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.

Incorporating Markov decision process on genetic algorithms to formulate trading strategies for stock markets

Abstract: The paper proposed a novel application for incorporating Markov decision process on genetic algorithms to develop stock trading strategies.This predicts the results of applying the Markov decision process with real-time computational power to help investors formulate correct timing (portfolio adjustment) and trading strategies (buy or sell).This study thus uses the excellent genetic algorithm parallel space searching ability to provide investors with the optimal stock selection strategy and capital allocation, and combines them with both constructs to solve the portfolio problem and improve return on investment for investors.This research can solve stock selection, market timing and capital allocation at the same time for investors when investing in the stock market. With the arrival of low interest rates, investors entered the stock market to seek higher returns. However, the stock market proved volatile, and only rarely could investors gain excess returns when trading in real time. Most investors use technical indicators to time the market. However the use of technical indicators is associated with problems, such as indicator selection, use of conflicting versus similar indicators. Investors thus have difficulty relying on technical indicators to make stock market investment decisions.This research combines Markov decision process and genetic algorithms to propose a new analytical framework and develop a decision support system for devising stock trading strategies. This investigation uses the prediction characteristics and real-time analysis capabilities of the Markov decision process to make timing decisions. The stock selection and capital allocation employ string encoding to express different investment strategies for genetic algorithms. The parallel search capabilities of genetic algorithms are applied to identify the best investment strategy. Additionally, when investors lack sufficient money and stock, the architecture of this study can complete the transaction via credit transactions. The experiments confirm that the model presented in this research can yield higher rewards than other benchmarks.

Stock Index Futures

Abstract: Preface Preliminaries: Stock market indices Introduction to futures trading Arbitrage: Arbitrage and the valuation of stock index futures Arbitrage in practice Arbitrage and relaxing the assumptions Prices: Basics, spreads and the risk premium Maturity, price volatility and volume Market efficiency Uses: Hedging The uses of stock index futures by fund managers Others: The design and regulation of futures contracts Further topics in index futures Questions Glossary References Index.

Strategic Trading by Index Funds and Liquidity Provision Around S&P 500 Index Additions

Abstract: We examine the trades of index funds and other institutions around S&P 500 index additions. We find index funds begin rebalancing their portfolios with the announcement of composition changes and do not fully establish their positions until weeks after the effective date. Trading away from the effective date is more prevalent for stocks with lower levels of liquidity and among large index funds, which is consistent with index funds accepting higher tracking error in order to reduce the price impact of their trades. Small and mid-cap funds provide liquidity to index funds around additions, and added stocks with a greater proportion of these natural liquidity providers experience lower inclusion returns.

An Analysis of the Role of "Insider Trading" on Futures Markets

Abstract: Section 23(b) of the Commodity Exchange Act requires the Commodity Futures Trading Commission (CFTC) to examine the possession by futures traders of material nonpublic information concerning other persons' cash market or futures activities and to report to Congress on the adequacy of the commission's authority to prevent abuses resulting from those circumstances. This request for a study of "insider trading" represented a compromise between groups that wanted to outlaw insider trading and those who felt that current regulations in the area were adequate. Unfortunately, section 23(b) does not define "material nonpublic information," or state what "abuses" are associated with its possession, or require the CFTC to produce a costbenefit analysis of the effects of increasing the CFTC's authority. This paper provides a framework in which to analyze the role of insider trading in futures markets. Within this framework it will be possible to provide meaningful content to the terms mentioned in section 23(b) and to consider what evidence could be collected regarding correctable abuses associated with insider trading.

Option Trading Costs Are Lower than You Think

Abstract: Conventional estimates of the costs of taking liquidity in equity options markets are large. This presents a puzzle, which we resolve by taking seriously the implication of models of dynamic limit order markets that the bid-ask midpoint can be a poor proxy for the true value of a security, conditional on the occurrence of a trade. Changes in option prices can be predicted using publically available information, and a large proportion of option trades exploit this high-frequency predictability to take liquidity at low cost, buying and selling immediately before option prices are expected to change. Conventional measures of effective spreads and price impact do not account for this execution timing but can be adjusted to do so. For the average trade, effective spreads that take account of trade timing are one-third smaller than the conventionally measured effective spreads; for trades that reflect execution timing, they are four times smaller. Conventional measures of price impact overstate it by a factor of more than two. These findings have striking implications for the profitability of options trading strategies that involve taking liquidity. Our main results are robust to recent changes in option market structure.