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.

Systems and methods for trading actively managed funds

Abstract: The invention provides systems and methods for intra-day trading of actively managed exchange traded funds (AMETFs). The invention provides creation and redemption structures for AMETF shares that allow arbitrage, intra-day value estimations for AMETF shares, and hedging portfolios for hedging risks associated with trading AMETF shares, all without requiring disclosure of the specific assets underlying the AMETF.

Trading Fees and Efficiency in Limit Order Markets

Abstract: We study competition between a dealer (OTC) market and a limit order market. In the limit order market, investors can choose to be "makers" (post limit orders) or "takers" (hit limit orders) whereas in the dealer market they must trade at dealers' quotes. Moreover, in the limit order market, investors pay a trading fee to the operator of this market ("the matchmaker"). We show that an increase in the matchmaker's trading fee can raise investors' ex-ante expected welfare. Actually, it induces makers to post more aggressive offers and thereby it raises the likelihood of a direct trade between investors. For this reason as well, a reduction in the matchmaker's trading fee can counter-intuitively raise the OTC market share. However, entry of a new matchmaker results in an improvement in investors' welfare, despite its negative effect on trading fees. The model has testable implications for the effects of a change in trading fees and their breakdown between makers and takers on various measures of market liquidity.

Revisiting the empirical linkages between stock returns and trading volume

Abstract: This paper investigates whether the empirical linkages between stock returns and trading volume differ over the fluctuations of stock markets, i.e., whether the return–volume relation is asymmetric in bull and bear stock markets. Using monthly data for the S&P 500 price index and trading volume from 1973M2 to 2008M10, strong evidence of asymmetry in contemporaneous correlation is found. As for a dynamic (causal) relation, it is found that the stock return is capable of predicting trading volume in both bear and bull markets. However, the evidence for trade volume predicting returns is weaker.

Speculation and Hedging in Segmented Markets

Abstract: We analyze a model where traders have different trading opportunities and learn information from prices The difference in trading opportunities implies that different traders may have different trading motives when trading in the same market -- some trade for speculation and others for hedging -- and thus they may respond to the same information in opposite directions This implies that adding more informed traders may reduce price informativeness and therefore provides a source for learning complementarities leading to multiple equilibria and price jumps Our model is relevant to various realistic settings and helps to understand a variety of modern financial markets