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Practice four
Practice four

... deviation with a higher expected return. 17 Not possible. Given these data, the SML is: E(r) = 10% + (18% – 10%) A portfolio with beta of 1.5 should have an expected return of: E(r) = 10% + 1.5  (18% – 10%) = 22% The expected return for Portfolio A is 16% so that Portfolio A plots below the SML (i ...
Download paper (PDF)
Download paper (PDF)

... This study provides new evidence on how the forecasting accuracy of event prediction markets depends on market liquidity. I use three years of intraday data on one-day binary outcome securities based on sports and financial events traded on an online exchange, TradeSports.com. I use three standard m ...
Market Quality and Contagion in Fragmented Markets
Market Quality and Contagion in Fragmented Markets

... obvious how one can infer the overall quality of a market from the spreads or volumes of individual securities. Usually this involves picking a few assets that are deemed representative of the market as a whole. Furthermore, since identical assets, or more generally payoffs, may not exist on multipl ...
The Anatomy of a Stock Market Winner
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... market capitalizationshad outperformed largecapitalization companies by about 20 per cent on an annual basis.2 Other "investment anomalies," characterizing peculiar patterns in the timing of stock returns, also emerged, ranging from a month-of-the-yearor Januaryeffect to a week-of-the-month effect t ...
Trade in the options empire
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... We think that prospective traders should remember that one of the key benefits of binary options trading is that it affords you a high level of control over your portfolio. It is the one form of trading that actually allows you to minimize and set your own levels of risk. This means that diligent tr ...
Abstract Edward Chamberlin, who initiated classroom
Abstract Edward Chamberlin, who initiated classroom

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... obtained or evaluated accurately as long as an incomplete futures market exists. Gardner argued that a futures market may be incomplete due to a missing long-term futures market. He evaluated this missing futures market for corn, soybeans and cotton, and found little empirical support for multi-year ...
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... • Suppose that F1 = rM − r0 – i.e. the single factor is excess return on the market • Then λ1 = µM − r0 and bj1 = βj . • Hence, CAPM can be given an APT interpretation. • CAPM and APT may also be consistent in a multifactor model. • But they are founded on different assumptions. Watch out: The CAPM ...
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... – If the “evading SEC scrutiny” story is correct then pattern of insider purchases prior to (positive) jumps should be at least as strong as pattern of insider sales prior to crashes – If our story is correct, then pattern should disappear Research Seminar Finrisk University of Zurich - 12/15/2006 ...
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... specialist’s fixed and variable transactions costs (including his time, inventory costs, etc.) are zero and when competition forces the specialist’s profit to zero. The core idea is that the specialist faces an adverse selection problem, since a customer agreeing to trade at the specialist’s ask or ...
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MicrosoftMarch07Slides - Duke University`s Fuqua School of

... • There are many equilibria • One has seller extracting all surplus – high prices • One has seller with almost no market power – low prices → Buyers have a credible threat • Equilibrium can be efficient (above cases) • There are inefficient equilibria – Trade is delayed – Upgrades are sold periodica ...
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... 0.71 in the year of 2013 and 2014 respectively. This receives a profit of ( λP − λUI ) . Here the author aims shows a steeply rising value of both load and only Genco’s prospective of participating in option generation dependency on spot prices. Therefore the market with objective of maximizing its ...
presentation
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... behave in strategic situations. Strategic decisions are those in which each person, in deciding what actions to take, must consider how others might respond to that action. ...
Oligoly
Oligoly

... strategic situations.  Strategic decisions are those in which each person, in deciding what actions to take, must consider how others might respond to that action. ...
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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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