Linked Names

As I have gone through reading various pieces, I have come across various people who have been doing work in the field. I will try and include links here to their sites/work. I will add to the list over time.

1. William Samuelson
2. Richard Zeckhauser
3. Chip Heath
4. Amos Tversky
5. Werner De Bondt
6. Richard Thaler
7. Terrance Odean
8. Nicholas Barberis
9. Daniel Kahneman
10. Robert Vishny
11. Michael Pompian
12. James Montier
13. John List
14. Annamaria Lusardi
15. Meir Statman
16. Jason Zweig
17. Paul Samuelson
18. Richard Deaves
19. B.F. Skinner

Terrance Odean

Terrance Odean had done a phenomenal amount of work in the field of behavioural finance, and apparently Daniel Kahneman, the man who won a Nobel for his work on the subject believes he will be a future winner of that prize.

I read and watched his talk on various studies on various investment foibles which was both entertaining and interesting.

A number of his papers can be found here.

I couldn't find any books that he had written, but he is quoted in a number, which I guess suggests he spends more time on his research papers.

Anyway, it was a pleasant change to listen to an academic who is an entertaining speaker.

Stock Picking or Asset Allocation Alpha

My search is more focused on the damage an individual with limited expertise can do through buying and selling at the wrong times. My gut feeling is that a skilled manager's abilities become redundant if that work is undone in that way.

Swenson's comment on asset allocation being the primary driver of returns is not a unusual opinion. My understanding of what he says though is that both Allocation and finding people who can add alpha once that decision has been made is important. The only thing he has a big problem with is excessive aggressive attempts at market timing. Even though he does talk about taking advantage of opportunities.

Prof. Shiller makes a comment along those lines in the opening remarks of his next lecture in the series.

(This is a follow up to Colin's comment on the David Swenson lecture.)

People Make Mistakes

Posts have been sparse while I have focused on doing a fair amount of reading around the subject and fighting with data.

I have been reading Stocks for the Long Runby Jeremy Siegel, and Behavioural Finance: Insights into Irrational Minds and Marketsby James Montier.

I have also got about half way through Robert Shiller's course on academicearth.org. Siegel's is one of the set books.

Montier's book is one of the best sources I have come across so far, and he sites further academic papers for every point he makes.

Siegel's study is fascinating from the length of time it looks at. A lot of investors feel 5 years is long enough to make a decent assessment of the likely success of a strategy. Siegel looks at over 200 years of financial market returns! Admittedly the large majority of well kept data comes from a single market (the US), but he does look at other markets too. I will discuss some of the findings in further posts.

Shillers's course is fascinating (though academics and portfolio managers perhaps need to have a chat to Garr Reynolds about improving their communication skills). What I particularly enjoy is the contrasting views that seem to get air time. So far, it has had guest lectures from David Swenson, Carl Ichan and Andy Redleaf.

I have not yet found a study that looks at the pros and cons of using IRR as a measure for investor behaviour. The latest DALBAR study has just been released. It is interesting seeing the effects of a very significant 2008 year on this ongoing study. I am working through their process, and will no doubt have my own critique of their method in time, but I haven't come across any alternative benchmarking methods to look at.

David Swenson

Lecture 9 of Robert Shiller's course on Markets is a guest lecture by David Swenson who is the head of the investment committee of Yale.


I particularly enjoyed his responses to the questions at the end, although you couldn't actually hear the questions as there were no microphones in the audience.

The talk is about his approach to investing and he discusses asset allocation, market timing and security selection.

He also talks about what I am looking at on this blog looking at the difference between the time-weighted returns that funds publish and the dollar weighted returns that investors experience. He refers to a morningstar study that also documents not only individual but also institutional investors in their habit of buying high and selling low.

He talks about the '87 crash and how just before the crash a lot of institutions had historically high weightings to equity, and at the bottom increased their weightings to bonds.

