Topic
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.
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TL;DR: In this paper, the authors explore the business-led advocacy of the UK emission trading scheme with special focus on the symbolic benefits of emission trading for the business community and find that although business originally supported emission trading as an alternative to taxation, more socio-symbolic motives shaped business interest in emission trading after announcement of the Climate Change Levy.
Abstract: This paper explores the business-led advocacy of the UK emission trading scheme with special focus on the symbolic benefi ts of emission trading for the business community. It traces the development of the UK Emissions Trading Group and links the group’s preferences for emission trading to socio-economic, operational and legislative contexts. The analysis reveals that, although business originally supported emission trading as an alternative to taxation, more socio-symbolic motives shaped business interest in emission trading after announcement of the Climate Change Levy. This suggests that ‘symbolic politics’ can drive industry support for economic instruments such as emission trading, even when the economic rationale for doing so is diminished or constrained by existing policy frameworks or wider socio-economic contexts. Copyright © 2008 John Wiley & Sons, Ltd and ERP Environment.
37 citations
TL;DR: In this paper, the authors analyze strategic speculators' incentives to trade on information in a model where firm value is endogenous to trading, due to feedback from the financial market to corporate decisions.
Abstract: We analyze strategic speculators' incentives to trade on information in a model where firm value is endogenous to trading, due to feedback from the financial market to corporate decisions. Trading reveals private information to managers and improves their real decisions, enhancing fundamental value. This feedback effect has an asymmetric effect on trading behavior: it increases (reduces) the profitability of buying (selling) on good (bad) news. This gives rise to an endogenous limit to arbitrage, whereby investors may refrain from trading on negative information. Thus, bad news is incorporated more slowly into prices than good news, potentially leading to overinvestment.
37 citations
TL;DR: In this paper, the authors analyzed the price discovery process and the informational role of trading in the Italian wholesale secondary markets for Treasury bonds: the businessto-business (B2B), interdealer and quote driven MTS cash and the business-to-consumer (b2C), order driven BondVision trading venues.
Abstract: This paper analyses the price discovery process and the informational role of trading in the Italian wholesale secondary markets for Treasury bonds: the business-to-business (B2B), interdealer and quote driven MTS cash and the business-to-consumer (B2C), order driven BondVision trading venues. Using daily data for a representative set of fixed rate government bonds over the period January 2007 - February 2012, we find that the B2C dealer-to-customer market contributes to the process of price formation to a greater extent than the B2B interdealer platform. The informational role of trading is found to be considerable: order flow is a key variable in the process of price formation and appears to continuously act on a cross market basis. Moreover, the explanatory role of order flow turns out to be stronger when liquidity conditions are poorer.
37 citations
TL;DR: In this paper, the authors identify Chinese bull and bear market regimes and their statistical properties, and their exante trading rule tests indicate the usefulness of such regime identification for Chinese investors.
37 citations
TL;DR: In this paper, the authors study competition among market designers who create new trading platforms, when boundedly rational traders learn to select among them, and they find that traders tend to select non-market clearing platforms with prices systematically above the market clearing level, provided at least one such platform is introduced by a market designer.
Abstract: We study competition among market designers who create new trading platforms, when boundedly rational traders learn to select among them. We ask whether ‘Walrasian’ platforms, leading to market-clearing trading outcomes, will dominate the market in the long run. If several market designers compete, we find that traders learn to select non-market clearing platforms with prices systematically above the market-clearing level, provided at least one such platform is introduced by a market designer. This in turn leads market designers to introduce non-market clearing platforms. Hence platform competition induces non-competitive market outcomes.
37 citations