Active Trading – Definition

Cite this article as:"Active Trading – Definition," in The Business Professor, updated September 9, 2019, last accessed June 4, 2020, https://thebusinessprofessor.com/lesson/active-trading-definition/.

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Active Trading Definition

Active trading is the purchase and sale of securities for fast profit on the basis of short-term price movements.

A Little More on What is Active Trading

Active trading seeks profit from price movements in markets of high liquidity. Because of this, active traders usually focus on foreign currency trades, derivatives, or volatile stocks. Active trading needs a more speculative viewpoint that strategies of buy and hold. This makes technical analysis a beneficial tool for active traders with its predictive techniques based on the price charts of equity.

Usually, a high volume of trades is utilized by active traders for profit-making, since price swings which may happen over the short-term tend to be small. Active traders also frequently use limit orders, which make it possible for traders to set exact price levels at which securities can be sold. Stop-loss orders, for instance, utilize a lower price point to limit a trade’s downside, ensuring maximum loss supposing the price’s movement goes against the trader. An upper limit is set to the price by take-profit orders. This might limit upside in cases where the price goes up unexpectedly, but this makes it possible for traders to lock in a specific amount of profit without monitoring price movements closely in a bid to sell precisely at the right time.

Active Trading Strategies

Active traders have various strategies in their arsenal depending on how long they intend holding a security.

Day trading refers to the purchase and sale of a security within the same day of trading, in order to capitalize on a particular event expected to affect the stock’s price. For instance, an investor might foretell short-term price movements based on the earnings announcement of a company or an announcement of the central bank’s change in interest rate targets.

Swing trading involves holding positions for several days. In situations such as these, investors expect price movement to range from a day to two weeks after entering a trade.

Scalping utilizes high trade volumes to capitalize on little price discrepancies over the short-term. For instance, traders may utilize the major leverage available from a foreign exchange platform in order to increase profits from small price movements based on both one-minute charts and tick charts.

Active Trading Compared to Active Investing

While these two terms sound familiar, active investing and active trading describe strategies that are entirely different. Active investing are activities which are entered into by fund managers or investors who want to rearrange securities’ portfolios. Active investors consistently seek alpha, which differentiates the return on an actively managed portfolio from that of a benchmark, index, or a passive investing strategy that’s similar. Passive investing proponents often state how difficult it can be for an active portfolio’s profits to overcome the additional expenses incurred by active investors. For this reason, most inexperienced investors would probably see more returns from index funds, as well as, similar products.

Reference for “Active Trading”

https://www.investopedia.com › Trading › Trading Strategy

https://www.greekshares.com/investing-education/active-versus-passive-trading

https://www.thebalance.com › Investing › Stocks

https://www.smartaboutmoney.org/Courses/…/Active-Trading-vs-Long-Term-Investin..

https://www.activetradingpartners.com/

Academic research on “Active Trading”

A model of active trading by using the properties of chaos, Ozkaya, A. (2015). A model of active trading by using the properties of chaos. Digital Signal Processing, 39, 15-21. This study introduces a research path to obtain alternative trading rules by using nonlinear dynamical analysis of stock returns. We examine the daily return data of Istanbul Stock Exchange index and Shenzhen Index B-Shares. Both stock returns series are shown to exhibit chaotic behavior and associated maximal Lyapunov exponents (LE) are computed. A new prediction method which bases on the properties of detected chaotic behavior is proposed to perform one-week out-of-sample prediction of the stock returns. Finally we develop a nonlinear model of active trading, in which traders rely only on their heterogeneous forecasts of future periods’ maximum and minimum returns. The model motivates active trading under chaotic behavior.

Active momentum trading versus passive ‘naive diversification’, N. BANERJEE, A. N. U. R. A. G., & D. HUNG, C. H. (2013). Active momentum trading versus passive ‘naive diversification’. Quantitative Finance, 13(5), 655-663.

Active trading and retail investors in Malaysia, Khan, M. T. I., Tan, S. H., & Chong, L. L. (2017). Active trading and retail investors in Malaysia. International Journal of Emerging Markets, 12(4), 708-726.

Assessing the Performance of Active and Passive Trading On the Ghana Stock Exchange, Dickson, G. K. (2015). Assessing the Performance of Active and Passive Trading On the Ghana Stock Exchange (Doctoral dissertation, University of Ghana). This paper sought to test the weak-form market efficiency of the Ghana Stock Exchange and to establish whether the application of technical trading rules like the Variable Moving Average (VMA) on the Composite Index (CI) would be profitable during the periods January 2011 to December 2014. The study also provides evidence that active trading on the GSE can be profitable and can outperform the buy-hold-strategy adopted by a passive trader or investor. To establish market efficiency, the random walk model is estimated using two different statistical methods, namely, the Augmented Dickey Fuller (ADF) Unit root test and the Lo & MacKinlay variance ratio test. Empirical results from the ADF unit root test and the Variance ratio test strongly reject the random walk hypothesis and evidently support previous empirical studies that the, Ghana Stock Exchange market is weak-form inefficient. To exploit the inefficiencies on the exchange, the study applied technical trading techniques called the Variable Moving Average (VMA). Five different variations of this rule i.e. (1, 50), (5, 50), (1, 150), (5, 150) and (2, 200) were applied to the index to investigate whether they would outperform the passive investment strategy. It was found that indeed the application of the VMA yielded positive returns and that out of the five combination of rules tested, four actually outperformed the results of a buy-and-hold strategy with the exception of the (2,200) rule. It was also found that employing technical rules with much shorter lengths yielded profits twice as much as the returns generated by a passive trader or investor who uses a buy-and-hold strategy.

Local News and Active Trading, Lindblom, T., Mavruk, T., & Sjögren, S. (2017). Local News and Active Trading. In Proximity Bias in Investors’ Portfolio Choice (pp. 185-211). Palgrave Macmillan, Cham. This chapter presents select preliminary findings from a recent study conducted by Mavruk (2016) on what role local media plays—if any—in the trading activity and equity returns in local markets. His study examines the sources of local information and tests its direct effects on the local investments made by individual investors. The focus is mainly on if and how news in local media affects the trading activity and portfolio returns of individual investors who exhibit proximity (locally and/or birthplace) bias. The results contribute to the local bias literature by allowing us to infer information asymmetry between proximate individual investors and other remote investors and also separate informed local trades from uninformed local trades. By paying more attention to the local webpage news, remote individual investors may reduce their information search costs, and hence information asymmetry between them and local investors.

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