He briefly looks at the allocation of the Yale fund and summarises his approach as having an equity orientated and diversified asset allocation, to avoid market timing and to focus your time and energy based on your skill levels and areas where it is possible to add value.

In choosing managers, a major factor is on the qualitative aspects. He looks for people of high integrity, with unimpeachable character who are smart and incredibly hard working. He wants managers who are obsessed with the markets and maniacally focused on beating the market and achieving superior returns rather than gathering assets.

An interesting talk, well worth the watch.

Robert Shiller Lecture

I get daily Google Alerts from http://www.google.com/alerts on Behavioural Finance. Normally it is rubbish searches, but it does bring up new stuff. Today, this came through....

Geary Behavioural Economics Blog: Robert Shiller Lecture from ...
By Liam Delaney 
A really superb lecture on behavioural finance from Robert Shiller - those doing my courses will recognise hopefully all of the topics - looking at him lecture is rekindling my ancient ambivalence with respect to powerpoint - chalk and ...
Geary Behavioural Economics Blog - http://gearybehaviourcenter.blogspot.com/

linking to a lecture by Robert Shiller on the role of psychology in Behavioural Finance. Tyler Cowen of Marginal Revolution also pointed out http://academicearth.org/ which records lectures from America's top universities. Very useful.

Behavioral Finance and Wealth Management

I had started by reviewing or summarising the descriptions Pompian gave of various Biases and their impact on financial decision making. What may be better is to do a post specifically dedicated to each bias with links I can accumulate over time to various sources which review those biases.

Pompian follows a formulaic examination of each bias, discussion of it's implications, a test for determining whether a client has the bias, and steps to try and counter that. In the end, his proposal is to adjust for a balance between the optimal portfolio according to traditional Markowitz's Efficient Frontier style analysis. His focus is very much at the individual Independent Advisor Level. How can they understand their client's and adjust the client's portfolio to maximise the possibility that they will stick to the optimal allocation strategy. Once that strategy is in place, it would be a case of reviewing the strategy to make sure it is still correct (given unexpected life changes) but otherwise attempting to interfere as little as possible.

The book is useful introduction to Behavioural Finance, and opens the door to questions that need to be asked when providing advice.

I still have an uneasy feeling about an over-reliance on a system (MPT) that has as it's basis a belief that returns are normally distributed and uses Volatility as a definition of risk.

What I like about the book therefore, is not so much it's solution but the fact that it highlights a question.

Finance for a long time has tried to understand the question of the optimal strategy and how best to allocate assets. While that question has not yet been answered, the parallel discussion of how to best approach the emotional side of investing.

Creating Benchmarks

The Dalbar study is the only one that I am aware of that attempts to create Benchmarks for Investor Behaviour. There are obvious advantages to having a widely established benchmarks that you can use to measure your performance. The key though is to establish that they make sense.

Here are the characteristics of a good benchmark as according to the AIMR Benchmark & Performance Attribution Subcomitee Report (1998) by 'Insurance Finance & Investment', Oct 31 2006 issue, a publication of World Trade Executive. Comment welcome.

1. Representative of the asset class or mandate
2. Investable
3. Constructed in a disciplined and objective manner
4. formulated from publicly available information
5. acceptable by the manager as a neutral position
6. consistent with underlying investor status (regarding tax, time horizon etc.)

A 'Behavioural Benchmark' would obviously be different from a fund benchmark. There are a few issues that come up.

How do you measure what the client did with the money when they withdrew it? Did it sit in cash or did they maybe put it in another vehicle that actually outperformed? Is it possible to measure the impact of client behaviour without knowledge of their entire portfolio?

You can use systematic behaviour patterns as benchmarks since they are implementable (i.e. like being 'investable'). This allows comparison with methods such as dollar-cost averaging and buy and hold strategies.

It is possible to know the actual return of the investor or IRR. Why is an attempt to work out the average investor behaviour better than just using the actual return they got? Why should we model what they would have got based on 'average behaviour' such as in the Dalbar study?

