As the markets melt down, remember Rule No. 1: Don't Panic! Going along with the herd and selling equities at a time like this is foolish. Personally, not only am I not selling, but I am buying and will continue to do so as prices fall, because equities are like anything else: the lower the price (assuming it's a sound company), the better for you in the long term.
The only scenario in which it makes sense to sell right now is if you honestly, truly believe that the world is coming to an end and stock prices will therefore never, ever recover. Barring that, though, this is the perfect time to buy stocks and I will be doing so aggressively as the slide continues. When the panic stops (and remember, it always does eventually) and stocks begin to recover, I will have taken advantage of buying in at much lower prices, while the herd will have dumped all theirs at the bottom and start buying again at the higher price points.
Showing posts with label Investing. Show all posts
Showing posts with label Investing. Show all posts
09 August 2011
27 January 2011
Fun with Artificial Intelligence, Part II
As promised when I initially wrote about artificial intelligence (and specifically about Eureqa), I have been playing around with economic data and have come up with some interesting results. After running dozens of experiments using different combinations of data and allowing Eureqa to use different computational operations, I have come to a fascinating conclusion: in terms of what helps the AI fit equity performance data points to a model based on all data available to it, it seems equities don't 'care' about anything but how we, the consumers, think about the economy, regardless of how well- or ill-informed we are about the economy. Give the AI GDP data, CPI, unemployment, CD rates (to provide opportunity cost), even prior year stock performance and price-to-earnings data: if you also provide consumer confidence data, it will systematically discard every other data type save that one. In some simulations with solutions that have similar complexity and fit, it might also use Fed Funds rate and/or CD rates, which I see as opportunity-cost stand-ins. But for the most part, it just wants to know, "How do you feel about the economy, regardless of how well or poorly it is actually performing and regardless of how much you even really understand it?"
As I said in the prior post, I am far from convinced that such data-mining can offer realistic predictive models. But ironically, the fact that the AI prefers the least rigid, least 'rational' data type, in fact makes me less skeptical about its predictive power. My reasoning is this: if the AI chose models that were based strictly on 'hard' data such as CPI, etc., I would suspect it was simply data-mining and that the results would be useless outside the confines of the already-given universe of data points, since equities markets are inherently irrational and are driven by things far less quantifiable than, say, GDP; but the very fact that it chooses the least 'rational' data type, consumer confidence, tells me that it may indeed be coming up with a decent predictive model, since it seems fitting that it has chosen the one data type that combines rigid metrics but applies them to decidedly 'soft' data (that is, consumer sentiment).
So the proof will be in the pudding, I guess. But that pudding will take quite a while to cook, so don't hold your breath. Meanwhile, for what it's worth, I will shortly add some of the predicted values from various experiments. Then we'll sit back and see what happens!
My next project is GDP. I want to see what data types the AI most prefers for predicting the performance of the US economy.
As I said in the prior post, I am far from convinced that such data-mining can offer realistic predictive models. But ironically, the fact that the AI prefers the least rigid, least 'rational' data type, in fact makes me less skeptical about its predictive power. My reasoning is this: if the AI chose models that were based strictly on 'hard' data such as CPI, etc., I would suspect it was simply data-mining and that the results would be useless outside the confines of the already-given universe of data points, since equities markets are inherently irrational and are driven by things far less quantifiable than, say, GDP; but the very fact that it chooses the least 'rational' data type, consumer confidence, tells me that it may indeed be coming up with a decent predictive model, since it seems fitting that it has chosen the one data type that combines rigid metrics but applies them to decidedly 'soft' data (that is, consumer sentiment).
So the proof will be in the pudding, I guess. But that pudding will take quite a while to cook, so don't hold your breath. Meanwhile, for what it's worth, I will shortly add some of the predicted values from various experiments. Then we'll sit back and see what happens!
My next project is GDP. I want to see what data types the AI most prefers for predicting the performance of the US economy.
09 January 2011
Changing gears for a moment: Investing Primer
Completely different theme today: real-world investing. This is actually just a copy-paste (with a few edits) from a document I wrote quite a while back and that I updated recently for a friend. Over the years, several people have asked me for my take on investing. I am not sure why: it's not something I usually discuss with people. Perhaps my overall nerdy aura makes people think I am an expert! I am not, and should never be mistaken for one. I can't emphasize that enough. I hope this 'primer' is a useful *starting* point for those interested in understanding investing, but it should never be an *ending* point in such a search. Talk to a professional! But this primer should give you the knowledge you need to ask the right questions of such a professional.
