Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.

Trading and Exchanges: Market Microstructure for Practitioners

Abstract: PART I: THE STRUCTURE OF TRADING PART II: THE BENEFITS OF TRADE PART III: SPECULATORS PART IV: LIQUIDITY SUPPLIERS PART V: ORIGINS OF LIQUIDITY AND VOLATILITY PART VI: EVALUATION AND PREDICTION PART VII: MARKET STRUCTURES

Informed Trading in Stock and Option Markets

Abstract: We investigate the contribution of option markets to price discovery, using a modification of Hasbrouck’s (1995) “information share” approach. Based on five years of stock and options data for 60 firms, we estimate the option market’s contribution to price discovery to be about 17% on average. Option market price discovery is related to trading volume and spreads in both markets, and stock volatility. Price discovery across option strike prices is related to leverage, trading volume, and spreads. Our results are consistent with theoretical arguments that informed investors trade in both stock and option markets, suggesting an important informational role for options. INVESTORS WHO HAVE ACCESS to private information can choose to trade in the stock market or in the options market. Given the high leverage achievable with options and the built-in downside protection, one might think the options market would be an ideal venue for informed trading. If informed traders do trade in the options market, we would expect to see price discovery in the options market. That is, we would expect at least some new information about the stock price to be reflected in option prices first. Establishing that price discovery straddles both the stock and options markets is important for several reasons. In a frictionless, dynamically complete market, options would be redundant securities. This paper contributes to the understanding of why options are relevant in actual markets, by providing the first unambiguous evidence that stock option trading contributes to price discovery in the underlying stock market. Further, we document that the level

Securities trading workstation

Abstract: A work station for use by a trader of securities on an established market The work station is integrated into a network of competing market makers for a plurality of securities for trading A centralized database provides a feed of data on current market events for the securities, including price and transaction data The work station is specifically programmed to receive the feed of data from the database and convert this datastream into a form conducive to enhanced trading Seven separate applications permit the trader to track the market, select securities, bid and ask pricing, market direction and market depth Traders equipped with the workstation are capable of entering transactions with more complete and copious knowledge about the extant market

Variations in Trading Volume, Return Volatility, and Trading Costs: Evidence on Recent Price Formation Models

Abstract: Patterns in stock market trading volume, trading costs, and return volatility are examined using New York Stock Exchange data from 1988. Intraday test results indicate that, for actively traded firms trading volume, adverse selection costs, and return volatility are higher in the first half-hour of the day. This evidence is inconsistent with the Admati and Pfleiderer (1988) model which predicts that trading costs are low when volume and return volatility are high. Interday test results show that, for actively traded firms, trading volume is low and adverse selection costs are high on Monday, which is consistent with the predictions of the Foster and Viswanathan (1990) model. ACADEMICS, INVESTORS, AND REGULATORS alike are now intensively focused upon understanding the volatility of asset returns and its relation to trading volume. This interest was undoubtedly piqued by the market break of October 1987-a time during which volatility and trading volume reached unprecedented levels. But, even beforehand, researchers observed regular differences in the return process for various hours of the day and days of the week. Research concerning temporal patterns in stock market volatility and volume falls in two groups-studies that document observed patterns and studies that develop models to predict patterns. Among the studies in the first group are Oldfield and Rogalski (1980), French and Roll (1986), Stoll and Whaley (1990), Harris (1986), and Wood, McInish, and Ord (1985), who report evidence on seasonalities in daily and weekly return variances. Among the regularities that have been documented using interday data is that volatility is higher when the market is open than when it is closed. Oldfield and Rogalski (1980), French and Roll (1986), and Stoll and Whaley (1990), for example, point out significant differences in return volatility between trading

Price impacts of block trading on the new york stock exchange

Abstract: IN AN EFFICIENT market, prices reflect underlying values. This insures the proper allocation of new funds to the most productive areas of the economy. Additionally, individual investors benefit by knowing that prices at which they trade are not subject to forces which have little or nothing to do with the underlying value of the company. Extensive empirical tests which tend to support the efficiency of the stock market have been carried out in the past.' Until recently, however, no tests have been carried out to assess directly the impact of institutional investors on the efficiency of the stock market.2 The purpose of this paper is to examine the extent to which block trading by institutional investors contributes to or detracts from efficient markets. A block trade can be defined as a transaction involving a larger number of shares than can readily be handled in the normal course of the auction market.