Portfolio of Global Futures Algorithmic Trading Strategies for Best Out-of-Sample Performance

2016 ◽  
pp. 424-435 ◽  
Author(s):  
Aistis Raudys
Keyword(s):  
Trading Strategies ◽  
Algorithmic Trading ◽  
Out Of Sample ◽  
Global Futures

Algorithmic Trading and Transaction Costs

2020 ◽  
pp. 189-219
Author(s):  
George Chalamandaris ◽  
Dimitrios Antonopoulos
Keyword(s):  
Transaction Costs ◽  
Hedge Fund ◽  
Relative Volume ◽  
Trading Strategies ◽  
Algorithmic Trading ◽  
Price Momentum ◽  
Out Of Sample ◽  
High Level ◽  
Implicit Transaction Costs

“Algos” are algorithmic trading strategies that are meant to optimize the execution quality of the trades in terms of transaction costs and market-timing. This chapter presents the transaction costs taxonomy and popular algorithmic execution strategies. Authors empirically examine a dataset of hedge fund transactions. Our results suggest that implicit transaction costs are characterized by a significant buy-sell asymmetry. To get some insight about the possible determinants of Implicit Transaction Costs, authors investigate the algo type and stock characteristics such as market capitalization, relative volume, inverse prior close, price momentum, buy indicator and trade duration. Both in-sample and out-of-sample tests show that a significant portion of transaction costs can be anticipated before the trade execution. Results show that high-level execution strategies can be constructed to optimize the algo choice.

FINRA proposes requiring registration of associated persons who develop algorithmic trading strategies

2015 ◽  
Vol 16 (3) ◽  
pp. 33-36
Author(s):  
Janet M. Angstadt ◽  
Michael T. Foley ◽  
Ross Pazzol ◽  
James D. Van De Graaff
Keyword(s):  
Design Methodology ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Algorithmic Trading ◽  
Design Development ◽  
Significant Modification ◽  
Current Form ◽  
Content Type ◽  
Rule Making

Purpose – To analyze FINRA’s proposal that would require registration with FINRA of associated persons of FINRA-member firms who are primarily responsible for the design, development or significant modification of an algorithmic trading strategy. Design/methodology/approach – This article discusses the rationale and details of the proposed requirements. Findings – If adopted in its current form, the proposed rule-making, particularly when combined with the SEC’s proposed amendments to Rule 15b9-1 under the Securities and Exchange Act, would result in many various individuals who currently are not subject to a FINRA registration requirement, to pass a qualification examination and register. Originality/value – This article contains valuable information about important FINRA rule making activity.

Firm-Specific Estimates of Differential Persistence and their Incremental Usefulness for Forecasting and Valuation

2015 ◽  
Vol 91 (3) ◽  
pp. 811-833 ◽  
Author(s):  
Andrew C. Call ◽  
Max Hewitt ◽  
Terry Shevlin ◽  
Teri Lombardi Yohn
Keyword(s):  
State Of The Art ◽  
Cash Flows ◽  
Trading Strategies ◽  
Significant Excess ◽  
Lower Growth ◽  
Cross Sectional ◽  
Operating Cash Flows ◽  
Earnings Components ◽  
Out Of Sample ◽  
Jel Classifications

ABSTRACT Although the differential persistence of accruals and operating cash flows is a firm-specific phenomenon, research seeking to exploit the differential persistence of these earnings components typically employs cross-sectional forecasting models. We find that a model based on firm-specific estimates of the differential persistence of accruals and operating cash flows is incrementally useful for out-of-sample forecasting relative to state-of-the-art cross-sectional models. In doing so, we show that firm-specific estimates of differential persistence are particularly useful when forecasting earnings for more stable firms (e.g., more profitable, lower growth, and less levered firms). We also demonstrate that a trading strategy exploiting investors' fixation on earnings and based on firm-specific estimates of differential persistence earns statistically and economically significant excess returns that are incremental to those generated by trading strategies based on the size of accruals. These results suggest that firm-specific estimates of differential persistence are incrementally informative for forecasting and valuation. JEL Classifications: M41.

A Step-By-Step Implementation of a Multi-Agent Currency Trading System

2011 ◽  
pp. 213-253
Author(s):  
Rui Pedro Barbosa ◽  
Orlando Belo
Keyword(s):  
Intelligent Agents ◽  
Trading Strategies ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
Out Of Sample ◽  
Multi Agent ◽  
Trading Agent ◽  
Classification And Regression ◽  
Trading Simulation ◽  
Case Based

With this chapter the authors intend to demonstrate the potential practical use of intelligent agents as autonomous financial traders. The authors propose an architecture to be utilized in the creation of this type of agents, consisting of an ensemble of classification and regression models, a case-based reasoning system and an expert system. This architecture was used to implement six intelligent agents, each being responsible for trading one of the following currency pairs with a 6-hour timeframe: CHF/JPY, EUR/CHF, EUR/JPY, EUR/USD, USD/CHF and USD/JPY. These agents simulated trades during an out-of-sample period going from February of 2007 till July of 2010, having all achieved an acceptable performance. However, their strategies resulted in relatively high drawdowns, and much of their profit disappeared once the trading costs were factored into the trading simulation. In order to overcome these problems, they integrated the agents in a multi-agent system, in which agents communicate their decisions to each other before sending the market orders, and work together to eliminate redundant trades. This system averaged out the returns of the agents, thus eliminating much of the risk associated with their individual trading strategies, and also originated considerable savings in trading expenses. Their results seem to vindicate the usefulness of the proposed trading agent architecture, and also demonstrate that there is indeed a place for intelligent agents in financial markets.

scholarly journals The Economic Value of Predicting Stock Index Returns and Volatility

2004 ◽  
Vol 39 (2) ◽  
pp. 407-429 ◽  
Author(s):  
Wessel Marquering ◽  
Marno Verbeek
Keyword(s):  
Linear Models ◽  
Market Timing ◽  
Economic Value ◽  
Trading Strategies ◽  
Stock Index ◽  
Short Sales ◽  
Monthly Data ◽  
Out Of Sample ◽  
Mean Variance ◽  
Stock Index Returns

AbstractIn this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data, we examine the economic value of a number of alternative trading strategies over the period 1970–2001. It appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility exceeds that of strategies that only employ timing in returns. Most of the profitability of the dynamic strategies, however, is located in the first half of our sample period.

scholarly journals Social signals and algorithmic trading of Bitcoin

2015 ◽  
Vol 2 (9) ◽  
pp. 150288 ◽  
Author(s):  
David Garcia ◽  
Frank Schweitzer
Keyword(s):  
Information Search ◽  
Large Scale ◽  
Emotional Valence ◽  
Trading Strategies ◽  
Algorithmic Trading ◽  
Social Signals ◽  
Large Scale Data ◽  
Media Sentiment ◽  
Returns On Investment ◽  
Robust Statistical Methods

The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading-based social media sentiment has the potential to yield positive returns on investment.

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