INVESTING 101: A STARTER’S GUIDE
The first rule of investing is straight from Douglass Adams: DON’T PANIC. (Sadly, the second rule is not ‘42’.) The reason many investors lose money is that right when it becomes the least expensive time to invest, they sell. To understand what I mean, imagine the following scenario. You need to buy a new suit. You go to your local department store and the clerk tells you that a big sale just ended, so it’s the most expensive time to buy. So you think to yourself, huh, most expensive time? Cool. Then suits must be worth a lot! I will take two!! The next week, the store has a huge 50% off sale. So your logic is, well, then suits must not be worth as much as I thought they were, so I’d better unload them before they lose even more value! So you return to the store for a refund. The clerk looks at you like you’re crazy: “Sir, you realize that since the sale is on, I have to refund the SALE price, not the price you paid? You will lose half your money!!” To which you reply, “Yeah, but I am nervous prices will fall even further...please give me money back.”
Now that little scenario may sound silly to the point of inane, but stop and think about the way most people handle their investments. This is exactly how they behave. They get into the stock market because it’s ‘hot’ (meaning prices for stocks are often at their HIGHEST). Then, right when prices get reasonable and smart investors are buying up cheap stocks (i.e. during market corrections), the foolish investors run out of the market in a panic, locking in their losses forever. They do this because they believe they are smarter than the markets, that they can ‘time’ the market. That is a fool's errand. Don't try it.
The key to making money in equities is a very simple strategy using two tools:
1. Dollar-cost averaging. The reason it is called ‘dollar-cost averaging’ is that you are buying steadily through all the market fluctuations, so you average out the cost of ownership of your equities. So it’s just a technical way of saying ‘slow and steady’. Invest a fixed amount every month (or every paycheck or whatever) and do NOT SELL during market corrections. In fact, quite the opposite: if the market is correcting, think of it as that sale at the department store: if you do anything at these times, it should be to buy more when all the panicking masses are 'returning their overpriced suits!'
2. Indexing. The reality is that unless investing actually interests you and you are willing and able to take the time to research stocks thoroughly, your best bet is simply to buy ETFs or mutual funds that are tied to the major indices that reflect your goals and risk-aversion level. DO NOT buy actively-managed funds. These are funds in which the managers actively buy and sell stocks in the belief they can beat the market consistently. But check the facts: the VAST majority of actively-managed mutual funds fail to beat the indices most comparable to their strategies and goals. Many can brag they have done it for, say, a year or two or even five, but in the end, the ‘invisible hand’ will always win. In fact, since they all fall sooner or later in comparison to the indices, any funds that brag they have beaten the market for a few years, are actually the very ones to avoid as it means they will sooner rather than later revert to the mean.
You might say, “Well, that sounds good in theory, Christopher, but look at the reality of the stock market over the past decade. I know a lot of people who lost a fortune in the markets during that time. So how can you defend equities as a sound investment?” The answer is: easily and justifiably. Go back to dollar-cost averaging and apply it to the wild and crazy markets over the past ten years. For example, the NASDAQ is actually lower as of this writing (January 2011) than it was exactly ten years ago today. Scary, huh? So if you were a foolish market-timer who went in when it was hot (right before the bubble burst) and ran out at every scary correction, you would be broke (or close enough anyway). But if you had been a smart investor following this simple strategy, look at what would have happened:
Buy $10,000 last day of every October starting 2000, so ten purchases spread over the decade (2000-2009). (Normally you would buy every month or few weeks, not just once a year, but let’s keep it simple for the sake of example.) Even though the NASDAQ is lower now than ten years ago, you would, as of last trading day in October 2010, have $124,769.24 on that $100,000 investment, thanks to dollar-cost averaging and a disciplined approach to investing, with no panicked withdrawals. That may not look huge – and by historical standards, it isn’t great for equities– but you are still ahead of the game, even after inflation. And compare it to what your ‘safe’ friend did when he ‘wisely’ sold all his stocks after the crash, got out of equities and just put it all in bank certificates of deposit (aka CDs) at, say, 2.5%: even if he escaped with that same starting $100,000, he now has $114,834.66. That barely (if at all) keeps up with inflation, so in real terms, he has lost money on his ‘safe’ approach.
