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IIIS Discussion Paper No. 304
IIIS Discussion Paper No. 304

... See section 2.2 for relevant literature. ...
Liquidity article - Zebra Capital Management
Liquidity article - Zebra Capital Management

... for which the investors should be compensated, resulting in lower valuation for a lowvolume stock. However, in another study, Lee and Swaminathan (1998) show that the liquidity hypothesis is not totally consistent with their evidence. They study the joint interaction between past stock price momentu ...
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... E-Margin is a leveraged trading facility. Positions created under this product can be squared off or converted to delivery till T+30 days in BSE exchange and T+5 in NSE exchange (T=Trade date) on or before the specified time i.e. 02:45 pm. Unlike for a 'Cash' order, you do not have to pay the full o ...
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... sophisticated and wealthier individuals are likely to pursue momentum buy-investing, and at the same time they also exhibit a strong disposition effect. Our study therefore proceeds to investigate how the above documented trading behavior contributes to future stock volatility and future stock retu ...
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... The dataset extracted from reverse auction software was processed and used for statistical examination. Evidenced based analysis in this field has several circumstances. First, economic indicators as savings are not so easy to be calculated. The problem is which price can be compared to winning pric ...
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... on major currency exchanges without incurring extra risk. This is significant because the results suggested that patterns can be found in historical prices and those patterns appeared to repeat themselves. Moreover, this work gave evidence that genetic programming can be used to find such patterns. ...
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... 2. Comment on the following quotation from a leading investment analyst. Stocks that move perfectly with the market have a beta of 1. Betas get higher as volatility goes up and lower as it goes down. Thus, Southern Co, a utility whose share have traded close to $12 for most of the past three years, ...
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... dealers who have less information. And as all dealers seek optimal diversification to minimize risk, only the team which has more information succeed, and this leads to asymmetry in risk and abnormal profits to who has information. Another study performed by (Gul & Qiu, 2002) aimed to test the relat ...
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... Our theory is related to the “volatility feedback” effect first studied by French et al. (1987), Malkiel (1979), and Pindyck (1984). They point out that greater stock market volatility can lead to a higher risk premium and thus to lower stock prices.5 Campbell and Hentschel (1992) show that this eff ...
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... relatively unbiased basis, and carefully collects the relevant information. In the extreme case, even one such trader can drive the market price to the underlying equilibrium price.9 The analysis of break points undertaken here does not identify historical facts, but rather the acquisition and asses ...
Derivatives and Volatility on Indian Stock Markets
Derivatives and Volatility on Indian Stock Markets

... (namely Dax index) on which derivative products are not introduced. This study shows that unlike the findings by Antoniou and Holmes (1995) for the London Stock Exchange (LSE), the introduction of index future, per se, has actually reduced the stock price volatility. Bologna and Covalla also found t ...
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... management must be related to the policy goals that are established. Reserve stocks provide a means of softening the effects of periodic fluctuations in grain supplies by leveling off the peaks of surplus production and filling in the troughs of short crops or large export demands. Limited stocks of ...
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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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