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Governing Multiple Firms"
Governing Multiple Firms"

... monitors, she may su¤er a shock, which forces her to sell and so she does not receive the full payo¤ from monitoring. Alternatively, she may not monitor and sell (“cut and run”). Selling leads to a relatively high price under separate ownership, as discussed above. Thus, the payo¤ from monitoring (n ...
BARCLAYS BANK PLC (Form: 424B2, Received: 12/30
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... situation where levering up low-beta stocks ends up being less efficient than buying high-beta stocks when speculating on the common factor of firms’ cash flows. In other words, higher beta assets are naturally more speculative. Our model yields the following key testable implications. When macro-di ...
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Common market makers and commonality in liquidity
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... estate, such as commingled funds. Return series on privately held real estate are impacted by infrequent appraisals, are often labeled as smoothed, and hence, do not update information as quickly. Recent studies of NCREIF, for example, attempt to refine the measurement of return indexes, and interpr ...
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... for the value of the underlyings against which claims are traded in the derivatives markets. For example, the euro forward contract we discussed earlier has a notional value of $118 million, and the interest rate swap had a notional amount of $200,000. Interest rate swaps represent 56 percent of the ...
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... case in which there are two perfectly correlated securities with different Sharpe ratios. In a portfolio choice model without illiquidity risk, this case presents an arbitrage opportunity; the investor takes positions of plus or minus infinity in the two different assets. When one asset is illiquid, ...
FREE Sample Here - We can offer most test bank and
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... from the above examples, if the premium margin (which is the same as the current value) for the Telstra Sep $4.00 call is $0.33 and the risk margin is $0.14, then the total margin is $0.47. If the premium margin for the Telstra Sep $3.75 put is $0.12 and the risk margin is $0.07, then the total marg ...
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... future will move in a particular way. Many private investors find technical analysis appealing since the information needed to perform these analysis is easy to obtain, it is much harder for the individual investor to obtain fundamental information about companies and even harder to use fundamental ...
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... markets. With that assumption (and, often, some other more technical assumptions), one can find a portfolio strategy that does not use the derivative and only requires an initial investment such that the portfolio pays the same as the derivative at maturity. The portfolio is called a replicating por ...
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... in the underlying assets. In fact, the derivatives can be formed on almost any variable, for example, from the price of hogs to the amount of snow falling at a certain ski resort. The term financial derivative relates with a variety of financial instruments which include stocks, bonds, treasury bil ...
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... prices (CBOE 2009b). This index is hosted by Standard & Poor’s and it includes 500 capitalization weighted large-cap common stocks actively traded in the United States. SPX is a good proxy for the U.S. market as a whole since it generalizes the market well. Furthermore, SPX is broad by including wid ...
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... each predictor. The average predictor’s long-short return declines by 26% out-of-sample. This 26% estimate is an upper bound on the effect of statistical biases, since some traders are likely to learn about the predictor before publication, and their trading will cause the return decay to be greate ...
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Algorithmic trading

Algorithmic trading, also called algo trading and blackbox trading, encompasses trading systems that are heavily reliant on complex mathematical formulas and high-speed, computer programs to determine trading strategies. These strategies use electronic platforms to enter trading orders with an algorithm which executes pre-programmed trading instructions accounting for a variety of variables such as timing, price, and volume. Algorithmic trading is widely used by investment banks, pension funds, mutual funds, and other buy-side (investor-driven) institutional traders, to divide large trades into several smaller trades to manage market impact and risk.Algorithmic trading may be used in any investment strategy or trading strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically.Many types of algorithmic or automated trading activities can be described as high-frequency trading (HFT), which is a specialized form of algorithmic trading characterized by high turnover and high order-to-trade ratios. As a result, in February 2012, the Commodity Futures Trading Commission (CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. Algorithmic trading and HFT have resulted in a dramatic change of the market microstructure, particularly in the way liquidity is provided.Profitability projections by the TABB Group, a financial services industry research firm, for the US equities HFT industry were US$1.3 billion before expenses for 2014, significantly down on the maximum of US$21 billion that the 300 securities firms and hedge funds that then specialized in this type of trading took in profits in 2008, which the authors had then called ""relatively small"" and ""surprisingly modest"" when compared to the market's overall trading volume. In March 2014, Virtu Financial, a high-frequency trading firm, reported that during five years the firm as a whole was profitable on 1,277 out of 1,278 trading days, losing money just one day, empirically demonstrating the law of large numbers benefit of trading thousands to millions of tiny, low-risk and low-edge trades every trading day.A third of all European Union and United States stock trades in 2006 were driven by automatic programs, or algorithms. As of 2009, studies suggested HFT firms accounted for 60-73% of all US equity trading volume, with that number falling to approximately 50% in 2012. In 2006, at the London Stock Exchange, over 40% of all orders were entered by algorithmic traders, with 60% predicted for 2007. American markets and European markets generally have a higher proportion of algorithmic trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algorithmic trading (about 25% of orders in 2006). Futures markets are considered fairly easy to integrate into algorithmic trading, with about 20% of options volume expected to be computer-generated by 2010. Bond markets are moving toward more access to algorithmic traders.Algorithmic trading and HFT have been the subject of much public debate since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission said in reports that an algorithmic trade entered by a mutual fund company triggered a wave of selling that led to the 2010 Flash Crash. The same reports found HFT strategies may have contributed to subsequent volatility by rapidly pulling liquidity from the market. As a result of these events, the Dow Jones Industrial Average suffered its second largest intraday point swing ever to that date, though prices quickly recovered. (See List of largest daily changes in the Dow Jones Industrial Average.) A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while ""algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010."" However, other researchers have reached a different conclusion. One 2010 study found that HFT did not significantly alter trading inventory during the Flash Crash. Some algorithmic trading ahead of index fund rebalancing transfers profits from investors.
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