If it’s so easy, why do so many people lose money? See rule number one. People panic. They buy at inflated values (when stocks are ‘hot’) and sell at deflated values (when it’s ‘time to get out’).
So the bottom line is simple: invest steadily, don’t panic, and stick with it!
The next question is, how aggressively should you invest? After all, there are stocks and then there are stocks...blue chip indices like the Dow tend to be less sexy and maybe not as lucrative, but they are less volatile and offer better dividends (i.e. fixed income regardless of price performance); small cap indices are very aggressive, but tend to fluctuate a lot and there is more downside risk that can offset some of that upside reward. And what about overseas indices? To decide the right blend, you must consider two things:
1) What is your risk tolerance? In simplest terms, people fall on a spectrum from ‘risk-adverse’ to ‘risk-seeking’. You either want steady but lower returns, or you want (potentially) higher returns but with more downside risk. It’s all fine and good to say ‘Don’t panic’, but if you are the type of person who WILL - despite all advice - panic when he sees an index tank, say, 30%, then you are more on the risk-averse side. The most important move you will ever make as an investor is when you truly ask yourself what kind of person you are and then honestly answer that question. If you are risk-adverse, admit that to yourself and proceed accordingly. This isn’t a test of your personal courage or self worth!
2) What is your time horizon? Equities are great investments, as I have shown above, but the shorter the investing time frame, the riskier they can be. Think of it this way: if you need to buy a car next week, you aren’t going to invest the money you need in Apple or Microsoft. Yes, they may go up several percentage points in a week. But they can also tank several points in a week. But if you are buying that car in, say, ten years, then part of your investment to save for it may well be in equities. So you need to blend accordingly and even create different portfolios (one for short term with CDs, bonds, and maybe just a very small amount in equities; another for long term, mostly in equities). For retirement savings, it is all about one portfolio, but one that evolves over time. Personally, I use a very simple approach: every year, roughly one percentage point more of my portfolio goes to safer investments (e.g. high-grade bonds, etc) and one point comes out of stocks. So by the time I retire, only around 30-35% of my money will be in stocks. That might seem high, but remember, retirement isn’t a one-day event: it also has a time horizon of its own, since some of the money will be needed ASAP, but the rest will not be needed til well after the day you retire.
Making mistakes around time horizon is why you hear about people who are shocked to lose their retirements just before they are due to retire. How many stories like that have we heard in recent years? “I was two years from retirement, then I lost 75% of my portfolio...now I have to work til I am 90!” So what two mistakes did this poor fellow make?* First of all, he broke Rule 1: he panicked and sold after the market correction, meaning losses are locked in. But the overarching issue is a mistake related to time horizons. The question you should ask that fellow is, “If you were 63 years old and needed that money at 65, why in the world did you have it all in equities?!?!” At 63, he should have had well over half his money in very safe places like CDs, highly rated bonds, etc. Money exposed to short-term fluctuations should just have been money he needed later in retirement, so there would be time to recoup.
In summary, first decide what kind of investor you are, then look at your time horizon, then make a plan to invest regularly, with a firm commitment to yourself not to panic at market downturns. Do this and you will be fine!
A small post script here: accept the fact that no matter how disciplined you are, you are still human and you will still do stupid and/or highly risky things. Set aside a ‘play portfolio’ with 2%-5% of your investing capital (or a few grand...whatever feels right, but no more than 5% of all capital) and use it to play hunches, buy ‘hot stocks’ your friends tell you about, whatever. Think of it as your investment playground. Silo this from your main portfolio, though: separate account, maybe even separate brokerage. (That’s to avoid the temptation to transfer money from your real portfolio.) This can also be your ‘lab’ where you can learn about investing, feeling safe to make mistakes. But remember not to get cocky: no matter how good you get, you will never beat the markets in the long term, so don’t get any stupid ideas about transferring your major assets here just because you do well for a while.
