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.

Financial Innovations and Market Volatility

Abstract: List of Tables. List of Figures. Foreword. Author's Preface. Part I: Before the Storm: 1. Financial Innovation: The Past Twenty Years and the Next. 2. Liquidity and Market Structure. 3. Financial Innovations and Market Volatility. Part II: The Crash of 1987 and Its Aftermath: 4. Index Futures During the Crash of 1987. 5. The Crash of 1987 and the Crash of 1946. 6. The Crash of 1987: Bubble of Fundamental. 7. Equilibrium Relations Between Cash Markets and Futures Markets. Part III: Markets and Volatility: Policy Issues: 8. Strategies for Capital Market Structure and Regulation. 9. Margin Regulation and Stock Market Volatility. 10. Should Short-Term Trading be Taxed? 11. Index Arbitrage and Volatility. 12. The International Competitiveness of US Futures Exchanges. 13. Volatility, Episodic Volatility and Coordinated Circuit Breakers. Part IV: The Academic Field of Finance: 14. The Academic Field of Finance: Some Observations on Its History and Prospects. Index.

Informed trading, investment, and welfare

Abstract: This article studies the welfare economics of informed stock market trading. We analyze the effect of more informative prices on investment, given that this dependence will itself be reflected in equilibrium prices. While a higher incidence of informed speculation always increases firm value through a more informative trading process, the effect on agents’ welfare depends on how revelation of information changes risk‐sharing opportunities in the market. Greater revelation of information that agents wish to insure against reduces their hedging opportunities. On the other hand, early revelation of information that is uncorrelated with hedging needs allows agents to construct better hedges.

Automated trading with performance weighted random forests and seasonality

Abstract: Seasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades. This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy. While simple approaches to trading these regularities can prove profitable, such trading leads to potential large drawdowns (peak-to-trough decline of an investment measured as a percentage between the peak and the trough) in profit. In this paper, we introduce an automated trading system based on performance weighted ensembles of random forests that improves the profitability and stability of trading seasonality events. An analysis of various regression techniques is performed as well as an exploration of the merits of various techniques for expert weighting. The performance of the models is analysed using a large sample of stocks from the DAX. The results show that recency-weighted ensembles of random forests produce superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. It is also found that using seasonality effects produces superior results than not having them modelled explicitly.

The Anatomy of the CDS Market

Abstract: Using novel position and trading data for single-name corporate credit default swaps (CDSs), we provide evidence that CDS markets emerge as “alternative trading venues” that serve a standardization and liquidity role. CDS positions and trading volume are larger for firms with bonds that are fragmented into many separate issues and have heterogeneous contractual terms. Whereas hedging motives are associated with trading volume in the bond and CDS markets, speculative trading concentrates in the CDS. Cross-market arbitrage links the CDS and bond market via the basis trade, compressing the negative CDS-bond basis and reducing price impact in the bond market.