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: The authors characterizes the temporal pattern of trading rule returns and official intervention for Australian, German, Swiss, and U.S. data to investigate whether intervention generates technical trading rule profits.
123 citations
TL;DR: An automated system that predicts the number of shares by adding a deep neural network (DNN) regressor to a deep Q-network, thereby combining reinforcement learning and a DNN is designed, outperforming the market and the reinforcement learning model.
Abstract: We study trading systems using reinforcement learning with three newly proposed methods to maximize total profits and reflect real financial market situations while overcoming the limitations of financial data. First, we propose a trading system that can predict the number of shares to trade. Specifically, we design an automated system that predicts the number of shares by adding a deep neural network (DNN) regressor to a deep Q-network, thereby combining reinforcement learning and a DNN. Second, we study various action strategies that use Q-values to analyze which action strategies are beneficial for profits in a confused market. Finally, we propose transfer learning approaches to prevent overfitting from insufficient financial data. We use four different stock indices—the S&P500, KOSPI, HSI, and EuroStoxx50—to experimentally verify our proposed methods and then conduct extensive research. The proposed automated trading system, which enables us to predict the number of shares with the DNN regressor, increases total profits by four times in S&P500, five times in KOSPI, 12 times in HSI, and six times in EuroStoxx50 compared with the fixed-number trading system. When the market situation is confused, delaying the decision to buy or sell increases total profits by 18% in S&P500, 24% in KOSPI, and 49% in EuroStoxx50. Further, transfer learning increases total profits by twofold in S&P500, 3 times in KOSPI, twofold in HSI, and 2.5 times in EuroStoxx50. The trading system with all three proposed methods increases total profits by 13 times in S&P500, 24 times in KOSPI, 30 times in HSI, and 18 times in EuroStoxx50, outperforming the market and the reinforcement learning model.
122 citations
TL;DR: In this paper, the authors used the daily Dow Jones Industrial Average Index from 1963 to 1988 to examine the linear and non-linear predictability of stock market returns with some simple technical trading rules.
Abstract: This paper uses the daily Dow Jones Industrial Average Index from 1963 to 1988 to examine the linear and non-linear predictability of stock market returns with some simple technical trading rules. Some evidence of non-linear predictability in stock market returns is found by using the past buy and sell signals of the moving average rules. In addition, past information on volume improves the forecast accuracy of current returns. The technical trading rules used in this paper are very popular and very simple. The results here suggest that it is worth while to investigate more elaborate rules and the profitability of these rules after accounting for transaction costs and brokerage fees. Copyright © 1998 John Wiley & Sons, Ltd.
120 citations
TL;DR: In this article, the impact of the stock market microstructure on return volatility and on the value discovery process in the Milan Stock Exchange is studied, where the primary trading mechanism employed by this exchange is a call market, which is usually preceded by trading in a continuous market.
Abstract: This paper studies the impact of the stock market microstructure on return volatility and on the value discovery process in the Milan Stock Exchange. The primary trading mechanism employed by this exchange is a call market, which is usually preceded and followed by trading in a continuous market. We find that the opening transaction in the continuous market has the highest volatility, and that opening the market with the call transaction seems to produce relatively lower volatility. In the closing transaction, investors correct perceived errors or noise in the prices set at the call. The implications of the results for market design are examined.
120 citations
TL;DR: In this paper, the authors examined the impact of option trading on individual investor performance and found that most investors incur substantial losses on their option investments, which are much larger than the losses from equity trading.
Abstract: This paper examines the impact of option trading on individual investor performance. The results show that most investors incur substantial losses on their option investments, which are much larger than the losses from equity trading. We attribute the detrimental impact of option trading on investor performance to poor market timing that results from overreaction to past stock market returns. High trading costs further contribute to the poor returns on option investments. Gambling and entertainment appear to be the most important motivations for trading options while hedging motives only play a minor role. We also provide strong evidence of performance persistence among option traders.
119 citations