Some sample portfolios:
Risk-seeking, long-term investor with at least 20 years til retirement:
5% in a safe money market fund or high-grade bond fund
5% in high-yield (lower grade) bond funds
20% in ETFs or index mutual funds tied to major world indices
30% in ETF or index mutual fund tied to Russell 2000 (small caps)
20% in ETF or index mutual fund tied to S&P Mid-Cap 400 (mid-caps)
20% in ETF or index mutual fund tied to S&P500 (large caps)
Mildly risk-adverse, long-term investor with at least 20 years til retirement:
10% distributed among safe money market funds, a high-grade bond fund, and maybe even some precious metal shares (e.g. ETFs tied to price of gold)
15% in ETFs or index mutual funds tied to major world indices (ex-USA)
10% in ETF or index mutual fund tied to Russell 2000 (small caps)
10% in ETF or index mutual fund tied to S&P Mid-Cap 400 (mid-caps)
55% in ETF or index mutual fund tied to S&P500 (large caps)
--------------------------------------------------------------------------------------
Footnotes:
*Well, the FIRST mistake was probably just plain greed, but that's another blog posting for another day. I truly do feel sorry for all the people who lost so much in such a short period of time, but I can't escape the reality that much of it was due to their own foolishness and greed. Even in cases of fraud. Take the Bernie Madoff debacle. Yes, he defrauded all those poor folks and he is a monster. But why weren't those people following the rule of 'if it seems too good to be true, IT IS'? And why were they sinking ALL of their money into his funds and not hedging against the risk by putting at least some of it elsewhere? Sadly, the answer in all cases is 'greed'. And let's be honest with ourselves: Madoff wasn't the only con artist. The SEC played a witting role in allowing his Ponzi scheme to flourish, despite many warnings from a fraud investigator who had been sounding the alarm about Madoff for YEARS.
INVESTING 101: A STARTER’S GUIDE
The first rule of investing is straight from Douglass Adams: DON’T PANIC. (Sadly, the second rule is not ‘42’.) The reason many investors lose money is that right when it becomes the least expensive time to invest, they sell. To understand what I mean, imagine the following scenario. You need to buy a new suit. You go to your local department store and the clerk tells you that a big sale just ended, so it’s the most expensive time to buy. So you think to yourself, huh, most expensive time? Cool. Then suits must be worth a lot! I will take two!! The next week, the store has a huge 50% off sale. So your logic is, well, then suits must not be worth as much as I thought they were, so I’d better unload them before they lose even more value! So you return to the store for a refund. The clerk looks at you like you’re crazy: “Sir, you realize that since the sale is on, I have to refund the SALE price, not the price you paid? You will lose half your money!!” To which you reply, “Yeah, but I am nervous prices will fall even further...please give me money back.”
Now that little scenario may sound silly to the point of inane, but stop and think about the way most people handle their investments. This is exactly how they behave. They get into the stock market because it’s ‘hot’ (meaning prices for stocks are often at their HIGHEST). Then, right when prices get reasonable and smart investors are buying up cheap stocks (i.e. during market corrections), the foolish investors run out of the market in a panic, locking in their losses forever. They do this because they believe they are smarter than the markets, that they can ‘time’ the market. That is a fool's errand. Don't try it.
The key to making money in equities is a very simple strategy using two tools:
1. Dollar-cost averaging. The reason it is called ‘dollar-cost averaging’ is that you are buying steadily through all the market fluctuations, so you average out the cost of ownership of your equities. So it’s just a technical way of saying ‘slow and steady’. Invest a fixed amount every month (or every paycheck or whatever) and do NOT SELL during market corrections. In fact, quite the opposite: if the market is correcting, think of it as that sale at the department store: if you do anything at these times, it should be to buy more when all the panicking masses are 'returning their overpriced suits!'
2. Indexing. The reality is that unless investing actually interests you and you are willing and able to take the time to research stocks thoroughly, your best bet is simply to buy ETFs or mutual funds that are tied to the major indices that reflect your goals and risk-aversion level. DO NOT buy actively-managed funds. These are funds in which the managers actively buy and sell stocks in the belief they can beat the market consistently. But check the facts: the VAST majority of actively-managed mutual funds fail to beat the indices most comparable to their strategies and goals. Many can brag they have done it for, say, a year or two or even five, but in the end, the ‘invisible hand’ will always win. In fact, since they all fall sooner or later in comparison to the indices, any funds that brag they have beaten the market for a few years, are actually the very ones to avoid as it means they will sooner rather than later revert to the mean.