Traditionally we try stripping out the effect of cash flows by giving the Time-Weighted Rate of Return rather than the IRR. The fund manager has no control of the cash flows and so we attempt to give a fairer view of the value add.

In order to accurately measure the impacts of a clients behaviour, you would need to have a detailed knowledge of their actual cash flows and then measure their decisions against their default or passive allocations. I am a bit concerned that the Dalbar measures don't really do this. By giving a simple flat $10,000/20 years it ignores the impact of cash flows and of what the clients alternative choices were.

Obviously though, benchmarks have to be simple and implementable as well which is what the Dalbar's benchmark is.

QAIB 2008

Quantitative Analysis of Investor Behaviour (2008)

This post is longer than I would like, and I will break it up at a later stage. I basically just wrote thoughts down as I read through the DALBAR study. The study looks at the 20 year period ended December 31 2007, and the equity section focuses on investments in the US through S&P Index.

The intention is that the QAIB be used to establish benchmarks. It further suggest that you can contact DALBAR for further information on using the QAIB benchmark and making comparable calculations.

The benchmark attempts to simulate the behaviour of the 'average investor' by tracking net inflows/outflows into Mutual Funds. This allows them to establish a pattern in which a $10,000 investment was made relative to that of a systematic investment of $10,000 over time.

Does the Dollar Cost Averaging/Systematic approach assume level contribution in nominal or in real terms?

Most mutual fund companies use the return of a lump sum invested at the beginning of an investment period. This only really works for someone who starts at the beginning of those periods and makes one contribution. Perhaps there should be three measures:

1) Lump sum from start of period
2) Annual Contribution from start of period
3) Monthly Contribution from start of period

The issue then becomes, what are the regular contributions? Do you use $1 per period, or do you allow for inflation. For Domestic Funds, you could allow for inflation. For Global Funds it becomes a little more difficult. You could benchmark it against the same currency benchmark as the fund's benchmark and use weighted average inflation measures.

The results show the average investor over the last 20 years having gained 4.48% vs. 11.81% performance of the underlying S&P index. This is often used to suggest a passive investment strategy and regular contributions. I think those are separate considerations.

1) Do you remove your decision making from when to invest?
2) Do you believe your chosen asset manager can beat a passive index?

The big problem is that both questions are not easy to answer and overconfidence would lead people to answer Yes to both. My feeling is that it is possible for a manager to beat an index, but finding those managers is tough. Partly because the best ones seem to make a lot less noise than the average ones. I think the first question is a more difficult one to claim that you can do.

The Guess Right Ratio

The next measure that is a useful suggestion is one of how often an investor makes the write call. So, if there are inflows and the next month has positive returns, they score 1. They also score if there are outflows and the market goes down. The average score over the period under investigation was 61%. Naturally, the period under investigation was an incredibly long bull market. It will be interesting to see how next years results to the end of 2008 look! If you invest every month, you would expect to have a ratio greater than 50% in a rising market.

Perhaps a better measure would be to divide the `Guess Right Ratio' by the % up months? If the market went down, so you were wrong more often, the base would also go down, so your `Guess Better than Randomly Ratio' would stay close to 100%. If you destroy value, it would fall below and if you are adding value it would go above 100%. Both the GRR and the GBTRR aren't weighted by money, so you wouldn't be able to use them to check consistency with actual value added or destroyed.

Holding Period

`The holding period reflects the length of time the average investor holds a fund if the current redemption rate persists'

This measure is useful if you assume that it is more likely the investor will share the funds returns if they stick around and don't do much. That seems intuitive.

The paper gives a methodology for calculating the investor return which can be applied to markets other than the S&P. I would like to see if others have taken DALBAR's work and applied it to other markets, or have any critical comment on the way they have done it. I need to let what they have done wallow in my head for a while to think of my take. First take is that it provides a very useful start to the process of providing measures of client behaviour.

