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RossFCF8ce_SM_ch13
RossFCF8ce_SM_ch13

... (LO3) If the market expected the growth rate in the coming year to be 2 percent, then there would be no change in security prices if this expectation had been fully anticipated and priced. However, if the market had been expecting a growth rate different than 2 percent and the expectation was incorp ...
Is There Price Discovery in Equity Options?
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... that option market price discovery appears to have declined since the sample period used in some of the earlier literature, e.g. Chakravarty, Gulen, and Mayhew (2004), and thus help reconcile our results with those in the earlier literature. In interpreting our results within the context of the exis ...
The Hybrid Consumer: Exploring the Drivers of a New
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... trading down to low-priced products and services and simultaneously trading up to premium ones and avoiding the “boring middle”, which is perceived as offering little added value: neither unbeatable prices nor unbeatable quality. Understanding the changing attitudes, behaviours and values of middle- ...
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Effect of Investor Sentiment on Market Response to Stock Splits
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... long vol or being short vol? The answer is short vol, at least since the beginning of the 2001 calendar year. Option time values were “taken in” during 2001 arguably because there has been a “volatility supply glut” or “volatility overhang”. If one thinks of volatility as a commodity like wheat, the ...
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... approximations to pure capital structure changes: intra-firm exchange offers and recapitalizations. These two events are unique in that they do not entail any firm cash inflows or outflows (with the exception of expenses), while they cause major changes in the firm's capital structure. These changes ...
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... The concept of VaR is easy to understand. VaR can be applied to different asset classes and security types. It allows us to boil down the risk of an entire portfolio, no matter how complex, to a single number. Importantly for the purposes of this paper, VaR is easy to backtest. Large financial insti ...
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... undertaken a stock split during the research period of 1988–1997 were the earlier master’s theses of Hovmöller & Wasing (1997) and Olsson & Söderblom (1996). For the years 1996– 1997 the primary source was the www-on-line service of the Stockholm Stock Exchange13. Furthermore, the Datastream system ...
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... companies like SOGO SHOSHA. As a result, general trading companies grew rapidly in South Korea and heavily contributed to its economic development. They have similarities to Japanese SOGO SHOSHA, but also have many differences such as that they couldn’t get out of the role of contact center they pla ...
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... get a paper promising that value of that paper at any point of time would be equal to certain number of gold coins. This system of book entry of coins against paper was the start of paper currency. With time, countries started trading across borders as they realized that everything cannot be produce ...
Mastering The Markets
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... The average person has absolutely no idea what drives the financial markets. Even more surprising is the fact that the average trader doesn’t understand what drives the markets either! Many traders are quite happy to blindly follow mechanical systems, based on mathematical formulas that have been ba ...
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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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