You might say, “Well, that sounds good in theory, Christopher, but look at the reality of the stock market over the past decade. I know a lot of people who lost a fortune in the markets during that time. So how can you defend equities as a sound investment?” The answer is: easily and justifiably. Go back to dollar-cost averaging and apply it to the wild and crazy markets over the past ten years. For example, the NASDAQ is actually lower as of this writing (January 2011) than it was exactly ten years ago today. Scary, huh? So if you were a foolish market-timer who went in when it was hot (right before the bubble burst) and ran out at every scary correction, you would be broke (or close enough anyway). But if you had been a smart investor following this simple strategy, look at what would have happened:
Buy $10,000 last day of every October starting 2000, so ten purchases spread over the decade (2000-2009). (Normally you would buy every month or few weeks, not just once a year, but let’s keep it simple for the sake of example.) Even though the NASDAQ is lower now than ten years ago, you would, as of last trading day in October 2010, have $124,769.24 on that $100,000 investment, thanks to dollar-cost averaging and a disciplined approach to investing, with no panicked withdrawals. That may not look huge – and by historical standards, it isn’t great for equities– but you are still ahead of the game, even after inflation. And compare it to what your ‘safe’ friend did when he ‘wisely’ sold all his stocks after the crash, got out of equities and just put it all in bank certificates of deposit (aka CDs) at, say, 2.5%: even if he escaped with that same starting $100,000, he now has $114,834.66. That barely (if at all) keeps up with inflation, so in real terms, he has lost money on his ‘safe’ approach.
If it’s so easy, why do so many people lose money? See rule number one. People panic. They buy at inflated values (when stocks are ‘hot’) and sell at deflated values (when it’s ‘time to get out’).
So the bottom line is simple: invest steadily, don’t panic, and stick with it!
The next question is, how aggressively should you invest? After all, there are stocks and then there are stocks...blue chip indices like the Dow tend to be less sexy and maybe not as lucrative, but they are less volatile and offer better dividends (i.e. fixed income regardless of price performance); small cap indices are very aggressive, but tend to fluctuate a lot and there is more downside risk that can offset some of that upside reward. And what about overseas indices? To decide the right blend, you must consider two things:
1) What is your risk tolerance? In simplest terms, people fall on a spectrum from ‘risk-adverse’ to ‘risk-seeking’. You either want steady but lower returns, or you want (potentially) higher returns but with more downside risk. It’s all fine and good to say ‘Don’t panic’, but if you are the type of person who WILL - despite all advice - panic when he sees an index tank, say, 30%, then you are more on the risk-averse side. The most important move you will ever make as an investor is when you truly ask yourself what kind of person you are and then honestly answer that question. If you are risk-adverse, admit that to yourself and proceed accordingly. This isn’t a test of your personal courage or self worth!
2) What is your time horizon? Equities are great investments, as I have shown above, but the shorter the investing time frame, the riskier they can be. Think of it this way: if you need to buy a car next week, you aren’t going to invest the money you need in Apple or Microsoft. Yes, they may go up several percentage points in a week. But they can also tank several points in a week. But if you are buying that car in, say, ten years, then part of your investment to save for it may well be in equities. So you need to blend accordingly and even create different portfolios (one for short term with CDs, bonds, and maybe just a very small amount in equities; another for long term, mostly in equities). For retirement savings, it is all about one portfolio, but one that evolves over time. Personally, I use a very simple approach: every year, roughly one percentage point more of my portfolio goes to safer investments (e.g. high-grade bonds, etc) and one point comes out of stocks. So by the time I retire, only around 30-35% of my money will be in stocks. That might seem high, but remember, retirement isn’t a one-day event: it also has a time horizon of its own, since some of the money will be needed ASAP, but the rest will not be needed til well after the day you retire.
Making mistakes around time horizon is why you hear about people who are shocked to lose their retirements just before they are due to retire. How many stories like that have we heard in recent years? “I was two years from retirement, then I lost 75% of my portfolio...now I have to work til I am 90!” So what two mistakes did this poor fellow make?* First of all, he broke Rule 1: he panicked and sold after the market correction, meaning losses are locked in. But the overarching issue is a mistake related to time horizons. The question you should ask that fellow is, “If you were 63 years old and needed that money at 65, why in the world did you have it all in equities?!?!” At 63, he should have had well over half his money in very safe places like CDs, highly rated bonds, etc. Money exposed to short-term fluctuations should just have been money he needed later in retirement, so there would be time to recoup.
