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.

Foreign‐Exchange Trading Volume and Federal Reserve Intervention

Abstract: We find a large positive correlation between daily trading volume in currency futures markets and foreign exchange intervention by the Federal Reserve over the period 1979-1996. Neither contemporaneous nor predicted volatility can fully account for the increases in trading activity. Whether or not the intervention operation is publicly reported appears to be an important determinant of trading volume.

A stock trading expert system based on the rule-base evidential reasoning using Level 2 Quotes

Abstract: Generally, stock trading expert systems (STES) called also ''mechanical trading systems'' are based on the technical analysis, i.e., on methods for evaluating securities by analyzing statistics generated by the market activity, such as past prices and volumes (number of transactions during a unit of a timeframe). In other words, such STES are based on the Level 1 information. Nevertheless, currently the Level 2 information is available for the most of traders and can be successfully used to develop trading strategies especially for the day trading when a significant amount of transactions are made during one trading session. The Level 2 tools show in-depth information on a particular stock. Traders can see not only the ''best'' bid (buying) and ask (selling) orders, but the whole spectrum of buy and sell orders at different volumes and different prices. In this paper, we propose some new technical analysis indices bases on the Level 2 and Level 1 information which are used to develop a stock trading expert system. For this purpose we adapt a new method for the rule-base evidential reasoning which was presented and used in our recent paper for building the stock trading expert system based the Level 1 information. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Warsaw Stock Exchange.

Quantifying StockTwits semantic terms' trading behavior in financial markets

Abstract: Provide linkage between changes in volume semantic terms and subsequent market moves.Sell short at higher prices resulted from decreased appearance of negative words.Buy or take long positions resulted from increased appearance of positive words.StockTwits contains valuable information and precede trading activity in the market. Growing evidence is suggesting that postings on online stock forums affect stock prices, and alter investment decisions in capital markets, either because the postings contain new information or they might have predictive power to manipulate stock prices. In this paper, we propose a new intelligent trading support system based on sentiment prediction by combining text-mining techniques, feature selection and decision tree algorithms in an effort to analyze and extract semantic terms expressing a particular sentiment (sell, buy or hold) from stock-related micro-blogging messages called "StockTwits". An attempt has been made to investigate whether the power of the collective sentiments of StockTwits might be predicted and how the changes in these predicted sentiments inform decisions on whether to sell, buy or hold the Dow Jones Industrial Average (DJIA) Index. In this paper, a filter approach of feature selection is first employed to identify the most relevant terms in tweet postings. The decision tree (DT) model is then built to determine the trading decisions of those terms or, more importantly, combinations of terms based on how they interact. Then a trading strategy based on a predetermined investment hypothesis is constructed to evaluate the profitability of the term trading decisions extracted from the DT model. The experiment results based on 122-tweet term trading (TTT) strategies achieve a promising performance and the (TTT) strategies dramatically outperform random investment strategies. Our findings also confirm that StockTwits postings contain valuable information and lead trading activities in capital markets.

High Frequency Trading in the Korean Index Futures Market

Abstract: We investigate the trading behavior of high frequency trading (HFT), the impact of HFT on market quality, its role in the price discovery process, and its profitability, using a very detailed data set of the KOSPI 200 index futures market. We find that high frequency traders (HFTs) do not provide liquidity in the futures market, nor does HFT have any role in enhancing market quality. Indeed, HFT is detrimental to the price discovery process. This finding is contrary to those in the existing literature on HFT in equity markets. We also find that profitable opportunities for HFTs are rare after transaction costs are considered, with the notable exception that foreign HFTs can earn a profit in the index futures market. © 2013 Wiley Periodicals, Inc. Jrl Fut Mark 35:31–51, 2015

Order-to-Trade Ratios and Market Liquidity

Abstract: We study the impact on market liquidity of the introduction of a penalty for high order-to-trade ratios (OTRs), implemented by the Italian stock exchange to curtail high-frequency quote submission. We find that the fee is associated with a collapse in the quoted depth of the stocks that make up the bulk of trading in Italian equities and an increase in price impacts of trading across the treated stocks. Spreads do not change, however. Stocks from a pan-European control sample show no such liquidity changes. Thus, the Italian OTR fee had the effect of making Italian stocks markets more shallow and less resilient. Large stocks are more severely affected than midcaps. We also find evidence of a limited decrease in turnover. Consolidated liquidity, constructed by aggregating across all electronic trading venues for these stocks, decreases just like that on the main exchange. Thus, liquidity was not simply diverted from the main exchange, it was reduced in aggregate.