Irrational Exuberance (2000)

I have just finished Irrational Exuberance by Robert Shiller. I am aware that there are updates, and new editions, as the edition I read was released in 2000 before the `Internet Bubble' burst.

I am wary of quoting too many of the passages that he wrote here. It is worth reading the book. Like a lot of the other literature, much time is spent on discussing whether or not the market is efficient. In an efficient market, you could safely leave people to do as they pleased since timing would neither help nor hinder them. I think Shiller makes a very compelling argument that this is simply not true. In this edition he closes the book by making various suggestions as to how we could improve market efficiency and opens the debate as to how we can prevent some of the wealth destruction. 

A large part of his discussion centers around the flow of information, and how that is used. Market Efficiency is based on perfect information being available. And it is true, we have easier access to an absolute wealth of information. The thing is not so much the availability of information, but also the ability to process that information. Ability and willingness. A lot of investors are not professionals and often `play the market' as a source of entertainment. In addition people have a lot of other things to worry about.

Shiller:

On the average investor...

'During the most significant financial events, most people are preoccupied with other personal matters, not with the financial markets at all.... People think they know more than they do. They like to express opinions on matters they know little about, and they often act on these opinions'

On the essence of the EMH:

`At its roots, the efficient markets theory holds that differing abilities do not produce differing investment performance. The Theory claims that the smartest people will not be able to do better than the least intelligent people ito investment performance. They can do no better because their superior understanding is already completely incorporated into the share price.'

One of his most convincing rebuttals:

'Stock Prices appear to be too volatile to be considered in accord with efficient markets. If stocks prices are supposed to be an optimal predictor of the dividend present value, then they should not jump around erratically when the true fundamental value is growing along a smooth trend.'

And, lastly a warning about another risk that EMH can incorrectly lead people to thinking that Equities will always outperform:

The evidence that stocks will always outperform bonds over long time intervals simply does not exist... at least one genuine fundamental truth about stocks: that they are a residual claim on corporate cash flows, available to stockholders only after everyone else has been paid. Stocks are therefore, by their very definition, risky.

For the most part, I think Shiller's book for me is a warning against accepting too closely conventional wisdom without doing your homework.

The stock market is after all just a price setting mechanism for businesses. Once set though, the price acts independently, and while there is an argument for the availability of information, a belief that in general, the aggregate average interpretation is right is not one that I subscribe to. 

As always, I am happy to read arguments that may convince me otherwise, but I think it is both possible to create and to destroy wealth over time.

My aim is to try find measures to help people stop themselves from destroying wealth.

Biases

The first four biases looked at by Pompian are Overconfidence, Representativeness, Anchoring and Adjustment and Cognitive Dissonance.

Overconfidence

The most commonly sited behavioural bias associated with overconfidence is that clients show too much trading activity. This may be something that can be measured at individual firm level.

The study which he sites (which I have heard of the result but not yet read) is `Boys will be boys' which shows evidence of the overconfidence in males feeding through to excessive trading which leads to underperformance.

Representativeness 

This bias leads to people making judgements based on stereotypes... eg. assuming a fund will outperform because it has a certain style, and that type of style outperforms in certain circumstances. Another form that it shows up in is `The Law of Small numbers' otherwise known as the `Gambler's Fallacy'... where people assume a pattern from a very small selection. This could apply to the way funds are judged on short term performance rather than processes and philosophy.

Anchoring and Adjustment

People base their expectations on an anchor, so if a fund has performed in a certain way regularly, that is what they expect the next time. This could maybe apply to expectations of return where someone has lived through a high inflationary period, or a bull market.

Cognitive Dissonance

This is the most interesting bias. Pompian looks at the way we respond when our actions and our beliefs conflict. Do we change our actions, change our beliefs, or change our perception of our actions?

This starts to genuinely delve into the psychologist's space... The advice he gives when it becomes obvious that your response to this bias is negatively affecting you is...