In summary, first decide what kind of investor you are, then look at your time horizon, then make a plan to invest regularly, with a firm commitment to yourself not to panic at market downturns. Do this and you will be fine!
A small post script here: accept the fact that no matter how disciplined you are, you are still human and you will still do stupid and/or highly risky things. Set aside a ‘play portfolio’ with 2%-5% of your investing capital (or a few grand...whatever feels right, but no more than 5% of all capital) and use it to play hunches, buy ‘hot stocks’ your friends tell you about, whatever. Think of it as your investment playground. Silo this from your main portfolio, though: separate account, maybe even separate brokerage. (That’s to avoid the temptation to transfer money from your real portfolio.) This can also be your ‘lab’ where you can learn about investing, feeling safe to make mistakes. But remember not to get cocky: no matter how good you get, you will never beat the markets in the long term, so don’t get any stupid ideas about transferring your major assets here just because you do well for a while.
Some sample portfolios:
Risk-seeking, long-term investor with at least 20 years til retirement:
5% in a safe money market fund or high-grade bond fund
5% in high-yield (lower grade) bond funds
20% in ETFs or index mutual funds tied to major world indices
30% in ETF or index mutual fund tied to Russell 2000 (small caps)
20% in ETF or index mutual fund tied to S&P Mid-Cap 400 (mid-caps)
20% in ETF or index mutual fund tied to S&P500 (large caps)
Mildly risk-adverse, long-term investor with at least 20 years til retirement:
10% distributed among safe money market funds, a high-grade bond fund, and maybe even some precious metal shares (e.g. ETFs tied to price of gold)
15% in ETFs or index mutual funds tied to major world indices (ex-USA)
10% in ETF or index mutual fund tied to Russell 2000 (small caps)
10% in ETF or index mutual fund tied to S&P Mid-Cap 400 (mid-caps)
55% in ETF or index mutual fund tied to S&P500 (large caps)
--------------------------------------------------------------------------------------
Footnotes:
*Well, the FIRST mistake was probably just plain greed, but that's another blog posting for another day. I truly do feel sorry for all the people who lost so much in such a short period of time, but I can't escape the reality that much of it was due to their own foolishness and greed. Even in cases of fraud. Take the Bernie Madoff debacle. Yes, he defrauded all those poor folks and he is a monster. But why weren't those people following the rule of 'if it seems too good to be true, IT IS'? And why were they sinking ALL of their money into his funds and not hedging against the risk by putting at least some of it elsewhere? Sadly, the answer in all cases is 'greed'. And let's be honest with ourselves: Madoff wasn't the only con artist. The SEC played a witting role in allowing his Ponzi scheme to flourish, despite many warnings from a fraud investigator who had been sounding the alarm about Madoff for YEARS.
Fun with Artificial Intelligence
I have been having a ball for the past week playing with Eureqa, an AI I mentioned in an earlier post. Eureqa is the brainchild of Profs. Schmidt and Lipson at Cornell. When it comes to data, my main fascination has always been economic data, so I have been toying with trying to find models that best account for why equities markets move in one direction or another. As I said in that previous post, there are far too many irrational factors involved in the movement of equities markets to come up with the S&P 500's answer to E=MC². Even if you managed to come up with a more or less reasonable predictive model, the theoretical economic characteristic of so-called 'perfect knowledge' eventually becomes not-so-theoretical economic reality; people begin operating under this new spotlight; next thing you know, the model is dead precisely because everyone knows about it and therefore behaves in ways not predictable by the model (since this new knowledge and resulting behavior are themselves major new variables).
Quite aside from the fact that eventual knowledge of a good model would itself make the model obsolete, is the fact that there is a HUGE difference between an equation that explains data and an equation that reveals cause and effect for data. Just ask all the people who have wasted good time and money 'data-mining' the history of equities markets. A perfect example is O'Shaughnessy's 'What Works on Wall Street'. The author dug through decades of data on the stock market and came up with elaborate models showing what would have been extremely effective ways of making money....assuming one had the knowledge of the entire period, but had gained that knowledge at the beginning of the period studied. It's amazing to me that an internet search of this man still pulls up almost universally positive, glowing articles and interviews, despite the fact that the mutual funds that he opened in the 1990s, funds entirely built on his 'research', were abject failures. He managed to spin this somehow, get out of mutual funds, and open a private wealth management company. This allowed him to continue making money and claiming he was right all along, but in fact freeing him to use completely unrelated methods of investing (since he isn't required to divulge his techniques). So he is undoubtedly a gifted marketer, and obviously even a good money manager...as long as he isn't following his own advice.
