7 ways to get savvier, wiser in 2007

There are two kinds of people: Those who wish they had invested in the stock market and those who wish they hadn't. Do you regret not investing when the Sensex fell to 8,800? Do you wish you had booked profits on those dicey small-cap stocks? Wonder why you did not listen to all the advice on prudent investing that experts spell out on TV and the papers ad nauseum? With the New Year just a few winks away, it's time to stop torturing ourselves about what we did and did not do. How about resolving to be wiser, savvier investors in 2007 instead? Here is a list of seven investment resolutions for 2007.

Resolution 1: I will invest only in sound stocks

How hard is it to resist the voice in your head (the broker's, that is) telling you that there is a fortune waiting to be unearthed in an unheard of penny stock? Very hard, we agree. But an 80-90 per cent drop in the stock price might just give us a little more perspective. That is how much one could lose if one does not know how to play the penny stock game. The odds of winning are almost always against you.

Forget penny stocks. There are several purported real-estate plays, turnaround stories, acquisition candidates and `proxies' that promise to be tomorrow's bluechip stock, with not an ounce of fundamentals to back them up. Remember how they sank when the market corrected? That memory is enough to ensure that we keep this resolution.

Resolution 2: I will do my own research

Now who has the time to do research except those who make a living out of it? True, but a weak argument. Nobody cares for money that is not theirs, so it is each one for herself when it comes to investing in the market. A quick glance through the annual report and recent earnings and steadily tracking news developments pertaining to the company are necessary at the most basic level. But if you have ambitions of beating the market, you will have to do more than that, which means more time, effort and resolve.

Resolution 3: I will stick to stocks that I understand

Even legendary investor Warren Buffett does not claim to understand everything. He stayed away from tech stocks during the bubble. We know how well that worked out for him!

So lets make things easy for us. Doing the spadework is much easier when we understand the industry. If you already work for the software industry, pick up some software stocks, even if it means buying shares of your employer's competitor. Think how well you would come off sounding if you ever were to go for a job interview with that company!

In the long term, buying stocks we understand will help us make good investment decisions. We probably will know when to buy or sell a stock ahead of the market and will not give in to temporary panic (or temporary insanity) and dump a good company when the market crashes due to some extraordinary event.

Resolution 4: I will not be influenced by market rumours

Buy the rumour and sell the news is the popular market adage. But fall for a baseless rumour and you could get burned. The worst of the market buzz these days are the absurd price targets. "Psst, the stock of ABC is trading at 50. The three-month price target is Rs 1,500. It is the next Unitech". If you have heard that, remember that there are vested interests behind these rumours and you could end up holding a lemon.

Resolution 5: I will not fret over my investments

Ever kept willing a stock to go up, only to find that the stock had a mind of its own and refused to budge from your buy price? Once you buy or sell a stock, stop fretting over your decision. Be clear on your investment rationale and objective. If you have invested on the basis of fundamentals, for instance, do not get influenced by a contrary technical view. Fundamental and technical analysis can be at odds from time to time. Similarly, if you decide to invest with a one-year perspective and are convinced about the fundamentals, stick to your guns. If the company performs contrary to your expectations, then you can review your decision.

Resolution 6: I shall cut my losses

A lesson we all would have learned from this year's market correction is to cut positions before we slip even further into losses. If you follow technical analysis, resolve this year to adhere strictly to your stop-losses. If you follow fundamentals and find you have gone wrong in your estimates, remember that it is impossible to be right every time. In a market such as ours, there are plenty of investment opportunities. When the market falls steeply, we are better off booking losses on momentum stocks and buying bluechips that are available for a song.

Resolution 7: I will stop procrastinating

"I spotted the stock at Rs 30 and thought of buying it but did not. The next time I saw it, it was at Rs 60". Sometimes, we experience a rare moment in the market when we actually know what to do. Then, we spoil it by not acting on our instinct. To succeed in the market, we need to be actively involved.

Sceptics among you may wonder if anyone will abide by these resolutions for more than a couple of months. But let's sharpen our pencils in all earnestness anyway and put down these promises. Keeping them could ensure that we all have a "happy and prosperous" new year indeed.

NY Times - A Smarter Computer to Pick Stocks

Ray Kurzweil, an inventor and new hedge fund manager, is describing the future of stock-picking, and it isn’t human.

“Artificial intelligence is becoming so deeply integrated into our economic ecostructure that some day computers will exceed human intelligence,” Mr. Kurzweil tells a room of investors who oversee enormous pools of capital. “Machines can observe billions of market transactions to see patterns we could never see.”

The listeners, attendees of a conference sponsored earlier this month by the Capital Group Companies, are slightly skeptical. Some have heard that Mr. Kurzweil, 58, who takes more than 150 vitamins and supplements a day, believes people will eventually live forever. Others know he has said that in 2045, man and machine will achieve “singularity,” and humans will hold their breath for hours thanks to nanomachines in our bloodstreams.

But some are aware that a former Microsoft executive and chairman of the Nasdaq stock market, Michael W. Brown, is an investor in Mr. Kurzweil’s new hedge fund, FatKat, and that Bill Gates once described him as “the best person I know at predicting the future of artificial intelligence.”

More important, many of them have seen Mr. Kurzweil’s ideas used by stock speculators. So, they want to learn more about his brave, new world.

“These ideas are the future,” said David Atkinson, a private investor who attended another lecture later that day by Mr. Kurzweil. “I’m not really sure I understand them, but they’re making some folks rich.”