`If you think you may have made a bad investment decision, analyze the decision; if your fears prove correct, confront the problem head-on and rectify the situation'

Behavioural Finance and Wealth Management

Michael Pompian takes the angle of applying Behavioural Finance to individual financial advice in his book, 'Behavioural Finance and Wealth Management'.

I have just finished the first part which provides and introduction to Behavioural Finance, the history of Behavioural Finance Micro, and his structure for incorporating investor behaviour into the asset allocation process.

He says...

'An optimal portfolio is one with which an investor can comfortably live, so that he or she has the ability to adhere to his or her investment program, while at the same time reach long-term financial goals'

`a client's best practical allocation may be a slightly underperforming long-term investment program to which the client can adhere, warding off the impulse to "change horses" in the middle of the race"

'the right allocation is the one that helps the client to attain financial goals while simultaneously providing psychological security for the client to sleep at night.'

'Instead of a universal theory of investor behaviour, behavioural finance research relies on a broad collection of evidence pointing to the ineffectiveness of human decision making in various economic decision-making circumstances.'

The next 22  chapters which I have just started go through a variety of biases in an attempt to provide practical advice on how to mitigate them.

My gut response is that it is a worthwhile attempt, and though I know I should delay judgement, I think he may be caught in the middle. The structure he proposes is still based on adjusting what a rational investor under traditional finance with portfolio optimisation should do, allowing for biases.

I have a deep distrust of using volatility as a measure of risk. That provides the basis of Portfolio Optimisation, where there is a tradeoff between volatility and return in the belief that to get higher returns, you need to accept higher volatility.

So... and I will delay judgement (as much as I can), I continue reading more in the hope of improving my understanding of the different biases rather than having any confidence in his methodology of providing a solution.

A useful distinction he makes is between cognitive biases, and emotional biases. Cognitive biases are easier to remedy since they can be `reasoned' with. Emotional biases are delving into a world where portfolio managers, advisers, accountants, actuaries and the like have far less claim to fame.

Daniel Kahneman, winner of the the Nobel Prize for Economics in 2002 for his work in Behavioural Finances is a psychologist rather than an economist.

The benefits of working across fields.

Voting and Weighing

Robin Bowerman talks about the influence of emotions on investing:




Benjamin Graham:

Graham wrote that the owner of equity stocks should regard them first and foremost as conferring part ownership of a business. With that perspective in mind, the stock owner should not be too concerned with erratic fluctuations in stock prices, since in the short term, the stock market behaves like a voting machine, but in the long term it acts like a weighing machine (i.e. its true value will in the long run be reflected in its stock price).

The Other Side

Fama (1998) abstract:

Market efficiency survives the challenge from literature on long-term return anomalies. Consistent with the EMH that the anomalies are chance results, apparent overreaction to information is about as common as underreaction, and post-event continuation of pre-event abnormal returns is about as frequent as post-event reversals. Most important, consistent with the market efficiency prediction that apparent anomalies can be due to methodology, most long-term return anomalies tend to disappear with reasonable changes in technique.

Working through this paper (I am not yet complete), Fama's argument is that because evidence has been found of both over-reaction and under-reaction, and because no model can be found to explain both sufficiently, that you can't reject Market Efficiency.

It is also a useful survey, though 10 years old, of the other work that had been done. I have not found a paper yet specific to what I am looking for. In other words, measures of investor behaviour in managed funds. This is an extension though of whether manager's themselves can add value. 

Author's such as Nassim Taleb cast doubt on our ability to identify these managers since the number out there, and chance, dictates that some will outperform over extended periods. So like Fama, `anomalies' can be attributed to randomness.

Stepping aside from academic models for a second though... doesn't it make intuitive sense that if the market is a fractional interest in a business, then doing your due diligence and allocating money properly is likely to lead to better returns than average? Clearly in order to outperform, you are going to have to make better decisions than average. Average decisions less costs will underperform no decision and an investment in `the average decision'/index.