If this is all still (quite understandably) rather abstruse, I'll illustrate with a metaphor. Imagine you stand outside on the street corner and observe the weather and the passing of cars and people. Three out of ten days it rains. On those days, you notice people wearing raincoats. You also notice there are no open convertibles. Data-mining your way to 'good' equations tells you that an absence of convertibles and a presence of raincoats cause it to rain. That's an example of mixing up cause and effect. In another example, imagine that you observe that on the days it rained, you observed ten percent more people named Jane. Aha! An excess of Janes is causing rain! No, this is just coincidence.* I could go on, but for more examples of these kinds of problematic reasoning, there's a far better resource: read Crimes Against Logic by Jamie Whyte. This book is one of my top fifteen all-time favorites, not so much because it taught me anything I didn't already know (though it did that, too), but because he so eloquently and clearly expressed ideas I had known well but that I had been unable to articulate.
However, an inability to find well-performing predictive models for equities markets, doesn't mean that feeding such data in Eureqa is itself useless. By watching how Eureqa treats all the different variables, you start to see how they interact and which ones haven't even a correlative relationship with equities market performance. For example, Eureqa rarely seems to 'care' much for inflation. There appears to be very little correlation. BUT, it does 'care' quite a lot about the Fed Funds rate, which is essentially the public policy reaction to inflation. It also 'likes' CD rates, which might be a decent stand-in for opportunity costs, though that implies some cause-and-effect (CD rates are low -> opportunity cost of foregoing them in favor of equities is low -> I will buy equities -> everyone does same -> equity prices rise)**, which requires a heavier burden of proof, one that I am far from meeting. And employment? Almost always tosses that out as irrelevant very quickly. But it 'loves' consumer confidence, which suggests that while the markets don't 'care'*** about how many people are out of work, they care very much about how confident people feel in the economy (which is presumably in turn driven by how many of them have jobs, though not directly). But again, there is no straight cause-and-effect here. You can't say Consumer Confidence = y ergo stock performance will = z as an exact function of y.
Early days yet, but so far, so fun! After I get bored with this round of experiments, I think I'll move on to GDP.
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Footnotes:
*Don't even get me started on people who say 'I don't believe in coincidences.' Do you have ANY idea what kind of universe we would live in WITHOUT A HUGE LOT OF COINCIDENCES? Randomness permeates the very fabric of existence. The 'problem' is that our human brains have evolved with this incessant need to find patterns. I say this facetiously because of course that very same 'problem' is doubtless one of the very core elements of our intelligence, not to mention a key explanation to our very survival as a species. But it does have the unfortunate side-effect of making us see Jesus in breakfast food far too often.
**This introduces an intriguing interplay itself. Perhaps the opportunity cost (in form of CD rates or T-bills) must reach a certain threshold before prompting consideration of equities, but that consideration is in turn colored by the confidence one has in the markets and the overall economy (as measured by U of M consumer confidence index?), and that interplay in turn drives the degree to which investors commit to equities, thus determining the demand for (and therefore value of) those equities. Add in a dash of price-to-earnings data (i.e., the 'real' cost of 'buying' the earnings behind an equity) and you might just have some soup worth tasting.
***Please forgive the anthropomorphic words here. And if you are a lefty like me, do not fall into the temptation of attributing 'feelings' to markets. That a market does not move in reaction to a tragedy like high unemployment, does NOT mean that the people who make up those markets do not care about unemployed people. The phenomenon is merely an observed outcome of the aggregate behavior of the people acting in the market, not the 'evil' intent of any group of people within the market. I enjoy demonizing Wall St fat-cats as much as the next liberal, but do so for their individual behaviors, not those of the markets in which they act.
