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Is Economics Performative? Option Theory and the Construction of Derivatives Markets
Is Economics Performative? Option Theory and the Construction of Derivatives Markets

... By the start of the 1970s, however, work by financial economists Fischer Black and Myron Scholes, with key additional input from their colleague Robert C. Merton, produced what has become the canonical theory of options (Black and Scholes 1973; Merton 1973). Although there were significant differenc ...
Trade the Right Stocks at the Right Time
Trade the Right Stocks at the Right Time

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... the effect of an increase in tick size of S&P 500 futures. They find increases in the S&P 500 futures bid–ask spreads, but the spreads remain low relative to those of S&P 500 ETFs, which is consistent with the empirical results here. DeJong and Donders (1998) examine the relations between futures, o ...
Market efficiency in emerging stock markets: A case study of the
Market efficiency in emerging stock markets: A case study of the

... 2008). The efficient market hypothesis (EMH), however, has a vital part in modern financial literature. As a result, for a sufficient market, Magnusson and Wydick (2002) state that movements in such market need to be characterized by a random walk based on current available information. Many researc ...
an empirical analysis - Indian Commerce Association (ICA)
an empirical analysis - Indian Commerce Association (ICA)

... concerned about the factors determining international investment, the performance of foreign capital investments, and the impact of foreign investment on local turnover and on the volatility of stock prices (Tesar and Werner, 1995). FII inflows on domestic stock market are important from government ...
Chapter 7
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... This is because when you buy an option; you have to be correct in determining not only the direction of the stock's movement, but also the magnitude and the timing of this movement. To succeed, you must correctly predict whether a stock will go up or down, and you have to be right about how much the ...
Mean-Reverting Models in Financial and Energy Markets
Mean-Reverting Models in Financial and Energy Markets

... financial markets (for Heston model + models with delay) • We can price options for an asset in energy markets • Drawbacks: 1) one-factor models (L is a constant) 2) W(phi_t^-1)-Gaussian process • Future work: 1) consider two-factor models: S (t) and L (t) (L->L (t)) (possibly with jumps) (analytica ...
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Dynamic Trading with Predictable Returns and

... position in security 1 decays more slowly than that in security 2, as the predictor that currently “likes” security 1 is more persistent. Therefore, the aim portfolio loads more heavily on security 1, and consequently the optimal trade buys more shares in security 1 than it would otherwise. We show ...
Stock Split Revisited: Evidence from U.S. and China Sheridan
Stock Split Revisited: Evidence from U.S. and China Sheridan

... United States and China. Specifically, we update the U.S. evidence by examining the more recent post-1999 data and examine Chinese data over this same time period. There are a number of reasons why the out-of-sample evidence in the United States may be of independent interest. In the recent period, ...
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... seasonality. Chen et al. (2010) found that exchange rates are very useful in forecasting future commodity prices but not vice versa. They also found positive relationship between exchange rate and international commodity prices. More recent studies consider a time period when China had already devel ...
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Short selling around the world with applications to the S…

... market, subsequent stock returns are far less than the market.5 In other words, when short selling is limited, investors pay prices that are too high for stocks. Although at first it might seem good to have stock prices that are too high, it is not good. As the stock price eventually falls, many inv ...
Insider Trading and CEO Pay - Chicago Unbound
Insider Trading and CEO Pay - Chicago Unbound

... deal that pays managers in part out of the hide of future shareholders. The firm should also internalize any costs arising from this payment scheme, since future shareholders should take this into account when deciding whether and at what price to buy shares. While there still may be good reasons to ...
Stale or Sticky Stock Prices?
Stale or Sticky Stock Prices?

... For each trade day t, we rank all stocks on their day t–1 market capitalization and partition the stocks into ten deciles. For each stock in a cap-decile, we record for day t the time of its last trade on its primary market and calculate the number of minutes between that trade and the time of the m ...
How Quickly Do Markets Learn? Private Information Dissemination
How Quickly Do Markets Learn? Private Information Dissemination

... Our study makes a distinct contribution to the vast literature on information and asset pricing by making usually unobservable private information the subject of empirical tests, complementing the common approaches that rely on transaction and order flow information (for a comprehensive survey, see ...
Financial Market Shocks and the Macroeconomy
Financial Market Shocks and the Macroeconomy

... Following Grossman and Stiglitz (1980) we assume there is a mass m of informed agents and 1 − m of uninformed agents, each with negative exponential utility with risk aversion R. Informed agents learn the realization of the technology shock θ perfectly after date 0 and prior to trade at date 1. We ...
Treasury bill rate - Chinhoyi University of Technology
Treasury bill rate - Chinhoyi University of Technology

... techniques employed in the past. One of the analysts suggested that the current literature has examined the relationship between Price– Earnings (P/E) ratios and securities returns. A number of studies had concluded that high P/E stocks tended to have higher betas and lower risk-adjusted returns tha ...
Earnings Release
Earnings Release

... captured by the dispersion observed between analysts’ predictions [Abarbanell, Lanen and Verrechia (1995)]. Finally, an alternative method presented by Maddala and Nimalendran (1995) is the non-observed component approach that uses earnings surprises as a non-observed explicative variable in differe ...
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Hard Times
Hard Times

... Other work has used implications from the cross section to derive new equity premium predictors. For example, Polk, Thompson, and Vuolteenaho (2006) point out that if the CAPM is true, a high equity premium implies low prices for stocks with high betas. Relative valuations of high-beta stocks can th ...
An international trend in market design: Endogenous effects of limit
An international trend in market design: Endogenous effects of limit

... than a hybrid of dealer- and order-driven markets. The studied change in market design is common for limit order book markets, while hybrid markets tend to provide some transparency of dealer identity. Although we recognize that the market design change investigated in this paper is complex, it is t ...
learning and evolution of trading strategies in limit order markets
learning and evolution of trading strategies in limit order markets

... trading strategies in limit order markets. We now summarize the main findings and discuss the main contributions of the paper. To help the discussion, we refer one-sided learning to the cases where either the informed or the uninformed traders learn and two-sided learning to the case where both the ...
The Impact of Serial Correlation on Option Prices in a Non
The Impact of Serial Correlation on Option Prices in a Non

... water. These modifications stand by the seminal Black-Scholes conception of an arbitrage based optionpricing model in which a perfect hedge can be formed between the option and the underlying asset. The inconsistency that has received the most attention is the so-called volatility smile. The volatil ...
Liquidity measures, liquidity drivers and expected returns on an
Liquidity measures, liquidity drivers and expected returns on an

... shocks. By highlighting the role of consumption smoothing and wealth rebalancing we contribute to the discussion on the drivers of the liquidity risk premium. It has been shown that liquidity risk is priced, and recent findings provide for higher premiums on emerging markets than on developed market ...
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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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