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Tools - Interest Rate and Currency Swaps
Tools - Interest Rate and Currency Swaps

Estimating a Structural Model of Herd Behavior in Financial Markets
Estimating a Structural Model of Herd Behavior in Financial Markets

... by the data, which implies both that herd behavior arises in equilibrium and that there is information content in the sequence of trades. Note that in each day of trading there is always high heterogeneity in trading decisions (i.e., even in days when the fundamental value has increased, we observe ...
Chapter 10
Chapter 10

... Assumptions of technical analysis directly oppose the notion of efficient markets Technicians believe that stock prices move in patterns that persist and are predictable to the informed investor. Technical analysts develop systems to detect trends and patterns in prices If the capital market is weak ...
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... Selection vs. Timing. Explain the meanings of market timing and security selection, highlighting their similarities and differences. ...
The Stock Market
The Stock Market

... the laptops determine 9 companies you wish to buy stock in, how much the stock is worth currently, and how much money you would have invested in that stock. – Turn to page 36 in your workbooks – Using the stapled packet I have given you pick nine companies, go to Google and type in ...
Price jump prediction in a limit order book
Price jump prediction in a limit order book

... give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that the liquidity balance on the best bid/best ask is quite informative for predicting the future market order’s direction. Moreover, we define price jump as a sell (buy) market order arriv ...
Study on Diversification Benefits and Financial Market Integration
Study on Diversification Benefits and Financial Market Integration

... The financial markets around the world became more integrated due to development of technologies and an increase of cross-border investments and foreign direct investments. Financial market integration and openness (e.g., liberalization and deregulation) may discipline domestic economic policies and ...
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... If your income is $10,000, your price elasticity of demand as the price of compact discs rises from $8 to $10 [Formula = [(Qi – Qf)/ Qi ]/[(Pi – Pf)/ Pi ] sometimes the formula will give a negative number but remember its an absolute value, so it is always positive, thus the calculation [(40 - 32)/4 ...
Optimal basket liquidation with finite time
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... controls. As a result, the corresponding Hamilton-Jacobi-Bellman (HJB) equation involves an infinite initial condition and cannot be solved in general. The case of exponential utility, however, exhibits a close connection to classical mechanics, and we are able to construct a classical solution to o ...
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Morningstar US Market Factor Tilt Index
Morningstar US Market Factor Tilt Index

... allows us to set separately degrees of value tilting and size tilting, each on a scale from 0 (no tilt) to 1 (full tilt). To select which combination of settings for thee tilting parameters, we first create 25 portfolios by using values of 0.1, 0.2, 0.3, 0.4, 0.5 for each of two parameters. We then ...
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Order Submission Strategies and Information

Technical Analysis Around the World: Does it Ever Add Value?
Technical Analysis Around the World: Does it Ever Add Value?

... The evidence of profitability over and above transactions costs appears to be the most compelling in emerging markets. Parisi and Vasquez (2000) document large profits to the BLL (1992) trading rules in the Chilean stock market. They do not consider transactions costs, however, several other authors ...
Durability, Re-trading and Market Performance
Durability, Re-trading and Market Performance

... investigate why efficiencies remain low throughout the RT experiments, we summarize each trader type’s trading activities. In Figure 6, each subject’s buying and selling activities are sampled over 4 RT experiments covering a total of 40 trading periods. The first question is whether subject types s ...
THE BEHAVIOR OF MALAYSIAN STOCK MARKET
THE BEHAVIOR OF MALAYSIAN STOCK MARKET

... corporate finance. The most prominent of the studies is the efficient market hypothesis (EMH) which was introduced by Fama, Fisher, Jensen, and Roll in 1969 (Fama, 1991; Fama, 1998). This line of research advocates an economically efficient market whereby the behavior of markets can be predicted and ...
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... This paper introduces an econometric framework that can be used to study short-run liquidity provision in limit order markets. This model can be applied with minor modifications to any sequence of events that occur at random, rather than predetermined, time intervals. In the context of electronic li ...
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... The Mariner team will employ a derivatives relative-value strategy that capitalizes on Mr. Loflin’s experience in structuring, analyzing, and managing portfolios of derivative securities at banks and hedge funds since 1999. The strategy will identify and seek to exploit recurring dislocations in der ...
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DOC - Europa.eu
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... reasons and since 2008 prices are fairly stable and less volatile than, for example, most energy commodities. There is no evidence of any pattern between the influx of investors and volatility in carbon prices. ...
Multi-market Trading and Liquidity: Evidence from Cross
Multi-market Trading and Liquidity: Evidence from Cross

... Atanasova: Corresponding author; Beedie School of Business, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada. E-mail: [email protected]. Gatev: Simon Fraser University. Li: Simon Fraser University. ...
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re-examining risk tolerance using worst

... diversified among various stock categories, and a commitment of less than 10% would not have a meaningful effect. Their bond holding can remain at 5% because of their other fixed-income assets—the pension and Social Security payments. Of course, the next question the Pinkertons must contend with is ...
Informational overshooting, booms, and crashes
Informational overshooting, booms, and crashes

... This paper presents an explanation to stock markets’ booms and crashes, which is based on informational dynamics. Episodes of booms and crashes have occured in many stock markets since the famous South Sea Bubble.1 The US stock market has experienced two such episodes during this century: the boom a ...
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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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