Quite aside from the fact that eventual knowledge of a good model would itself make the model obsolete, is the fact that there is a HUGE difference between an equation that explains data and an equation that reveals cause and effect for data. Just ask all the people who have wasted good time and money 'data-mining' the history of equities markets. A perfect example is O'Shaughnessy's 'What Works on Wall Street'. The author dug through decades of data on the stock market and came up with elaborate models showing what would have been extremely effective ways of making money....assuming one had the knowledge of the entire period, but had gained that knowledge at the beginning of the period studied. It's amazing to me that an internet search of this man still pulls up almost universally positive, glowing articles and interviews, despite the fact that the mutual funds that he opened in the 1990s, funds entirely built on his 'research', were abject failures. He managed to spin this somehow, get out of mutual funds, and open a private wealth management company. This allowed him to continue making money and claiming he was right all along, but in fact freeing him to use completely unrelated methods of investing (since he isn't required to divulge his techniques). So he is undoubtedly a gifted marketer, and obviously even a good money manager...as long as he isn't following his own advice.
If this is all still (quite understandably) rather abstruse, I'll illustrate with a metaphor. Imagine you stand outside on the street corner and observe the weather and the passing of cars and people. Three out of ten days it rains. On those days, you notice people wearing raincoats. You also notice there are no open convertibles. Data-mining your way to 'good' equations tells you that an absence of convertibles and a presence of raincoats cause it to rain. That's an example of mixing up cause and effect. In another example, imagine that you observe that on the days it rained, you observed ten percent more people named Jane. Aha! An excess of Janes is causing rain! No, this is just coincidence.* I could go on, but for more examples of these kinds of problematic reasoning, there's a far better resource: read Crimes Against Logic by Jamie Whyte. This book is one of my top fifteen all-time favorites, not so much because it taught me anything I didn't already know (though it did that, too), but because he so eloquently and clearly expressed ideas I had known well but that I had been unable to articulate.
However, an inability to find well-performing predictive models for equities markets, doesn't mean that feeding such data in Eureqa is itself useless. By watching how Eureqa treats all the different variables, you start to see how they interact and which ones haven't even a correlative relationship with equities market performance. For example, Eureqa rarely seems to 'care' much for inflation. There appears to be very little correlation. BUT, it does 'care' quite a lot about the Fed Funds rate, which is essentially the public policy reaction to inflation. It also 'likes' CD rates, which might be a decent stand-in for opportunity costs, though that implies some cause-and-effect (CD rates are low -> opportunity cost of foregoing them in favor of equities is low -> I will buy equities -> everyone does same -> equity prices rise)**, which requires a heavier burden of proof, one that I am far from meeting. And employment? Almost always tosses that out as irrelevant very quickly. But it 'loves' consumer confidence, which suggests that while the markets don't 'care'*** about how many people are out of work, they care very much about how confident people feel in the economy (which is presumably in turn driven by how many of them have jobs, though not directly). But again, there is no straight cause-and-effect here. You can't say Consumer Confidence = y ergo stock performance will = z as an exact function of y.
Early days yet, but so far, so fun! After I get bored with this round of experiments, I think I'll move on to GDP.
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Footnotes:
*Don't even get me started on people who say 'I don't believe in coincidences.' Do you have ANY idea what kind of universe we would live in WITHOUT A HUGE LOT OF COINCIDENCES? Randomness permeates the very fabric of existence. The 'problem' is that our human brains have evolved with this incessant need to find patterns. I say this facetiously because of course that very same 'problem' is doubtless one of the very core elements of our intelligence, not to mention a key explanation to our very survival as a species. But it does have the unfortunate side-effect of making us see Jesus in breakfast food far too often.
**This introduces an intriguing interplay itself. Perhaps the opportunity cost (in form of CD rates or T-bills) must reach a certain threshold before prompting consideration of equities, but that consideration is in turn colored by the confidence one has in the markets and the overall economy (as measured by U of M consumer confidence index?), and that interplay in turn drives the degree to which investors commit to equities, thus determining the demand for (and therefore value of) those equities. Add in a dash of price-to-earnings data (i.e., the 'real' cost of 'buying' the earnings behind an equity) and you might just have some soup worth tasting.
***Please forgive the anthropomorphic words here. And if you are a lefty like me, do not fall into the temptation of attributing 'feelings' to markets. That a market does not move in reaction to a tragedy like high unemployment, does NOT mean that the people who make up those markets do not care about unemployed people. The phenomenon is merely an observed outcome of the aggregate behavior of the people acting in the market, not the 'evil' intent of any group of people within the market. I enjoy demonizing Wall St fat-cats as much as the next liberal, but do so for their individual behaviors, not those of the markets in which they act.
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