It is difficult to come up with measures for wealth destruction if there is no consensus on whether or not added value can be gained at all.

Fortunately, I don't need to look for consensus. While I will continue to read and comment on papers from both sides... I find the evidence that it is possible to create and destroy value compelling.

Bias and Exuberance

It is rather interesting reading Robert Shiller's book 'Irrational Exuberance' after/during a major market crash given that it was written at the height of the bubble in 2000.

Chapter 2 lists a number of precipitating factors that he believed led to the bubble.

1. The baby boom and its perceived effects on the market
2. A Republican Congress and Capital Gains Tax cuts
3. Cultural changes favouring business success or the appearance thereof
4. Triumphalism and the decline of Foreign Economic Rivals
5. The arrival of the Internet at a time of solid earnings growth
6. Expansion in media reporting
7. Analysts increasingly optimistic forecasts
8. The expansion of DC pension schemes
9. The growth of mutual funds
10. The decline of inflation and the effects of money illusion
11. Expansion of the volume of trade: discount brokers, day traders and 24 hour trading
12. The rise of gambling opportunities

An interesting quote is:

'... it is worth remembering that there is no air-tight science of stock market pricing. Economists have certainly made progress in understanding financial markets, but the complexity of real life continues to prevail.'

In order for you to believe that it is possible for investors to be able to destroy the work done by good managers by exhibiting the wrong behaviour, you have to reject the efficient market hypothesis (EMH). That is an extension of believing that it is indeed possible for there to be good managers.

The argument in favour of index funding because of the average manager fund not out performing the market while incurring additional costs, implicitly accepts the EMH.

If the EMH holds, basically the timing of entering and exiting funds shouldn't really affect the chance of you performing better or worse than the fund you are invested in, since the price is always fair.

I have so far worked through the first half of Barberis and Thaler's (2003) Survey of Behavioural Finance. The arguments of Behavioural Finance to counter the EMH are centered around two major areas. The first is 'Limits to Arbitrage', where the argument is that even when the market is incorrectly priced, mechanisms to correct the mispricing are limited. The second is Psychology and looks into the various biases that people exhibit.

The most striking thing for me is that it seems self-evident that the psychological errors in particular are clearly real. The argument over whether markets are rational and always correctly priced seems to be a distraction from trying to find ways to correct the investor biases. They also seem so consistent with biases people exhibit beyond the world of finance that the problem is one that the field of finance can learn a lot from areas outside of our traditional academic world.

An Introduction

I found a copy of item 3, of the to read list Irrational Exuberance - Shleifer (2000), so that is where I have started. The book was published just before the burst of the Internet bubble as Shleifer warned that the market was overpriced. The argument from academia was that the market was efficient so this shouldn't be the case. The market also stayed overpriced for so long that people who argued this were simply sidelined for not accepting the `new world'. It is a little strange reading this book after the burst, and subsequent inflation of a new bubble, and subsequent burst.

But this is case and point for the aim of this study. If an asset manager correctly assesses the market and makes the right decisions, aware that it is difficult/impossible to time the market... Can they keep their clients through that? If clients join just after significant outperformance, and leave just after the crash... the skill of the asset manager counts for nothing.

From behaviouralfinance.net , here is an introduction to the field of behavioural finance.

Wikipedia also provides a:

• History
• Methodology
• List of Topics
• ... Key Conclusions
• ... Key Figures and Scholars
• ... References 

Sewell's Behavioural Finance

Having discovered behaviouralfinance.net through the references to Mauboussin's `Decision Making for Investors', I realised the necessity for a fair amount of background reading into Behavioural Finance.

Martin Sewell, from University College London, gives a survey of all the major contributions since the field started in `An Introduction to Behavioural Finance.'

