market reconfiguration. In the old days, this meant stock exchanges located in just a few financial centers—geographic locations with asset class specialties and a known membership of participants. Today, technology allows transactions to occur virtually any “place” where buyers and sellers can meet electronically. These alternative trading venues feature additional pools of liquidity (e.g., dark pools) that are offered and executed outside of a traditional exchange. One problem, however, is the growing expense of maintaining access and transparency through all that fragmentation. Connecting to all those disparate centers makes it very difficult for any one buyer to see whether he or she is getting a good deal. But the group agreed on an eventual solution: an overarching, network-based liquidity center. This would be cloud-based and capable of aggregating thousands of liquidity centers into one logical, buying-and-selling marketplace, assuring transparency. As Julio Gomez put it, tech trumps fragmentation.
  3. The Commoditization of Transactions:  The more people know about what’s for sale, the less you can charge for making the transaction possible. After all, a higher price comes with information that no one else has. But with a more transparent, unified market, customers will know exactly what’s for sale. Which leads to another trading challenge: commoditization of trading. This is creating a conflict in the industry: Do we want an efficient marketplace with low costs per transaction, or an inefficient marketplace with high cost per transaction? Regardless, the group discussed how some industry players—including exchanges (for example, NYSE, Chicago Mercantile Exchange, and Direct Edge) and market participants (buy-side and sell-side)—are examining ways to branch out from their core role: seeking new revenue streams by offering technology services, distributing market data content, providing order management execution services, or offering pre-trade analytics services.

Ideally, I would like to see the Electronic Trading Innovation Council convene three times a year, with an open invitation to expand its level of participation and base of knowledge. The industry can only benefit from the brainstorming and strategies offered by this formidable collection of experts.

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Using Twitter To Predict Financial Markets

Past research has looked at the sentiment on Twitter to predict stock price, but until now little research has focused on the volume of tweets and the way tweets are linked.

Reblogged from Wall Street & Technology

Researchers have developed a model that uses data from Twitter to help predict the traded volume and value of a stock the following day.

While past research has looked at the sentiment, positive or negative, of tweets to predict stock price, little research has focused on the volume of tweets and the ways that tweets are linked to other tweets, topics or users. Past work has also mostly studied the overall stock market indexes, and not individual stocks.

Vagelis Hristidis, an associate professor at the Bourns College of Engineering, one of his graduate students and three researchers at Yahoo! in Spain, set out to study how activity in Twitter is correlated to stock prices and traded volume.

They obtained the daily closing price and the number of trades from Yahoo! Finance for 150 randomly selected companies in the S&P 500 Index for the first half of 2010. Then, they developed filters to select only relevant tweets for those companies during that time period. For example, if they were looking at Apple, they needed to exclude tweets that focused on the fruit.

“They expected to find the number of trades was correlated with the number of tweets. Surprisingly, the number of trades is slightly more correlated with the number of what they call “connected components.” That is the number of posts about distinct topics related to one company. For example, using Apple again, there might be separate networks of posts regarding Apple’s new CEO, a new product it released and its latest earnings report,” according to a statement.

The trading strategy outperformed other baseline strategies by between 1.4 percent and nearly 11 percent and also did better than the Dow Jones Industrial Average during a four-month simulation.

Still, Hristidis notes several potential weaknesses in the study. First, the trading strategy worked in a period when the Dow Jones dropped, but it may not produce the same results when the Dow Jones is rising. There is also sensitivity related to the duration of the trading. For example, it took 30 days in the simulation to start outperforming the Dow Jones.

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Trade to improve your portfolio

Continuing the Debate from The Economist

An  interesting write-up from Abnormal Returns

One of the persistent themes on Wall Street is that investing strategies go in and out of favor on a regular basis.  Oftentimes this has to do with the performance of the strategies, sometimes based on the structure of markets and always having to do with the media hype surrounding a strategy.  Most strategies that reach the level of public consciousness are likely.

The fact of the matter is that even the most well-crafted investment strategy will have periods of underperformance.  As Jeff Miller at A Dash of Insight writes:

Your system will not always work!

The most recent investment strategy to take its hits in the eyes of the public is “buy and hold.”  This shouldn’t be all that surprising given the generally dismal performance of the US equity market over the past thirteen years, which for most investors is all they can remember. Given the volatility of the markets all manner of strategies that advocate various levels of active investing have been proposed to help alleviate the risks of continued exposure to the equity markets.

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Debate – High Frequency Trading Beneifits the Quality of Markets

The debate hosted by The Economist

For the Motion

Jim Overdahl

Vice-president, Securities and Finance Practice, National Economic Research Associates
High-frequency trading has improved the overall quality of markets. Trading costs are lower, markets are deeper and more liquid, discrepancies in prices across related markets are reduced, and prices better reflect information about the value of stocks and commodities.

Against the Motion

Seth Merrin

Founder and CEO, Liquidnet
High-frequency traders are, by design, trading ahead of market orders to the detriment of long-term investors. HFT benefits the very few at the expense of the very many, which defies the purpose of why a market exists and as a result has lessened the overall quality of the markets.

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The next big UI challenge is making big data human

Reblogged from GigaOm

IBM’s Jeopardy-playing supercomputer Watson is now getting a gig in the retail banking sector as part of an IBM partnership with Citi. This is in addition to its position as a diagnostic assistant for doctors. But the many careers of Watson aren’t just a fun story for the tech press; they illustrate a very big technological and business opportunity for companies like IBM and Microsoft — the rendering of big data into human scale.

For Citi, Watson will be used by retail bankers and loan officers to help them sell to consumers by taking all the information the bank has on a customer and trying to understand what they might want next. So if someone signs up their kid for a credit card, Watson will help the bank understand what type of financial products a customer might want once they send a child to college. For example, my parents remodeled their kitchen. So a Watson-using Citi banker might have offered them a remodeling loan a few months after I left home.

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Quantitative Trading: Economist Approach vs. Mathematician Approach

Algorithmic Trading, Investment, by Haksun Li.

Thank you Lewis for introducing me to the field of “Quantitative Equity Portfolio Management”. It opens my eyes to the other spectrum of “Quantitative Trading.” Apparently what Lewis considers quantitative trading is very different from what I consider quantitative trading. I call the former an economist approach and the latter a mathematician approach. This blog piece does a very brief comparison and points out some new research directions by taking the advantages of both.

Briefly, the economist approach is a two-step approach. The first step tries to predict the exceptional excess returns alpha by examining its relationships with macroeconomic factors, such as momentum, dividends, growth, fundamentals and etc. The second step is capitals allocation. The focus in the economist approach is on identifying the “right” economic factors. The mathematics  employed is relatively simple: linear regression, (constrained) quadratic programming. The trading horizon is month-on-month, quarter-on-quarter, or even years. An example of such is factor model in QEPM.

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