Financial Markets Reporter October , 2010 Free Markets vs Centralized Markets

8/8/2019 Financial Markets Reporter October , 2010 Free Markets vs Centralized Markets

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When the markets rise and fall based on computerised trades, where are the checks andbalances for price discovery and fair market value? This current trend takes us away from freemarket principles and it leads us towards centralized or politicized markets. The Politicians andtheir special interest constituency are funnelling subsidies at projects with no natural marketdemand; thus, creating Artificial Demand in the market place. Then they attempt to resell theproject in the financial markets relying on automated computer programs as oppose to convincing

investors.

Computerized Trading Supplanted Investors

These trading programs can be categorized as liquidity rebate traders, predatory algorithmictraders, automated market makers, and program traders. They are exploiting the new marketdynamics and negatively affecting real investors.


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LIQUIDITY REBATE TRADERS

To attract volume, all market centers (the exchanges and the ECNs) now offer rebates ofabout penny a share to broker dealers who post orders. It can be a buy or sell order, aslong as it is offering to do something on the exchange or ECN in question. If the order isfilled, the market center pays the broker dealer a rebate and charges a larger amount to thebroker dealer who took liquidity away from the market. This has led to trading strategiessolely designed to obtain the liquidity rebate.

In this case, our institutional investor is willing to buy shares in a price range of $20.00 to$20.05. The algo gets hit, and buys 100 shares at $20.00. Next, it shows it wants to buy500 shares. It gets hit on that, and buys 500 more shares. Based on that information, arebate trading computer program can spot the institution as having an algo order. Then,the rebate trading computer goes ahead of the algo by a penny, placing a bid to buy 100shares at $20.01. Whoever had been selling to the institutional investor at $20.00 is likelyto sell to the rebate trading computer at $20.01. That happens, and the rebate tradingcomputer is now long 100 shares at $20.01 and has collected a rebate of penny a share.Then, the computer immediately turns around and offers to sell its 100 shares at $20.01.Chances are that the institutional algo will take them.

The rebate trading computer makes no money on the shares, but collects another penny formaking the second offer. Net, net, the rebate trading computer makes a penny pershare, and has caused the institutional investor to pay a penny higher per share.

PREDATORY ALGOS

More than half of all institutional algo orders are pegged to the National Best Bid orOffer (NBBO). The problem is, if one trader jumps ahead of another in price, it can causea second trader to go along side of the first one. Very quickly, every algo trading order ina given stock is following each other up or down (or down and up), creating huge, whiplike price movements on relatively little volume.

This has led to the development of predatory algo trading strategies. These strategies aredesigned to cause institutional algo orders to buy or sell shares at prices higher or lowerthan where the stock had been trading, creating a situation where the predatory algo canlock in a profit from the artificial increase or decrease in the price.

To illustrate, lets use an institutional algo order pegged to the NBBO with discretion topay up to $20.10. First, the predatory algo uses methods similar to the liquidity rebatetrader to spot this as an institutional algo order. Next, with a bid of $20.01, the predatoryalgo goes on the attack. The institutional algo immediately goes to $20.01. Then, thepredatory algo goes $20.02, and the institutional algo follows. In similar fashion, thepredatory algo runs up the institutional algo to its $20.10 limit. At that point, the predatoryalgo sells the stock short at $20.10 to the institutional algo, knowing it is highly likely that

the price of the stock will fall. When it does, the predatory algo covers. This is how a stock can move10 or 15 cents on a handful of 100 or 500 share trades.

AUTOMATED MARKET MAKERS

Automated market maker (AMM) firms run trading programs that provideliquidity to the NYSE, NASDAQ and ECNs. AMMs are supposed to function likecomputerized specialists or market makers, stepping in to provide inside buy and sells, tomake it easier for retail and institutional investors to trade.


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AMMs, however, often work counter to real investors. AMMs have the ability to ping stocks toidentify reserve book orders. In pinging, an AMM issues an order ultra fast, and if nothing happens,

it cancels it. But if it is successful, the AMM learns a tremendous amount of hidden information thatit can use to its advantage.

To show how this works, this time our institutional trader has input discretion into the algoto buy shares up to $20.03, but nobody in the outside world knows that. First, the AMMspots the institution as an algo order. Next, the AMM starts to ping the algo. The AMMoffers 100 shares at $20.05. Nothing happens, and it immediately cancels. It offers$20.04. Nothing happens, and it immediately cancels.

Then it offers $20.03 and the institutional algo buys. Now, the AMM knows it has founda reserve book buyer willing to pay up to $20.03. The AMM quickly goes back to a pennyabove the institutions original $20.00 bid, buys more shares at $20.01 before the

institutional algo can, and then sell those shares to the institution at $20.03.

PROGRAM TRADERS

Program traders buy or sell small quantities of a large number of stocks at the same time,to trigger NBBO or discretionary algo orders, so as to quickly juice a market alreadymoving up or down into a major drop or spike up.

Because so many algo orders are pegged and are being pushed around by other highfrequency traders, program traders are like a fuse. When they light it, thats when thingsget really going. This is especially so in volatile markets when things are very shaky andpeople are very nervous like they are now. Keep in mind that many algo orders mustachieve a percentage of volume that matches the market in the stock. So if the programtraders can increase the volume on an individual stock just enough, they will trigger evenmore algo buying or selling.

Program traders profit by having an option on the market. Their objective is to push thatoption into the money by a greater amount than what they used to get the market moving.

Our conclusion is that the prices of currencies, commodities, and equities must be setby fundamental analysis. Criteria such as supply, demand, quality of the underlying assetmust be the issues at the core of the price discovery process. The allures of ComputerizedTrading such as liquidity and volume are destroying the fabric of capitalism by creating

a centrally controlled market place. The alternative is that every country must strive tosecure its own farms for its own food security. The market has become a facade.


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Financial Markets Reporter October , 2010 Free Markets vs Centralized Markets