The bits that jumped out at me to look at further include a study by Thaler in 1980 where he identifies 5 problems that arise through biases in our decision making:

1. Underweighting of opportunity costs
2. Failure to ignore sunk costs
3. Search Behaviour
4. Choosing not to choose and regret
5. precommitment and self-control

The books I am probably going to try get ahold of and read (comments welcome of a better place to start) are, in likely order:

1. Inefficient Markets: An introduction to Behavioural Finance - Shleifer (2000)
2. Heurisitics and Biases: The Psychology of Intuitive Judgement - Khaneman et al. (2002)
3. Irrational Exuberance - Shleifer (2000)
4. The Psychology of Judgement and Decision Making - Plous (1993)
5. Beyond Greed and Fear - Shefrin (2000)
6. Simple Heuristics That Make Us Smart - Gigerenzer, Todd & the ABC Research Group (1999)

Clearly I can't buy every book that I would like to read, so I am going to have to ask people who may have books on a wish list if I can borrow them...

Decision Making for Investors

Michael.J.Mauboussin of Legg Mason provides some very useful insights and a whole lot of references to further read into in the paper, 'Decision Making for Investors'.

Unlike the believers in the efficient market hypothesis and advocates of index-linked investments, Mauboussin emphasises the need for a focus on process over short-term results in order to distinguish between luck and skill of investors.

He does believe that you can outperform arguing that 'super performers have more in common with one another that they do with the average participant in their own field.'

His paper approaches investor decision making from three angles:

1. Inputs from successful people in various domains.
2. Expected Value.
3. Heuristics and Biases and why we fail to calibrate probabilities and outcomes properly.

He highlights a further 3 strategies to avoid the problems:

1. Focus on process rather than the outcome.
2. A constant search for favourable odds.
3. An understanding of the role of time.

'Focus on establishing a superior process with the understanding that outcomes take care of themselves' - MJM

He quotes Robert Rubin:

Any individual decision can be badly thought through, and yet be successful, or exceedingly well thought through, but be unsuccessful, because of the recognised possibility of failure in fact occurs. But over time, more thoughtful decision-making can be encouraged by evaluating decisions on how well they were made rather than on outcome.

He quotes Nassim Taleb and discusses the asymmetric nature of stock market returns and the importance of fat tails. On the same subject he quotes Philip Anderson (Nobel Prize winning physicist):

Much of the real world is controlled as much by the "tails" of the distribution as by the averages: by the exceptional, not the mean; by the catastrophe, not the steady drip; by the very rich, not by the "middle class". We need to free ourselves from "average" thinking.

Finally he gives a number of challenges in order to overcome many of the problems he identified. These seem fairly generic and valuable not just in the sphere of making investment decisions. My challenge in the research I am doing will be to find tangible measures of how well we are doing at meeting these goals. Again, it seems though that these valid points all point to emotional intelligence, making a high quality decision and then the ability to 'just do nothing'. Here they are:

1. Try not to overestimate your abilities.
2. Actively challenge your own assumptions.
3. Ask disconfirming questions.
4. Realise that past events or prices are sign posts, not answers.
5. View decisions from various perspectives.
6. Seek information from a variety of sources.
7. Reframe questions.
8. Consider only future costs and benefits.

I would encourage the more eager readers to go to the original paper and then post any thoughts you might have on this paper in the comments section of this post.

Any other suggestions of other papers/books/articles to read would be appreciated.

Measures

I have yet to find Bogle's paper. One of the frustrations of doing a literature review is finding the literature to review! One article which I have found which take a different approach to Bogle is Davis Advisors.

They conclude with 7 pieces of advise:

• Avoid Self Destructive Investor Behaviour
• Understand That Crisises are Inevitable
• Don't Attempt to Time the Market
• Be Patient
• Don't Let Emotions Guide Your Investment Decisions
• Recognise That Short Term Performance Is Inevitable
• Disregard Short Term Forecasts and Predictions

Dalbar do an annual study called 'A Quantitative Analysis of Investor Behaviour' which at $750 is not something I can just download, but is referred to in the online Davis Article. The graph on page 2 illustrates the issue that is at the heart of why I am doing this research.

