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In financial markets, implementation shortfall is the difference between the decision price and the final execution price (including commissions, taxes, etc.) for a trade. This is also known as the "slippage". Agency trading is largely concerned with minimizing implementation shortfall and finding liquidity.
The decision price is the price of the stock that prompted the decision to buy or sell. The most common decision price is the close price or the arrival price. If we split the decision to buy a stock from the actual trading of the stock, as is often the case with fund managers (decision makers) and brokers (trade executors), you can see why both are used.
From the fund manager's point of view, his decision to trade is often based on the closing price of the day's trading (along with the entire history of the stock and other signals/indicators). When he decides to buy a particular stock the next day, it is because he believes that the price will go up from that closing price. Thus his decision price is the close price.
However the broker, unless she is explicitly told what levels to buy at or what prompted the desire to buy, does not know when or why the decision was made. Her best guess is that the current price at the time the order is received is what prompted the decision and thus her decision price is the arrival price. There is no common definition of this price, but the broker normally uses the last traded price or the "mid price" - equal to the average of the current bid and ask prices being quoted at the time the order was received.
Brokerage firms specialize in developing algorithmic strategies, and providing them to the institutional investment community, that aid in the quest to minimise slippage from benchmarks such as implementation shortfall, volume-weighted average price or time-weighted average price. Alpha Profiling is an example of an algorithmic method of minimising implementation shortfall.
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The following outline is provided as an overview of and topical guide to finance:
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Alpha profiling is an application of machine learning to optimize the execution of large orders in financial markets by means of algorithmic trading. The purpose is to select an execution schedule that minimizes the expected implementation shortfall, or more generally, ensures compliance with a best execution mandate. Alpha profiling models learn statistically-significant patterns in the execution of orders from a particular trading strategy or portfolio manager and leverages these patterns to associate an optimal execution schedule to new orders. In this sense, it is an application of statistical arbitrage to best execution. For example, a portfolio manager specialized in value investing may have a behavioral bias to place orders to buy while an asset is still declining in value. In this case, a slow or back-loaded execution schedule would provide better execution results than an urgent one. But this same portfolio manager will occasionally place an order after the asset price has already begun to rise in which case it should best be handled with urgency; this example illustrates the fact that Alpha Profiling must combine public information such as market data with private information including as the identity of the portfolio manager and the size and origin of the order, to identify the optimal execution schedule.