Александр Попов. Блог.

1. Покупка дивитикеров, т.е. акции компаний, предлагающим высокие дивиденды.


2. Покупка сильно недооцененных активов, которые имеют .

Goldman Sachs was a badly tarnished institution in 2009 in the aftermath of the taxpayer bailout of Wall Street, and its reputation would sink even lower in 2010 with revelations that it had bet against the success of investments it had sold to its clients. The public was steamed in March 2009 when it learned that Goldman Sachs had finagled the government into bailing out counterparties involved in credit default swap deals with troubled insurance giant AIG, which was the recipient of more than $80 billion in taxpayer funds. The public was in an anti-Wall Street mood owing to the financial cataclysm caused by its investment bankers. It felt that Goldman Sachs should take a financial hit as a consequence of its high-risk investments and viewed it as a manipulative scheming firm with too much power in Washington, DC. Critics called it “Government Sachs.” Banking expert Christopher Whalen described Goldman Sachs as “a political organization masquerading as an investment bank, and they’re sitting at the table with the top people in government.”

Grassley had been a long-time critic of government regulators and Capitol Hill staff who used their positions to land better-paying jobs with the private-sector companies under their purview. He fired off a letter to the SEC’s inspector general David Kotz requesting an investigation into the “revolving door” at the agency. He cited an article in the April 5, 2010, Wall Street Journal that reported numerous instances in which an SEC employee had left and represented clients before the commission only days later. Reporter Tom McGinty had found that lower-level employees of the commission could legally appear at the SEC on behalf of clients the day after they had left their agency jobs, as long as they had filed letters of disclosure.

Kaufman angrily wrote a protest letter to Schapiro and then took to the floor of the Senate to bring his complaint to the attention of his colleagues and the press. He said, “It appears as though it (the panel) was chosen primarily to hear testimony that reinforces the top-line defenses of the current market structure—that high-frequency trading provides liquidity and reduces spreads—rather than a deep dive into the problems that caused severe market dislocation on May 6 and damaged our market’s credibility. I have called on the SEC to add more participants to give the panels some semblance of balance. Frankly, Mr. President, I find the preliminary reports to be so stacked in favor of the entrenched money that has caused the very problems we seek to address that the panel itself stands as a symbolic failure of the regulators and regulatory system—that is, with the exception of a few brave souls who have been invited to critique the conventional industry wisdom.”

Five of the panelists had written letters to the SEC prior to May 6 commenting on its proposed reexamination of the National Market Structure. All of them said they were in favor of the status quo.

Kaufman said one expected panelist wrote: “Over the past 18 months—since the height of the financial crisis—the Commission has been very active with rule-making proposals. Nearly all the issues that may have contributed to diminishing investor confidence have been addressed by Commission rule-making.”

Kaufman said that a second expected participant, a representative of a stock exchange, had written a widely disseminated e-mail stating that the equities market was not broken. The e-mail said, “To the contrary, we would argue that the U.S. equity markets were a shining model of reliability and healthy function during what some are calling one of the most challenging and difficult times in recent market history.”

A third participant wrote the SEC in advance of the meeting, “Implementing any type of regulation that would limit the tools or the effectiveness of automation available for use by any class of investor in the name of ‘fairness’ would turn back the clock on the U.S. equity market and undo years of innovation and investment.”

The only mutual fund company invited to sit on the panel was Vanguard Funds. It was an outlier among mutual fund firms in that it staunchly defended HFT, arguing that the activity kept costs down by narrowing stock spreads and reducing transaction costs. Vanguard had a personal agenda: It was one of the largest managers of exchange-traded funds (ETFs), a product that was a cross between a mutual fund and an index option. Arnuk quipped in a blog that Vanguard churned out ETFs faster than Hasbro churned out Hannah Montana toys. Most mutual fund companies were of the mind that high-frequency traders were actually raising their trading costs by using algorithms to front-run their orders. True, spreads had narrowed—but there was a hidden tax on the mutual fund industry and its 90 million customers as a result of this front-running, they claimed.

The machines bought and the machines sold, and they didn’t care what they were selling as long as they could get rid of the shares in 2 minutes or less. The machines were dominating the market. What did all of it mean?

Jeff Silver and Ben Hunt, managing directors of Iridian Asset Management, tried to answer this question in a letter to their investors at the end of the second quarter of 2010. “At its most basic level, it means that the prices of securities bear little or no relationship to the fundamental economic reality of the corporations that issue those securities,” they wrote. “It means that price discovery, which is the fundamental goal of the open bid-ask system of a public exchange, is no longer meaningful on terms that make sense to the investor whose decisions are based on fundamental economic reality. It means the U.S. equity market is no longer an effective capital market where shares of a stock represent ownership interest in an economic entity with cash flows and assets, but is more accurately conceptualized as a casino where shares or stocks are simply placeholders for money—chips.”

The high-frequency trader was playing the markets the way card counters play blackjack, they said, providing liquidity if a market for a particular security is staked in his favor and backing off at other times.

In general, high-frequency traders favored four strategies.

The most publicized strategy—the one least offensive to the public and the politicians—was automated liquidity provision, in which high-frequency traders functioned like market makers, making millions of trades each day to earn sub-penny rebates per share from exchanges for bringing volume to those venues.

Another strategy, called “sniping” and which brought the traders considerable public relations problems, tried to identify large orders in the market by using algorithms to detect trading patterns in small orders that showed they were flowing at intervals from a single source, such as a pension fund or a mutual fund. Credit Suisse, a large provider of HFT solutions, hawked algorithms to traders with names like “Guerilla” and “Sniper” to detect big orders in both the public markets and in dark pools, where mutual funds, pensions, and other big buyers and sellers attempt to trade without rippling the markets.

A third strategy, called event trading, tried to capitalize on the news of the day and predict which direction the markets would take in reaction to the latest development. Harvey Houtkin used to instruct his trading students, “The trend is your friend,” and this was a variation of that theme. Large quantitative-trading firms such as Medallion engaged heavily in this type of momentum trading.

The fourth strategy was old-fashioned arbitrage, in which the traders attempted to find price discrepancies between seemingly unrelated instruments, like stocks and sugar futures, for instance. Generally speaking, the algorithms compared data of past stock and commodities movements to build an understanding of how they might behave in the present. This allowed computers programmed with these algorithms to look for pricing inefficiencies. If on a given day a stock rose in value by X dollars, for instance, the traders might judge the move to be extreme, based on 20 years’ worth of pricing, volume, and related data, and short the stock, expecting it to correct back down. If the underlying company was a big food producer, the stock’s fall might affect the prices of agricultural futures on the commodities exchange. The super-fast computers would exploit such correlations.