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Algorithmic Trading
Abstract
In a broad sense, the term algorithmic trading is used to describe trading in an automated manner according to a set of rules. It is often used interchangeably with statistical trading or statistical arbitrage, which may or may not be automated, but is based on signals derived from statistical analyses or models. Smart order routing, program trading, and rules-based trading are some of the other terms associated with algorithmic trading. More recently, the range of functions and activities associated with algorithmic trading has grown to include market impact modeling, execution risk analytics, cost aware portfolio construction, and the use of market microstructure effects. In this article, we first explain the basic ideas of market impact and optimal execution from both the sell- and buy-side perspectives. We then provide an overview of the most popular algorithmic trading strategies.
Investing in financial markets involves the buying and selling of various financial instruments such as stocks, bonds, currencies, and commodities with the aim of making a profit. Participants in financial markets include individual investors, institutional investors, and traders who engage in transactions through various platforms. Two types of investing techniques used by investors are manual and algorithmic. Manual traders depend on their experience, intuition, and real-time market analysis to make investing decisions. While algorithmic investing is known for its efficiency, speed, and ability to handle large volumes of data, manual investing allows for a more intuitive and adaptive approach, incorporating the trader's qualitative insights into decision-making. Both methods have their merits and demerits, and their effectiveness often depends on factors such as the trader's expertise, market conditions, and the specific goals of the trading strategy. This article aims to investigate the investors’ attitudes towards manual as well as algo trading strategies. For this purpose, a sample of 80 investors employed in the IT sector is considered from Bangalore East. The data is analyzed based on variables like demography of investors, trading experience, preference towards trading strategies: manual or automated, and technological literacy. This research enhances our understanding of investor sentiment in the dynamic intersection of algorithmic and manual trading.
Researchers are increasingly using data from the Nasdaq market to examine pricing behavior, market design, and other microstructure phenomena. The validity of any study that classifies trades as buys or sells depends on the accuracy of the classification method. Using a Nasdaq proprietary data set that identifies trade direction, we examine the validity of several trade classification algorithms. We find that the quote rule, the tick rule, and the Lee and Ready (1991) rule correctly classify 76.4%, 77.66%, and 81.05% of the trades, respectively. However, all classification rules have only a very limited success in classifying trades executed inside the quotes, introducing a bias in the accuracy of classifying large trades, trades during high volume periods, and ECN trades. We also find that extant algorithms do a mediocre job when used for calculating effective spreads. For Nasdaq trades, we propose a new and simple classification algorithm that improves over extant algorithms.
The price reaction to a single transaction depends on transaction volume, the identity of the stock, and possibly many other factors. Here we show that, by taking into account the differences in liquidity for stocks of different size classes of market capitalization, we can rescale both the average price shift and the transaction volume to obtain a uniform price-impact curve for all size classes of firm for four different years (1995-98). This single-curve collapse of the price-impact function suggests that fluctuations from the supply-and-demand equilibrium for many financial assets, differing in economic sectors of activity and market capitalization, are governed by the same statistical rule.
I have used a portfolio analysis framework to capture the dynamic essentials of an unconstrained (long-short) investment management strategy. It is a bare-bones system with only two parameters. These parameters can be measured, and they are linked to familiar investment concepts. This minimalist setup yields a surprisingly abundant set of results. We can characterize the efficiency of the implementation, analyze and explain many aspects of the strategy, and optimize the rate of trading. More important than the specific results is the change in perception. Looking at the portfolio as a moving object provides a new perspective and opens up various interesting possibilities.
Electronic trading of equities and other securities makes heavy use of "arrival price" algorithms, that determine optimal trade sched-ules by balancing the market impact cost of rapid execution against the volatility risk of slow execution. In the standard formulation, mean-variance optimal strategies are static: they do not modify the execution speed in response to price motions observed dur-ing trading. We show that with a more realistic formulation of the mean-variance tradeoff, and even with no momentum or mean re-version in the price process, substantial improvements are possible for adaptive strategies that spend trading gains to reduce risk, by accelerating execution when the price moves in the trader's favor. The improvement is larger for large initial positions.
Transaction costs in trading involve both risk and return. The return is associated with the cost of immediate execution and the risk is a result of price movements during a more gradual trading. The paper shows that the trade-off between risk and return in optimal execution should reflect the same risk preferences as in ordinary investment. The paper develops models of the joint optimization of positions and trades, and shows conditions under which optimal execution does not depend upon the other holdings in the portfolio. Optimal execution however may involve trades in assets other than those listed in the order; these can hedge the trading risks. The implications of the model for trading with reversals and continuations are developed. The model implies a natural measure of liquidity risk
We derive dynamic optimal trading strategies that minimize the expected cost of trading a large block of equity over a fixed time horizon. Specifically, given a fixed block of shares to be executed within a fixed finite number of periods T, and given a price-impact function that yields the execution price of an individual trade as a function of the shares traded and market conditions, we obtain the optimal *sequence* of trades or "best execution strategy" as a function of market conditions---closed-form expressions in some cases---that minimizes the expected cost of executing within T periods. Our analysis is extended to the portfolio case in which price impact *across* stocks can have an important effect on the total cost of trading a portfolio. We also discuss generalizations to other price impact functions, imposing constraints, and algorithms for performing the optimization numerically. (The text "" comes from a text-processor called TeX and stands for a mathematical symbol which is an upper case S with a bar over it.)
This paper evaluates alternative methods for classifying individual trades as market buy or market sell orders using intraday trade and quote data. The authors document two potential problems with quote-based methods of trade classification: quotes may be recorded ahead of trades that triggered them, and trades inside the spread are not readily classifiable. These problems are analyzed in the context of the interaction between exchange floor agents. The authors then propose and test relatively simple procedures for improving trade classifications. Copyright 1991 by American Finance Association.
In an environment where trading volume affects security prices and where prices are uncertain when trades are submitted, quasi-arbitrage is the availability of a series of trades that generate infinite expected profits with an infinite Sharpe ratio. We show that when the price impact of trades is permanent and time-independent, only linear price-impact functions rule out quasi-arbitrage and thus support viable market prices. When trades have also a temporary price impact, only the permanent price impact must be linear while the temporary one can be of a more general form. We also extend the analysis to a time-dependent framework. Copyright The Econometric Society 2004.
This paper suggests that the interactions of security trades and quote revisions be modeled as a vector autoregressive system. Within this framework, a trade's information effect may be meaningfully measured as the ultimate price impact of the trade innovation. Estimates for a sample of NYSE issues suggest a trade's full price impact arrives only with a protracted lag; the impact is a positive and concave function of the trade size; large trades cause the spread to widen; trades occurring in the face of wide spreads have larger price impacts; and information asymmetries are more significant for smaller firms. Copyright 1991 by American Finance Association.
Small Delta Continuous Trading for Dynamic Portfolios
- P Kolm
- L Maclin
Kolm, P. & Maclin, L. (2009). Small Delta Continuous Trading for Dynamic Portfolios, Courant Institute, New York University.
Dynamic portfolio analysis, Jour-nal of Portfolio Management
- R C Grinold
Grinold, R.C. (2007). Dynamic portfolio analysis, Jour-nal of Portfolio Management 34(1), 16–26.
Algorithmic Trading III: Precision, Control, Execution
- R. Almgren
- J. Lorenz