What I am also interested in doing is trying to find measures of how individual investment companies can implement to see how they are doing at helping their clients get the full performance of the fund.

Making the assumption that you have done your Due Dilligence on an investment company, understand and agree with their philosophy and have confidence in their process to achieve their goals... the obvious message from the articles, and even from Bogle (even though he rejects active management) is to do nothing.

There are likely two things that need to be measured:

1) Historical Gap between Fund and Clients Performance

2) Behaviour Measures that link to the suspected causes of value destruction. 

Are there quantitative measures for the 7 points mentioned by Davis?

Perhaps

1. Level of client activity measures like volatility of contributions. 
   
2. Measures of correlation between client activity and market volatility. If the market goes up, are there more subscriptions? If the market goes down, are there more redemptions?
   
3. Lapse Rates or measures of clients leaving.

Is it possible to get quantitative measures of client understanding and emotional capacity to handle the ups and downs? You certainly can't start doing tests for clients! It may require qualitative evaluation. But that obviously makes analysis harder.

Let me see if I can find some suggestion!

John Bogle

In my last post, I mentioned John Bogle's study. I have not yet read of much of his work. He does have a website, but what I am trying to find is the original study that he did comparing the Fund's Return to the average investor's return.

Most Fund's measure their historical performance since inception or over set periods (say 1, 3, 5 ,10 years). The measure used most often is the Time-Weighted Rate of Return (TWR) since this is not impacted by the cash flows into and out of the fund. It is a clean measure of what $1 invested in the fund would grow to over the period measure. The actual return (Internal Rate of Return  or IRR) is affected by cash flows. 

If you invest $1 and it grows to $5 in 6 months, so you get excited... who wouldn't be with 400% growth, and top your investment up to $100 ($95 more), then there is a 50% drop in the next 6 months.

Your Net Investment = $96

TWR = 150%

IRR = -72.6%

Clearly the fund manager has no control over when you choose to invest or disinvest. The problem comes in if you regularly choose to invest just after the fund has done really well and sell just after it has done badly.

So in this example, the TWR of 150% shows a very successful year for the fund, but the client has lost a lot.

This is a very short term and extreme example, but over time if the client is very active in entering and leaving the fund... there is no reason why their return should be the same as the funds.

Overview

Performance Alpha is not generated smoothly over time. There are inevitable periods of both extreme outperformance and underperformance. I do not believe it is possible to predict when these periods will be. The problem is that excluding the most significant periods of outperformance can dramatically affect a clients performance relative to the fund's performance. Anecdotally and from popular papers by John Boggle, for example, Clients have a habit of leaving after shorter periods of underperformance, and joining after periods of outperformance leading to clients within a fund underperforming the fund itself. I would like to do

• a literature review both of what research has been done on this subject.
  
• What measures of destructive client behaviour have been proposed
  
  • Pros and Cons of each measure
    
  • Examples
    
    • Behavioural Alpha
      
      • Fund Return over period vs. Clients IRR
        
      • Return if net contributions level over period vs. Clients IRR
        
    • Period Invested
      
    • Contribution Volatility Measure
      
• Whether there are additional measures that could be used
  
• What can be done to assist clients to protect any alpha creation
  
  • Have there been successful studies on how to do this?
    

Collaborative Research

I am starting this blog with the intention of doing a research paper and a presentation on the topic at the Convention of the Actuarial Society of South Africa in late 2009, and the International Convention in 2010.

The intention is two pronged.

Firstly, I am interested in the topic and think it is important that we find ways of measuring our effectiveness in meeting the needs of investors. This does not stop at creating out performance if that out performance can not be fed through to clients because of their decision making.

Secondly, I am interested in whether using new communication technology, we can increase the number of people willingly contributing to research.

I hope you will join me in seeing if it works?