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Top 10 Stock Screening Strategies That Make
Top 10 Stock Screening Strategies That Make

... (portfolio) is generated. The period’s returns are calculated using the % change in price from the beginning of the holding period to the end of the holding period, plus any applicable dividends. The returns for the portfolio is the arithmetic mean of the returns for the individual companies in the ...
What Does Average Directional Index - ADX Mean
What Does Average Directional Index - ADX Mean

... Buy signal is triggered when there is a crossover ie +DI line moves above DI line and sell signal when +DI line moves below -DI line. The time periods most commonly used are 10 or 14 days. I prefer to use 10 days since the signal can be triggered sooner and can use it to confirm with other indicator ...
Spillover Effect of Disagreement
Spillover Effect of Disagreement

... A growing body of research has recently highlighted that investor heterogeneity can play an important role in understanding the behavior of asset prices (see, e.g., Basak (2005) for a recent survey). These studies have focused on the case of a single risky asset and examined how investor disagreemen ...
Trading Volume Reaction to the Earnings Reconciliation from IFRS
Trading Volume Reaction to the Earnings Reconciliation from IFRS

... from IFRS to U.S. GAAP for all IFRS-based foreign firms from 2005 to 2006. As noted by Holthausen and Watts (2001, 3), “Unless those underlying theories are descriptive of accounting, the value-relevance literature’s reported associations between accounting numbers and common equity valuations have ...
Did Stop Signs Stop Investor Trading?
Did Stop Signs Stop Investor Trading?

... Leuz 2005). This led the number of PS firms to double yet these firms, which are known for their illiquidity, have been largely overlooked by prior research.2 Finally, our study extends the line of research that addresses the role of mandated disclosures in improving market efficiency. Bushee and L ...
Make and Take Fees in the US Equity Market
Make and Take Fees in the US Equity Market

... Colliard and Foucault (2012) show that an increase in the total fee can be associated with increased trading activity due to heterogeneous patience across investors. With a fee increase, patient investors submit more aggressive quotes, increasing the likelihood of a transaction. We assess empiricall ...
Chapter 22: Credit Risk Modeling
Chapter 22: Credit Risk Modeling

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The Role of Size and Book-to-Market Ratio as Proxies for

... factors are proxies for firm-specific and/or macroeconomic risk in the ISE. We believe that the ISE is a suitable market to test the size and book-to-market effects and their rational risk explanation. First, it is still not fully integrated with the world markets, is in a stage of institutional dev ...
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The Myths and Fallacies about Diversified Portfolios
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What is an Exchange Traded Fund? How are ETFs bought and sold

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Challenges arising from alternative investment management

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TRANSMISSION OF INFORMATION ACROSS INTERNATIONAL
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Price Discrimination - Department of Economics
Price Discrimination - Department of Economics

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Morningstar Strategic Beta Guide
Morningstar Strategic Beta Guide

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Seasons Series Trust - Mid Cap Value Portfolio - Annuities
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... investing in the Portfolio with the cost of investing in other mutual funds. The Example assumes that you invest $10,000 in the Portfolio for the time periods indicated and then redeem all of your shares at the end of those periods. The Example also assumes that your investment has a 5% return each ...
A Model of Intertemporal Asset Prices Under Asymmetric
A Model of Intertemporal Asset Prices Under Asymmetric

... expectations about future cash flows and noise trading. When investors are less informed about the true growth rate of dividends, their expectation about future cash flows becomes less variable. This has the effect of reducing price volatility. On the other hand, there is more uncertainty in the sto ...
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short term patterns

... Outside Bars can be either Bullish or Bearish depending on the direction of the inbound trend. If the inbound price trend is down, then upon identification of an Outside Bar, taking a long position or closing a short position is recommended. Conversely, if the inbound price trend is up, then upon id ...
Financial Liberalization and Emerging Stock Market Volatility
Financial Liberalization and Emerging Stock Market Volatility

... pre and post-change. Aggarwal et al. (1999) follow a different route and, instead of specifying a priori the dates of the breaks, they detect shifts in volatility from the data by using an iterated cumulative sum of squares (ICSS) algorithm. This procedure identifies the points of shocks or sudden ch ...
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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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