Complicated stock picking methods are nothing new. For decades, Wall Street firms and hedge funds like D. E. Shaw have snapped up math and engineering Ph.D.s and assigned them to find hidden market patterns. When these analysts discover subtle relationships, like similarities in the price movements of Microsoft and I.B.M., investors seek profits by buying one stock and selling the other when their prices diverge, betting historical patterns will eventually push them back into synchronicity.

Today, such methods have achieved a widespread use unimaginable just five years ago. The Internet has put almost every data source within easy reach. New software programs, like the Apama Algorithmic Trading Platform, have made it possible for day traders to build complicated trading algorithms almost as easily as they drag an icon across a digital desktop.

“Five years ago it would have taken $500,000 and 12 people to do what today takes a few computers and co-workers,” said Louis Morgan, managing director of HG Trading, a three-person hedge fund in Wisconsin. “I’m executing 1,500 to 2,000 trades a day and monitoring 1,500 pairs of stocks. My software can automatically execute a trade within 20 milliseconds — five times faster than it would take for my finger to hit the buy button.”

Studies estimate that a third of all stock trades in the United States were driven by automatic algorithms last year, contributing to an explosion in stock market activity. Between 1995 and 2005, the average daily volume of shares traded on the New York Stock Exchange increased to 1.6 billion from 346 million.

But in recent years, as algorithms and traditional quantitative techniques have multiplied, their successes have slowed.

“Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”

So investment firms have increasingly begun exploring mathematics’ furthest edges and turning to people like Mr. Kurzweil, who became an expert in pattern recognition building a reading machine for the blind.

For years, computer scientists had tried to help machines perform mundane tasks like reading printed words or telling faces apart. With algorithms similar to those used by stock pickers, programmers created millions of rules designed to tell an “A” from an “a.” But no machine could read a page of text as well as the average child.

So Mr. Kurzweil and others took a different tack: instead of creating sequential rules to instruct a computer to read, they thought, why not create thousands of random rules and let the computer figure out what works?

The result was nonlinear decision making processes more akin to how a brain operates. So-called “neural networks” and “genetic algorithms” have become common in higher-level computer science. Neural networks permit computers to create new rules and automatically change underlying assumptions by experimenting with thousands of random sequences and processes. Genetic algorithms encourage software to “evolve” by letting different rules compete, and combining the most successful outcomes.

Wall Street has rushed to mimic the techniques. Because arbitrage opportunities disappear so quickly now, neural networks have emerged that can consider thousands of scenarios at once. It is unlikely, for instance, that Microsoft will begin selling ice-cream or I.B.M. will declare bankruptcy, but a nonlinear system can consider such possibilities, and thousands of others, without overtaxing computers that must be ready to react in milliseconds.

“Most software fails in pattern recognition because there aren’t enough sequential rules in the world to teach a computer to discern between two faces, or to find almost imperceptible relationships between stocks,” said Orhan Karaali, a computer scientist and director at Advanced Investment Partners, a $1.7 billion hedge fund. “But a machine that can generate complicated rules a person would never have thought of, and that can learn from past mistakes is a powerful tool.”

Last year, the funds using Mr. Karaali’s model returned in excess of 20 percent by using nonlinear techniques, according to his company. Whereas older methods of stock analysis rely on certain assumptions — for instance, that market volatility always reverts to the mean — Mr. Karaali’s model calculates probabilities and generates assumptions on the fly, and might predict that during a panic, investors will sell Microsoft but, for seemingly irrational reasons, hold onto I.B.M.

“Only an elite group of people are using these ideas, but a lot of people are thinking about them,” said Stacy Williams, director of quantitative strategies at HSBC Global Markets. HSBC is working with Cambridge University in using models based on how viruses spread to forecast foreign currency markets.

“The downside with these systems is their black box-ness,” Mr. Williams said. “Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it’s not always intuitive or clear why the black box latched onto certain data or relationships.”

Such qualms, however, have not stopped Wall Street from scouring university doctoral programs or listening to people like Mr. Kurzweil.

In the pursuit of previously undetectable patterns, hedge funds are racing to quantify things — like newspaper headlines — that were previously immune from number-crunching.

Both Dow Jones Newswires and Reuters have transformed decades of news archives into numerical data for use in designing and testing algorithmic systems. The companies are beginning to structure news so it can be absorbed by quantitative models within milliseconds of release.

Moreover, companies like Progress Software are working with news agencies to create computer programs that instantly translate news — for example, a headline regarding Microsoft’s earnings — into data. M.I.T. is examining, among other things, evaluating companies by seeing how many positive versus negative words are used in a newspaper article.

Software in development could potentially respond automatically to almost anything; changes in weather forecasts on television news, shifting analyst sentiments or what a particular movie critic said about the new blockbuster.

“Right now, everyone basically has access to the same data,” said John Bates, a Progress Software executive. “To get an edge, we want to give investors the ability to immediately turn news into numbers. We want to automate what before required human analysis.”

But as these new techniques proliferate, some worry that promotion is outpacing reality. These techniques may be better for marketing than stock picking.

“Investment firms fall over themselves advertising their latest, most esoteric systems,” said Mr. Lo of M.I.T., who was asked by a $20 billion pension fund to design a neural network. He declined after discovering the investors had no real idea how such networks work.

“There are some pretty substantial misconceptions about what these things can and cannot do,” he said. “As with any black box, if you don’t know why it works, you won’t realize when it’s stopped working. Even a broken watch is right twice a day.”